Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality

Topic Information

Dear Colleagues,

Today, the problem of air pollution affects almost the entire world. In many regions of Asia, Africa, South America and Europe, exceedances of not only the relatively restrictive recommendations of the World Health Organization (WHO), but also local, often more liberal, allowable concentrations of air pollutants are recorded. Pollutants enter the air from natural sources (e.g. volcanic eruptions, forest fires), but human activities are the key factor responsible for changes in air quality. The most important sources of anthropogenic emissions, affecting air quality on a local, regional and global scale, include:

•    Combustion of fuels in the energy production and distribution sector;
•    Combustion of fuels in the municipal and household sector;
•    Manufacturing / industrial processes;
•    Transportation, especially road transport.

As a result, many different chemical substances are released into the air, both gaseous, such as sulfur oxides, nitrogen oxides, volatile organic compounds, as well as particulate matter, on the surface of which particularly harmful substances belonging to the group of polycyclic aromatic hydrocarbons, other persistent organic pollutants or heavy metals often adsorb. As a consequence of their influence, the natural composition of the air changes, thus contributing to a negative impact on almost all components of the environment. The pressure of air pollution on the environment may be more direct, resulting in a negative impact on living organisms (both plants and animals), in particular on human health and quality of life, but also on various types of structures or building materials. In turn, an indirect effect is observed in the case of, for example, leaching of pollutants into soils, or their accumulation in the tissues of living organisms.

Due to the health effects associated with air pollution, including mainly respiratory, cardiovascular and nervous systems diseases, but also neoplasms, it is necessary, above all, to precisely identify areas exposed to the influence of pollution, as well as to reduce and ultimately eliminate the emission sources. The first aspect is related primarily to the development of air quality monitoring systems, including the improvement, expansion and usage of modern measurement methods based on the so-called low-cost devices. In such a case, however, it is necessary to ensure the appropriate quality of the measurement results. On the other hand, reduction of pressure from emission sources concern activities on the development of renewable energy sources and their widespread implementation, especially in countries that base their economy on the combustion of fossil fuels. These works should also focus on the implementation of environmentally friendly production technologies and finally on the development of means of transport and transport systems, characterized by a significant reduction in the emission of pollutants into the air.

This multidisciplinary topic will be dedicated to the presentation of scientific papers on all these aspects that can be collected under the common title “Air pollution”. Articles on the sources of air pollutants emissions, types of emitted substances, but also their impact on air quality, the environment and, above all, human health will be presented. Particular emphasis will be placed on the issues of minimizing not only emissions as such, but above all limiting health effects and improving the quality of life. For this purpose, a key aspect will be the presentation of modern, but also accurate methods for identifying air pollutants and assessing the air quality, as well as innovative technologies that can significantly contribute to reducing the pressure of emission sources on air quality.

We welcome submission that cover, but are not limited to the following topics:

•    Air pollution
•    Air pollutants
•    Particulate matter
•    Gaseous pollutants

•    Exposure to air pollution
•    Environmental exposure
•    Environmental determinants of health
•    Adverse health effects
•    Respiratory diseases
•    Cardiovascular diseases
•    Nervous system diseases
•    Cancers
•    Local and general inflammatory processes
•    Exacerbations of diseases
•    Neuro-cognition
•    Morbidity
•    Mortality

•    Measurement methods
•    Air quality monitoring
•    Air quality assessment
•    Air quality modelling

•    Combustions of fuels
•    Fossil fuels
•    Energy systems
•    Energy production and distributions
•    Municipal and households emission
•    Traffic-related air pollutants
•    Renewable energy sources
•    Mitigating the air quality problem

Dr. Chris G. Tzanis
Prof. Dr. Artur Badyda
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400
Atmosphere atmosphere	2.5	4.6	2010	15.8 Days	CHF 2400
Journal of Clinical Medicine jcm	3.0	5.7	2012	17.3 Days	CHF 2600
Pollutants pollutants	-	-	2021	28.9 Days	CHF 1000

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Published Papers (40 papers)

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17 pages, 5325 KiB  

Open AccessArticle

Impact of COVID-19 Mobility Changes on Air Quality in Warsaw

by Artur Badyda, Andrzej Brzeziński, Tomasz Dybicz, Karolina Jesionkiewicz-Niedzińska, Piotr Olszewski, Beata Osińska, Piotr Szagała and Dominika Mucha

Appl. Sci. 2022, 12(15), 7372; https://doi.org/10.3390/app12157372 - 22 Jul 2022

Cited by 3 | Viewed by 1559

Abstract

During a pandemic, the mobility of people changes significantly from the normal situation (the number of trips made, the directions of travel and the modes of transport used). Changes in mobility depend on the scale of the pandemic threat and the scale of [...] Read more.

During a pandemic, the mobility of people changes significantly from the normal situation (the number of trips made, the directions of travel and the modes of transport used). Changes in mobility depend on the scale of the pandemic threat and the scale of the restrictions introduced and assessing the impact of these changes is not straightforward. This raises the question of the social cost of changes in mobility and their impact on the environment, including air quality. The article shows that it is possible to determine this impact using big data from mobile operators’-SIM card movements and data from air quality monitoring stations. Data on SIM card movements allows for reconstructing the state of the transport system before and during the different phases of a pandemic. The changes in mobility of people determined in this way can be related to the results of measurements of pollutant concentrations in the air. In this way, it is possible to identify links between mobility changes and air quality. The article presents the extent (in relation to the state without the pandemic) of changes in the mobility of the population during the pandemic and the related impact on air quality using the example of Warsaw. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Figure 1

Figure 1
<p>Traffic air quality monitoring station in Al. Niepodległości in Warsaw, located directly next to the roadway.</p> Full article ">Figure 2
<p>Hierarchy of analysis of the trips related to Warsaw: (<b>A</b>)-from abroad, (<b>B</b>)-domestic, (<b>C</b>)-from the Mazovia region, (<b>D</b>)-from the Warsaw Metropolitan Area (WMA).</p> Full article ">Figure 3
<p>Numbers of trips during the pandemic phases in 2020 and the 2019 baseline period.</p> Full article ">Figure 4
<p>Annual concentrations of NO₂ in Warsaw 2004–2019. Measuring stations: blue bar–Al. Niepodległości (traffic station), orange bar–Targówek (urban background station), grey bar–Ursynów (urban background station) (developed by the author on the basis of GIOŚ data).</p> Full article ">Figure 5
<p>Mean NO₂ concentrations in al. Niepodległości in 2020 compared to the mean concentrations from 2017 to 2020. Calculation based on all days of the year (developed by the author).</p> Full article ">Figure 6
<p>Mean morning peak hour NO₂ concentrations in al. Niepodległości for the period March–June 2020 compared to the averages for the same period 2017–2020. Calculation based on all Tuesdays, Wednesdays and Thursdays (developed by the author).</p> Full article ">Figure 7
<p>Comparison of the average number of trips generated with the mean NO₂ concentration, March–June 2020-al. Niepodległości in Warsaw.</p> Full article ">Figure 8
<p>Comparison of the average number of trips generated against NO₂ concentrations calculated as the averages over the peak hours. 2020-al. Niepodległości.</p> Full article ">Figure 9
<p>Summary of the mean NO₂ concentrations [μg/m³] at the air quality monitoring station at al. Niepodległości in Warsaw. Calculation for all days in the period 2017–2020.</p> Full article ">Figure 10
<p>Concentrations of PM2.5 and PM10 recorded at the air quality monitoring station in Wawer district in March and April 2020. Red colour indicates increase and green colour indicates decrease in relation to 2019.</p> Full article ">Figure 10 Cont.
<p>Concentrations of PM2.5 and PM10 recorded at the air quality monitoring station in Wawer district in March and April 2020. Red colour indicates increase and green colour indicates decrease in relation to 2019.</p> Full article ">Figure 11
<p>Average number of trips generated in March, April, May and June in Wawer in relation to daily average hourly concentrations of PM2.5 and PM10.</p> Full article ">

17 pages, 4127 KiB  

Open AccessArticle

Effectiveness of the National Pollutant Release Inventory as a Policy Tool to Curb Atmospheric Industrial Emissions in Canada

by Tony R. Walker

Pollutants 2022, 2(3), 289-305; https://doi.org/10.3390/pollutants2030019 - 1 Jul 2022

Cited by 3 | Viewed by 2338

Abstract

To curb greenhouse gas emissions and reduce atmospheric pollutants in Canada, many pieces of environment legislation are targeted at reducing industrial emissions. Traditional regulation prescribes penalties through fines to discourage industries from polluting, but, in the past two decades, alternative forms of environmental [...] Read more.

To curb greenhouse gas emissions and reduce atmospheric pollutants in Canada, many pieces of environment legislation are targeted at reducing industrial emissions. Traditional regulation prescribes penalties through fines to discourage industries from polluting, but, in the past two decades, alternative forms of environmental regulation, such as the National Pollutant Release Inventory (NPRI), have been introduced. NPRI is an information management tool which requires industries to self-report emissions data based on a set of guidelines determined by Environment and Climate Change Canada, a federal agency. The tool works to inform the public regarding industry emissions and provides a database that can be analyzed by researchers and regulators to inform emissions trends in Canada. These tools have been successful in other jurisdictions (e.g., United States and Australia). However, research assessing the U.S. Toxic Release Inventory suggests there are fundamental weaknesses in the self-reported nature of the data and incidences of under-reporting. This preliminary study aimed to explore NPRI in Canada and test its effectiveness against the National Air Pollutant Surveillance Network (NAPS), an air quality monitoring program administered by the federal government. While instances of under-reporting were undetected, this study identified areas of weakness in the NPRI tool and instances of increasing emissions across various industrial sectors in Canada. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Figure 1
<p>Changes made to the NPRI substance list over time (adapted from ECCC [<a href="#B10-pollutants-02-00019" class="html-bibr">10</a>]).</p> Full article ">Figure 2
<p>Locations of NAPS monitoring stations across Canada (adapted from ECCC [<a href="#B21-pollutants-02-00019" class="html-bibr">21</a>]).</p> Full article ">Figure 3
<p>Steel industry NPRI data (kg) from 2002 to 2015.</p> Full article ">Figure 4
<p>Power generation NPRI data (kg) from 2002 to 2015.</p> Full article ">Figure 5
<p>Oil and gas NPRI data (kg) from 2002 to 2015.</p> Full article ">Figure 6
<p>Comparison of NPRI and NAPS data for each substance for a steel-making facility from 2002 to 2015.</p> Full article ">Figure 7
<p>Comparison of NPRI and NAPS data for sulfur dioxide and nitrogen dioxide for an oil and gas facility from 2002 to 2015.</p> Full article ">Figure 8
<p>Comparison of NPRI and NAPS data for sulfur dioxide and nitrogen dioxide for a power generation facility from 2002 to 2015.</p> Full article ">

11 pages, 3099 KiB  

Open AccessArticle

Ambient Size-Segregated Particulate Matter Characterization from a Port in Upstate New York

by Omosehin D. Moyebi, Brian P. Frank, Shida Tang, Gil LaDuke, David O. Carpenter and Haider A. Khwaja

Atmosphere 2022, 13(6), 984; https://doi.org/10.3390/atmos13060984 - 18 Jun 2022

Cited by 2 | Viewed by 2167

Abstract

Air pollution impacts human health and the environment, especially in urban cities with substantial industrial activities and vehicular traffic emissions. Despite increasingly strict regulations put in place by regulatory agencies, air pollution is still a significant environmental problem in cities across the world. [...] Read more.

Air pollution impacts human health and the environment, especially in urban cities with substantial industrial activities and vehicular traffic emissions. Despite increasingly strict regulations put in place by regulatory agencies, air pollution is still a significant environmental problem in cities across the world. The objective of this study was to evaluate the environmental pollution from stationary and mobile sources using real-time monitoring and sampling techniques to characterize size-segregated particulate matter (PM), black carbon (BC), and ozone (O₃) at the Port of Albany, NY. Air pollution monitoring was carried out for 3 consecutive weeks under a 24-hour cycle in 2018 at the New York State Department of Environmental Conservation (NYSDEC) site within the Port. Sampling was done with an AEROCET 531, optical particle sizer (OPS), ozone monitor, and MicroAeth AE51. Higher mass and number concentrations of size-segregated particles were observed during the daytime. PM_2.5 and PM₁₀ concentrations ranged from 1 to 271 micrograms per cubic meter (µg/m³) and 1 to 344 µg/m³, respectively. While these values do not exceed the level of the USEPA 24-hour standards, frequent sharp peaks were observed at higher concentrations. Size-segregated PM at sizes 0.3 µm and 0.374 µm recorded maximum concentrations of 101,631 particle number per cubic centimeter (#/cm³) and 43,432 #/cm³, respectively. Wide variations were observed in the particle number concentrations for 0.3 µm, 0.374 µm, and 0.465 µm sizes, which ranged from 1521 to 101,631 #/cm³; 656 to 43,432 #/cm³; and 311 to 29,271 #/cm³, respectively. BC concentration increased during morning and evening rush hours with the maximum concentration of 11,971 ng/m³ recorded at 8:00 AM. This suggests that mobile sources are the primary contributor to anthropogenic sources of BC within the Port. Episodic elevations in the concentrations of size-segregated PM and BC confirmed the contribution of industrial and vehicular activities around the Port of Albany. This study underscores the importance of measuring particles on a size-segregated basis in order to more fully understand the contributions of the multiple sources present within and surrounding a port environment. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Figure 1

Figure 1
<p>(<b>a</b>) Map of New York State showing the sampling site at the Port of Albany. (<b>b</b>). Rooftop monitoring location adjacent to the Port of Albany.</p> Full article ">Figure 2
<p>Daily variations of PM_2.5 and PM₁₀ concentrations during 8-9 November 2018.</p> Full article ">Figure 3
<p>Wind rose plots for maximum and minimum concentrations of daily air pollutants at the Port of Albany.</p> Full article ">Figure 4
<p>Plots of 72-hr backward-in-time trajectories depicting wind direction and its effect on the daily transport and dilution of air pollutants during the study period for the maximum and minimum concentrations at the Port of Albany. Note: Maximum concentrations: (<b>a</b>) 0.374 and 0.465 µm on 8 November 2018; (<b>b</b>) BC ng/m³ and 0.3 µm on 9 November 2018; (<b>c</b>) PM₁, PM_2.5, PM₇, PM₁₀, and TSP µg/m³ on 15 November 2018; (<b>d</b>) Minimum concentrations for 0.3, 0.374, 0.465 µm, PM, and BC on 6 November 2018.</p> Full article ">Figure 5
<p>Size segregated sub-micron PM particle number variations at the Port of Albany on 13 November 2018.</p> Full article ">Figure 6
<p>Size segregated sub-micron PM particle number variations at the Port of Albany on 15 November 2018.</p> Full article ">Figure 7
<p>Variation of black carbon concentrations at the Port of Albany during 13-14 November 2018 (weekday: Tuesday and Wednesday).</p> Full article ">Figure 8
<p>Black carbon variability at the Port of Albany during 16-17 November 2018 (<b>a</b>) weekday and (<b>b</b>) weekend: Friday and Saturday).</p> Full article ">

17 pages, 8828 KiB  

Open AccessArticle

A Case Analysis of Dust Weather and Prediction of PM₁₀ Concentration Based on Machine Learning at the Tibetan Plateau

by Changrong Tan, Qi Chen, Donglin Qi, Liang Xu and Jiayun Wang

Atmosphere 2022, 13(6), 897; https://doi.org/10.3390/atmos13060897 - 1 Jun 2022

Cited by 5 | Viewed by 2162

Abstract

Dust weather is common and disastrous at the Tibetan Plateau. This study selected a typical case of dust weather and analyzed its main development mechanism in the northeast of the Tibetan Plateau, then applied six machine learning methods and a time series regression [...] Read more.

Dust weather is common and disastrous at the Tibetan Plateau. This study selected a typical case of dust weather and analyzed its main development mechanism in the northeast of the Tibetan Plateau, then applied six machine learning methods and a time series regression model to predict PM₁₀ concentration in this area. The results showed that: (1) The 24-h pressure change was positive when the front intruded on the surface; convergence of vector winds with a sudden drop in temperature and humidity led by a trough on 700 hPa; a “two troughs and one ridge” weather situation appeared on 500 hPa while the cold advection behind the trough was strong and a cyclone vorticity was formed in the east of Inner Mongolia. (2) The trajectory of air mass from the Hexi Corridor was the main air mass path influencing Xining City, in this case, since a significant lag in the peak of PM₁₀ concentration appeared in Xining City when compared with Zhangye City. (3) The Multiple Linear Regression was not only timely and effective in predicting the PM₁₀ concentration but had great abilities for anticipating the transition period of particle concentration and the appearance date of maximum values in such dust weather. (4) The M_A and M_P in the clean period were much lower than that in the dust period; the PM₁₀ of Zhangye City as an eigenvalue played an important role in predicting the PM₁₀ of Xining City even in clean periods. Different from dust periods, the prediction effect of Random Forest Optimized by Bayesian hyperparameter was superior to Multiple Linear Regression in clean periods. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Figure 1
<p>One of the 700 hPa wind fields of ECMWF fine grid prediction products (the spatial resolution was 0.25° × 0.25°).</p> Full article ">Figure 2
<p>Terrain height of the Tibetan Plateau, geographical location of Qaidam Basin, Qilian Mountains, Hexi Corridor, Inner Mongolia, and the main meteorological observation sites.</p> Full article ">Figure 3
<p>Distribution of dust weather in northern China from 08:00 on 14 March to 11:00 on 16 March 2021 (Provided by the Central Meteorological Observatory of China.).</p> Full article ">Figure 4
<p>Superposition diagram of 24-h pressure change (isolines), wind field, and minimum visibility on surface. (<b>a</b>) 08:00, 15 March 2021; (<b>b</b>) 08:00, 16 March 2021.</p> Full article ">Figure 5
<p>Superposition diagram of geopotential height (purple contours), temperature (red isotherms), wind field, and relative humidity at 700 hPa. (<b>a</b>) 08:00, 15 March 2021; (<b>b</b>) 08:00, 16 March 2021.</p> Full article ">Figure 6
<p>Superposition diagram of geopotential height (black contours), temperature (red isotherms), wind field, and relative humidity at 500 hPa. (<b>a</b>) 08:00, 15 March 2021; (<b>b</b>) 08:00, 16 March 2021.</p> Full article ">Figure 7
<p>Cluster analysis of HYSPLIT backward trajectory in Xining City during March 2021.</p> Full article ">Figure 8
<p>Hourly variation curves of surface temperature, relative humidity, 24-h pressure change, and minimum visibility in Xining City from 13 to 23 March 2021.</p> Full article ">Figure 9
<p>Structure of the T-lnp diagram (emagram) of Xining City at 08:00 on 16 March 2021.</p> Full article ">Figure 10
<p>Hourly variation curves of surface PM₁₀ concentration in Xining and Zhangye cities from 13 to 23 March 2021.</p> Full article ">Figure 11
<p>Comparison between predicted and observed daily mean values of PM₁₀ concentration in Xining City from 13 to 23 March 2021 (<b>a</b>): Multiple Linear Regression, (<b>b</b>): Random Forecast.</p> Full article ">Figure 12
<p>Comparison between predicted and observed daily mean values of PM₁₀ concentration in Xining City from 16 to 24 April, 2022 (<b>a</b>): Multiple Linear Regression, (<b>b</b>): Random Forecast.</p> Full article ">

17 pages, 7455 KiB  

Open AccessArticle

Application of the Gaussian Model for Monitoring Scenarios and Estimation of SO₂ Atmospheric Emissions in the Salamanca Area, Bajío, Mexico

by Amanda Enrriqueta Violante Gavira, Wadi Elim Sosa González, Ramón de Jesús Pali Casanova, Marcial Alfredo Yam Cervantes, Manuel Aguilar Vega, Javier Chacha Coto, José del Carmen Zavala Loría, Luis Alonso Dzul López and Eduardo García Villena

Atmosphere 2022, 13(6), 874; https://doi.org/10.3390/atmos13060874 - 27 May 2022

Viewed by 2434

Abstract

Population and industrial growth in Mexico’s Bajío region demand greater electricity consumption. The production of electricity from fuel oil has severe implications on climate change and people’s health due to SO₂ emissions. This study describes the simulation of eight different scenarios for [...] Read more.

Population and industrial growth in Mexico’s Bajío region demand greater electricity consumption. The production of electricity from fuel oil has severe implications on climate change and people’s health due to SO₂ emissions. This study describes the simulation of eight different scenarios for SO₂ pollutant dispersion. It takes into account distance, geoenvironmental parameters, wind, terrain roughness, and Pasquill–Gifford–Turner atmospheric stability and categories of dispersion based on technical information about SO₂ concentration from stacks and from one of the atmospheric monitoring stations in Salamanca city. Its transverse character, its usefulness for modeling, and epidemiological, meteorological, and fluid dynamics studies, as suggested by the models approved by the Environmental Protection Agency (EPA), show a maximum average concentration of 399 µg/m³, at an average distance of 1800 m. The best result comparison in the scenarios was scenery 8. Maximum nocturnal dispersion was shown at a wind speed of 8.4 m/s, and an SO₂ concentration of 280 µg/m³ for stack 4, an atypical situation due to the geography of the city. From the validation process, a relative error of 14.7 % was obtained, which indicates the reliability of the applied Gaussian model. Regarding the mathematical solution of the model, this represents a reliable and low-cost tool that can help improve air quality management, the location or relocation of atmospheric monitoring stations, and migration from the use of fossil fuels to environmentally friendly fuels. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Figure 1
<p>Emissions of SO₂ in Salamanca 2000–2017. Source: Adapted from INECC [<a href="#B18-atmosphere-13-00874" class="html-bibr">18</a>]. Note: Upper light gray indicates the maximum level of SO₂ emission, dark gray shows the percentile 90th; the band gray below shows the average and finally the dark band indicates the 10th percentile of emissions registered by the red cross station during the signal period.</p> Full article ">Figure 2
<p>(<b>a</b>) High soil and water pollution of the Salamanca entity, (<b>b</b>), Guanajuato location in the Mexican Republic Map (<b>c</b>) Salamanca, the most Polluted entity area in Guanajuato. Source: Adapted from Google Earth (2020) [<a href="#B32-atmosphere-13-00874" class="html-bibr">32</a>].</p> Full article ">Figure 3
<p>Tract and dispersion of SO₂ are indicated in the closest neighborhood on Salamanca’s Map from the TTP emission source monitored by the Red Cross station. Source: Adapted from Google Earth (2020) [<a href="#B32-atmosphere-13-00874" class="html-bibr">32</a>].</p> Full article ">Figure 4
<p>Dispersion coefficients. (<b>a</b>) Top view of dispersion σ_y, (<b>b</b>) dispersion z exe, σ_z. Source: own elaboration and adapted from the two-dimensional Gaussian model [<a href="#B29-atmosphere-13-00874" class="html-bibr">29</a>].</p> Full article ">Figure 5
<p>(<b>a</b>) 24 h average atmospheric pressure and room temperature and (<b>b</b>) 24 h SO₂ average concentration. Source: own elaboration.</p> Full article ">Figure 6
<p>Wind at the RC station Google Earth. Source: Adapted from Google Earth 2020.</p> Full article ">Figure 7
<p>Modeling sceneries.</p> Full article ">Figure 8
<p>(<b>a</b>) Scenario 1 and (<b>b</b>) scenario 2. Source: Author’s elaboration.</p> Full article ">Figure 9
<p><b>(a</b>) Scenery 3 and (<b>b</b>) scenery 4. Source: Author´s elaboration.</p> Full article ">Figure 10
<p>(<b>a</b>) Scenary 7 and (<b>b</b>) scenery 8. Source: Author’s own elaboration.</p> Full article ">

18 pages, 5977 KiB  

Open AccessArticle

Characterizing Air Pollution and Its Association with Emission Sources in Lahore: A Guide to Adaptation Action Plans to Control Pollution and Smog

by Mifrah Ali, Iffat Siddique and Sawaid Abbas

Appl. Sci. 2022, 12(10), 5102; https://doi.org/10.3390/app12105102 - 19 May 2022

Cited by 8 | Viewed by 5502

Abstract

Lahore, the home of 11 million people, is one of the most polluted cities in the world. Pollution causes deaths, birth defects, and years of life lost. This study’s real-time data analysis of the air quality index (AQI) showed that air pollution remained [...] Read more.

Lahore, the home of 11 million people, is one of the most polluted cities in the world. Pollution causes deaths, birth defects, and years of life lost. This study’s real-time data analysis of the air quality index (AQI) showed that air pollution remained “unhealthy for everyone” for 54% of the time, and “unhealthy for sensitive groups” for 88% of the time, during the last three years (June 2019–September 2021). The air quality index (AQI) value in Lahore reached 175 µg/m³ in 2021. This alarmingly hazardous air situation was analyzed by selecting fourteen sites based on the provenance of industrialization and tailpipe emissions. An analysis of remote sensing data for these sites was performed, in addition to field surveys, to identify the relationship between pollutant concentration and on-ground current practices. The key primary and secondary air pollutants selected for analysis were carbon monoxide (CO), nitrogen dioxide (NO₂), sulphur dioxide (SO₂), aerosol optical depth (AOD), methane (CH₄), and formaldehyde (HCHO). The assessment was carried out for the study period of July 2018 to April 2021. The real-time AQI was plotted against each pollutant’s monthly concentration, which showed a significant positive correlation of AQI with SO₂, NO₂, and CO. A plotting of the percentage contribution of each pollutant with its emission sources highlighted the main pollutant to take action to reduce, as a priority on those particular sites. The pollutant hotspot within each economic activity was also determined. Assessments showed that the AQI value was higher on weekends than on weekdays. These findings can help to develop smart adaptation action plans for immediate implementation, to dilute the current environmental risks in the city. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Figure 1
<p>Study area map indicating selected sites in the Lahore district and its surrounding areas.</p> Full article ">Figure 2
<p>Temporal patterns of pollutant concentrations across spatially dispersed sites belonging to three sectors (roads, industrial estates, and brick kilns): (<b>a</b>) carbon monoxide (CO), (<b>b</b>) nitrogen dioxide (NO₂), (<b>c</b>) methane (CH₄), (<b>d</b>) sulphur dioxide (SO₂), (<b>e</b>) formaldehyde (HCHO), (<b>f</b>) aerosol optical depth (AOD).</p> Full article ">Figure 3
<p>Total monthly contribution of different pollutants and their relevancy to maximum AQI in Lahore, from March 2018 to December 2020: (<b>a</b>) CO, (<b>b</b>) NO₂, (<b>c</b>) SO₂, (<b>d</b>) HCHO, (<b>e</b>) CH₄, (<b>f</b>) AOD.</p> Full article ">Figure 4
<p>The percentage contribution of each site on the corresponding pollutant.</p> Full article ">Figure 5
<p>The top contributor of specific pollutants within various economic activities. The top contributor of (<b>a</b>) CO in Roads (<b>b</b>) CO in Brick kilns (<b>c</b>) CO in Industrial Estates (<b>d</b>) NO₂ in Roads (<b>e</b>) NO₂ in Brick kilns (<b>f</b>) NO₂ in Industrial Estates (<b>g</b>) SO₂ in Roads (<b>h</b>) SO₂ in Brick kilns (<b>i</b>) SO₂ in Industrial Estates (<b>j</b>) HCHO in Roads (<b>k</b>) HCHO in Brick kilns (<b>l</b>) HCHO in Industrial Estates (<b>m</b>) CH₄ in Roads (<b>n</b>) CH₄ in Brick kilns (<b>o</b>) CH₄ in Industrial Estates (<b>p</b>) AOD in Roads (<b>q</b>) AOD in Brick kilns (<b>r</b>) AOD in Industrial Estates.</p> Full article ">Figure 6
<p>The comparative contribution of pollutants based on vehicle type.</p> Full article ">Figure 7
<p>The comparative contribution of pollutants based on the brick kiln type, i.e., traditional brick kilns (TBK) and contemporary brick kilns (CBK).</p> Full article ">Figure 8
<p>The daily trend of AQI in Lahore to identify the difference in weekday and weekend trends.</p> Full article ">

11 pages, 3171 KiB  

Open AccessArticle

Study on the Catalytic Decomposition Reaction of N₂O on MgO (100) in SO₂ and CO Environments

by Xiaoying Hu, Erbo Zhang, Wenjun Li, Lingnan Wu, Yiyou Zhou, Hao Zhang and Changqing Dong

Appl. Sci. 2022, 12(10), 5034; https://doi.org/10.3390/app12105034 - 16 May 2022

Cited by 1 | Viewed by 1571

Abstract

To study the role of MgO in the reduction of N₂O in circulating fluidized bed boilers, density functional theory was used to evaluate heterogeneous decomposition. The interference of SO₂ and CO on N₂O was considered. N₂O [...] Read more.

To study the role of MgO in the reduction of N₂O in circulating fluidized bed boilers, density functional theory was used to evaluate heterogeneous decomposition. The interference of SO₂ and CO on N₂O was considered. N₂O on MgO (100) is a two-step process that includes O transfer and surface recovery processes. The O transfer process is the rate-determining step with barrier energy of 1.601 eV, while for the Langmuir–Hinshelwood and Eley–Rideal surface recovery mechanisms, the barrier energies are 0.840 eV and 1.502 eV, respectively. SO₂ has a stronger interaction with the surface-active O site than that of N₂O. SO₂ will occupy the active site and hinder N₂O decomposition. CO cannot improve the catalysis of MgO (100) for N₂O because O transfer is the rate-determining step. Compared with homogeneous reduction by CO, MgO has a limited catalytic effect on N₂O, where the barrier energy decreases from 1.691 eV to 1.601 eV. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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<p>Surface energy of MgO (100) and MgO (110) surface models with different layers.</p> Full article ">Figure 2
<p>Surface model and state density analysis of MgO (100).</p> Full article ">Figure 3
<p>Stable adsorption structure of N₂O on MgO (100) surface. (<b>a</b>) The O is at O atop; (<b>b</b>) The O is at the top of the surface Mg; (<b>c</b>) The O is at the surface bridge; (<b>d</b>) O vacancy on the surface; (<b>e</b>) The N is at the top of the surface O; (<b>f</b>) The N is at the top of the surface Mg; (<b>g</b>) The N is at the surface bridge; (<b>h</b>) N vacancy on the surface.</p> Full article ">Figure 4
<p>Oxygen atom transfer process of N₂O on MgO (100) surface.</p> Full article ">Figure 5
<p>The surface reduction process of MgO (100) following the Langmuir–Hinshelwood (LH) mechanism.</p> Full article ">Figure 6
<p>The surface reduction process of MgO (100) following the Eley–Rideal (ER) mechanism.</p> Full article ">Figure 7
<p>Stable adsorption structure of SO₂ molecules on the surface of MgO (100). (<b>a</b>) The S is at the top of the surface O; (<b>b</b>) The S is at the top of the surface Mg; (<b>c</b>) The S is at the surface bridge; (<b>d</b>) O vacancy on the surface; (<b>e</b>) The S is at the top of the surface O; (<b>f</b>) The S is at the top of the surface Mg; (<b>g</b>) The S is at the surface bridge; (<b>h</b>) S vacancy on the surface.</p> Full article ">Figure 8
<p>Energy line diagram of MgO (100) surface reduced by CO.</p> Full article ">

15 pages, 3652 KiB  

Open AccessReview

A Brief Review of Formaldehyde Removal through Activated Carbon Adsorption

by Yu-Jin Kang, Hyung-Kun Jo, Min-Hyeok Jang, Xiaoliang Ma, Yukwon Jeon, Kyeongseok Oh and Joo-Il Park

Appl. Sci. 2022, 12(10), 5025; https://doi.org/10.3390/app12105025 - 16 May 2022

Cited by 29 | Viewed by 11586

Abstract

Formaldehyde is a highly toxic indoor pollutant that can adversely impact human health. Various technologies have been intensively evaluated to remove formaldehyde from an indoor atmospheres. Activated carbon (AC) has been used to adsorb formaldehyde from the indoor atmosphere, which has been commercially [...] Read more.

Formaldehyde is a highly toxic indoor pollutant that can adversely impact human health. Various technologies have been intensively evaluated to remove formaldehyde from an indoor atmospheres. Activated carbon (AC) has been used to adsorb formaldehyde from the indoor atmosphere, which has been commercially viable owing to its low operational costs. AC has a high adsorption affinity due to its high surface area. In addition, applications of AC may be diversified by the surface modification. Among the different surface modifications for AC, amination treatments of AC have been reported and evaluated. Specifically, the amine functional groups of the amine-treated AC have been found to play an important role in the adsorption of formaldehyde. Surface modifications of AC by impregnating and/or grafting the amine functional groups onto the AC surface have been reported in the literature. The impregnation of the amine-containing species on AC is mainly achieved by physical interaction or H-bond of the amines to the AC surface. Meanwhile, the grafting of the amine functional groups is mainly conducted through chemical reactions occurring between the amines and the AC surface. Herein, the carboxyl group, as a representative functional group for grafting on the surface of AC, plays a key role in the amination reactions. A qualitative comparison of amination chemicals for the surface modification of AC has also been discussed. Thermodynamics and kinetics for adsorption of formaldehyde on AC are firstly reviewed in this paper, and then the major factors affecting the adsorptive removal of formaldehyde over AC are highlighted and discussed in terms of humidity and temperature. In addition, new strategies for amination, as well as the physical modification option for AC application, are proposed and discussed in terms of safety and processability. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>FA removal efficiency on the (modified) bead-type activated carbon at the various RH contents: (<b>a</b>) non-modified bead-type activated carbon, (<b>b</b>) halogen modified bead-type activated carbon, and (<b>c</b>) sulfur modified bead-type activated carbon. Operation conditions: 100 ppmv; space velocity: 40,000/h; temperature: ambient.</p> Full article ">Figure 2
<p>Conceptual diagram of impregnated aniline on the surface of activated carbon.</p> Full article ">Figure 3
<p>Grafting reactions (<b>a</b>) between melamine and activated carbon and (<b>b</b>) between diethylene triamine and activated carbon via halogenated intermediates for both cases.</p> Full article ">Figure 4
<p>The mechanism of FA over amine species (urea) onto the AC (modification from our previous work [<a href="#B63-applsci-12-05025" class="html-bibr">63</a>]).</p> Full article ">Figure 5
<p>Conceptual diagram of recycling process for customized bead-type AC with 0.5 mm diameter, on average.</p> Full article ">

15 pages, 2295 KiB  

Open AccessArticle

Source Apportionment and Toxic Potency of PM_2.5-Bound Polycyclic Aromatic Hydrocarbons (PAHs) at an Island in the Middle of Bohai Sea, China

by Lin Qu, Lin Yang, Yinghong Zhang, Xiaoping Wang, Rong Sun, Bo Li, Xiaoxue Lv, Yuehong Chen, Qin Wang, Chongguo Tian and Ling Ji

Atmosphere 2022, 13(5), 699; https://doi.org/10.3390/atmos13050699 - 28 Apr 2022

Cited by 3 | Viewed by 1872

Abstract

Polycyclic aromatic hydrocarbons (PAHs) have attracted more attention because of their high atmospheric concentration and toxicity in recent decades. In this study, a total of 60 PM_2.5 samples were collected from Beihuangcheng Island in Bohai Sea, China, from August 2017 to March [...] Read more.

Polycyclic aromatic hydrocarbons (PAHs) have attracted more attention because of their high atmospheric concentration and toxicity in recent decades. In this study, a total of 60 PM_2.5 samples were collected from Beihuangcheng Island in Bohai Sea, China, from August 2017 to March 2018 for analyzing 16 congeners of PAHs (Σ₁₆PAHs). Sources of PAHs were apportioned by a positive matrix factorization (PMF) model and the carcinogenic risk due to exposure to the PAHs was estimated by the toxicity equivalent of BaP (BaPeq). The results showed that the average concentration of Σ₁₆PAHs was 35.3 ± 41.8 ng/m³. The maximum concentration of Σ₁₆PAHs occurred in winter, followed by spring and autumn, and summer. The PMF modeling apportioned the PAHs into four sources, coal combustion, biomass burning, vehicle exhaust, and petroleum release, contributing 43.1%, 25.8%, 24.7%, and 6.39%, respectively. The average ΣBaPeq concentration was 2.32 ± 4.95 ng/m³ during the sampling period, and vehicle exhaust was the largest contributor. The finding indicates that more attention should be paid to reduce the emissions from coal combustion and vehicle exhaust because they were the largest contributors to the PAH concentration in PM_2.5 and ΣBaPeq concentration, respectively. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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<p>The location of the sampling site on the Beihuangcheng Island.</p> Full article ">Figure 2
<p>The 120 h back trajectory clusters during the sampling period.</p> Full article ">Figure 3
<p>Factor profiles of PAH components in PM_2.5 identified by the PMF modeling.</p> Full article ">Figure 4
<p>Seasonal contributions of four source factors to PAHs in PM_2.5 identified by the PMF modeling.</p> Full article ">Figure 5
<p>Contributions of four source factors to ΣBaPeq in PM_2.5 identified by the PMF modeling.</p> Full article ">

14 pages, 4889 KiB  

Open AccessArticle

Research on the Spatial Heterogeneity and Influencing Factors of Air Pollution: A Case Study in Shijiazhuang, China

by Yuan Sun, Jian Zeng and Aihemaiti Namaiti

Atmosphere 2022, 13(5), 670; https://doi.org/10.3390/atmos13050670 - 22 Apr 2022

Cited by 4 | Viewed by 2329

Abstract

Rapid urbanization causes serious air pollution and constrains the sustainable development of society. The influencing factors of urban air pollution are complex and diverse. Multiple factors act together to interact in influencing air pollution. However, most of the existing studies on the influencing [...] Read more.

Rapid urbanization causes serious air pollution and constrains the sustainable development of society. The influencing factors of urban air pollution are complex and diverse. Multiple factors act together to interact in influencing air pollution. However, most of the existing studies on the influencing factors of air pollution lack consideration of the interaction mechanisms between the factors. Using multisource data and geographical detectors, this study analyzed the spatial heterogeneity characteristics of air pollution in Shijiazhuang City, identified its main influencing factors, and analyzed the interaction effects among these factors. The results of spatial heterogeneity analysis indicate that the distribution of aerosol optical depth (AOD) has obvious agglomeration characteristics. High agglomeration areas are concentrated in the eastern plain areas, and low agglomeration areas are concentrated in the western mountainous areas. Forests (q = 0.620), slopes (q = 0.616), elevation (q = 0.579), grasslands (q = 0.534), and artificial surfaces (q = 0.506) are the main individual factors affecting AOD distribution. Among them, natural factors such as topography, ecological space, and wind speed are negatively correlated with AOD values, whereas the opposite is true for human factors such as roads, artificial surfaces, and population. Each factor can barely affect the air pollution status significantly alone, and the explanatory power of all influencing factors showed an improvement through the two-factor enhanced interaction. The associations of elevation ∩ artificial surface (q = 0.625), elevation ∩ NDVI (q = 0.622), and elevation ∩ grassland (q = 0.620) exhibited a high explanatory power on AOD value distribution, suggesting that the combination of multiple factors such as low altitude, high building density, and sparse vegetation can lead to higher AOD values. These results are conducive to the understanding of the air pollution status and its influencing factors, and in future, decision makers should adopt different strategies, as follows: (1) high-density built-up areas should be considered as the key areas of pollution control, and (2) a single-factor pollution control strategy should be avoided, and a multi-factor synergistic optimization strategy should be adopted to take full advantage of the interaction among the factors to address the air pollution problem more effectively. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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<p>Map of Shijiazhuang (<b>a</b>) in the air pollution transmission channel area of the Beijing-Tianjin-Hebei region; (<b>b</b>) administrative region of Shijiazhuang City.</p> Full article ">Figure 2
<p>Spatial distribution of air pollution (AOD).</p> Full article ">Figure 3
<p>Results of standardized classification of variables.</p> Full article ">Figure 4
<p>Results of spatial autocorrelation analysis: (<b>a</b>) Moran’s I results; (<b>b</b>) general G results).</p> Full article ">Figure 5
<p>Clustering and hotspot analysis results: (<b>a</b>) Anselin local Moran results; (<b>b</b>) Getis–Ord-Gi* results.</p> Full article ">Figure 6
<p>Results of factor detector.</p> Full article ">Figure 7
<p>Results of interaction detector.</p> Full article ">Figure 8
<p>Influencing factors of air pollution.</p> Full article ">Figure 9
<p>Analysis of the interaction mechanism of factors.</p> Full article ">

17 pages, 1184 KiB  

Open AccessArticle

Can Environmental Regulation Reduce Urban Haze Concentration from the Perspective of China’s Five Urban Agglomerations?

by Xinfei Li, Yueming Li, Chang Xu, Jingyang Duan, Wenqi Zhao, Baodong Cheng and Yuan Tian

Atmosphere 2022, 13(5), 668; https://doi.org/10.3390/atmos13050668 - 22 Apr 2022

Cited by 2 | Viewed by 1464

Abstract

Based on the perspective of urban agglomerations, this paper explores the impact mechanism of environmental regulation on haze, and tries to find the most suitable environmental regulation intensity for haze control in urban agglomerations. This paper uses the fixed-effect model and panel threshold [...] Read more.

Based on the perspective of urban agglomerations, this paper explores the impact mechanism of environmental regulation on haze, and tries to find the most suitable environmental regulation intensity for haze control in urban agglomerations. This paper uses the fixed-effect model and panel threshold model to verify the effect of environmental regulations on haze concentration in 206 cities in China. A grouping test is also conducted to verify whether a regional heterogeneity arises due to different regional development levels for five urban agglomerations and non-five urban agglomerations, respectively. The results show that: (1) In the linear model, strengthening environmental regulation can reduce the haze concentration, but this effect is not significant. The effect of environmental regulation on haze control in the five major urban agglomerations is better than that in the non-five major urban agglomerations; (2) In the nonlinear model, the impact of environmental regulation on haze shows a “U” trend in the five major urban agglomerations and an inverted “U” trend in the non-five major urban agglomerations. Although the results are not significant, we can still conclude that the impact of environmental regulation on haze varies depending on the level of regional economic development. Therefore, the environmental regulation should be formulated according to local conditions; (3) In the threshold model, the impact of environmental regulation on the haze concentration in five major urban agglomerations has a threshold effect. In the five major urban agglomerations, although environmental regulation can effectively reduce haze concentration, the governance effect will weaken as the environmental regulation increases. This study plays a positive role in guiding local governments to adjust environmental regulation intensity according to local conditions and helping local environmental improvement. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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<p>Location of five urban agglomerations in China.</p> Full article ">Figure 2
<p>Changes in environmental regulation intensity of five major urban agglomerations and non-five major urban agglomerations.</p> Full article ">Figure 3
<p>Changes in PM2.5 of five major urban agglomerations and non-five major urban agglomerations.</p> Full article ">Figure 4
<p>Effect of environmental regulation on haze in five urban agglomerations.</p> Full article ">

14 pages, 7093 KiB  

Open AccessArticle

Atmospheric Hg Levels in Tree Barks Due to Artisanal Small-Scale Gold Mining Activity in Bunut Seberang Village in Indonesia

by Azhary Taufiq, Melya Riniarti, Endang Linirin Widiastuti, Hendra Prasetia, Slamet Budi Yuwono, Ceng Asmarahman and Tedy Rendra

Atmosphere 2022, 13(4), 633; https://doi.org/10.3390/atmos13040633 - 15 Apr 2022

Cited by 3 | Viewed by 2068

Abstract

Mercury (Hg) is a useful heavy metal; however, it is toxic to both humans and the environment. Tree bark is an excellent bioindicator, which has been proven to be effective in studying the level of atmospheric Hg contamination. This study aimed to determine [...] Read more.

Mercury (Hg) is a useful heavy metal; however, it is toxic to both humans and the environment. Tree bark is an excellent bioindicator, which has been proven to be effective in studying the level of atmospheric Hg contamination. This study aimed to determine the distribution of evaporated Hg using the total weight of Hg (THg) in tree barks in Indonesia at the artisanal and small-scale gold mining (ASGM) area of Bunut Seberang Village and Lampung University, respectively. Samples were taken using purposive sampling, based on the criteria of forestry trees at a height level of 1.3 m above ground as wide as 100 cm². The samples were analyzed by atomic absorption spectrometry and Scanning electron microscopy to determine the levels of THg and to investigate the bark structures. Results showed that the highest THg values were found in a Magnolia champaca sample (56.5 µg), followed by Swietenia mahagoni (45.8 µg) and Swietenia mahagoni (33.5 µg). All species studied showed THg levels in the tree barks at an elevation from 30 to 320 m above sea level. The Hg amounts found in the sampled barks indicated the dispersion of Hg throughout the ASGM area, which signified hazardous atmospheric conditions in the area. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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<p>The research sampling locations were (<b>a</b>) Bunut Seberang Village, Lampung Province, Pesawaran District, Indonesia (test samples), and (<b>b</b>) University of Lampung, Lampung Province, Bandar Lampung, Indonesia (control samples).</p> Full article ">Figure 1 Cont.
<p>The research sampling locations were (<b>a</b>) Bunut Seberang Village, Lampung Province, Pesawaran District, Indonesia (test samples), and (<b>b</b>) University of Lampung, Lampung Province, Bandar Lampung, Indonesia (control samples).</p> Full article ">Figure 2
<p>The locations of the sampled trees at Bunut Seberang Village including <span class="html-italic">C. pentandra</span> (<span class="html-italic">n</span> = 1), <span class="html-italic">M. champaca</span> (<span class="html-italic">n</span> = 1), <span class="html-italic">P. falcataria</span> (<span class="html-italic">n</span> = 2), <span class="html-italic">P. acerifolium</span> (<span class="html-italic">n</span> = 2), and <span class="html-italic">S. mahagoni</span> (<span class="html-italic">n</span> = 7) and at Bunut Seberang Village such as <span class="html-italic">T.grandis</span> (<span class="html-italic">n</span> = 5) (<span class="html-italic">n</span> (total sampled trees) = 18).</p> Full article ">Figure 3
<p>Atmospheric Hg distribution in the Bunut Seberang Village between 2010 and 2020.</p> Full article ">Figure 4
<p>SEM micrographs of <span class="html-italic">M. champaca</span> at magnifications of (<b>a</b>) 1000, (<b>b</b>) 3000, and (<b>c</b>) 5000×. SEM micrographs of <span class="html-italic">S. mahagoni</span> at magnifications of (<b>d</b>) 1000, (<b>e</b>) 3000, and (<b>f</b>) 5000×. The colored box (<span class="html-fig-inline" id="atmosphere-13-00633-i001"> <img alt="Atmosphere 13 00633 i001" src="/atmosphere/atmosphere-13-00633/article_deploy/html/images/atmosphere-13-00633-i001.png"/></span>: <span class="html-fig-inline" id="atmosphere-13-00633-i002"> <img alt="Atmosphere 13 00633 i002" src="/atmosphere/atmosphere-13-00633/article_deploy/html/images/atmosphere-13-00633-i002.png"/></span>, <span class="html-fig-inline" id="atmosphere-13-00633-i003"> <img alt="Atmosphere 13 00633 i003" src="/atmosphere/atmosphere-13-00633/article_deploy/html/images/atmosphere-13-00633-i003.png"/></span>, or <span class="html-fig-inline" id="atmosphere-13-00633-i004"> <img alt="Atmosphere 13 00633 i004" src="/atmosphere/atmosphere-13-00633/article_deploy/html/images/atmosphere-13-00633-i004.png"/></span>) in a panel depicts the analyzed area.</p> Full article ">Figure 4 Cont.
<p>SEM micrographs of <span class="html-italic">M. champaca</span> at magnifications of (<b>a</b>) 1000, (<b>b</b>) 3000, and (<b>c</b>) 5000×. SEM micrographs of <span class="html-italic">S. mahagoni</span> at magnifications of (<b>d</b>) 1000, (<b>e</b>) 3000, and (<b>f</b>) 5000×. The colored box (<span class="html-fig-inline" id="atmosphere-13-00633-i001"> <img alt="Atmosphere 13 00633 i001" src="/atmosphere/atmosphere-13-00633/article_deploy/html/images/atmosphere-13-00633-i001.png"/></span>: <span class="html-fig-inline" id="atmosphere-13-00633-i002"> <img alt="Atmosphere 13 00633 i002" src="/atmosphere/atmosphere-13-00633/article_deploy/html/images/atmosphere-13-00633-i002.png"/></span>, <span class="html-fig-inline" id="atmosphere-13-00633-i003"> <img alt="Atmosphere 13 00633 i003" src="/atmosphere/atmosphere-13-00633/article_deploy/html/images/atmosphere-13-00633-i003.png"/></span>, or <span class="html-fig-inline" id="atmosphere-13-00633-i004"> <img alt="Atmosphere 13 00633 i004" src="/atmosphere/atmosphere-13-00633/article_deploy/html/images/atmosphere-13-00633-i004.png"/></span>) in a panel depicts the analyzed area.</p> Full article ">Figure 5
<p>(<b>a</b>) Bark thickness (mm) versus THg found, (<b>b</b>) bark thickness versus THg found (control), (<b>c</b>) distance from ASGM (m) versus THg found, (<b>d</b>) distance from ASGM (m) versus THg found (control), (<b>e</b>) elevation versus THg found, and (<b>f</b>) elevation versus THg found (control).</p> Full article ">Figure 6
<p>(<b>a</b>) Distance, (<b>b</b>) thickness, and (<b>c</b>) elevation. <span class="html-fig-inline" id="atmosphere-13-00633-i005"> <img alt="Atmosphere 13 00633 i005" src="/atmosphere/atmosphere-13-00633/article_deploy/html/images/atmosphere-13-00633-i005.png"/></span> = 2q box, <span class="html-fig-inline" id="atmosphere-13-00633-i006"> <img alt="Atmosphere 13 00633 i006" src="/atmosphere/atmosphere-13-00633/article_deploy/html/images/atmosphere-13-00633-i006.png"/></span> = 3q box.</p> Full article ">Figure 6 Cont.
<p>(<b>a</b>) Distance, (<b>b</b>) thickness, and (<b>c</b>) elevation. <span class="html-fig-inline" id="atmosphere-13-00633-i005"> <img alt="Atmosphere 13 00633 i005" src="/atmosphere/atmosphere-13-00633/article_deploy/html/images/atmosphere-13-00633-i005.png"/></span> = 2q box, <span class="html-fig-inline" id="atmosphere-13-00633-i006"> <img alt="Atmosphere 13 00633 i006" src="/atmosphere/atmosphere-13-00633/article_deploy/html/images/atmosphere-13-00633-i006.png"/></span> = 3q box.</p> Full article ">

21 pages, 3854 KiB  

Open AccessArticle

Characterization of Annual Air Emissions Reported by Pulp and Paper Mills in Atlantic Canada

by Gianina Giacosa, Codey Barnett, Daniel G. Rainham and Tony R. Walker

Pollutants 2022, 2(2), 135-155; https://doi.org/10.3390/pollutants2020011 - 8 Apr 2022

Cited by 8 | Viewed by 6363

Abstract

The pulp and paper industry is a major contributor to water and air pollution globally. Pulp and paper processing is an intensive energy consuming process that produces multiple contaminants that pollute water, air, and affect ecological and human health. In Canada, the National [...] Read more.

The pulp and paper industry is a major contributor to water and air pollution globally. Pulp and paper processing is an intensive energy consuming process that produces multiple contaminants that pollute water, air, and affect ecological and human health. In Canada, the National Pollutant Release Inventory (NPRI) is used to assess the release of air pollutants into the atmosphere from industrial facilities (including pulp and paper mills) and provides a repository of annual emissions reported by individual facilities. This study compared annual air emissions of carbon monoxide, nitrogen oxides, total particulate matter (TPM), PM_2.5, PM₁₀, sulphur dioxide, and volatile organic compounds from nine different pulp and/or paper mills in Atlantic Canada from three provinces (Nova Scotia, New Brunswick, and Newfoundland and Labrador) between 2002 and 2019. Results revealed that annual releases were several orders of magnitude higher than federal reporting thresholds suggested by Environment and Climate Change Canada. Pulp mills emit higher pollutant loads than those producing paper. The highest exceedance of a reporting threshold was for particulate matter (PM_2.5) at Northern Pulp in Nova Scotia. The emissions of PM_2.5 were on average (over a 17-year period) about 100,000% above the reporting threshold of 0.3 tonnes per year. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Figure 1
<p>Location of mills and major populated centres. Pulp mills are represented with a circle, paper mills with a square, and the pulp and paper mill with a triangle.</p> Full article ">Figure 2
<p>Air emissions registered in the APEI for Nova Scotia, New Brunswick and Newfoundland, and Labrador for pollutants (<b>a</b>) CO, (<b>b</b>) NO_x, (<b>c</b>) TPM, (<b>d</b>) PM₁₀, (<b>e</b>) PM_2.5, (<b>f</b>) SO₂, and (<b>g</b>) VOC.</p> Full article ">Figure 2 Cont.
<p>Air emissions registered in the APEI for Nova Scotia, New Brunswick and Newfoundland, and Labrador for pollutants (<b>a</b>) CO, (<b>b</b>) NO_x, (<b>c</b>) TPM, (<b>d</b>) PM₁₀, (<b>e</b>) PM_2.5, (<b>f</b>) SO₂, and (<b>g</b>) VOC.</p> Full article ">Figure 3
<p>Air pollutant emissions from 2002 to 2019 for each facility for pollutants (<b>a</b>) CO, (<b>b</b>) NO_x, (<b>c</b>) TPM, (<b>d</b>) PM₁₀, (<b>e</b>) PM_2.5, (<b>f</b>) SO₂, and (<b>g</b>) VOC.</p> Full article ">Figure 4
<p>Distribution of air pollutant emissions from 2002 to 2019 for each facility for the pollutants (<b>a</b>) CO, (<b>b</b>) NO_x, (<b>c</b>) TPM, (<b>d</b>) PM₁₀, (<b>e</b>) PM_2.5, (<b>f</b>) SO₂, and (<b>g</b>) VOC.</p> Full article ">Figure 5
<p>Mean difference from reporting thresholds (DRT) over (<b>a</b>) the sites, (<b>b</b>) the pollutants, and (<b>c</b>) the entire period. White represents no data.</p> Full article ">Figure 6
<p>Percentual difference from the ARL on the CPMAEPPF for (<b>a</b>) TPM and (<b>b</b>) SO₂. Pulp mills are represented by a solid line and paper mills by a dotted line. The black dotted line represents the mean on all mills and the black slashed line represents when a release equals the threshold suggested by CPMAEPPF.</p> Full article ">

21 pages, 7277 KiB  

Open AccessArticle

Development of Vehicle Emission Model Based on Real-Road Test and Driving Conditions in Tianjin, China

by Yi Zhang, Ran Zhou, Shitao Peng, Hongjun Mao, Zhiwen Yang, Michel Andre and Xin Zhang

Atmosphere 2022, 13(4), 595; https://doi.org/10.3390/atmos13040595 - 7 Apr 2022

Cited by 6 | Viewed by 2484

Abstract

Based on the demand of vehicle emission research and control, this paper presents the development of a portable vehicle measurement system (PEMS) based on SEMTECH-DS and ELPI+, the vehicle emission tests carried out on actual roads, and the data obtained for the establishment [...] Read more.

Based on the demand of vehicle emission research and control, this paper presents the development of a portable vehicle measurement system (PEMS) based on SEMTECH-DS and ELPI+, the vehicle emission tests carried out on actual roads, and the data obtained for the establishment and validation of a vehicle emission model. Based on the results of the vehicle emission test, it was found that vehicle driving conditions (speed, acceleration, vehicle specific power (VSP), etc.) had a significant impact on the pollutant emission rate. In addition, local driving cycles were generated and the frequency distribution of VSP-bin under different cycles was analyzed. Then, through the establishment of an emission rate database, calculation of emission factors and validation of the emission model, a vehicle emission model based on actual road driving conditions was developed by taking VSP as the “surrogate variables”. It showed that the emission factor model established in this study could better reflect the vehicle transient emissions on the actual road with high accuracy and local adaptability. Through this study, it could be found that due to the great differences in traffic development modes and vehicle driving conditions in different cities in China, the emission model based on driving conditions was a better choice to carry out the research on vehicle emission in Chinese cities. Compared with directly applying international models or quoting the recommended values of relevant macroscopic guidelines, the emission factor model established in this study, using actual driving conditions, could better reflect the vehicle transient emissions on the actual road with high accuracy and local adaptability. In addition, due to the rapid development of China’s urban traffic and the rapid change of driving conditions, it was of great significance to regularly update China’s urban conditions to improve the accuracy of the model, no matter which model was chosen. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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<p>Location of Tianjin on the map of China.</p> Full article ">Figure 2
<p>The technical route of the development of emission factor model based on driving condition.</p> Full article ">Figure 3
<p>The composition of PEMS and its real installation example.</p> Full article ">Figure 4
<p>Part of tested vehicles equipped with PEMS.</p> Full article ">Figure 5
<p>The test routes for different types of vehicles; (<b>a</b>) Rout A for LDV, MDV and LDT; (<b>b</b>) Route B for LDV, MDV and HDV; (<b>c</b>) Route C for HDV, MDT and HDT.</p> Full article ">Figure 6
<p>The typical relationship between vehicular speed, acceleration and pollutant emission rate; (<b>a</b>) Relationship between vehicular speed, acceleration and CO emission rate; (<b>b</b>) Relationship between vehicular speed, acceleration and HC emission rate; (<b>c</b>) Relationship between vehicular speed, acceleration and NOx emission rate; (<b>d</b>) Relationship between vehicular speed, acceleration and PM emission rate.</p> Full article ">Figure 7
<p>The typical relationship between vehicular VSP and pollutant emission rate. (<b>a</b>) The relationship between vehicular VSP and CO emission rate; (<b>b</b>) The relationship between vehicular VSP and HC emission rate; (<b>c</b>) The relationship between vehicular VSP and NOx emission rate; (<b>d</b>) The relationship between vehicular VSP and PM emission rate.</p> Full article ">Figure 8
<p>The typical distribution of speed acceleration driving condition points; (<b>a</b>) Peak hours (7:00–9:00, 17:00–19:00); (<b>b</b>) Off-peak hours (other time).</p> Full article ">Figure 9
<p>The localized vehicle driving cycle in Tianjin.</p> Full article ">Figure 10
<p>The typical vehicle driving cycles of different speed intervals; (<b>a</b>) The typical vehicle driving cycles under low-speed driving condition; (<b>b</b>) The typical vehicle driving cycles under middle-speed driving condition; (<b>c</b>) The typical vehicle driving cycles under high-speed driving condition.</p> Full article ">Figure 10 Cont.
<p>The typical vehicle driving cycles of different speed intervals; (<b>a</b>) The typical vehicle driving cycles under low-speed driving condition; (<b>b</b>) The typical vehicle driving cycles under middle-speed driving condition; (<b>c</b>) The typical vehicle driving cycles under high-speed driving condition.</p> Full article ">Figure 11
<p>The VSP-bin frequency distributions of different typical vehicle driving cycles; (<b>a</b>) The VSP-bin frequency distributions of low-speed driving condition; (<b>b</b>) The VSP-bin frequency distributions of middle-speed driving condition; (<b>c</b>) The VSP-bin frequency distributions of high-speed driving condition; the areas divided by 4 red dotted lines from left to right corresponds to 5 speed intervals (i.e., “Deceleration”, “Idling”, “Low speed”, “Middle speed”, and “High speed”) in VSP-bin division standard respectively.</p> Full article ">Figure 11 Cont.
<p>The VSP-bin frequency distributions of different typical vehicle driving cycles; (<b>a</b>) The VSP-bin frequency distributions of low-speed driving condition; (<b>b</b>) The VSP-bin frequency distributions of middle-speed driving condition; (<b>c</b>) The VSP-bin frequency distributions of high-speed driving condition; the areas divided by 4 red dotted lines from left to right corresponds to 5 speed intervals (i.e., “Deceleration”, “Idling”, “Low speed”, “Middle speed”, and “High speed”) in VSP-bin division standard respectively.</p> Full article ">Figure 12
<p>The vehicle emission rate (g/s) based on VSP-bin. (<b>a</b>) CO emission rate (g/s) based on VSP-binl; (<b>b</b>) HC emission rate (g/s) based on VSP-bin; (<b>c</b>) NO_x emission rate (g/s) based on VSP-bin; (<b>d</b>) PM emission rate (g/s) based on VSP-bin; the areas divided by 4 red dotted lines from left to right corresponds to 5 speed intervals (i.e., “Deceleration”, “Idling”, “Low speed”, “Middle speed”, and “High speed”) in VSP-bin division standard respectively.</p> Full article ">Figure 13
<p>The emission factors of different tested vehicles; (<b>a</b>) Emission factors of CO; (<b>b</b>) Emission factors of HC; (<b>c</b>) Emission factors of NOx; (<b>d</b>) Emission factors of PM.</p> Full article ">Figure 14
<p>A section of driving condition data was randomly selected from the validation database.</p> Full article ">Figure 15
<p>Comparison of measurement and simulation values of instantaneous pollutant emission rate under the driving condition for validation; (<b>a</b>) Comparison of measurement and simulation values of in-stantaneous CO emission rate; (<b>b</b>) Comparison of measurement and simulation values of instantaneous HC emission rate; (<b>c</b>) Comparison of measurement and simulation values of instantaneous NOx emission rate; (<b>d</b>) Comparison of measurement and simulation values of instantaneous PM emission rate.</p> Full article ">

13 pages, 2433 KiB  

Open AccessArticle

Analysis of the Diurnal Changes in the Water-Soluble Ion Concentration in Wuhan between 2016 and 2019

by Yingying Sun, Jinhui Zhao, Chao He, Zhouxiang Zhang, Nan Chen, Jiaqi Hu, Huanhuan Liu and Xinlei Wang

Atmosphere 2022, 13(4), 582; https://doi.org/10.3390/atmos13040582 - 4 Apr 2022

Cited by 1 | Viewed by 1907

Abstract

This study uses online monitoring data from the Hubei Environmental Monitoring Center’s Atmospheric Compound Pollution Automatic Monitoring Station from 2016 to 2019 to analyze the diurnal changes in the concentration of water-soluble ions in particulate matter in Wuhan. During the study period, the [...] Read more.

This study uses online monitoring data from the Hubei Environmental Monitoring Center’s Atmospheric Compound Pollution Automatic Monitoring Station from 2016 to 2019 to analyze the diurnal changes in the concentration of water-soluble ions in particulate matter in Wuhan. During the study period, the concentrations of SO₂, NO₃⁻, and SO₄²⁻ changed significantly, while those of NH₄⁺, NH₃, and Ca²⁺ exhibited minimal differences. SO₂ and NO₃⁻ showed an annually increasing trend, while NH₄⁺ and SO₄²⁻ exhibited an annually decreasing trend. The ion concentration was generally higher in the winter and spring and lower in the summer and autumn. The concentration of water-soluble ions was generally higher during the day than at night. However, the “weekend effect” on the change in ion concentrations was substantial and higher during the day than at night. This effect was the strongest for NO₃⁻ and the weakest for NH₃. These changes in the weekend effect of water-soluble ions in particulate matter clearly revealed the impact of periodic human activities on atmospheric pollution. Taken together, the results of this novel study reveal the diurnal pollution characteristics and “weekend effect” of water-soluble ions with high concentrations in atmospheric aerosols in Wuhan over a four-year period, thus providing relevant insights for Wuhan’s atmospheric mitigation plan. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Sampling point locations ((<b>a</b>) represents Wuhan city; (<b>b</b>) represents Hongshan District; (<b>c</b>) represents the sampling site).</p> Full article ">Figure 2
<p>Concentrations of water-soluble ions in particulate matter in Wuhan, China during the period 2016–2019. The red dotted line represents the four-year trend line for each ion. R² represents the degree of fit of the trend line. <span class="html-italic">P</span> represents statistical significance.</p> Full article ">Figure 3
<p>Seasonal changes in the water-soluble ion concentrations. (<b>a</b>–<b>g</b>) Changes in the water-soluble ion concentrations of NH₄⁺, Ca²⁺, NO₃⁻, SO₄²⁻, SO₂, and NH₃, respectively.</p> Full article ">Figure 4
<p>Changes in the concentrations of water-soluble ions on weekdays and weekends.</p> Full article ">Figure 5
<p>Water-soluble ion correlation heatmap in Wuhan City.</p> Full article ">

17 pages, 9301 KiB  

Open AccessArticle

Personal Exposure and Inhaled Dose Estimation of Air Pollutants during Travel between Albany, NY and Boston, MA

by Vineet Kumar Pal and Haider A. Khwaja

Atmosphere 2022, 13(3), 445; https://doi.org/10.3390/atmos13030445 - 9 Mar 2022

Cited by 2 | Viewed by 2259

Abstract

Out of eight deaths caused worldwide, one death is caused due to air pollution exposure, making it one of the top global killers. Personal exposure measurement for real-time monitoring has been used for inhaled dose estimation during various modes of workplace commuting. However, [...] Read more.

Out of eight deaths caused worldwide, one death is caused due to air pollution exposure, making it one of the top global killers. Personal exposure measurement for real-time monitoring has been used for inhaled dose estimation during various modes of workplace commuting. However, dose-exposure studies during long commutes are scarce and more information on inhaled doses is needed. This study focuses on personal exposures to size-fractionated particulate matter (PM₁, PM_2.5, PM₄, PM₇, PM₁₀, TSP) and black carbon (BC) inside a bus traveling more than 270 kms on a highway between Albany, NY and Boston, MA. Measurements were also made indoors, outdoors, and while walking in each city. Mean PM (PM₁, PM_2.5, PM₄, PM₇, PM₁₀, TSP) and mean BC concentrations were calculated to estimate the inhaled exposure dose. The highest average PM_2.5 and PM₁₀ exposures concentrations were 30 ± 12 and 111 ± 193 µg/m³, respectively, during Boston to Albany. Notably, personal exposure to BC on a bus from Albany to Boston (5483 ± 2099 ng/m³) was the highest measured during any commute. The average inhaled dose for PM_2.5 during commutes ranged from 0.018 µg/km to 0.371 µg/km. Exposure concentrations in indoor settings (average PM_2.5 = 37 ± 55 µg/m³, PM₁₀ = 78 ± 82 µg/m³, BC = 5695 ± 1774 ng/m³) were higher than those in outdoor environments. Carpeted flooring, cooking, and vacuuming all tended to increase the indoor particulate level. A high BC concentration (1583 ± 1004 ng/m³) was measured during walking. Typical concentration profiles in long-haul journeys are presented. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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<p>A schematic diagram of the bus route from Albany, NY to Boston, MA. (Source: Google Maps, 2019. Accessed 12 May 2019, Wadsworth Center, Albany, NY 12201-0509).</p> Full article ">Figure 2
<p>A scehematic diagram of the evening walk route in Boston, Massachussetts. (Source: Google Maps, 2019. Accessed 12 May 2019, Wadsworth Center, Albany, NY 12201-0509).</p> Full article ">Figure 3
<p>Exposure concentration for a typical bus journey from Boston to Albany during the Campaign: 3 (<b>a</b>) PM₁₀, (<b>b</b>) PM_2.5, (<b>c</b>) PM₁, and (<b>d</b>) BC.</p> Full article ">Figure 3 Cont.
<p>Exposure concentration for a typical bus journey from Boston to Albany during the Campaign: 3 (<b>a</b>) PM₁₀, (<b>b</b>) PM_2.5, (<b>c</b>) PM₁, and (<b>d</b>) BC.</p> Full article ">Figure 4
<p>(<b>a</b>) PM₁₀, (<b>b</b>) PM_2.5, and (<b>c</b>) BC exposure concentrations for a walk in Boston (Campaign 2). Note: MicroAeth AE51 background noise (or negative values) were excluded from BC exposure analysis.</p> Full article ">Figure 4 Cont.
<p>(<b>a</b>) PM₁₀, (<b>b</b>) PM_2.5, and (<b>c</b>) BC exposure concentrations for a walk in Boston (Campaign 2). Note: MicroAeth AE51 background noise (or negative values) were excluded from BC exposure analysis.</p> Full article ">Figure 5
<p>Exposure concentration variations for 24 h in Albany, NY (Campaign 6): (<b>a</b>) PM₁₀, (<b>b</b>) PM_2.5, (<b>c</b>) PM₁, and (<b>d</b>) BC.</p> Full article ">Figure 5 Cont.
<p>Exposure concentration variations for 24 h in Albany, NY (Campaign 6): (<b>a</b>) PM₁₀, (<b>b</b>) PM_2.5, (<b>c</b>) PM₁, and (<b>d</b>) BC.</p> Full article ">Figure 6
<p>Exposure concentrations variations for 24 h in Albany (Campaign 7): (<b>a</b>) PM₁₀, (<b>b</b>) PM_2.5, (<b>c</b>) PM₁, and (<b>d</b>) BC.</p> Full article ">Figure 6 Cont.
<p>Exposure concentrations variations for 24 h in Albany (Campaign 7): (<b>a</b>) PM₁₀, (<b>b</b>) PM_2.5, (<b>c</b>) PM₁, and (<b>d</b>) BC.</p> Full article ">Figure 7
<p>Mass concentration of (<b>a</b>) PM₁₀, (<b>b</b>) PM_2.5, (<b>c</b>) PM₁, and (<b>d</b>) BC inside house in Boston (Campaign 5).</p> Full article ">Figure 7 Cont.
<p>Mass concentration of (<b>a</b>) PM₁₀, (<b>b</b>) PM_2.5, (<b>c</b>) PM₁, and (<b>d</b>) BC inside house in Boston (Campaign 5).</p> Full article ">

10 pages, 3833 KiB  

Open AccessArticle

Improvement of the Standard Chimney Electrostatic Precipitator by Dividing the Flue Gas Stream into a Larger Number of Pipes

by Juraj Trnka, Jozef Jandačka and Michal Holubčík

Appl. Sci. 2022, 12(5), 2659; https://doi.org/10.3390/app12052659 - 4 Mar 2022

Cited by 7 | Viewed by 2640

Abstract

Combustion of biomass-based solid fuels is becoming increasingly popular, especially in small heat sources. A major problem in the combustion of biomass is the increased production of emissions and especially the solid component of PM particles. Currently, the most used solution to this [...] Read more.

Combustion of biomass-based solid fuels is becoming increasingly popular, especially in small heat sources. A major problem in the combustion of biomass is the increased production of emissions and especially the solid component of PM particles. Currently, the most used solution to this problem is the application of electrostatic chimney separators, which innovations are discussed in our article. Two models of electrostatic precipitators were constructed in this work. The aim of this work was to compare the use of a standard single-pipe chimney electrostatic precipitator with a newer four-pipe variant. Eight measurements were performed on both devices with and without the use of an electrostatic precipitator, on the basis of which the separation efficiency was evaluated for both variants. The results of the measurements showed the initial value of the average PM production in the one-pipe variant decreased from 1012 to 416 $\mathrm{mg}.{\mathrm{m}}^{-3}$ when using the separator, while in the use of the four-pipe variant it decreased from the starting value 342 to only 152 $\mathrm{mg}.{\mathrm{m}}^{-3}$. These results show that the improvement of the classic single-pipe separator by increasing the number of tubes significantly reduced the production of PM emissions and increased the separation efficiency from 66 to 85%. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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<p>The course of the phases of the combustion process during measurement.</p> Full article ">Figure 2
<p>Experimental setup: (<b>a</b>) combustion device with standardized chimney electrostatic precipitator; (<b>b</b>) improved four-pipe chimney electrostatic precipitator.</p> Full article ">Figure 3
<p>Experimental HV power supply: (<b>a</b>) model CX-120A with power 100 W and voltage 20 kV; (<b>b</b>) model CX-600A with power and 300 W and voltage 60 kV.</p> Full article ">Figure 4
<p>Measuring probes: (<b>a</b>) probe for measuring PM production; (<b>b</b>) probe for measuring chimney temperature and flow rate; (<b>c</b>) layout of cascade sampling probe parts.</p> Full article ">Figure 5
<p>Sets of collection filters F1 PM &gt; 10, F2 PM 10–2.5, and F3 PM &lt; 2.5 for individual measurements of variant 1: (<b>a</b>) M1, (<b>b</b>) M2, (<b>c</b>) M3, (<b>d</b>) M4, (<b>e</b>) M5, (<b>f</b>) M6, (<b>g</b>) M7, and (<b>h</b>) M8.</p> Full article ">Figure 6
<p>Amount of PM emissions for measurements of variant 1.</p> Full article ">Figure 7
<p>Amount of PM emissions in measurements of variant 2.</p> Full article ">Figure 8
<p>Sets of collection filters F1 PM &gt; 10, F2 PM 10–2.5, and F3 &lt; 2.5 for individual measurements of variant 2: (<b>a</b>) M1, (<b>b</b>) M2, (<b>c</b>) M3, (<b>d</b>) M4, (<b>e</b>) M5, (<b>f</b>) M6, (<b>g</b>) M7, and (<b>h</b>) M8.</p> Full article ">Figure 9
<p>Graphical dependence of separation efficiency on the size of the collection surface.</p> Full article ">Figure 10
<p>Graphical dependence of separation efficiency on the distance between electrodes.</p> Full article ">

21 pages, 2937 KiB  

Open AccessArticle

A Machine Learning-Based Ensemble Framework for Forecasting PM_2.5 Concentrations in Puli, Taiwan

by Peng-Yeng Yin, Alex Yaning Yen, Shou-En Chao, Rong-Fuh Day and Bir Bhanu

Appl. Sci. 2022, 12(5), 2484; https://doi.org/10.3390/app12052484 - 27 Feb 2022

Cited by 4 | Viewed by 2355

Abstract

Forecasting of PM_2.5 concentration is a global concern. Evidence has shown that the ambient PM_2.5 concentrations are harmful to human health, climate change, plant species mortality, etc. PM_2.5 concentrations are caused by natural and anthropogenic activities, and it is challenging [...] Read more.

Forecasting of PM_2.5 concentration is a global concern. Evidence has shown that the ambient PM_2.5 concentrations are harmful to human health, climate change, plant species mortality, etc. PM_2.5 concentrations are caused by natural and anthropogenic activities, and it is challenging to predict them due to many uncertain factors. Current research has focused on developing a new model while overlooking the fact that every single model for PM_2.5 prediction has its own strengths and weaknesses. This paper proposes an ensemble framework which combines four diverse learning models for PM_2.5 forecasting in Puli, Taiwan. It explores the synergy between parametric and non-parametric learning, and short-term and long-term learning. The feature set covers periodic, meteorological, and autoregression variables which are selected by a spiral validation process. The experimental dataset, spanning from 1 January 2008 to 31 December 2019, from Puli Township in central Taiwan, is used in this study. The experimental results show the proposed multi-model framework can synergize the advantages of the embedded models and obtain an improved forecasting result. Further, the benefit obtained by blending short-term learning with long-term learning is validated, in surpassing the performance obtained by using just single type of learning. Our multi-model framework compares favorably with deep-learning models on Puli dataset. It also shows high adaptivity, such that our multi-model framework is comparable to the leading methods for PM_2.5 forecasting in Delhi, India. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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<p>Architecture of our multi-model framework.</p> Full article ">Figure 2
<p>The spiral model of software development life cycle.</p> Full article ">Figure 3
<p>(<b>a</b>) The daily and (<b>b</b>) yearly periodic trends of PM_2.5 time series.</p> Full article ">Figure 4
<p>The autocorrelation characteristic of PM_2.5 series.</p> Full article ">Figure 5
<p>Variations of forecasting RMSE with the retained feature set in each spiral iteration.</p> Full article ">Figure 6
<p>Illustration of PM_2.5 series approximation by using the Fourier polynomial of degree <span class="html-italic">n</span> equivalent to 50, 60, and 70, respectively.</p> Full article ">Figure 7
<p>Location of the studied field. (<b>a</b>) Puli Township and western plausible pollution sources. (<b>b</b>) Basin geography of Puli Township. The EPA supersite marked by a red star is located in the central Puli downtown.</p> Full article ">Figure 7 Cont.
<p>Location of the studied field. (<b>a</b>) Puli Township and western plausible pollution sources. (<b>b</b>) Basin geography of Puli Township. The EPA supersite marked by a red star is located in the central Puli downtown.</p> Full article ">

20 pages, 1343 KiB  

Open AccessReview

A Review of Air Pollution Mitigation Approach Using Air Pollution Tolerance Index (APTI) and Anticipated Performance Index (API)

by Ibironke Titilayo Enitan, Olatunde Samod Durowoju, Joshua Nosa Edokpayi and John Ogony Odiyo

Atmosphere 2022, 13(3), 374; https://doi.org/10.3390/atmos13030374 - 23 Feb 2022

Cited by 16 | Viewed by 5282

Abstract

Air pollution is a global environmental issue, and there is an urgent need for sustainable remediation techniques. Thus, phytoremediation has become a popular approach to air pollution remediation. This paper reviewed 28 eco-friendly indigenous plants based on both the air pollution tolerance index [...] Read more.

Air pollution is a global environmental issue, and there is an urgent need for sustainable remediation techniques. Thus, phytoremediation has become a popular approach to air pollution remediation. This paper reviewed 28 eco-friendly indigenous plants based on both the air pollution tolerance index (APTI) and anticipated performance index (API), using tolerance level and performance indices to evaluate the potential of most indigenous plant species for air pollution control. The estimated APTI ranged from 4.79 (Syzygium malaccense) to 31.75 (Psidium guajava) among the studied indigenous plants. One of the selected plants is tolerant, and seven (7) are intermediate to air pollution with their APTI in the following order: Psidium guajava (31.75) > Swietenia mahogany (28.08) > Mangifera indica L. (27.97) > Ficus infectoria L. (23.93) > Ficus religiosa L. (21.62) > Zizyphus Oenoplia Mill (20.06) > Azadirachta indica A. Juss. (19.01) > Ficus benghalensis L. (18.65). Additionally, the API value indicated that Mangifera indica L. ranges from best to good performer; Ficus religiosa L. and Azadirachta indica A. Juss. from excellent to moderate performers; and Cassia fistula L. from poor to very poor performer for air pollution remediation. The Pearson correlation shows that there is a positive correlation between API and APTI (R2 = 0.63), and this implies that an increase in APTI increases the API and vice versa. This paper shows that Mangifera indica L., Ficus religiosa L., and Azadirachta indica A. Juss. have good potential for sustainable reduction in air pollution for long-term management and green ecomanagement development. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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<p>Comparisons of particulate matter size (PM) [<a href="#B44-atmosphere-13-00374" class="html-bibr">44</a>].</p> Full article ">Figure 2
<p>Formation of ozone.</p> Full article ">Figure 3
<p>Schematic representation of phytoremediation techniques.</p> Full article ">Figure 4
<p>Variations of APTI and API in the four different plants from eight different studies.</p> Full article ">

19 pages, 3610 KiB  

Open AccessArticle

Effects of COVID-19-Epidemic-Related Changes in Human Behaviors on Air Quality and Human Health in Metropolitan Parks

by Wen-Pei Sung and Chun-Hao Liu

Atmosphere 2022, 13(2), 276; https://doi.org/10.3390/atmos13020276 - 7 Feb 2022

Cited by 2 | Viewed by 1518

Abstract

The outbreak of the new coronavirus pneumonia (Coronavirus disease 2019, COVID-19) created a serious impact on the lives of people around the world. Humans, affected by the COVID-19 virus, must reduce related activities to suppress the spread of this disease. However, the pandemic [...] Read more.

The outbreak of the new coronavirus pneumonia (Coronavirus disease 2019, COVID-19) created a serious impact on the lives of people around the world. Humans, affected by the COVID-19 virus, must reduce related activities to suppress the spread of this disease. However, the pandemic had a positive impact on the environment due to reduced outdoor activities. The correlation between reduced human outdoor activities and health effects was investigated in this study through two Metropolitan parks in Taichung, Taiwan. The developed low-cost air quality sensors were installed in these two parks to detect the variances in PM_2.5 concentrations during the epidemic outbreak. Experimental results indicated that PM_2.5 concentrations in these two parks were reduced from about 23.25 and 22.96 μg/m³ to 8.19 and 8.48 μg/m³, respectively, the median absolute deviations (MAD) decreased from 4.21 and 4.57 to 1.71 and 1.35, respectively after the epidemic outbreak, and the calculated standard deviation of all normal-to-normal interval (SDNN) and the ratio of low-frequency power to high-frequency (LF/HF) indicated that the drops of PM_2.5 concentrations caused the increased health-related benefits by 73.53% with the variances being low. These results showed that the PM_2.5 concentrations displayed high correlations with human activities, which also played important roles in human health effects. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Figure 1
<p>The hardware circuit of the developed aerosol sensing system.</p> Full article ">Figure 2
<p>The experimental field of metropolitan parks at the north of Beitun District, Taichung, Taiwan.</p> Full article ">Figure 3
<p>Photos of the (<b>a</b>) Dunhua park and (<b>b</b>) 823 Memorial park at the north of Beitun District, Taichung, Taiwan.</p> Full article ">Figure 4
<p>The experimental data comparison of the PMS3003 aerosol sensor with the AEROCET 531S portable dust meter.</p> Full article ">Figure 5
<p>The change of PM_2.5 concentration at the Xitun station in Taichung City of the Central Meteorological Administration.</p> Full article ">Figure 6
<p>The temperature variation at the Xitun station in Taichung City of the Central Meteorological Administration.</p> Full article ">Figure 7
<p>The east-west wind speed variation at the Xitun station in Taichung City of the Central Meteorological Administration.</p> Full article ">Figure 8
<p>The relative humidity and rainfall variation at the Xitun station in Taichung City of the Central Meteorological Administration.</p> Full article ">Figure 9
<p>The relationship between epidemic changes and the variation of PM_2.5 concentration.</p> Full article ">Figure 10
<p>The box and whisker plot of the variation of PM_2.5 concentration at different epidemic outbreak stages.</p> Full article ">Figure 11
<p>The comparison of the PM_2.5 concentration changes at Xitun station before the epidemic outbreak and at various periods of the epidemic outbreak.</p> Full article ">Figure 12
<p>The comparison of the PM_2.5 concentration changes at Dunhua Park before the epidemic outbreak and at various periods of the epidemic outbreak.</p> Full article ">Figure 13
<p>The comparison of the PM_2.5 concentration changes at 823 Park before the epidemic outbreak and at various periods of the epidemic outbreak.</p> Full article ">Figure 14
<p>Improvement benefits for SDNN reduction and LF/HF promotion with the PM_2.5 concentration change in Dunhua Park.</p> Full article ">Figure 15
<p>Improvement benefits for SDNN reduction and LF/HF promotion with the PM_2.5 concentration change in 823 Park.</p> Full article ">

23 pages, 7718 KiB  

Open AccessArticle

Poor Visibility in Winter Due to Synergistic Effect Related to Fine Particulate Matter and Relative Humidity in the Taipei Metropolis, Taiwan

by Li-Wei Lai

Atmosphere 2022, 13(2), 270; https://doi.org/10.3390/atmos13020270 - 5 Feb 2022

Cited by 6 | Viewed by 2100

Abstract

Visibility is important because it influences transportation safety. This study examined the relationships among sea–land breezes, relative humidity (RH), and the urban heat island (UHI) effect. The study also sought to understand how the synergistic effects of fine particulate matter (PM_2.5) [...] Read more.

Visibility is important because it influences transportation safety. This study examined the relationships among sea–land breezes, relative humidity (RH), and the urban heat island (UHI) effect. The study also sought to understand how the synergistic effects of fine particulate matter (PM_2.5) and RH influence visibility. Hourly meteorological, PM_2.5 concentration, and visibility data from 2016 to 2019 were obtained from government-owned stations. This study used quadratic equations, exponential functions, and multi-regression models, along with a comparison test, to analyse the relationships between these variables. While sea breezes alone cannot explain the presence of PM_2.5, UHI circulation coupled with sea breezes during winter can promote the accumulation of PM_2.5. The synergistic effects of RH, PM_2,5, and aerosol hygroscopicity exist in synoptic patterns type I and type III. PM_2.5 was negatively correlated with visibility in the winter, when the RH was 67–95% and the continental cold high-pressure (CCHP) system was over the Asian continent (type I), or when the RH was 49–89% and the CCHP had moved eastward, with its centre located beyond 125° E (type III). The synergistic predictor variable PM_2.5×RH was more important than PM_2.5 and RH individually in explaining the variation in visibility. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Figure 1

Figure 1
<p>Taiwan geographical information: (<b>a</b>) terrain of the Taipei metropolis; (<b>b</b>) location of Taiwan and the Taipei metropolis; (<b>c</b>) distribution of the sites. The numbers of the sites are shown in <a href="#atmosphere-13-00270-t001" class="html-table">Table 1</a>. Blue circles and blue stars indicate the weather stations operated by the Central Weather Bureau, triangles indicate the air-quality monitoring stations operated by the Environmental Protection Administration, and blue stars indicate the urban and rural sites used to calculate the urban heat island index.</p> Full article ">Figure 2
<p>Negative relationship between PM_2.5 concentration and visibility during different situations from 2016 to 2019 in the Taipei metropolis: (<b>a</b>) synoptic weather pattern type I when 67% <math display="inline"><semantics> <mo>≤</mo> </semantics></math> RH <math display="inline"><semantics> <mo>≤</mo> </semantics></math> 95%; (<b>b</b>) synoptic weather pattern type III when 49% <math display="inline"><semantics> <mo>≤</mo> </semantics></math> RH <math display="inline"><semantics> <mo>≤</mo> </semantics></math> 89%.</p> Full article ">Figure 3
<p>Examples of the three main synoptic weather patterns influencing Taiwan weather conditions in the winter: (<b>a</b>) 6 February 2016 14:00 LST; type I: a continental high-pressure system was over the Asian continent, and the north-easterly monsoon winds prominently influenced the weather conditions in the Taipei metropolis; (<b>b</b>) 23 January 2017 14:00 LST; type II: a continental high-pressure system had left the Asian continent, but its centre was not further east than 125° E; (<b>c</b>) 18 April 2019 8:00 LST; type III: a continental high-pressure system had moved eastward, moving its centre east of 125° E, and the easterly or south-easterly winds affected the weather conditions in the Taipei metropolis (courtesy of the Department of Atmospheric Sciences at the Chinese Culture University).</p> Full article ">Figure 3 Cont.
<p>Examples of the three main synoptic weather patterns influencing Taiwan weather conditions in the winter: (<b>a</b>) 6 February 2016 14:00 LST; type I: a continental high-pressure system was over the Asian continent, and the north-easterly monsoon winds prominently influenced the weather conditions in the Taipei metropolis; (<b>b</b>) 23 January 2017 14:00 LST; type II: a continental high-pressure system had left the Asian continent, but its centre was not further east than 125° E; (<b>c</b>) 18 April 2019 8:00 LST; type III: a continental high-pressure system had moved eastward, moving its centre east of 125° E, and the easterly or south-easterly winds affected the weather conditions in the Taipei metropolis (courtesy of the Department of Atmospheric Sciences at the Chinese Culture University).</p> Full article ">Figure 3 Cont.
<p>Examples of the three main synoptic weather patterns influencing Taiwan weather conditions in the winter: (<b>a</b>) 6 February 2016 14:00 LST; type I: a continental high-pressure system was over the Asian continent, and the north-easterly monsoon winds prominently influenced the weather conditions in the Taipei metropolis; (<b>b</b>) 23 January 2017 14:00 LST; type II: a continental high-pressure system had left the Asian continent, but its centre was not further east than 125° E; (<b>c</b>) 18 April 2019 8:00 LST; type III: a continental high-pressure system had moved eastward, moving its centre east of 125° E, and the easterly or south-easterly winds affected the weather conditions in the Taipei metropolis (courtesy of the Department of Atmospheric Sciences at the Chinese Culture University).</p> Full article ">Figure 4
<p>Relationships among relative humidity (RH), visibility obtained from Songsang International Airport, urban heat island (UHI) effect, and sea–land breezes from 2016 to 2019 in the winter, October to April, in the Taipei metropolis: (<b>a</b>) Tamsui; (<b>b</b>) wind rose for Tamsui; (<b>c</b>) Keelung; (<b>d</b>) wind rose for Keelung.</p> Full article ">Figure 4 Cont.
<p>Relationships among relative humidity (RH), visibility obtained from Songsang International Airport, urban heat island (UHI) effect, and sea–land breezes from 2016 to 2019 in the winter, October to April, in the Taipei metropolis: (<b>a</b>) Tamsui; (<b>b</b>) wind rose for Tamsui; (<b>c</b>) Keelung; (<b>d</b>) wind rose for Keelung.</p> Full article ">Figure 4 Cont.
<p>Relationships among relative humidity (RH), visibility obtained from Songsang International Airport, urban heat island (UHI) effect, and sea–land breezes from 2016 to 2019 in the winter, October to April, in the Taipei metropolis: (<b>a</b>) Tamsui; (<b>b</b>) wind rose for Tamsui; (<b>c</b>) Keelung; (<b>d</b>) wind rose for Keelung.</p> Full article ">Figure 5
<p>Wind roses for G2-A, G4-A, and G4-B in the upwind areas, Tamsui and Keelung stations, and the downwind area, Taipei station, of the Taipei Basin when rainfall amount was lower than 0.1 mm between 11:00 and 19:00 LST in the winter from 2016 to 2019: (<b>a</b>) G2-A: longitude of high-pressure centre &gt; 125<math display="inline"><semantics> <mrow> <mo>°</mo> </mrow> </semantics></math> E; T_m &gt; 20 °C; UHI &gt; 0 °C; sea breeze; (<b>b</b>) G4-A: longitude of high-pressure centre &gt; 125° E; T_m &gt; 20 °C; UHI &lt; 0 °C; sea breeze; (<b>c</b>) G4-B: longitude of high-pressure centre &gt; 125<math display="inline"><semantics> <mrow> <mo>°</mo> </mrow> </semantics></math> E; T_m &gt; 20 °C; UHI &lt; 0 °C; non-sea breeze. Red triangle symbols indicate the Tamsui, Taipei, and Keelung stations.</p> Full article ">Figure 5 Cont.
<p>Wind roses for G2-A, G4-A, and G4-B in the upwind areas, Tamsui and Keelung stations, and the downwind area, Taipei station, of the Taipei Basin when rainfall amount was lower than 0.1 mm between 11:00 and 19:00 LST in the winter from 2016 to 2019: (<b>a</b>) G2-A: longitude of high-pressure centre &gt; 125<math display="inline"><semantics> <mrow> <mo>°</mo> </mrow> </semantics></math> E; T_m &gt; 20 °C; UHI &gt; 0 °C; sea breeze; (<b>b</b>) G4-A: longitude of high-pressure centre &gt; 125° E; T_m &gt; 20 °C; UHI &lt; 0 °C; sea breeze; (<b>c</b>) G4-B: longitude of high-pressure centre &gt; 125<math display="inline"><semantics> <mrow> <mo>°</mo> </mrow> </semantics></math> E; T_m &gt; 20 °C; UHI &lt; 0 °C; non-sea breeze. Red triangle symbols indicate the Tamsui, Taipei, and Keelung stations.</p> Full article ">Figure 6
<p>Comparison of the distributions of relative humidity (RH) and PM_2.5 concentrations between the two cases when the synoptic weather pattern was type I and the urban heat island effect was greater than 0 °C: (<b>a</b>,<b>c</b>) mean PM_2.5 concentration was 25.9 µg/m³, and mean RH was 86.3% on 5 January 2019 14:00 LST; (<b>b</b>,<b>d</b>) mean PM_2.5 concentration was 5.1 µg/m³, and mean RH was 86.3% on 22 February 2019 14:00 LST. The red star symbol indicates Songsang International Airport (121°33′09″ E, 25°04′11″ N).</p> Full article ">Figure 7
<p>Comparison of the distributions of relative humidity (RH) and PM_2.5 concentrations between the two cases when the synoptic weather pattern was type I and the urban heat island effect was greater than 0 °C: (<b>a</b>,<b>c</b>) mean PM_2.5 concentration was 25.9 µg/m³, and mean RH was 84.7% on 24 February 2016 14:00 LST; (<b>b</b>,<b>d</b>) mean PM_2.5 concentration was 25.9 µg/m³, and mean RH was 68% on 14 March 2016 14:00 LST. The red star symbol indicates Songsang International Airport (121°33′09″ E, 25°04′11″ N).</p> Full article ">

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Supplementary material:
Supplementary File 1 (ZIP, 1216 KiB)

18 pages, 4318 KiB  

Open AccessArticle

Development and Characterization of a Time-Sequenced Cascade Impactor: Application to Transient PM_2.5 Pollution Events in Urbanized and Industrialized Environments

by Soulemane Halif Ngagine, Karine Deboudt, Pascal Flament, Marie Choël, Pierre Kulinski and Fabien Marteel

Atmosphere 2022, 13(2), 244; https://doi.org/10.3390/atmos13020244 - 31 Jan 2022

Cited by 2 | Viewed by 2674

Abstract

To set up a sampling and analysis strategy for particulate matter (PM) based on the time periods used in international standards is often inadequate for assessing the impact of day/night cycles or episodic emissions on urban air quality. To obtain a detailed physico–chemical [...] Read more.

To set up a sampling and analysis strategy for particulate matter (PM) based on the time periods used in international standards is often inadequate for assessing the impact of day/night cycles or episodic emissions on urban air quality. To obtain a detailed physico–chemical characterization of urban PM when concentrations exceed the regulatory thresholds, a new rotary cascade impactor named the Time-Resolved Atmospheric Particle Sampler (TRAPS) was designed and tested for coarse and fine particle sampling. The TRAPS implementation, coupled with Optical Particle Counter measurements, provides time-resolved samples that can be analyzed by a wide range of single-particle analysis techniques. The TRAPS theoretical design was verified experimentally. Experimental cut-off diameters of 1.32 and 0.13 µm, respectively, for coarse and fine stages, were found in good agreement with theoretical values. Additionally, good trace separation, preventing inter-sample contamination, was evidenced by Scanning Electron Microscopy (SEM). The homogeneous distribution of particles of different types over a trace was also verified. As a case study, automated SEM-EDX analysis of 2500 particles, collected during two pollution peaks of a transient PM_2.5 pollution event, revealed that individual particles’ chemical composition was influenced by local sources during the first pollution peak, and mainly transported during the second peak. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Figure 1

Figure 1
<p>Internal view of the TRAPS impactor (<b>a</b>) and scheme of the acceleration nozzles with their dimensions and internal views (A-A cross-sectional drawing), for the coarse (<b>b</b>,<b>d</b>) and fine (<b>c</b>,<b>e</b>) stages.</p> Full article ">Figure 2
<p>Schematic view of the experimental setup for determining the fine stage collection efficiency using nebulized monodisperse silica spheres.</p> Full article ">Figure 3
<p>Schematic representation of the experimental setup for determining the coarse stage collection efficiency using vortex shaking of powders of monodisperse silica spheres.</p> Full article ">Figure 4
<p>Schematic representation of the four configurations used for the experimental determination of the collection efficiency. The nozzles of the coarse and fine stages are represented, respectively, in red and yellow. The motors and plates were removed in configurations (<b>B</b>,<b>D</b>), while the stages were fully assembled (with all their nozzles, motors and collection plates) in configurations (<b>A</b>,<b>C</b>).</p> Full article ">Figure 5
<p>Theoretical (blue dotted line) and experimental (black line) collection efficiency curves for the coarse (<b>a</b>) and fine (<b>b</b>) stages (Error bars = 1 S.D with <span class="html-italic">n</span> = 5 for the coarse stage and 3 S.D with <span class="html-italic">n</span> = 5 for the fine stage).</p> Full article ">Figure 6
<p>TSEM images (magnification: 50×, accelerating voltage: 15 kV) of the fine stage samples showing sodium chloride particles (<b>a</b>) and silica nanospheres (<b>b</b>) collected for 1 and 5 min, respectively.</p> Full article ">Figure 7
<p>TSEM images (Magnification: 25×, Accelerating voltage: 15 kV) of the coarse stage samples showing silica microspheres collected for 6 min.</p> Full article ">Figure 8
<p>Image of the TEM grid (fine stage) used to evaluate the homogeneity of the repartition of impacted particles. The width of the impaction trace is marked by 2 horizontal lines and the nozzle width by 2 dotted lines. Green, blue and purple rectangles represent the analyzed areas.</p> Full article ">Figure 9
<p>Distribution of the 6 particle types over the analyzed areas identified in <a href="#atmosphere-13-00244-f007" class="html-fig">Figure 7</a>. Each point represents a particle and each color a particle type. A total of 405, 763 and 567 particles are observed in the green, blue and purple areas, respectively.</p> Full article ">Figure 10
<p>Time series evolution of PM_2.5 mass concentration at our sampling site. Colored rectangles represent the TRAPS sampling periods, where P1 and P5 (cyan) are the analyzed samples whose results are discussed in the paper.</p> Full article ">Figure 11
<p>Relative contribution of different particle types during P1 and P5, respectively, for the TRAPS fine fraction ((<b>a</b>) coarse stage) and ultrafine fraction ((<b>b</b>) fine stage).</p> Full article ">

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Supplementary material:
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11 pages, 257 KiB  

Open AccessArticle

The Impact of Short-Term Outdoor Air Pollution on Clinical Status and Prognosis of Hospitalized Patients with Coronary Artery Disease Treated with Percutaneous Coronary Intervention

by Piotr Desperak, Aneta Desperak, Bożena Szyguła-Jurkiewicz, Piotr Rozentryt, Andrzej Lekston and Mariusz Gąsior

J. Clin. Med. 2022, 11(3), 484; https://doi.org/10.3390/jcm11030484 - 18 Jan 2022

Viewed by 1573

Abstract

Background: The aim of this study was to determine the influence of acute exposure to air pollutants on patients’ profile, short- and mid-term outcomes of hospitalized patients with coronary artery disease (CAD) treated with coronary angioplasty. Methods: Out of 19,582 patients of the [...] Read more.

Background: The aim of this study was to determine the influence of acute exposure to air pollutants on patients’ profile, short- and mid-term outcomes of hospitalized patients with coronary artery disease (CAD) treated with coronary angioplasty. Methods: Out of 19,582 patients of the TERCET Registry, 7521 patients living in the Upper Silesia and Zaglebie Metropolis were included. The study population was divided into two groups according to the diagnosis of chronic (CCS) or acute coronary syndromes (ACS). Data on 24-h average concentrations of particulate matter with aerodynamic diameter <10 μm (PM10), sulfur dioxide (SO₂), nitrogen monoxide (NO), nitrogen dioxide (NO₂), and ozone (O₃) were obtained from eight environmental monitoring stations. Results: No significant association between pollutants’ concentration with baseline characteristic and in-hospital outcomes was observed. In the ACS group at 30 days, exceeding the 3rd quartile of PM10 was associated with almost 2-fold increased risk of adverse events and more than 3-fold increased risk of death. Exceeding the 3rd quartile of SO₂ was connected with more than 8-fold increased risk of death at 30 days. In the CCS group, exceeding the 3rd quartile of SO₂ was linked to almost 2,5-fold increased risk of 12-month death. Conclusions: The acute increase in air pollutants’ concentrations affect short- and mid-term prognosis in patients with CAD. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

18 pages, 2673 KiB  

Open AccessArticle

Characteristics of Volatile Organic Compounds in the Pearl River Delta Region, China: Chemical Reactivity, Source, and Emission Regions

by Weiqiang Yang, Qingqing Yu, Chenglei Pei, Chenghao Liao, Jianjun Liu, Jinpu Zhang, Yanli Zhang, Xiaonuan Qiu, Tao Zhang, Yongbo Zhang and Xinming Wang

Atmosphere 2022, 13(1), 9; https://doi.org/10.3390/atmos13010009 - 21 Dec 2021

Cited by 13 | Viewed by 3917

Abstract

Volatile organic compounds (VOCs) are important precursors of photochemical ozone and secondary organic aerosol (SOA). Here, hourly variations of ambient VOCs were monitored with an online system at an urban site (Panyu, PY) in the Pearl River Delta region during August–September of 2020 [...] Read more.

Volatile organic compounds (VOCs) are important precursors of photochemical ozone and secondary organic aerosol (SOA). Here, hourly variations of ambient VOCs were monitored with an online system at an urban site (Panyu, PY) in the Pearl River Delta region during August–September of 2020 in order to identify reactive VOC species and major sources of VOCs, OH loss rate (L_OH), SOA formation potential (SOAFP), and corresponding emission source regions. The average concentration of VOCs at PY was 31.80 ± 20.82 ppbv during the campaign. The C₂–C₅ alkanes, aromatics, and ≥C₆ alkanes contributed for the majority of VOC, alkenes and aromatics showed the highest contribution to L_OH and SOAFP. Further, m/p-xylene, propene, and toluene were found to be the top three most reactive anthropogenic VOC species, with respective contributions of 11.6%, 6.1%, and 5.8% to total L_OH. Toluene, m/p-xylene, and o-xylene constituted a large fraction of calculated SOAFP. Seven major sources were identified by using positive matrix factorization model. Vehicle exhaust made the most significant contribution to VOCs, followed by liquefied petroleum gas and combustion sources. However, industrial-related sources (including industrial solvent use and industrial process emission) had the largest contribution to L_OH and SOAFP. By combining source contribution with wind direction and wind speed, the regions of different sources were further identified. Based on high-resolution observation data during ozone pollution, this study clearly exhibits key reactive VOC species and the major emission regions of different VOC sources, and thus benefits the accurate emission control of VOCs in the near future. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Figure 1
<p>Location of VOC monitoring sites in the Pearl River Delta region.</p> Full article ">Figure 2
<p>Time series of observed VOCs, O₃, NO₂, PM_2.5, and meteorological parameters (temperature, humidity, wind speed, wind direction) at PY site during the campaign.</p> Full article ">Figure 3
<p>Composition and top ten species of (<b>a</b>) VOCs, (<b>b</b>) L_OH, and (<b>c</b>) SOAFP, respectively.</p> Full article ">Figure 4
<p>Diurnal variations of VOCs and the main components.</p> Full article ">Figure 5
<p>Correlations between mixing ratio of VOCs and L_OH.</p> Full article ">Figure 6
<p>Ratios of (<b>a</b>) i-pentane/n-pentane and (<b>b</b>) toluene/benzene at PY site during campaign.</p> Full article ">Figure 7
<p>Source profiles of VOCs resolved from PMF.</p> Full article ">Figure 8
<p>Source contributions of (<b>a</b>) VOCs, (<b>b</b>) L_OH, and (<b>c</b>) SOAFP.</p> Full article ">Figure 9
<p>The distributions of mixing ratio of each source in wind direction and speed.</p> Full article ">

17 pages, 4083 KiB  

Open AccessArticle

Study on the Law of Harmful Gas Release from Limnoperna fortunei (Dunker 1857) during Maintenance Period of Water Tunnel Based on K-Means Outlier Treatment

by Ruonan Wang, Xiaoling Wang, Songmin Li, Jupeng Shen, Jianping Wang, Changxin Liu, Yazhi Zheng, Yitian Chen and Chaoyuan Ding

Appl. Sci. 2021, 11(24), 11995; https://doi.org/10.3390/app112411995 - 16 Dec 2021

Cited by 2 | Viewed by 2263

Abstract

It is of great significance for air pollution control and personnel safety guarantee to master the release characteristics of harmful gases in the process of Limnoperna fortunei corruption. In view of the lack of research on the environmental pollution caused by the corruption [...] Read more.

It is of great significance for air pollution control and personnel safety guarantee to master the release characteristics of harmful gases in the process of Limnoperna fortunei corruption. In view of the lack of research on the environmental pollution caused by the corruption of Limnoperna fortunei, a model experiment was designed to study the three harmful gases of NH₃, H₂S, and CH₄ in the putrid process of Limnoperna fortunei by considering the density of Limnoperna fortunei and the time of leaving water. The results show that: (1) The recognition and processing of outliers based on wavelet decomposition and K-means algorithm can effectively reduce the standard deviation and coefficient of variation of the data set and improve the accuracy of the data set. (2) The variation of NH₃ and H₂S gas concentrations with the time of water separation satisfies polynomial linear regression (R² > 99%). (3) At a density of 0.5–7.0 × 10⁴ mussels/m², the highest concentration of NH₃ reached 47.9777–307.9454 mg/m³ with the increase in the density of Limnoperna fortunei and the extension of the time away from water, far exceeding the occupational exposure limit of NH₃ of 30 mg/m³, potentially threatening human health and safety. The highest detection value of H₂S concentration is 0.1909–5.0946 mg/m³, and the highest detection concentration of CH₄ is 0.02%, both of which can be ignored. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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<p>Sampling site of <span class="html-italic">Limnoperna fortunei</span>.</p> Full article ">Figure 2
<p><span class="html-italic">Limnoperna fortunei</span> sample collection.</p> Full article ">Figure 3
<p>Schematic diagram of test device (<b>a</b>) and site layout (<b>b</b>); (a—mussel cluster; b—multi-purpose hole; c—gas sampling hole; d—gas reflux hole; e—gas sampling pipe; f—gas reflux pipe; g—online gas detector).</p> Full article ">Figure 4
<p>Morphological changes of <span class="html-italic">Limnoperna fortunei</span> during putrid process; ((<b>a</b>)—day 1; (<b>b</b>)—day 3; (<b>c</b>)—day 5; (<b>d</b>)—day 10).</p> Full article ">Figure 5
<p>Changes of NH₃ concentration during the putrefaction of <span class="html-italic">Limnoperna fortunei</span>.</p> Full article ">Figure 6
<p>Changes of H₂S concentration during the putrefaction of <span class="html-italic">Limnoperna fortunei</span>.</p> Full article ">Figure 7
<p>Decomposition of NH₃ (<b>a</b>) and H₂S (<b>b</b>) concentration data by db5 wavelet.</p> Full article ">Figure 8
<p>K-means outlier recognition.</p> Full article ">Figure 9
<p>Pretreatment results of NH₃ concentration data.</p> Full article ">Figure 10
<p>Pretreatment results of H₂S concentration data.</p> Full article ">Figure 11
<p>The change of NH₃ concentration under different densities of <span class="html-italic">Limnoperna fortunei</span>.</p> Full article ">Figure 12
<p>The change of H₂S concentration under different densities of <span class="html-italic">Limnoperna fortunei</span>.</p> Full article ">

31 pages, 9657 KiB  

Open AccessArticle

Evaluating the Parameters of a Systematic Long-Term Measurement of the Concentration and Mobility of Air Ions in the Environment inside Císařská Cave

by Zdeněk Roubal, Eva Gescheidtová, Karel Bartušek, Zoltán Szabó, Miloslav Steinbauer, Jarmila Überhuberová and Ariana Lajčíková

Atmosphere 2021, 12(12), 1615; https://doi.org/10.3390/atmos12121615 - 3 Dec 2021

Cited by 4 | Viewed by 2206

Abstract

Determining the concentration and mobility of light air ions is an indispensable task to ensure the successful performance and progress of various operations within multiple fields and branches of human activity. This article discusses a novel methodology for measuring air ions in an [...] Read more.

Determining the concentration and mobility of light air ions is an indispensable task to ensure the successful performance and progress of various operations within multiple fields and branches of human activity. This article discusses a novel methodology for measuring air ions in an environment with high relative humidity, such as that of a cave. Compared to common techniques, the proposed method exhibits a lower standard deviation and analyses the causes of spurious oscillations in the measured patterns obtained from FEM-based numerical simulations on the one hand and a model with concentrated parameters on the other. The designed ion meter utilises a gerdien tube to facilitate long-term measurement in cold and very humid spaces, an operation that can be very problematic if executed with other devices. Importantly, the applied procedure for calculating the mobility spectra of air ions from the acquired saturation characteristics is insensitive to fluctuations and noises in the measured patterns, and it also enables us to confirm the presence of very mobile air ions generated by fragmenting water droplets. During the sensing cycles, the concentration of light negative ions was influenced by the active gerdien tube. For the investigated cave, we had designed a measuring sequence to cover not only the time dependence of the concentration of light negative ions but also their mobility; this approach then allowed monitoring the corresponding impact of the patients’ presence in the cave, an effect neither described nor resolved thus far. Such comprehensive research, especially due to its specific character, has not been frequently conducted or widely discussed in the literature; the efforts characterised herein have therefore expanded the relevant knowledge and methodology, thus contributing towards further advancement in the field. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The original layout and revised profiles of Císařská Cave, authored by Absolon and Faimon et Lang, respectively. The latter two drawings indicate the air flows as characterised in sources [<a href="#B70-atmosphere-12-01615" class="html-bibr">70</a>,<a href="#B71-atmosphere-12-01615" class="html-bibr">71</a>].</p> Full article ">Figure 2
<p>Two identical units of the AK-UTEE-v2 system to measure negative and positive ions.</p> Full article ">Figure 3
<p>The current layout of Císařská Cave, showing the measurement spots and related transverse cross section through Nagel’s Dome: a scheme customised according to the emergency response plan for the children’s therapy resort in Ostrov u Macochy (drawn by Dr. Pavel Slavík).</p> Full article ">Figure 4
<p>The sequence patterns of the light ion concentration measurement at points No. 3 (Ostrov Chambers 2; <b>up</b>) and No. 1 (Nagel’s Dome; <b>bottom</b>) at 16:00.</p> Full article ">Figure 5
<p>The sequence patterns of the light ion concentration measurement, points No. 5 (relaxation room, lower section; <b>up</b>) and No. 7 (Deep Lake; <b>bottom</b>) at 16:52 and 17:48, respectively.</p> Full article ">Figure 6
<p>The concentration of light negative ions related to the level above the ground (<b>up</b>) and distance from the reference wall (<b>bottom</b>) in Nagel’s Dome.</p> Full article ">Figure 7
<p>The patterns (<b>up</b>) and histograms (<b>bottom</b>) of the light air ion concentrations detected with the gerdien tube in the earthed (blue) and unearthed (red) modes; the interval width (difference between the columns) corresponds to 250 ions/cm³, and 200 measured values are assumed. Time: 11:00 to 11:45.</p> Full article ">Figure 8
<p>The distribution of the electric potential through a section of the GT, assuming a disturbing positive potential of 1 V on the shielding electrode.</p> Full article ">Figure 9
<p>The equivalent diagram of the capacitive sensor in the no-earthing mode.</p> Full article ">Figure 10
<p>The equivalent diagram of the capacitive sensor in the earthing mode.</p> Full article ">Figure 11
<p>The fundamental configuration of the sensing element (gerdien tube) in a space comprising a free electric charge, without the outer electrode earthing.</p> Full article ">Figure 12
<p>The fundamental configuration of the sensing element (gerdien tube) in a space comprising a free electric charge, with the outer electrode earthing.</p> Full article ">Figure 13
<p>The basic (<b>up</b>) and normalised (<b>bottom</b>) saturation characteristics of the concentration of negative ions in Nagel’s Dome.</p> Full article ">Figure 13 Cont.
<p>The basic (<b>up</b>) and normalised (<b>bottom</b>) saturation characteristics of the concentration of negative ions in Nagel’s Dome.</p> Full article ">Figure 14
<p>The approximated saturation characteristics of negative ions taken in Nagel’s Dome, 8 August 2015.</p> Full article ">Figure 15
<p>Fitting the saturation characteristic of positive ions as measured in Nagel’s Dome on 8 August 2015.</p> Full article ">Figure 16
<p>The humid air density related to the temperature, pressure, and relative humidity in Císařská Cave during a year; the data are represented for two limit values of relative humidity.</p> Full article ">Figure 17
<p>The concentration of light negative ions over the course of the week-long (17 October 2015 to 23 October 2015) measurement procedure, and the related differences between the inside and the outside temperatures. Measuring sequence: No. 1.</p> Full article ">Figure 18
<p>The daytime variations in the individual fractions of the aerosol particles, with the child patients present.</p> Full article ">Figure 19
<p>The rapid post-exercise fall in the concentration of particles exhibiting the aerodynamic radii of 5 and 10 μm (<b>left-hand image</b>), and the slow decrease in the 1 μm and 2.5 μm particles (<b>right-hand image</b>).</p> Full article ">Figure 20
<p>Comparing the aerosol particle quantities: Regular conditions involving separate clusters of patients, and performance tests without the splitting.</p> Full article ">Figure 21
<p>The air ion mobility variations due to the impact of the child patients´presence (17 October 2015 to 23 October 2015). Measuring sequence: No. 1.</p> Full article ">Figure 22
<p>The long-term measurement from 10 to 14 November 2015: The relationship between the concentrations of light negative ions during the relevant week and the corresponding differences between the outer and the inner temperatures. Measuring sequence: No. 1.</p> Full article ">Figure 23
<p>The long-term measurement from 10 to 14 November 2015: The relationship between the air ion mobility and the corresponding differences between the outer and the inner temperatures. Measuring sequence: No. 1.</p> Full article ">Figure 24
<p>The long-term measurement from 19 to 24 November 2015: The relationship between the concentrations of light negative ions during the relevant week and the corresponding differences between the outer and the inner temperatures. Measuring sequence: No. 2.</p> Full article ">Figure 25
<p>The long-term measurement from 19 to 24 November 2015: The relationship between the air ion mobility and the differences between the outer and the inner temperatures. Measuring sequence: No. 2.</p> Full article ">Figure 26
<p>The long-term measurement from 19 to 24 November 2015: The relationship between the unipolarity coefficient <span class="html-italic">P</span> and the outer—inner temperature differences. Measuring sequence: No. 2.</p> Full article ">Figure 27
<p>The long-term measurement from 22 to 29 February 2016: The relationship between the concentrations of light negative ions during the relevant week and the corresponding differences between the outer and the inner temperatures. Measuring sequence: No. 2.</p> Full article ">Figure 28
<p>The long-term measurement from 22 to 29 February 2016: The relationship between the air ion mobility and the differences between the outer and the inner temperatures. Measuring sequence: No. 2.</p> Full article ">Figure 29
<p>The long-term measurement from 22 to 29 February 2016: The dependence of the unipolarity coefficient P on the differences between the outer and the inner temperatures. Measuring sequence: No. 2.</p> Full article ">

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18 pages, 3037 KiB  

Open AccessArticle

Has COVID-19 Lockdown Affected on Air Quality?—Different Time Scale Case Study in Wrocław, Poland

by Tomasz Turek, Ewa Diakowska and Joanna A. Kamińska

Atmosphere 2021, 12(12), 1549; https://doi.org/10.3390/atmos12121549 - 24 Nov 2021

Cited by 9 | Viewed by 2175

Abstract

Due to the COVID-19 pandemic, there are series of negative economic consequences, however, in limiting mobility and reducing the number of vehicles, positive effects can also be observed, i.e., improvement of air quality. The paper presents an analysis of air quality measured by [...] Read more.

Due to the COVID-19 pandemic, there are series of negative economic consequences, however, in limiting mobility and reducing the number of vehicles, positive effects can also be observed, i.e., improvement of air quality. The paper presents an analysis of air quality measured by concentrations of NO₂, NO_x and PM_2.5 during the most restrictive lockdown from 10 March to 31 May 2020 on the case of Wrocław. The results were compared with the reference period—2016–2019. A significant reduction in traffic volume was identified, on average by 26.3%. The greatest reduction in the concentration of NO₂ and NO_x was recorded at the station farthest from the city center, characterized by the lowest concentrations: 20.1% and 22.4%. Lower reduction in the average concentrations of NO₂ and NO_x was recorded at the municipal station (7.9% and 7.7%) and the communication station (6.7% and 10.2%). Concentrations of PMs in 2020 were on average 15% and 13.4% lower than in the reference period for the traffic station and the background station. The long-term impact of the lockdown on air quality was also examined. The analysis of the concentrations of the pollutants throughout 2020, and in the analyzed period of 2021, indicated that the reduction of concentrations and the improvement in air quality caused by the restrictions should be considered as a temporary anomaly, without affecting long-term changes and trends. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Figure 1

Figure 1
<p>Wrocław city and its location on Poland map. Red triangle—traffic station, blue dots—air pollution measuring stations.</p> Full article ">Figure 2
<p>Spearman correlation coefficients between pollution concentrations and traffic flow for reference period 2016–2019 (<b>left side</b>) and analyzed period 2020 (<b>right side</b>); B, K—background stations, W—traffic station.</p> Full article ">Figure 3
<p>Nitrogen dioxide hourly variability from 10 March to 30 May 2016–2019.</p> Full article ">Figure 4
<p>Nitrogen oxide hourly variability from 10 March to 30 May 2016–2019.</p> Full article ">Figure 5
<p>Particulate matter hourly variability from 10 March to 30 May 2016–2019.</p> Full article ">Figure 6
<p>Nitrogen dioxide hourly variability from 10 March to 30 May 2020: NO₂ on the left and NO_x on the right side.</p> Full article ">Figure 7
<p>PM_2.5 hourly variability from 10 March to 30 May 2020.</p> Full article ">Figure 8
<p>Average daily values of traffic flow.</p> Full article ">Figure 9
<p>Average daily values of NO₂ and NO_x concentrations at three research stations—B and K background and W traffic.</p> Full article ">Figure 9 Cont.
<p>Average daily values of NO₂ and NO_x concentrations at three research stations—B and K background and W traffic.</p> Full article ">Figure 10
<p>Average daily values of PM_2.5 concentrations at two research stations—K background (<b>left</b>) and W traffic (<b>right</b>).</p> Full article ">Figure 11
<p>Average values of concentrations of air pollutants in the period 10 March–31 May 31 with standard deviation.</p> Full article ">Figure 12
<p>Average daily values of NO_x concentrations in the period 10 March–31 May.</p> Full article ">Figure 13
<p>Annual average NO₂ and NO_x concentrations with trend lines, 2020 marked by red frame.</p> Full article ">

19 pages, 3170 KiB  

Open AccessArticle

Spatial-Temporal Variation of Air PM_2.5 and PM₁₀ within Different Types of Vegetation during Winter in an Urban Riparian Zone of Shanghai

by Jing Wang, Changkun Xie, Anze Liang, Ruiyuan Jiang, Zihao Man, Hao Wu and Shengquan Che

Atmosphere 2021, 12(11), 1428; https://doi.org/10.3390/atmos12111428 - 29 Oct 2021

Cited by 5 | Viewed by 2316

Abstract

Particulate matter (PM) in urban riparian green spaces are undesirable for human participation in outdoor activities, especially PM_2.5 and PM₁₀. The PM deposition, dispersion and modification are influenced by various factors including vegetation, water bodies and meteorological conditions. This study [...] Read more.

Particulate matter (PM) in urban riparian green spaces are undesirable for human participation in outdoor activities, especially PM_2.5 and PM₁₀. The PM deposition, dispersion and modification are influenced by various factors including vegetation, water bodies and meteorological conditions. This study aimed to investigate the impact of vegetation structures and the river’s presence on PM in riparian zones. The spatial-temporal variations of PM_2.5 and PM₁₀ concentrations in three riparian vegetation communities with different structures (open grassland (G), arbor-grass (AG) and arbor-shrub-grass (ASG) woodlands) were monitored under relatively stable environment. The removal percentages (RP) and ratios of PM_2.5 and PM₁₀ were calculated and compared to identify the removal effect of vegetation structures and the river’s presence. It is found that: (1) when the wind was static (hourly wind speed < 0.2 m/s), the RP was ranked as follows: G > AG > ASG. When the wind was mild (0.2 m/s < hourly wind speed < 2 m/s), the RP was ranked as follows: G > ASG > AG. Generally, the G had the best removal effect during the monitoring period; (2) the lowest RP occurred in the middle of the G (–3.4% for PM_2.5, 1.8% for PM₁₀) while the highest RP were found in middle of the AG and ASG, respectively (AG: 2.1% for PM_2.5, 6.7% for PM₁₀; ASG: 2.4% for PM_2.5, 6.3% for PM₁₀). Vegetation cover changed the way of natural deposition and dispersion; (3) compared with static periods, PM removal percentages were significantly reduced under mild wind conditions, and they were positively correlated with wind speed during the mild-wind period. Thus, a piecewise function was inferred between wind speed and PM removal percentage; (4) for all three communities, the 1 m-to-river PM_2.5/PM₁₀ ratio was significantly lower than that at 6 m and 11 m, even lower than that in the ambient atmosphere. The river likely promoted the hygroscopic growth of PM_2.5 and the generation of larger-sized particles by coagulation effect. Based on these findings, open grassland space is preferred alongside rivers and space for outdoor activities is suggested under canopies in the middle of woodlands. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>a</b>) The location of the sampling sites in Shanghai, China; (<b>b</b>) the locations of three sampling sites in the campus of Shanghai Jiao Tong University, Minhang district; the instrumentation locations of measuring stations beside the Danshui River in three vegetation types: (<b>c</b>) arbor-shrub-grass woodland (ASG), (<b>d</b>) open grassland (G) and (<b>e</b>) arbor-grass woodland (AG); the instrumentation locations and heights of measuring stations in three vegetation types: (<b>f</b>) arbor-shrub-grass woodland (ASG), (<b>g</b>) open grassland (G) and (<b>h</b>) arbor-grass woodland (AG).</p> Full article ">Figure 2
<p>(<b>a</b>) Hourly trends in the average PM_2.5 and PM₁₀ concentrations within the three vegetation types: open grassland (G), arbor-grass woodland (AG) and arbor-shrub-grass woodland (ASG); (<b>b</b>) ANOVA results for the PM_2.5 and PM₁₀ concentrations in different vegetation types and distances from the river across the entire observation period. ** <span class="html-italic">p</span> ≤ 0.01; Error bar: standard deviation.</p> Full article ">Figure 3
<p>Results of the cluster analysis of the PM_2.5 and PM₁₀ spatial distributions across time. (<b>a</b>) Spatial-temporal distribution of the PM_2.5 and PM₁₀ concentrations in (i) grassland (G); (ii) arbor-grass (AG) woodland; (iii) arbor-shrub-grass (ASG) woodland generated using hourly data points at the distances of 1 m, 6 m, and 11 m from the river. (<b>b</b>) The distribution across time of five clusters in (i) grassland (G); (ii) arbor-grass (AG) woodland; (iii) arbor-shrub-grass (ASG) woodland as a result of the hierarchical cluster analysis. (<b>c</b>) The spatial-temporal distribution of PM_2.5 and PM₁₀ in the background atmosphere at 10 m above ground. (<b>d</b>) The average PM_2.5 and PM₁₀ concentrations at the stepped distance of the five clusters based on the hierarchical cluster analysis.</p> Full article ">Figure 4
<p>(<b>a</b>) Hourly trends in all three types of vegetation at (i) 1 m, (ii) 6 m, and (iii) 11 m distances from the river. (<b>b</b>) The spatial-temporal distribution of the removal percentages of the PM_2.5 and PM₁₀ in: (i) grassland (G); (ii) arbor-grass (AG) woodland; (iii) arbor-shrub-grass (ASG) woodland. (<b>c</b>) The spatial-temporal distribution of the wind speed in (i) grassland (G), (ii) arbor-grass (AG) woodland, and (iii) arbor-shrub-grass (ASG) woodland.</p> Full article ">Figure 5
<p>ANOVA results of the (<b>a</b>) wind speed, (<b>b</b>) relative humidity (RH), and (<b>c</b>) temperature in different vegetation types and distances from the river across the entire observation period. ** <span class="html-italic">p ≤</span> 0.01 *** <span class="html-italic">p ≤</span> 0.001; Error bar: standard deviation; G: open grassland; AG: arbor-grass woodland; ASG: arbor-shrub-grass woodland.</p> Full article ">Figure 6
<p>ANOVA results of (<b>a</b>) the PM_2.5 removal percentage and (<b>b</b>) the PM₁₀ removal percentage during the entire observation period; ANOVA results of (<b>c</b>) the PM_2.5 removal percentage and (<b>d</b>) the PM₁₀ removal percentage during mild-wind and static periods, respectively. * <span class="html-italic">p</span> ≤ 0.05 ** <span class="html-italic">p</span> ≤ 0.01 *** <span class="html-italic">p</span> ≤ 0.001. Error bar: standard deviation; G: open grassland; AG: arbor-grass woodland; ASG: arbor-shrub-grass woodland.</p> Full article ">Figure 7
<p>Scatter plots of all the data points during mild-wind periods from all vegetation types: (<b>a</b>) wind speeds and PM_2.5 removal percentages, (<b>b</b>) wind speeds and PM₁₀ removal percentages. Scatter plots of the data points during mild-wind periods for different vegetation types separately: (<b>c</b>) wind speeds and PM_2.5 removal percentages, (<b>d</b>) wind speeds and PM₁₀ removal percentages. G: open grassland; AG: arbor-grass woodland; ASG: arbor-shrub-grass woodland.</p> Full article ">Figure 8
<p>ANOVA results of the PM_2.5/PM₁₀ ratio in different vegetation types and distances: (<b>a</b>) during the entire observation period, (<b>b</b>) under static conditions, and (<b>c</b>) under mild-wind conditions. * <span class="html-italic">p</span> ≤ 0.05 ** <span class="html-italic">p</span> ≤ 0.01 *** <span class="html-italic">p</span> ≤ 0.001. Error bar: standard deviation; G: open grassland; AG: arbor-grass woodland; ASG: arbor-shrub-grass woodland.</p> Full article ">

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18 pages, 74100 KiB  

Open AccessArticle

Modeling the Impacts of City-Scale “Ventilation Corridor” Plans on Human Exposure to Intra-Urban PM2.5 Concentrations

by Chao Liu, Qian Shu, Sen Huang and Jingwei Guo

Atmosphere 2021, 12(10), 1269; https://doi.org/10.3390/atmos12101269 - 29 Sep 2021

Cited by 6 | Viewed by 2221

Abstract

Increasingly, Chinese cities are proposing city-scale ventilation corridors (VCs) to strengthen wind velocities and decrease pollution concentrations, although their influences are ambiguous. To assess VC impacts, an effort has been made to predict the impact of VC solutions in the high density and [...] Read more.

Increasingly, Chinese cities are proposing city-scale ventilation corridors (VCs) to strengthen wind velocities and decrease pollution concentrations, although their influences are ambiguous. To assess VC impacts, an effort has been made to predict the impact of VC solutions in the high density and diverse land use of the coastal city of Shanghai, China, in this paper. One base scenario and three VC scenarios, with various VC widths, locations, and densities, were first created. Then, the combination of the Weather Research and Forecasting/Single-Layer Urban Canopy Model (WRFv.3.4/UCM) and Community Multiscale Air Quality (CMAQv.5.0.1) numerical simulation models were employed to comprehensively evaluate the impacts of urban spatial form and VC plans on PM2.5 concentrations. The modeling results indicated that concentrations increased within the VCs in both summer and winter, and the upwind concentration decreased in winter. These counter-intuitive results could be explained by decreased planetary boundary layer (PBL), roughness height, deposition rate, and wind speeds induced by land use and urban height modifications. PM2.5 deposition flux decreased by 15–20% in the VCs, which was attributed to the roughness height decrease for it weakens aerodynamic resistance (Ra). PBL heights within the VCs decreased 15–100 m, and the entire Shanghai’s PBL heights also decreased in general. The modeling results suggest that VCs may not be as functional as certain urban planners have presumed. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Figure 1

Figure 1
<p>Flow chart of the research on the VCs’ effects on PM2.5 pollution on an urban scale.</p> Full article ">Figure 2
<p>(<b>a</b>) Shanghai area map and meteorology/PM2.5 monitor locations; (<b>b</b>) 10 base land uses of the city of Shanghai.</p> Full article ">Figure 3
<p>Base and VC morphology maps of Shanghai, China: (<b>a</b>) base; (<b>b</b>) VC_ECO2KM; (<b>c</b>) VC_ECO5KM; (<b>d</b>) VC_HW2KM.</p> Full article ">Figure 4
<p>Shanghai Ecological Protective Plan (draft version) in 2020 [<a href="#B25-atmosphere-12-01269" class="html-bibr">25</a>].</p> Full article ">Figure 5
<p>Research domains in the WPS.</p> Full article ">Figure 6
<p>(<b>a</b>) Modeling wind speed at 10 m high (m/s) in summer 2014; (<b>b</b>) modeling wind speed differences at 10 m high (m/s) in the ECO02 scenario and base scenario; (<b>c</b>) modeling wind speed differences at 10 m high (m/s) in the ECO05 scenario and base scenario; (<b>d</b>) modeling wind speed differences at 10 m high (m/s) in the HW02 scenario and base scenario.</p> Full article ">Figure 6 Cont.
<p>(<b>a</b>) Modeling wind speed at 10 m high (m/s) in summer 2014; (<b>b</b>) modeling wind speed differences at 10 m high (m/s) in the ECO02 scenario and base scenario; (<b>c</b>) modeling wind speed differences at 10 m high (m/s) in the ECO05 scenario and base scenario; (<b>d</b>) modeling wind speed differences at 10 m high (m/s) in the HW02 scenario and base scenario.</p> Full article ">Figure 7
<p>(<b>a</b>) Modeling wind speed at 2 m high (m/s) in winter 2014; (<b>b</b>) modeling wind speed differences at 2 m high (m/s) in the ECO02 scenario and base scenario; (<b>c</b>) modeling wind speed differences at 2 m high (m/s) in the ECO05 scenario and base scenario; (<b>d</b>) modeling wind speed differences at 2 m high (m/s) in the HW02 scenario and base scenario.</p> Full article ">Figure 8
<p>(<b>a</b>) Modeling ground-level PM2.5 concentrations (μg/m³) in summer 2014; (<b>b</b>) modeling ground-level PM2.5 concentration differences (μg/m³) in the ECO02 scenario and base scenario; (<b>c</b>) modeling ground-level PM2.5 concentration differences (μg/m³) in the ECO05 scenario and base scenario; (<b>d</b>) modeling ground-level PM2.5 concentration differences (μg/m³) in the HW02 scenario and base scenario.</p> Full article ">Figure 9
<p>(<b>a</b>) Modeling ground-level PM2.5 concentrations (μg/m³) in winter 2014; (<b>b</b>) modeling ground-level PM2.5 concentration differences (μg/m³) in the ECO02 scenario and base scenario; (<b>c</b>) modeling ground-level PM2.5 concentration differences (μg/m³) in the ECO05 scenario and base scenario; (<b>d</b>) modeling ground-level PM2.5 concentration differences (μg/m³) in the HW02 scenario and base scenario.</p> Full article ">

14 pages, 2689 KiB  

Open AccessArticle

Contributing towards Representative PM Data Coverage by Utilizing Artificial Neural Networks

by Chris G. Tzanis and Anastasios Alimissis

Appl. Sci. 2021, 11(18), 8431; https://doi.org/10.3390/app11188431 - 11 Sep 2021

Cited by 3 | Viewed by 1538

Abstract

Atmospheric aerosol particles have a significant impact on both the climatic conditions and human health, especially in densely populated urban areas, where the particle concentrations in several cases can be extremely threatening (increased anthropogenic emissions). Most large cities located in high-income countries have [...] Read more.

Atmospheric aerosol particles have a significant impact on both the climatic conditions and human health, especially in densely populated urban areas, where the particle concentrations in several cases can be extremely threatening (increased anthropogenic emissions). Most large cities located in high-income countries have stations responsible for measuring particulate matter and various other parameters, collectively forming an operating monitoring network, which is essential for the purposes of environmental control. In the city of Athens, which is characterized by high population density and accumulates a large number of economic activities, the currently operating monitoring network is responsible, among others, for PM₁₀ and PM_2.5 measurements. The need for satisfactory data availability though can be supported by using machine learning methods, such as artificial neural networks. The methodology presented in this study uses a neural network model to provide spatiotemporal estimations of PM₁₀ and PM_2.5 concentrations by utilizing the existing PM data in combination with other climatic parameters that affect them. The overall performance of the predictive neural network models’ scheme is enhanced when meteorological parameters (wind speed and temperature) are included in the training process, lowering the error values of the predicted versus the observed time series’ concentrations. Furthermore, this work includes the calculation of the contribution of each predictor, in order to provide a clearer understanding of the relationship between the model’s output and input. The results of this procedure showcase that all PM input stations’ concentrations have an important impact on the estimations. Considering the meteorological variables, the results for PM_2.5 seem to be affected more than those for PM₁₀, although when examining PM₁₀ and PM_2.5 individually, the wind speed and temperature contribution is on a similar level with the corresponding contribution of the available PM concentrations of the neighbouring stations. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Figure 1

Figure 1
<p>The Attic peninsula and the locations of the air quality monitoring stations.</p> Full article ">Figure 2
<p>Monthly averaged concentrations for PM₁₀ and PM_2.5 ((<b>a</b>,<b>b</b>) respectively) and for the three-year time period (2016–2018) at AGP target station.</p> Full article ">Figure 3
<p>An example of an artificial neuron and its basic characteristics.</p> Full article ">Figure 4
<p>PM₁₀ and PM_2.5 scatter diagrams of the predicted versus the observed concentrations for the different input cases: (<b>a</b>,<b>e</b>) PM only, (<b>b</b>,<b>f</b>) PM and T, (<b>c</b>,<b>g</b>) PM and WS and (<b>d</b>,<b>h</b>) PM, T and WS.</p> Full article ">

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11 pages, 2501 KiB  

Open AccessArticle

Characterization of PM-Bound Heavy Metal at Road Environment in Tianjin: Size Distribution and Source Identification

by Qijun Zhang, Hongjun Mao, Yanjie Zhang and Lin Wu

Atmosphere 2021, 12(9), 1130; https://doi.org/10.3390/atmos12091130 - 2 Sep 2021

Cited by 9 | Viewed by 2266

Abstract

To determine the size distribution and source identification of PM-bound heavy metals in roadside environments, four different particle size (<0.2 μm, 0.2–0.5 μm, 0.5–1.0 μm and 1.0–2.5 μm) samples were collected and analyzed from four different types of roads during the summer of [...] Read more.

To determine the size distribution and source identification of PM-bound heavy metals in roadside environments, four different particle size (<0.2 μm, 0.2–0.5 μm, 0.5–1.0 μm and 1.0–2.5 μm) samples were collected and analyzed from four different types of roads during the summer of 2015 in Tianjin. The results showed that the concentrations of PM-bound heavy metal from the roadside environment sampling sites were 597 ± 251 ng/m³ (BD), 546 ± 316 ng/m³ (FK), 518 ± 310 ng/m³ (JY) and 640 ± 237 ng/m³ (WH). There were differences in the concentrations of the heavy metal elements in the four different particle size fractions. The concentrations of Cu, Zn, Cd, Sn and Pb were the highest in the larger particle size fraction (0.5–2.5 μm). Cd, Cu, Zn and Pb were the elements that indicated emissions from tire wear and brake pad wear. The concentrations of Cr, Co and Ni were the highest in the smallest particle size fraction (<0.5 μm), indicating that motor vehicle exhaust was their main source. The correlation analysis results showed that there are differences in the concentration, distribution and correlation of different PM-bound heavy metals in different particle size fractions. The PCA results show that the accumulative interpretation variances of PM_0.2, PM_0.2–0.5, PM_0.5–1.0 and PM_1.0–2.5 reached 80.29%, 79.56%, 79.57% and 71.42%, respectively. Vehicle exhaust was the primary source of PM-bound heavy metal collected from the roadside sampling sites, while brake pad wear and tire wear were the second most common sources of the heavy metal. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Figure 1
<p>Location of sampling sites.</p> Full article ">Figure 2
<p>Concentrations of the heavy metal elements measured in the samples collected at the four road environment sampling sites in Tianjin.</p> Full article ">Figure 3
<p>Size-resolved concentration of heavy metal elements in particulate matter.</p> Full article ">Figure 3 Cont.
<p>Size-resolved concentration of heavy metal elements in particulate matter.</p> Full article ">Figure 4
<p>Enrichment factors of metals in PM_2.5 at roadside.</p> Full article ">

12 pages, 336 KiB  

Open AccessReview

Ammonia Emission in Poultry Facilities: A Review for Tropical Climate Areas

by Matheus Dias Oliveira, Fernanda Campos Sousa, Jairo Osorio Saraz, Arele Arlindo Calderano, Ilda Fátima Ferreira Tinôco and Antônio Policarpo Souza Carneiro

Atmosphere 2021, 12(9), 1091; https://doi.org/10.3390/atmos12091091 - 25 Aug 2021

Cited by 15 | Viewed by 4358

Abstract

Brazil is the largest broiler meat exporter in the world. This important economic activity generates income in different branches of the production chain. However, the decomposition of residues incorporated in the poultry litter generates several gases, among them ammonia. When emitted from the [...] Read more.

Brazil is the largest broiler meat exporter in the world. This important economic activity generates income in different branches of the production chain. However, the decomposition of residues incorporated in the poultry litter generates several gases, among them ammonia. When emitted from the litter to the air, ammonia can cause several damages to animals and man, in addition to being able to convert into a greenhouse gas. Thus, the aim of this article was to carry out a review of the ammonia emission factors in the production of broilers, the methodologies for measuring, and the inventories of emissions already carried out in several countries. The main chemical processes for generating ammonia in poultry litter have been introduced and some practices that can contribute to the reduction of ammonia emissions have been provided. The PMU, Portable Monitoring Unit, and the SMDAE, Saraz Method for Determination of Ammonia Emissions, with the required adaptations, are methodologies that can be used to quantify the ammonia emissions in hybrid facilities with a natural and artificial ventilation system. An ammonia emission inventory can contribute to the control and monitoring of pollutant emissions and is an important step towards adopting emission reductions. However, quantifying the uncertainties about ammonia emission inventories is still a challenge to be overcome. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

14 pages, 4712 KiB  

Open AccessArticle

Variation of Particle-Induced Oxidative Potential of PM_2.5 in Xinjiang, NW-China

by Juqin An, Dilnurt Talifu, Xiang Ding, Longyi Shao, Xinming Wang, Abulikemu Abulizi, Yalkunjan Tursun, Huibin Liu, Yuanyu Zhang and Turhun Aierken

Atmosphere 2021, 12(8), 1028; https://doi.org/10.3390/atmos12081028 - 11 Aug 2021

Viewed by 2079

Abstract

In order to evaluate the toxicity of PM_2.5 in the Dushanzi area, PM_2.5 samples were collected from December 2015 to July 2016, and a plasmid DNA damage assessment method was used to analyze the variation in the oxidative damage ability and [...] Read more.

In order to evaluate the toxicity of PM_2.5 in the Dushanzi area, PM_2.5 samples were collected from December 2015 to July 2016, and a plasmid DNA damage assessment method was used to analyze the variation in the oxidative damage ability and its relationship with sampling conditions and toxic components (polycyclic aromatic hydrocarbons, and heavy metals) loaded on the surface of PM_2.5. The results showed that the TD₃₀ values (toxic dosage of PM_2.5 causing 30% of plasmid DNA damage) of both the whole samples and the water-soluble fractions were lower during the heating period (369 μg/mL and 536 μg/mL, respectively), but higher in the dust period and non-heating period (681 μg/mL and 498 μg/mL, respectively; and 804 μg/mL and 847 μg/mL, respectively). Studies on the effect of meteorological parameters showed an increasing trend in TD₃₀ values for the whole samples and the water-soluble fractions as relative humidity, temperature and wind speed decrease. TD₃₀ values for the whole samples and the water-soluble fractions were negatively correlated with Flu (r = −0.690,r = −0.668; p < 0.05), Flt (r =−0.671, r = −0.760; p < 0.05), BaP (r = −0.672, r = −0.725; p < 0.05), IcdP (r = −0.694, r = −0.740; p < 0.05), Pyr (r = −0.727, r = −0.768; p < 0.01) and BghiP (r = −0.874, r = −0.845; p < 0.01) during the heating period, while As (r = 0.792, r = 0.749; p < 0.05) and Sr (r = 0.776, r = 0.754; p < 0.05) during the dust period showed significant positive correlation. In addition, the TD₃₀ values of PM_2.5 collected during sand blowing weather was the highest (1458 μg/mL and 1750 μg/mL), while the average TD₃₀ value of PM_2.5 collected on hazy days were the lowest (419.8 μg/mL and 488.6 μg/mL). Particles collected on the first day after snowfall showed a lower oxidizing capacity (676 μg/mL and 1330 μg/mL). The characteristic TD₃₀ values combined with back trajectory analysis indicated that hazy days were heavily influenced by air masses originating from the southern continent and local emissions, whereas the sand blowing weather came from the north of the Taklimakan Desert. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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<p>Location of the sampling site.</p> Full article ">Figure 2
<p>The seasonal distribution of oxidative capacity and PM2.5 mass concentrations. (W: whole samples; S: water-soluble fractions).</p> Full article ">Figure 3
<p>Correlations between the TD₃₀ values of the whole sample and water-soluble fractions with PM_2.5 mass concentrations.</p> Full article ">Figure 4
<p>Seasonal variation in meteorological parameters in Dushanzi District.</p> Full article ">Figure 5
<p>Correlations between the TD₃₀ values of whole samples and corresponding water-soluble fractions and the examined environmental factors.</p> Full article ">Figure 6
<p>Concentrations of the components of PM_2.5 under special weather condition.</p> Full article ">Figure 7
<p>Forty-eight hour back trajectories of air masses arriving at the sampling site.</p> Full article ">

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Supplementary material:
Supplementary File 1 (ZIP, 351 KiB)

10 pages, 1154 KiB  

Open AccessTechnical Note

Evaluation of Using Satellite-Derived Aerosol Optical Depth in Land Use Regression Models for Fine Particulate Matter and Its Elemental Composition

by Chun-Sheng Huang, Ho-Tang Liao, Tang-Huang Lin, Jung-Chi Chang, Chien-Lin Lee, Eric Cheuk-Wai Yip, Yee-Lin Wu and Chang-Fu Wu

Atmosphere 2021, 12(8), 1018; https://doi.org/10.3390/atmos12081018 - 8 Aug 2021

Cited by 1 | Viewed by 2967

Abstract

This study introduced satellite-derived aerosol optical depth (AOD) in land use regression (LUR) modeling to predict ambient concentrations of fine particulate matter (PM_2.5) and its elemental composition. Twenty-four daily samples were collected from 17 air quality monitoring sites (N = 408) [...] Read more.

This study introduced satellite-derived aerosol optical depth (AOD) in land use regression (LUR) modeling to predict ambient concentrations of fine particulate matter (PM_2.5) and its elemental composition. Twenty-four daily samples were collected from 17 air quality monitoring sites (N = 408) in Taiwan in 2014. A total of 12 annual LUR models were developed for PM_2.5 and 11 elements, including aluminum, calcium, chromium, iron, potassium, manganese, sulfur, silicon, titanium, vanadium, and zinc. After applied AOD and a derived-predictor, AOD percentage, in modeling, the number of models with leave-one-out cross-validation R² > 0.40 significantly increased from 5 to 9, indicating the substantial benefits for the construction of spatial prediction models. Sensitivity analyses of using data stratified by PM_2.5 concentrations revealed that the model performances were further improved in the high pollution season. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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<p>Locations of 17 selected TEPA monitoring sites including in this study.</p> Full article ">Figure 2
<p>Concentration distributions of PM measures for annual value, high PM_2.5 season (HPS) and low PM_2.5 season (LPS) (the box represents 25th–75th percentiles and median, and the whiskers represent 10th and 90th percentiles) (N = 17).</p> Full article ">

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Supplementary material:
Supplementary File 1 (ZIP, 731 KiB)

22 pages, 3415 KiB  

Open AccessArticle

The Potential Ozone Impacts of Landfills

by Eduardo P. Olaguer

Atmosphere 2021, 12(7), 877; https://doi.org/10.3390/atmos12070877 - 7 Jul 2021

Cited by 6 | Viewed by 4112

Abstract

Landfill gas produces ozone precursors such as nitrogen oxides and formaldehyde when combusted in flares or stationary engines. Solid waste landfills are also the third largest anthropogenic source of methane in the United States. Methane is both a greenhouse gas and a tropospheric [...] Read more.

Landfill gas produces ozone precursors such as nitrogen oxides and formaldehyde when combusted in flares or stationary engines. Solid waste landfills are also the third largest anthropogenic source of methane in the United States. Methane is both a greenhouse gas and a tropospheric ozone precursor. Despite its low photochemical reactivity, methane may noticeably affect urban ozone if released in large quantities along with other organic compounds in landfill gas. A fine-scale 3D Eulerian chemical transport model was used to demonstrate that, under meteorological and background chemical conditions conducive to high ozone concentrations, typical emissions of ozone precursors from a single hypothetical landfill may result in persistent daytime additions to ozone of over 1 part per billion (ppb) by volume tens of kilometers downwind. Large leaks of landfill gas can enhance this ozone pollution by over a tenth of a ppb, and external sources of non-methane ozone precursors may further exacerbate this impact. In addition, landfill gas combustion may increase near-source exposure to toxic formaldehyde by well over half a ppb. In Southeast Michigan, the combined influence of several landfills upwind of key monitoring sites may contribute significantly to observed exceedances of the U.S. ozone standard. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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<p>Locations of major solid waste landfills (blue diamonds) in the seven counties of the Southeast Michigan ozone non-attainment area relative to ozone monitoring stations (orange markers) run by the Michigan Department of Environment, Great Lakes, and Energy (EGLE). Stations are shown with corresponding 2018–2020 ozone design values.</p> Full article ">Figure 2
<p>Profile of the vertical diffusivity used in the model simulations.</p> Full article ">Figure 3
<p>Near Source: Baseline Scenario.Final surface concentrations of (<b>a</b>) NO, (<b>b</b>) NO₂, (<b>c</b>) O₃, (<b>d</b>) HCHO, (<b>e</b>) CH₄, and (<b>f</b>) PAR at the end of the 3 h simulation in the Baseline scenario for the 4 km × 4 km domain.</p> Full article ">Figure 3 Cont.
<p>Near Source: Baseline Scenario.Final surface concentrations of (<b>a</b>) NO, (<b>b</b>) NO₂, (<b>c</b>) O₃, (<b>d</b>) HCHO, (<b>e</b>) CH₄, and (<b>f</b>) PAR at the end of the 3 h simulation in the Baseline scenario for the 4 km × 4 km domain.</p> Full article ">Figure 3 Cont.
<p>Near Source: Baseline Scenario.Final surface concentrations of (<b>a</b>) NO, (<b>b</b>) NO₂, (<b>c</b>) O₃, (<b>d</b>) HCHO, (<b>e</b>) CH₄, and (<b>f</b>) PAR at the end of the 3 h simulation in the Baseline scenario for the 4 km × 4 km domain.</p> Full article ">Figure 4
<p>Near Source: Combustion Only ― Baseline. Difference plots of (<b>a</b>) NO, (<b>b</b>) NO₂, (<b>c</b>) O₃, (<b>d</b>) HCHO, (<b>e</b>) CH₄, and (<b>f</b>) PAR between the Combustion Only and Baseline scenarios at the end of the 3-h simulation for the 4 km × 4 km domain.</p> Full article ">Figure 4 Cont.
<p>Near Source: Combustion Only ― Baseline. Difference plots of (<b>a</b>) NO, (<b>b</b>) NO₂, (<b>c</b>) O₃, (<b>d</b>) HCHO, (<b>e</b>) CH₄, and (<b>f</b>) PAR between the Combustion Only and Baseline scenarios at the end of the 3-h simulation for the 4 km × 4 km domain.</p> Full article ">Figure 5
<p>Near Source: Fugitives + Combustion − Combustion Only. Difference plots of (<b>a</b>) NO, (<b>b</b>) NO₂, (<b>c</b>) O₃, (<b>d</b>) HCHO, (<b>e</b>) CH₄, and (<b>f</b>) PAR between the Fugitives + Combustion and Combustion Only scenarios at the end of the 3-h simulation for the 4 km × 4 km domain.</p> Full article ">Figure 5 Cont.
<p>Near Source: Fugitives + Combustion − Combustion Only. Difference plots of (<b>a</b>) NO, (<b>b</b>) NO₂, (<b>c</b>) O₃, (<b>d</b>) HCHO, (<b>e</b>) CH₄, and (<b>f</b>) PAR between the Fugitives + Combustion and Combustion Only scenarios at the end of the 3-h simulation for the 4 km × 4 km domain.</p> Full article ">Figure 6
<p>Near Source: Fugitives + Combustion − Combustion Only. Final surface concentrations of (<b>a</b>) NO, (<b>b</b>) NO₂, (<b>c</b>) O₃, (<b>d</b>) HCHO, (<b>e</b>) CH₄, and (<b>f</b>) PAR at the end of the 3-h simulation in the Baseline scenario for the 30 km × 30 km domain.</p> Full article ">Figure 6 Cont.
<p>Near Source: Fugitives + Combustion − Combustion Only. Final surface concentrations of (<b>a</b>) NO, (<b>b</b>) NO₂, (<b>c</b>) O₃, (<b>d</b>) HCHO, (<b>e</b>) CH₄, and (<b>f</b>) PAR at the end of the 3-h simulation in the Baseline scenario for the 30 km × 30 km domain.</p> Full article ">Figure 6 Cont.
<p>Near Source: Fugitives + Combustion − Combustion Only. Final surface concentrations of (<b>a</b>) NO, (<b>b</b>) NO₂, (<b>c</b>) O₃, (<b>d</b>) HCHO, (<b>e</b>) CH₄, and (<b>f</b>) PAR at the end of the 3-h simulation in the Baseline scenario for the 30 km × 30 km domain.</p> Full article ">Figure 7
<p>Ozone difference plot between the Combustion Only and Baseline scenarios at the end of the 3 h simulation for the 30 km × 30 km domain.</p> Full article ">Figure 8
<p>Ozone difference plot between the Fugitives + Combustion and Combustion Only scenarios at the end of the 3 h simulation for the 30 km × 30 km domain.</p> Full article ">Figure 9
<p>Methane difference plot between the Fugitives + Combustion and Combustion Only scenarios at the end of the 3 h simulation for the 30 km × 30 km domain.</p> Full article ">

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Supplementary material:
Supplementary File 1 (ZIP, 160 KiB)

13 pages, 3650 KiB  

Open AccessArticle

Real-World Vehicle Volatile Organic Compound Emissions and Their Source Profile in Chengdu Based on a Roadside and Tunnel Study

by Miao Feng, Xiang Hu, Li Zhou, Tianyue Zhang, Xiao Zhang, Qinwen Tan, Zihang Zhou, Ye Deng, Danlin Song and Chengmin Huang

Atmosphere 2021, 12(7), 861; https://doi.org/10.3390/atmos12070861 - 2 Jul 2021

Cited by 4 | Viewed by 2520

Abstract

With the continuous progress of air pollution prevention and control in China, the study of the emission characteristics of vehicles has become increasingly important. An in situ experiment was performed in the Tianfu tunnel in Chengdu to determine the vehicle emissions of volatile [...] Read more.

With the continuous progress of air pollution prevention and control in China, the study of the emission characteristics of vehicles has become increasingly important. An in situ experiment was performed in the Tianfu tunnel in Chengdu to determine the vehicle emissions of volatile organic compounds (VOCs). A total of 50 species of VOCs were quantified in the tunnel, with total concentrations in the range of 32.25–162.18 ppbv in the entrance and 52.90–233.92 ppbv in the exit, respectively. Alkanes were the most abundant group, followed by alkenes, aromatic hydrocarbons, oxygenated VOCs, alkynes and chlorocarbons. The general emission factors of the measured VOCs ranged from 141.71 mg veh⁻¹ km⁻¹ to 236.12 mg veh⁻¹ km⁻¹, and the average ± std was 177.31 ± 24.59 mg veh⁻¹ km⁻¹. The emission factors of diesel-fuelled vehicles, gasoline-fuelled vehicles and natural gas-fuelled vehicles were estimated based on linear regression analysis, with values of 272.39 ± 191.17 mg veh⁻¹ km⁻¹, 185.08 ± 12.85 mg veh⁻¹ km⁻¹ and 158.72 ± 3.21 mg veh⁻¹ km⁻¹, respectively. The results of roadside experiments indicate that the roadside ambience atmosphere contains many species characterized with vehicle emission features. Especially, there were fuel evaporation emission related substances, which were higher in content than tunnel samples. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Sampling sites at Tianfu Tunnel and three roads. Renmin South Road (RSR) represents main roads, Fanglin Road (FLR) represents branch roads and Hongguang Avenue (HGA) represents expressways.</p> Full article ">Figure 2
<p>Schematic diagram of sampling sites (A and B) in the Tianfu tunnel (TFT).</p> Full article ">Figure 3
<p>Time series of the numbers of three types of vehicles passing through the tunnel and NO₂ concentrations at the inlet (NO₂-A) and outlet (NO₂-B) of the Tianfu tunnel (TFT).</p> Full article ">Figure 4
<p>Temporal pattern of VOCs at the outlet of the Tianfu tunnel (TFT-B).</p> Full article ">Figure 5
<p>Top 20 species with the highest OFP from vehicular emissions.</p> Full article ">Figure 6
<p>VOC concentrations and their group compositions at Fanglin Road (FLR), Hongguang Avenue (HGA), Tianfu tunnel (TFT) and Renmin South Road (RSR).</p> Full article ">

15 pages, 2526 KiB  

Open AccessArticle

Improved Measurement Performance for the Sharp GP2Y1010 Dust Sensor: Reduction of Noise

by Jonathan E. Thompson

Atmosphere 2021, 12(6), 775; https://doi.org/10.3390/atmos12060775 - 16 Jun 2021

Cited by 1 | Viewed by 3209

Abstract

Sharp GP2Y1010 dust sensors are increasingly being used within distributed sensing networks and for personal monitoring of exposure to particulate matter (PM) pollution. These dust sensors offer an easy-to-use solution at an excellent price point; however, the sensors are known to offer limited [...] Read more.

Sharp GP2Y1010 dust sensors are increasingly being used within distributed sensing networks and for personal monitoring of exposure to particulate matter (PM) pollution. These dust sensors offer an easy-to-use solution at an excellent price point; however, the sensors are known to offer limited dynamic range and poor limits of detection (L.O.D.), often >15 μg m⁻³. The latter figure of merit precludes the use of this inexpensive line of dust sensors for monitoring PM_2.5 levels in environments within which particulate pollution levels are low. This manuscript presents a description of the fabrication and circuit used in the Sharp GP2Y1010 dust sensor and reports several effective strategies to minimize noise and maximize limits of detection for PM. It was found that measurement noise is primarily introduced within the photodiode detection circuitry, and that electromagnetic interference can influence dust sensor signals dramatically. Through optimization of the external capacitor and resistor used in the LED drive circuit—and the inter-pulse delay, electromagnetic shielding, and data acquisition strategy—noise was reduced approximately tenfold, leading to a projected noise equivalent limit of detection of 3.1 μg m⁻³. Strategies developed within this manuscript will allow improved limits of detection for these inexpensive sensors, and further enable research toward unraveling the spatial and temporal distribution of PM within buildings and urban centers—as well as an improved understanding of effect of PM on human health. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Figure 1
<p>Sharp dust sensor optical layout (<b>A,B</b>) and circuit board (<b>C</b>). Resistances noted were measured using a multimeter, while all components were installed in the circuit, thus, these may not represent the true values. Small yellow and red circles denote common contacts/connections.</p> Full article ">Figure 2
<p>(<b>A</b>) External circuit and connections required to operate the Sharp dust sensor. In this study, values for both R and C were varied to optimize performance. (<b>B</b>) Effect of capacitor value on indicated LED brightness and pulse-to-pulse variability as measured by the percent relative standard deviation (% RSD) of replicate pulses. Dust sensor #1 was used.</p> Full article ">Figure 3
<p>(<b>A</b>) Image plot of resistance of external LED resistor resistance vs. software programmed delay time between pulses vs. LED signal. The color indicates LED brightness (signal). (<b>B</b>) Image plot of resistance of external LED resistor resistance vs. software programmed delay time between pulses vs. percent relative standard deviation (% R.S.D.) of LED pulses. The color indicates % R.S.D. of sequential LED pulses.</p> Full article ">Figure 4
<p>Plot of indicated LED brightness (signal) vs. indicated LED peak current. As R² between these variables was &lt;0.001, no correlation was observed, indicating that LED drive current is not useful to correct for pulse-to-pulse variability in Sharp dust sensors.</p> Full article ">Figure 5
<p>(<b>A</b>) Twelve individual traces of dust sensor dark signal acquired with an Arduino sampling at 66.4 kHz. Signals indicate random dark noise transients observed in dust sensor output. The LED was not activated during these experiments. (<b>B</b>) Histogram of voltages of observed dark noise spikes. (<b>C</b>) Histogram of dark noise spikes with noise magnitudes reported as a 10-bit integer value.</p> Full article ">Figure 6
<p>(<b>A</b>) Traces of dust sensor dark signal (LED not triggered) acquired with an Arduino sampling at 66.4 kHz. Data on far-left reports observed signal when a cellular phone was brought near to the dust sensor. The middle plot of 6A reports when the cell phone was approximately 8 feet from the sensor and not being actively used. The far-right plot of 6A reports data observed when the dust sensor and circuitry were electromagnetically shielded by the end-user and a cellular phone was being used near the device. (<b>B</b>) reports pulse-to-pulse percent relative standard deviation (% R.S.D.) of sequential LED pulses under the stated conditions. (<b>C</b>) illustrates a histogram of pulse heights from the dust sensor. During this experiment, the sensor and circuit were shielded by the end-user, but a cellular phone was actively used nearby.</p> Full article ">Figure 7
<p>Noise power spectra for three individual dust sensors. All dust sensors indicated presence of interference noise of approx. 4.6 Hz. The origin of this noise is unknown. Other than this sole interfering frequency, noise approximated a white spectrum, suggesting signal averaging will improve signal-to-noise ratio.</p> Full article ">Figure 8
<p>Reduction of noise for the Sharp dust sensor through signal averaging. Signal averaging allowed reduction of noise by 10-fold compared to single pulse analysis. When N = 32 medians were averaged, a noise equivalent limit of detection of approximately 3 μg m⁻³ was found.</p> Full article ">

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Supplementary material:
Supplementary File 1 (ZIP, 229 KiB)

14 pages, 1666 KiB  

Open AccessArticle

Seasonality of the Airborne Ambient Soot Predominant Emission Sources Determined by Raman Microspectroscopy and Thermo-Optical Method

by Natalia Zioła, Kamila Banasik, Mariola Jabłońska, Janusz Janeczek, Barbara Błaszczak, Krzysztof Klejnowski and Barbara Mathews

Atmosphere 2021, 12(6), 768; https://doi.org/10.3390/atmos12060768 - 14 Jun 2021

Cited by 3 | Viewed by 2575

Abstract

Raman microspectroscopy and thermo-optical-transmittance (TOT) method were used to study airborne ambient soot collected at the suburban air monitoring station in southern Poland during the residential heating (January-February) and non-heating (June–July) seasons of 2017. Carbonaceous material constituted on average 47.2 wt.% of PM [...] Read more.

Raman microspectroscopy and thermo-optical-transmittance (TOT) method were used to study airborne ambient soot collected at the suburban air monitoring station in southern Poland during the residential heating (January-February) and non-heating (June–July) seasons of 2017. Carbonaceous material constituted on average 47.2 wt.% of PM_2.5 during the heating season and 26.9 wt.% in the non-heating season. Average concentrations of OC (37.5 ± 11.0 μg/m³) and EC (5.3 ± 1.1 μg/m³) during the heating season were significantly higher than those in the non-heating season (OC = 2.65 ± 0.78 μg/m³, and EC = 0.39 ± 0.18 μg/m³). OC was a chief contributor to the TC mass concentration regardless of the season. All Raman parameters indicated coal combustion and biomass burning were the predominant sources of soot in the heating season. Diesel soot, which is structurally less ordered than soot from other sources, was dominant during the non-heating season. The D1 and G bands area ratio (D1_A/G_A) was the most sensitive Raman parameter that discriminated between various soot sources, with D1_A/G_A > 1 for diesel soot, and less than 1 for soot from coal and wood burning. Due to high daily variability of both TOT and Raman spectroscopy data, single-day measurements can be inconclusive regarding the soot source apportionment. Long-time measurement campaigns are recommended. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Figure 1
<p>The sampling site location (red dot) in Racibórz in the Silesia province (yellow area on the map of Poland).</p> Full article ">Figure 2
<p>Relationship between OC and EC mass concentrations (μg/m³) during heating (H) and non-heating (NH; see also enlarged view in the inset) seasons in Racibórz in 2017.</p> Full article ">Figure 3
<p>Deconvoluted Raman spectra of soot from wood combustion (samples W1 (<b>a</b>) and W2 (<b>b</b>)), coal-fired furnace (<b>c</b>), and diesel engine exhaust (<b>d</b>).</p> Full article ">Figure 4
<p>Exemplary deconvoluted Raman spectra of soot in PM_2.5 collected during heating (H3) (<b>a</b>) and non-heating (NH4) (<b>b</b>) seasons in Racibórz.</p> Full article ">Figure 5
<p>Season-dependent changes in the D1_STA/G_STA and RAR parameters in the Raman spectra of soot from Racibórz in 2017. H—heating season and NH non-heating season.</p> Full article ">

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Supplementary material:
Supplementary File 1 (ZIP, 372 KiB)

16 pages, 2768 KiB  

Open AccessArticle

Source Apportionment and Health Risk Assessment of Metal Elements in PM_2.5 in Central Liaoning’s Urban Agglomeration

by Qingyuan Guo, Liming Li, Xueyan Zhao, Baohui Yin, Yingying Liu, Xiaoli Wang, Wen Yang, Chunmei Geng, Xinhua Wang and Zhipeng Bai

Atmosphere 2021, 12(6), 667; https://doi.org/10.3390/atmos12060667 - 24 May 2021

Cited by 14 | Viewed by 2895

Abstract

To better understand the source and health risk of metal elements in PM_2.5, a field study was conducted from May to December 2018 in the central region of the Liaoning province, China, including the cities of Shenyang, Anshan, Fushun, Benxi, Yingkou, [...] Read more.

To better understand the source and health risk of metal elements in PM_2.5, a field study was conducted from May to December 2018 in the central region of the Liaoning province, China, including the cities of Shenyang, Anshan, Fushun, Benxi, Yingkou, Liaoyang, and Tieling. 24 metal elements (Na, K, V, Cr, Mn, Co, Ni, Cu, Zn, As, Mo, Cd, Sn, Sb, Pb, Bi, Al, Sr, Mg, Ti, Ca, Fe, Ba, and Si) in PM_2.5 were measured by ICP-MS and ICP-OES. They presented obvious seasonal variations, with the highest levels in winter and lowest in summer for all seven cities. The sum of 24 elements were ranged from to in these cities. The element mass concentration ratio was the highest in Yingkou in the spring (26.15%), and the lowest in Tieling in winter (3.63%). The highest values of elements in PM_2.5 were mostly found in Anshan and Fushun among the studied cities. Positive matrix factorization (PMF) modelling revealed that coal combustion, industry, traffic emission, soil dust, biomass burning, and road dust were the main sources of measured elements in all cities except for Yingkou. In Yingkou, the primary sources were identified as coal combustion, metal smelting, traffic emission, soil dust, and sea salt. Health risk assessment suggested that Mn had non-carcinogenic risks for both adults and children. As for Cr, As, and Cd, there was carcinogenic risks for adults and children in most cities. This study provides a clearer understanding of the regional pollution status of industrial urban agglomeration. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Figure 1
<p>Location of sampling sites (<span class="html-italic">n</span> = 10) in the seven cities of the urban agglomeration.</p> Full article ">Figure 2
<p>(<b>a</b>) The seasonal mean concentrations of PM_2.5 measured at the seven cities. (<b>b</b>) The seasonal mean concentrations of the sum of elements in PM_2.5 measured at the seven cities. Shown in each subfigure are the mean (dot symbol), the median (horizontal line), the central 50% data (25th–75th percentiles; box), and the central 90% data (5th–95th percentiles; whiskers).</p> Full article ">Figure 3
<p>(<b>a</b>) Annual average concentrations of 24 elements in PM_2.5 at seven cities, (<b>b</b>) The average annual concentration of each element as a percentage of the total element concentration in PM_2.5 at seven cities.</p> Full article ">Figure 4
<p>The mean concentrations of total 24 PM_2.5 elemental components in different seasons at seven cities, the y-coordinate is the logarithmic coordinate.</p> Full article ">Figure 5
<p>Contribution percentage of the identified sources to PM_2.5 at seven cities; while the first six cities share the same source, Yingkou is different from them.</p> Full article ">Figure 6
<p>The non-carcinogenic risks (sum HI) of total nine metal elements to adults and children at seven cities. The y-coordinate is the logarithmic coordinate.</p> Full article ">Figure 7
<p>Non-carcinogenic risks (sum HI) to adults and children from the PMF-identified sources of PM_2.5 in seven cities ((<b>a</b>) non-carcinogenic risks to adults and (<b>b</b>) non-carcinogenic risks to children). While the first six cities share the same sources, Yingkou is different from them.</p> Full article ">Figure 8
<p>Carcinogenic risks from the PMF-identified sources to PM_2.5 in seven cities. While the first six cities share the same source, Yingkou is different from them.</p> Full article ">

14 pages, 1996 KiB  

Open AccessArticle

Geochemical Characterization and Heavy Metal Sources in PM₁₀ in Arequipa, Peru

by Jianghanyang Li, Greg Michalski, Elizabeth Joy Olson, Lisa R. Welp, Adriana E. Larrea Valdivia, Juan Reyes Larico, Francisco Alejo Zapata and Lino Morales Paredes

Atmosphere 2021, 12(5), 641; https://doi.org/10.3390/atmos12050641 - 18 May 2021

Cited by 4 | Viewed by 3213

Abstract

Particulate matter smaller than 10 μm (PM₁₀) is an important air pollutant that adversely affects human health by increasing the risk of respiratory and cardiovascular diseases. Recent studies reported multiple extreme PM₁₀ levels at high altitude Peruvian cities, which resulted [...] Read more.

Particulate matter smaller than 10 μm (PM₁₀) is an important air pollutant that adversely affects human health by increasing the risk of respiratory and cardiovascular diseases. Recent studies reported multiple extreme PM₁₀ levels at high altitude Peruvian cities, which resulted from a combination of high emissions and limited atmospheric circulation at high altitude. However, the emission sources of the PM₁₀ still remain unclear. In this study, we collected PM₁₀ samples from four sites (one industrial site, one urban site, and two rural sites) at the city of Arequipa, Peru, during the period of February 2018 to December 2018. To identify the origins of PM₁₀ at each site and the spatial distribution of PM₁₀ emission sources, we analyzed major and trace element concentrations of the PM₁₀. Of the observed daily PM₁₀ concentrations at Arequipa during our sampling period, 91% exceeded the World Health Organization (WHO) 24-h mean PM₁₀ guideline value, suggesting the elevated PM₁₀ strongly affected the air quality at Arequipa. The concentrations of major elements, Na, K, Mg, Ca, Fe, and Al, were high and showed little variation, suggesting that mineral dust was a major component of the PM₁₀ at all the sites. Some trace elements, such as Mn and Mo, originated from the mineral dust, while other trace elements, including Pb, Sr, Cu, Ba, Ni, As and V, were from additional anthropogenic sources. The industrial activities at Rio Seco, the industrial site, contributed to significant Pb, Cu, and possibly Sr emissions. At two rural sites, Tingo Grande and Yarabamba, strong Cu emissions were observed, which were likely associated with mining activities. Ni, V, and As were attributed to fossil fuel combustion emissions, which were strongest at the Avenida Independencia urban site. Elevated Ba and Cu concentrations were also observed at the urban site, which were likely caused by heavy traffic in the city and vehicle brake wear emissions. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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Figure 1

Figure 1
<p>Map of the sampling sites. Industrial site (Rio Seco), urban site (Av. Independencia), and suburban sites (Tingo Grande and Yarabamba). Urban land cover appears grey in this image.</p> Full article ">Figure 2
<p>PM₁₀ concentrations during the sampling period at all the sites. (<b>A</b>) Time series of observed PM₁₀ concentrations in this study; (<b>B</b>) box plot of PM₁₀ concentrations at each site.</p> Full article ">Figure 3
<p>Box plot of major element concentrations (<b>A</b>: Fe, <b>B</b>: Na, <b>C</b>: K, <b>D</b>: Mg, in %) at each site.</p> Full article ">Figure 4
<p>Box plot of the air concentrations (in ng/m³) of trace elements at each site.</p> Full article ">Figure 5
<p>Box plot of mass-weighted concentrations (in ppm) of trace elements in the PM₁₀ at each site.</p> Full article ">

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