Atmosphere

12 pages, 1873 KiB

Open AccessArticle

A New Method Proposed for the Estimation of Exposure to Atmospheric Pollution through the Analysis of Black Pigments on the Lung Surface

by Dunia Waked, Mariana Matera Veras, Paulo Hilário Nascimento Saldiva and Ana Paula Cremasco Takano

Atmosphere 2024, 15(9), 1126; https://doi.org/10.3390/atmos15091126 (registering DOI) - 17 Sep 2024

Abstract

Megacities can be considered excellent laboratories for studying the effects of the urban environment on human health. Typically, exposure to pollution is estimated according to daily or annual averages of pollutant concentrations, collected at monitoring stations, using satellite data for remote sensing of [...] Read more.

Megacities can be considered excellent laboratories for studying the effects of the urban environment on human health. Typically, exposure to pollution is estimated according to daily or annual averages of pollutant concentrations, collected at monitoring stations, using satellite data for remote sensing of pollutant levels, considering proximity to major roads, or through personal exposure monitoring with portable sensors. However, these approaches fall short in identifying individual exposure values over a lifetime. It is well established that individuals living in large urban areas inhale atmospheric particles containing carbonaceous components, resulting in the deposition of black pigments in lung tissue, known as black carbon or anthracosis. This study aims to detail the procedures for assessing the deposition of such pigments, which serve as an estimate of an individual’s exposure to atmospheric pollution particles. Data collection involves administering detailed questionnaires and capturing lung images in the autopsy room. The analysis is based on macroscopic quantification of black pigments, supplemented by an evaluation of personal habits and the clinical histories of the individuals. This method of estimating lifetime exposure to inhaled particles provides a valuable tool for understanding the correlation between urban living and its potential health effects. Full article

(This article belongs to the Special Issue Research on Air Pollution and Human Exposures)

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27 pages, 18384 KiB

Open AccessArticle

Calibration of Typhoon Track Forecasts Based on Deep Learning Methods

by Chengchen Tao, Zhizu Wang, Yilun Tian, Yaoyao Han, Keke Wang, Qiang Li and Juncheng Zuo

Atmosphere 2024, 15(9), 1125; https://doi.org/10.3390/atmos15091125 (registering DOI) - 17 Sep 2024

Abstract

An accurate forecast of typhoon tracks is crucial for disaster warning and mitigation. However, existing numerical weather prediction models, such as the Weather Research and Forecasting (WRF) model, still exhibit significant errors in track forecasts. This study aims to improve forecast accuracy by [...] Read more.

An accurate forecast of typhoon tracks is crucial for disaster warning and mitigation. However, existing numerical weather prediction models, such as the Weather Research and Forecasting (WRF) model, still exhibit significant errors in track forecasts. This study aims to improve forecast accuracy by correcting WRF-forecasted tracks using deep learning models, including Bidirectional Long Short-Term Memory (BiLSTM) + Convolutional Long Short-Term Memory (ConvLSTM) + Wide and Deep Learning (WDL), BiLSTM + Convolutional Gated Recurrent Unit (ConvGRU) + WDL, and BiLSTM + ConvLSTM + Extreme Deep Factorization Machine (xDeepFM), with a comparison to the Kalman Filter. The results demonstrate that the BiLSTM + ConvLSTM + WDL model reduces the 72 h track prediction error (TPE) from 255.18 km to 159.23 km, representing a 37.6% improvement over the original WRF model, and exhibits significant advantages across all evaluation metrics, particularly in key indicators such as Bias², Mean Squared Error (MSE), and Sequence. The decomposition of MSE further validates the importance of the BiLSTM, ConvLSTM, WDL, and Temporal Normalization (TN) layers in enhancing the model’s spatio-temporal feature-capturing ability. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Atmospheric Sciences)

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18 pages, 2158 KiB

Open AccessArticle

Application of an Empirical Model to Improve Maximum Value Predictions in CFD-RANS: Insights from Four Scientific Domains

by George Efthimiou

Atmosphere 2024, 15(9), 1124; https://doi.org/10.3390/atmos15091124 - 16 Sep 2024

Abstract

This study introduces an empirical model designed to predict the maximum values of time-dependent data across four turbulence-related fields: hydrogen combustion in renewable energy systems, urban microclimate effects on cultural heritage, shipping emissions, and road vehicle emissions. The model, which is based on [...] Read more.

This study introduces an empirical model designed to predict the maximum values of time-dependent data across four turbulence-related fields: hydrogen combustion in renewable energy systems, urban microclimate effects on cultural heritage, shipping emissions, and road vehicle emissions. The model, which is based on the mean, standard deviation, and integral time scale, employs two parameters: a fixed exponent ‘ν’ (0.3) reflecting time scale sensitivity, and a variable parameter ‘b’ that accounts for application-specific uncertainties. Integrated into the Computational Fluid Dynamics (CFD) framework, specifically the Reynolds-Averaged Navier–Stokes (RANS) methodology, the model addresses the RANS approach’s limitation in predicting extreme values due to its inherent averaging process. By incorporating the empirical model, this study enhances RANS simulations’ ability to predict critical values, such as peak hydrogen concentrations and maximum urban wind speeds, which is essential for safety and reliability assessments. Validation against experimental and numerical data across the four fields demonstrates strong agreement, highlighting the model’s potential to improve CFD-RANS predictions of extreme events. This advancement offers significant implications for future CFD-RANS applications, particularly in scenarios demanding fast and reliable maximum value predictions. Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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27 pages, 35504 KiB

Open AccessArticle

A Multi-Scale Analysis of the Extreme Precipitation in Southern Brazil in April/May 2024

by Michelle Simões Reboita, Enrique Vieira Mattos, Bruno César Capucin, Diego Oliveira de Souza and Glauber Willian de Souza Ferreira

Atmosphere 2024, 15(9), 1123; https://doi.org/10.3390/atmos15091123 - 16 Sep 2024

Abstract

Since 2020, southern Brazil’s Rio Grande do Sul (RS) State has been affected by extreme precipitation episodes caused by different atmospheric systems. However, the most extreme was registered between the end of April and the beginning of May 2024. This extreme precipitation caused [...] Read more.

Since 2020, southern Brazil’s Rio Grande do Sul (RS) State has been affected by extreme precipitation episodes caused by different atmospheric systems. However, the most extreme was registered between the end of April and the beginning of May 2024. This extreme precipitation caused floods in most parts of the state, affecting 2,398,255 people and leading to 183 deaths and 27 missing persons. Due to the severity of this episode, we need to understand its drivers. In this context, the main objective of this study is a multi-scale analysis of the extreme precipitation between 26 April and 5 May, i.e., an analysis of the large-scale patterns of the atmosphere, a description of the synoptic environment, and an analysis of the mesoscale viewpoint (cloud-top features and lightning). Data from different sources (reanalysis, satellite, radar, and pluviometers) were used in this study, and different methods were applied. The National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN) registered accumulated rainfall above 400 mm between 26 April and 5 May using 27 pluviometers located in the central-northern part of RS. The monthly volumes reached 667 mm and 803 mm, respectively, for April and May 2024, against a climatological average of 151 mm and 137 mm for these months. The maximum precipitation recorded was 300 mm in a single day on 30 April 2024. From a large-scale point of view, an anomalous heat source in the western Indian Ocean triggered a Rossby wave that contributed to a barotropic anticyclonic anomalous circulation over mid-southeastern Brazil. While the precipitant systems were inhibited over this region (the synoptic view), the anomalous stronger subtropical jet southward of the anticyclonic circulation caused uplift over RS State and, consequently, conditions leading to mesoscale convective system (MCS) development. In addition, the low-level jet east of the Andes transported warm and moist air to southern Brazil, which also interacted with two cold fronts that reached RS during the 10-day period, helping to establish the precipitation. Severe deep MCSs (with a cloud-top temperature lower than −80 °C) were responsible for a high lightning rate (above 10 flashes km⁻² in 10 days) and accumulated precipitation (above 600 mm in 10 days), as observed by satellite measurements. This high volume of rainfall caused an increase in soil moisture, which exceeded a volume fraction of 0.55, making water infiltration into the soil difficult and, consequently, favoring flood occurrence. Full article

(This article belongs to the Special Issue Weather and Climate Extremes: Observations, Modeling, and Impacts (2nd Edition))

18 pages, 1912 KiB

Open AccessArticle

Tire Wear Emissions by Highways: Impact of Season and Surface Type

by Jason A. Miech, Saed Aker, Zhaobo Zhang, Hasan Ozer, Matthew P. Fraser and Pierre Herckes

Atmosphere 2024, 15(9), 1122; https://doi.org/10.3390/atmos15091122 - 15 Sep 2024

Abstract

With the increasing number of electric vehicles taking to the roads, the impact of tailpipe emissions on air quality will decrease, while resuspended road dust and brake/tire wear will become more significant. This study quantified PM₁₀ emissions from tire wear under a [...] Read more.

With the increasing number of electric vehicles taking to the roads, the impact of tailpipe emissions on air quality will decrease, while resuspended road dust and brake/tire wear will become more significant. This study quantified PM₁₀ emissions from tire wear under a range of real highway conditions with measurements across different seasons and roadway surface types in Phoenix, Arizona. Tire wear was quantified in the sampled PM₁₀ using benzothiazoles (vulcanization accelerators) as tire markers. The measured emission factors had a range of 0.005–0.22 mg km⁻¹ veh⁻¹ and are consistent with an earlier experimental study conducted in Phoenix. However, these results are lower than values typically found in the literature and values calculated from emissions models, such as MOVES (MOtor Vehicle Emission Simulator). We found no significant difference in tire wear PM₁₀ emission factors for different surface types (asphalt vs. diamond grind concrete) but saw a significant decrease in the winter compared to the summer. Full article

(This article belongs to the Special Issue Particulate Matters in Ambient Air: Characteristics, Composition, and Sources)

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Map of sampling sites with diamond-ground concrete surfaces (DG) sites in maroon and Asphalt Rubber Friction Course surfaces (AR) sites in gold. Whole numbers correspond to the sample numbers in <a href="#atmosphere-15-01122-t001" class="html-table">Table 1</a>, with numbers in boxes representing sites that winter samples were also collected at. Full article ">Figure 2
Sr concentrations by impactor size cut from a residential background test, flare test, and two highway background tests by 33rd Ave and I-10. Full article ">Figure 3
PM1 (blue solid) and PM10 (green dashed) measurements from the MODULAIR™-PM sensor for samples 2.1 & 2.2. The black solid vertical lines represent lighting of flare sets for 2.1 and the red-dashed vertical lines for 2.2. Full article ">Figure 4
Weighted average %2PB compositions for each sample based on <a href="#atmosphere-15-01122-t002" class="html-table">Table 2</a> and <a href="#app1-atmosphere-15-01122" class="html-app">Figure S3</a>. Full article ">Figure 5
Average PM10 concentrations with standard deviation from all samples as measured by gravimetry on the quartz fiber filters. The shaded box represents the winter samples. Full article ">Figure 6
MOVES simulated tire wear EFs for the summer samples (A) and winter samples (B). Full article ">

19 pages, 12774 KiB

Open AccessArticle

Monthly Characteristics and Source–Receptor Relationships of Anthropogenic Total Nitrate in Northeast Asia

by Moon-Seok Kang, Da-Som Park, Chan-Byeong Chae, Young Sunwoo and Ki-Ho Hong

Atmosphere 2024, 15(9), 1121; https://doi.org/10.3390/atmos15091121 - 15 Sep 2024

Abstract

The complex nonlinear characteristics of atmospheric chemistry necessitate the development of new methods for calculating source–receptor (S–R) relationships for secondary air pollutants. In this study, the monthly characteristics and S–R relationships of anthropogenic total nitrate (i.e., the sum of N from nitric acid, [...] Read more.

The complex nonlinear characteristics of atmospheric chemistry necessitate the development of new methods for calculating source–receptor (S–R) relationships for secondary air pollutants. In this study, the monthly characteristics and S–R relationships of anthropogenic total nitrate (i.e., the sum of N from nitric acid, inorganic nitrate, and peroxyacetyl nitrate) in Northeast Asia were simulated and analyzed. The Community Multiscale Air Quality (CMAQ), Fifth-Generation NCAR/Penn State Mesoscale (MM5), and Sparse Matrix Operator Kernel Emissions (SMOKE) models were employed for air quality modeling, meteorological fields, and emissions processing, respectively. The study area encompassed Republic of Korea, Japan, and most of China. Five source/receptor regions were defined to derive the S–R relationships: three in China, one in Republic of Korea, and one in Japan. To produce data for the calculation of the S–R relationship, several experiments were conducted with a 20% reduction in NO_x emission sources. As a result of the S–R relationships, China was rarely impacted by the other two countries. The total depositions in other countries were significantly dominated by China (i.e., 43.5% and 40.7% in Republic of Korea and Japan, respectively, and up to 82.3% in December for Republic of Korea). Full article

(This article belongs to the Section Air Quality)

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15 pages, 3387 KiB

Open AccessArticle

Distribution and Characteristics of Ammonia Concentration by Region in Korea

by In-Ho Song, Hyun-Woong Kim, Jong-Sung Park, Seung-Myung Park, Jae-Yun Lee, Eun-Jung Nam, Yong-Jae Lim, Jung-Min Park, Myung-Soo Yoo, Seog-Yeon Cho and Hye-Jung Shin

Atmosphere 2024, 15(9), 1120; https://doi.org/10.3390/atmos15091120 - 14 Sep 2024

Abstract

In this study, the characteristics of ammonia and their effects on secondary particulate matter (PM) formation were analyzed by region in Korea in 2020. The NH₃ concentration was high in GJ (11.4 ppb), a neighboring agricultural area, followed by DJ (9.0 ppb) [...] Read more.

In this study, the characteristics of ammonia and their effects on secondary particulate matter (PM) formation were analyzed by region in Korea in 2020. The NH₃ concentration was high in GJ (11.4 ppb), a neighboring agricultural area, followed by DJ (9.0 ppb) and SE (8.6 ppb), which are located in urban areas. On the other hand, BI (2.6 ppb) and JI (4.5 ppb), which are background regions, demonstrated a lower concentration than other areas. Seasonally, ammonia was high in spring and summer, and it generally increased when human activities are active. Therefore, it is believed that the ammonia in the atmosphere not only changes depending on local emissions, but also based on temperature-dependent phase distribution characteristics. For SE and GJ, regions with relatively high ammonia concentrations, investigations into the effect of ammonia on secondary PM formation were conducted. In both regions, the ammonium-to-sulfate mole ratio tended to increase with increasing ammonia or PM_2.5 concentration. It can be assumed that the PM_2.5 concentration increases as nitrates are formed under the ammonia-sufficient condition. The adjusted gas ratio is generally greater than 4, indicating that there is a lot of free ammonia. Thus, it is estimated that a reduction in ammonia would not be effective to restrain nitrate formation. Full article

(This article belongs to the Section Air Quality)

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22 pages, 27897 KiB

Open AccessArticle

Evaluation of the Urban Canopy Scheme TERRA-URB in the ICON Model at Hectometric Scale over the Naples Metropolitan Area

by Davide Cinquegrana, Myriam Montesarchio, Alessandra Lucia Zollo and Edoardo Bucchignani

Atmosphere 2024, 15(9), 1119; https://doi.org/10.3390/atmos15091119 - 14 Sep 2024

Abstract

The present work is focused on the validation of the urban canopy scheme TERRA-URB, implemented in ICON weather forecast model. TERRA-URB is used to capture the behavior of urbanized areas as sources of heat fluxes, mainly due to anthropogenic activities that can influence [...] Read more.

The present work is focused on the validation of the urban canopy scheme TERRA-URB, implemented in ICON weather forecast model. TERRA-URB is used to capture the behavior of urbanized areas as sources of heat fluxes, mainly due to anthropogenic activities that can influence temperature, humidity, and other atmospheric variables of the surrounding areas. Heat fluxes occur especially during the nighttime in large urbanized areas, characterized by poor vegetation, and are responsible for the formation of Urban Heat and Dry Island, i.e., higher temperatures and lower humidity compared to rural areas. They can be exacerbated under severe conditions, with dangerous consequences for people living in these urban areas. For these reasons, the need of accurately forecasting these phenomena is particularly felt. The present work represents one of the first attempts of using a very high resolution (about 600 m) in a Numerical Weather Prediction model. Performances of this advanced version of ICON have been investigated over a domain located in southern Italy, including the urban metropolitan area of Naples, considering a week characterized by extremely high temperatures. Results highlight that the activation of TERRA-URB scheme entails a better representation of temperature, relative humidity, and wind speed in urban areas, especially during nighttime, also allowing a proper reproduction of Urban Heat and Dry Island effects. Over rural areas, instead, no significant differences are found in model results when the urban canopy scheme is used. Full article

(This article belongs to the Section Meteorology)

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18 pages, 5110 KiB

Open AccessArticle

Development of Dust Emission Prediction Model for Open-Pit Mines Based on SHPB Experiment and Image Recognition Method

by Shanzhou Du, Hao Chen, Xiaohua Ding, Zhouquan Liao and Xiang Lu

Atmosphere 2024, 15(9), 1118; https://doi.org/10.3390/atmos15091118 - 14 Sep 2024

Abstract

Open-pit coal mining offers high resource recovery, excellent safety conditions, and large-scale production. However, the process generates significant dust, leading to occupational diseases such as pneumoconiosis among miners and adversely affecting nearby vegetation through dust deposition, which hinders photosynthesis and causes ecological damage. [...] Read more.

Open-pit coal mining offers high resource recovery, excellent safety conditions, and large-scale production. However, the process generates significant dust, leading to occupational diseases such as pneumoconiosis among miners and adversely affecting nearby vegetation through dust deposition, which hinders photosynthesis and causes ecological damage. This limits the transition of open-pit mining to a green, low-carbon model. Among these processes, blasting generates the most dust and has the widest impact range, but the specific amount of dust generated has not yet been thoroughly studied. This study integrates indoor experiments, theoretical analyses, and field tests, employing the Split Hopkinson Pressure Bar (SHPB) system to conduct impact loading tests on coal–rock samples under pressures ranging from 0.13 MPa to 2.0 MPa. The results indicate that as the impact load increases, the proportion of large-sized blocks decreases while smaller fragments and powdered samples increase, signifying intensified sample fragmentation. Using stress wave attenuation theory, this study translates indoor impact loadings to field blast shock waves, revealing the relationship between blasting dust mass fraction and impact pressure. Field tests at the Haerwusu open-pit coal mine validated the formula. Using image recognition technology to analyze post-blast muck-pile fragmentation, the estimated dust production closely matched the calculated values, with an error margin of less than 10%. This formula provides valuable insights for estimating dust production and improving dust control measures during open-pit mine blasting operations. Full article

(This article belongs to the Section Air Pollution Control)

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15 pages, 7750 KiB

Open AccessArticle

Effects of Wildfire and Logging on Soil CO₂ Efflux in Scots Pine Forests of Siberia

by Elena A. Kukavskaya, Anna V. Bogorodskaya, Ludmila V. Buryak, Olga P. Kalenskaya and Susan G. Conard

Atmosphere 2024, 15(9), 1117; https://doi.org/10.3390/atmos15091117 - 14 Sep 2024

Abstract

Wildfires and logging play an important role in regulating soil carbon fluxes in forest ecosystems. In Siberia, large areas are disturbed by fires and logging annually. Climate change and increasing anthropogenic pressure have resulted in the expansion of disturbed areas in recent decades. [...] Read more.

Wildfires and logging play an important role in regulating soil carbon fluxes in forest ecosystems. In Siberia, large areas are disturbed by fires and logging annually. Climate change and increasing anthropogenic pressure have resulted in the expansion of disturbed areas in recent decades. However, few studies have focused on the effects of these disturbances on soil CO₂ efflux in the vast Siberian areas. The objective of our research was to evaluate differences in CO₂ efflux from soils to the atmosphere between undisturbed sites and sites affected by wildfire and logging in Scots pine forests of southern Siberia. We examined 35 plots (undisturbed forest, burned forest, logged plots, and logged and burned plots) on six study sites in the Angara region and four sites in the Zabaikal region. Soil CO₂ efflux was measured using an LI-800 infrared gas analyzer. We found that both fire and logging significantly reduced soil efflux in the first years after a disturbance due to a reduction in vegetation biomass and consumption of the forest floor. We found a substantially lower CO₂ efflux in forests burned by high-severity fires (74% less compared to undisturbed forests) than in forests burned by moderate-severity (60% less) and low-severity (37% less) fires. Clearcut logging resulted in 6–60% lower soil CO₂ efflux at most study sites, while multiple disturbances (logging and fire) had 48–94% lower efflux. The soil efflux rate increased exponentially with increasing soil temperature in undisturbed Scots pine forests (p < 0.001) and on logged plots (p < 0.03), while an inverse relationship to soil temperature was observed in burned forests (p < 0.03). We also found a positive relationship (R = 0.60–0.83, p < 0.001) between ground cover depth and soil CO₂ efflux across all the plots studied. Our results demonstrate the importance of disturbance factors in the assessment of regional and global carbon fluxes. The drastic changes in CO₂ flux rates following fire and logging should be incorporated into carbon balance models to improve their reliability in a changing environment. Full article

(This article belongs to the Special Issue Carbon Fluxes in the Pan-Arctic Region)

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The location of the study sites. The inserts show the Angara (a) and the Zabaikal (b) regions and the locations of our study areas. Full article ">Figure 2
Typical study sites in the Angara region (I) and the Zabaikal region (II): undisturbed forest (a), burned forest (b), logged plot (c), logged and burned plot (d). Full article ">Figure 3
Soil CO2 efflux at 6 study sites with various disturbance types in the Angara region. The box denotes 25th–75th percentiles, the center line indicates the median, the dot inside the box stands for the mean, the whiskers indicate minimum/maximum values, and dots outside the whiskers show outliers. Full article ">Figure 4
Soil CO2 efflux at 4 study sites with various disturbance types in the Zabaikal region. The box denotes 25th–75th percentiles, the center line indicates the median, the dot inside the box stands for the mean, the whiskers indicate minimum/maximum values, and dots outside the whiskers show outliers. Full article ">Figure 5
Soil CO2 efflux as a function of soil temperature in the undisturbed forests (a), burned forests (b), logged sites (c), and burned and logged sites (d) in the Angara region. Full article ">Figure 6
Soil CO2 efflux as a function of soil temperature in the undisturbed forests (a), burned forests (b), logged sites (c), and burned and logged sites (d) in the Zabaikal region. Full article ">Figure 7
Relationship between soil CO2 efflux and ground fuel depth in the Angara (a) and Zabaikal (b) regions with respect to disturbance types. Full article ">

19 pages, 1623 KiB

Open AccessArticle

Research on the Mechanism of the Influence of Thermal Stress on Tourists’ Environmental Responsibility Behavior Intention: An Example from a Desert Climate Region, China

by Dong Li, Pengtao Wang, Jingyun Guan, Xiaoliang Xu and Kaiyu Li

Atmosphere 2024, 15(9), 1116; https://doi.org/10.3390/atmos15091116 - 13 Sep 2024

Abstract

The desert climate region attracts a multitude of tourists due to its distinctive landforms and climatic conditions, however, it also presents challenges for environmental protection. This article constructs a theoretical model that examines the influence of thermal stress on tourists’ environmental responsibility behavior [...] Read more.

The desert climate region attracts a multitude of tourists due to its distinctive landforms and climatic conditions, however, it also presents challenges for environmental protection. This article constructs a theoretical model that examines the influence of thermal stress on tourists’ environmental responsibility behavior intention (ERBI), with anticipated pride and anticipated guilt serving as mediating factors. An empirical study is conducted in Turpan, Xinjiang, which represents a typical inland arid area in China. The results indicate that: (1) thermal stress does not have a significant direct impact on ERBI, nevertheless, anticipated pride and anticipated guilt play crucial mediating roles between thermal stress and this intention. (2) Furthermore, environmental knowledge positively moderates the relationship between anticipated pride, anticipated guilt, and the ERBI. This research contributes to the understanding of how tourists’ anticipatory emotions affect their ERBI in desert climate regions while deepening our comprehension of the driving mechanisms behind such intentions among tourists. Moreover, it provides theoretical references for promoting environmentally responsible behaviors among tourists visiting desert climate regions. Full article

(This article belongs to the Special Issue Extreme Climate Events: Causes, Risk and Adaptation)

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24 pages, 2008 KiB

Open AccessReview

A Review on the Arctic–Midlatitudes Connection: Interactive Impacts, Physical Mechanisms, and Nonstationary

by Shuoyi Ding, Xiaodan Chen, Xuanwen Zhang, Xiang Zhang and Peiqiang Xu

Atmosphere 2024, 15(9), 1115; https://doi.org/10.3390/atmos15091115 - 13 Sep 2024

Abstract

In light of the rapid Arctic warming and continuous reduction in Arctic Sea ice, the complex two-way Arctic–midlatitudes connection has become a focal point in recent climate research. In this paper, we review the current understanding of the interactive influence between midlatitude atmospheric [...] Read more.

In light of the rapid Arctic warming and continuous reduction in Arctic Sea ice, the complex two-way Arctic–midlatitudes connection has become a focal point in recent climate research. In this paper, we review the current understanding of the interactive influence between midlatitude atmospheric variability and Arctic Sea ice or thermal conditions on interannual timescales. As sea ice diminishes, in contrast to the Arctic warming (cooling) in boreal winter (summer), Eurasia and North America have experienced anomalously cold (warm) conditions and record snowfall (rainfall), forming an opposite oscillation between the Arctic and midlatitudes. Both statistical analyses and modeling studies have demonstrated the significant impacts of autumn–winter Arctic variations on winter midlatitude cooling, cold surges, and snowfall, as well as the potential contributions of spring–summer Arctic variations to midlatitude warming, heatwaves and rainfall, particularly focusing on the role of distinct regional sea ice. The possible physical processes can be categorized into tropospheric and stratospheric pathways, with the former encompassing the swirling jet stream, horizontally propagated Rossby waves, and transient eddy–mean flow interaction, and the latter manifested as anomalous vertical propagation of quasi-stationary planetary waves and associated downward control of stratospheric anomalies. In turn, atmospheric prevailing patterns in the midlatitudes also contribute to Arctic Sea ice or thermal condition anomalies by meridional energy transport. The Arctic–midlatitudes connection fluctuates over time and is influenced by multiple factors (e.g., continuous melting of climatological sea ice, different locations and magnitudes of sea ice anomalies, internal variability, and other external forcings), undoubtedly increasing the difficulty of mechanism studies and the uncertainty surrounding predictions of midlatitude weather and climate. In conclusion, we provide a succinct summary and offer suggestions for future research. Full article

(This article belongs to the Special Issue Arctic Atmosphere–Sea Ice Interaction and Impacts)

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Per ten-year variation in the annual cycle of Arctic Sea ice extent with eight lowest sea ice years (e.g., 2007, 2011, 2012, 2016, 2018, 2019, 2020, 2021). Figure and Table is from the National Snow and Ice Data Center (NSIDC; <a href="https://nsidc.org/arcticseaicenews/charctic-interactive-sea-ice-graph/" target="_blank">https://nsidc.org/arcticseaicenews/charctic-interactive-sea-ice-graph/</a>, accessed on 1 June 2024) and ref. [<a href="#B28-atmosphere-15-01115" class="html-bibr">28</a>]. Full article ">Figure 2
Schematic diagram of how the autumn (a) EsCB and (b) BK sea ice loss affects winter Eurasian temperature anomalies. Shadings in cylindrical equidistant and polar projection maps denote air temperature anomalies at 1000 hPa and sea ice anomalies, respectively. Red (blue) represents positive (negative) anomalies. Contours in cylindrical equidistant and polar projection maps denote geopotential anomalies at 1000 and 500 hPa, respectively. Dots denote more frequent extreme low temperatures. Black arrows denote the horizontal propagation of planetary waves. Orange (green) empty arrows denote stratospheric (tropospheric) pathways. Full article ">Figure 3
Schematic diagram of the connections between summer Arctic changes and midlatitude temperature and precipitation anomalies. Shadings denote air temperature anomalies at 1000 hPa. Red (blue) represents positive (negative) anomalies. Contours denote geopotential anomalies at 500 hPa. Green (brown) dots denote excessive (deficient) rainfall. Black arrows denote the horizontal propagation of planetary waves. Full article ">Figure 4
A brief synopsis of the Arctic–midlatitudes connection: interactive impacts, possible physical mechanisms (tropospheric and stratospheric pathways), and nonstationary. BK Seas denotes Barents–Kara Seas, EsCB Seas denotes East Siberian–Chukchi–Beaufort Seas, NA denotes North Atlantic, NP denotes North Pacific, AO/NAO denotes Arctic/North Atlantic Oscillation, UB denotes Ural blocking, PNA denotes Pacific–North American, ENSO denotes El Niño–Southern Oscillation, and MJO denotes Madden–Julian Oscillation. Full article ">

19 pages, 3350 KiB

Open AccessArticle

MSLKSTNet: Multi-Scale Large Kernel Spatiotemporal Prediction Neural Network for Air Temperature Prediction

by Feng Gao, Jiaen Fei, Yuankang Ye and Chang Liu

Atmosphere 2024, 15(9), 1114; https://doi.org/10.3390/atmos15091114 - 13 Sep 2024

Abstract

The spatiotemporal forecasting of temperature is a critical issue in meteorological prediction, with significant implications for fields such as agriculture and energy. With the rapid advancement of data-driven deep learning methods, deep learning-based spatiotemporal sequence forecasting models have seen widespread application in temperature [...] Read more.

The spatiotemporal forecasting of temperature is a critical issue in meteorological prediction, with significant implications for fields such as agriculture and energy. With the rapid advancement of data-driven deep learning methods, deep learning-based spatiotemporal sequence forecasting models have seen widespread application in temperature spatiotemporal forecasting. However, statistical analysis reveals that temperature evolution varies across temporal and spatial scales due to factors like terrain, leading to a lack of existing temperature prediction models that can simultaneously learn both large-scale global features and small to medium-scale local features over time. To uniformly model temperature variations across different temporal and spatial scales, we propose the Multi-Scale Large Kernel Spatiotemporal Attention Neural Network (MSLKSTNet). This model consists of three main modules: a feature encoder, a multi-scale spatiotemporal translator, and a feature decoder. The core module of this network, Multi-scale Spatiotemporal Attention (MSSTA), decomposes large kernel convolutions from multi-scale perspectives, capturing spatial feature information at different scales, and focuses on the evolution of multi-scale spatial features over time, encompassing both global smooth changes and local abrupt changes. The results demonstrate that MSLKSTNet achieves superior performance, with a 35% improvement in the MSE metric compared to SimVP. Ablation studies confirmed the significance of the MSSTA unit for spatiotemporal forecasting tasks. We apply the model to the regional ERA5-Land reanalysis temperature dataset, and the experimental results indicate that the proposed method delivers the best forecasting performance, achieving a 42% improvement in the MSE metric over the widely used ConvLSTM model for temperature prediction. This validates the effectiveness and superiority of MSLKSTNet in temperature forecasting tasks. Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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12 pages, 2197 KiB

Open AccessArticle

Risk Associations between Air Pollution Exposure and Cardiovascular Diseases: A Residential Retrospective Cohort Study

by Elisa Bustaffa, Cristina Mangia, Liliana Cori, Marco Cervino, Fabrizio Bianchi and Fabrizio Minichilli

Atmosphere 2024, 15(9), 1113; https://doi.org/10.3390/atmos15091113 - 13 Sep 2024

Abstract

The population of the Venafro Valley (Southern Italy) faces various type of air pollution problems (industrial facilities, traffic, and biomass combustion). To estimate exposure to various pollution sources, a multi-stage random forest model was used, integrating particulate matter (PM) data with satellite observations, [...] Read more.

The population of the Venafro Valley (Southern Italy) faces various type of air pollution problems (industrial facilities, traffic, and biomass combustion). To estimate exposure to various pollution sources, a multi-stage random forest model was used, integrating particulate matter (PM) data with satellite observations, land-use patterns, and meteorological information generating maps of PM_2.5 concentration. Four distinct PM_2.5 exposure categories were established using the quartile method. To assess the association between PM_2.5 and cause-specific mortality and morbidity, a time-dependent and sex-specific Cox multiple regression analysis was conducted, adjusting for age classes. In addition, the hazard ratios were accompanied by a probability measure of the strength of the evidence toward a hypothesis of health risk associated with the exposure under study (1−p value). The whole cohort was exposed to PM_2.5 annual levels exceeding the 5 µg/m³ limit recommended by the World Health Organization. Mortality excesses were observed in class 3 for both sexes for cardiac heart diseases. Excesses of cardiovascular diseases were observed for both sexes in class 3 and 4. The study highlights significant signals warranting mitigation actions, which regional authorities are currently considering. Full article

(This article belongs to the Special Issue Research on Air Pollution and Human Exposures)

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17 pages, 3949 KiB

Open AccessArticle

Assessment of Numerical Forecasts for Hub-Height Wind Resource Parameters during an Episode of Significant Wind Speed Fluctuations

by Jingyue Mo, Yanbo Shen, Bin Yuan, Muyuan Li, Chenchen Ding, Beixi Jia, Dong Ye and Dan Wang

Atmosphere 2024, 15(9), 1112; https://doi.org/10.3390/atmos15091112 - 13 Sep 2024

Abstract

This study conducts a comprehensive evaluation of four scenario experiments using the CMA_WSP, WRF, and WRF_FITCH models to enhance forecasts of hub-height wind speeds at multiple wind farms in Northern China, particularly under significant wind speed fluctuations during high wind conditions. The experiments [...] Read more.

This study conducts a comprehensive evaluation of four scenario experiments using the CMA_WSP, WRF, and WRF_FITCH models to enhance forecasts of hub-height wind speeds at multiple wind farms in Northern China, particularly under significant wind speed fluctuations during high wind conditions. The experiments apply various wind speed calculation methods, including the Monin–Obukhov similarity theory (ST) and wind farm parameterization (WFP), within a 9 km resolution framework. Data from four geographically distinct stations were analyzed to assess their forecast accuracy over a 72 h period, focusing on the transitional wind events characterized by substantial fluctuations. The CMA_WSP model with the ST method (CMOST) achieved the highest scores across the evaluation metrics. Meanwhile, the WRF_FITCH model with the WFP method (FETA) demonstrated superior performance to the other WRF models, achieving the lowest RMSE and a greater stability. Nevertheless, all models encountered difficulties in predicting the exact timing of extreme wind events. This study also explores the effects of these methods on the wind power density (WPD) distribution, emphasizing the boundary layer’s influence at the hub-heighthub-height of 85 m. This influence leads to significant variations in the central and coastal regions. In contrast to other methods that account for the comprehensive effects of the entire boundary layer, the ST method primarily relies on the near-surface 10 m wind speed to calculate the hub-height wind speed. These findings provide important insights for enhancing wind speed and WPD forecasts under transitional weather conditions. Full article

(This article belongs to the Special Issue Solar Irradiance and Wind Forecasting)

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(a) Model domain and BTH region in red rectangle (b) Zoomed-in BTH region and wind farm locations (red triangles) with terrain altitude. Full article ">Figure 2
The averaged statistical indicators of the BTH region for the 0-72 h forecast period. Full article ">Figure 3
Box plot for observations (black) and CMOST (red), WMOST (yellow), FETA (blue), and WETA (green) scenarios’ prediction over 72 h. Full article ">Figure 4
Time series of RMSE of CMOST (red), WMOST (yellow), FETA (blue), and WETA (green) scenarios among all stations over 72 h (070916 to 071216 UTC), and the corresponding average observation (black). Full article ">Figure 5
Time series of observed and forecasted wind speeds at each station over 72 h (07091600 to 071216 UTC), black line for observation, red for CMOST, yellow for WMOST, blue for FETA, and green for WETA. Full article ">Figure 6
Hub-height wind power density forecast average distribution over 72 h: for the CMOST scenario (a), the WMOST scenario (b), the FETA scenario (c), the WETA scenario (d), and the difference of WETA minus WMOST (e). Black triangles represent the locations of wind farms, arrows represent wind vector. Full article ">

15 pages, 6338 KiB

Open AccessArticle

Climate Classification in the Canadian Prairie Provinces Using Day-to-Day Thermal Variability Metrics

by William A. Gough and Zhihui Li

Atmosphere 2024, 15(9), 1111; https://doi.org/10.3390/atmos15091111 - 13 Sep 2024

Abstract

The data from thirty-one climate stations in the Canadian Prairie provinces of Alberta, Saskatchewan, and Manitoba are analyzed using a number of day-to-day thermal variability metrics. These are used to classify each climate station location using a decision tree developed previously. This is [...] Read more.

The data from thirty-one climate stations in the Canadian Prairie provinces of Alberta, Saskatchewan, and Manitoba are analyzed using a number of day-to-day thermal variability metrics. These are used to classify each climate station location using a decision tree developed previously. This is the first application of the decision tree to identify stations as having rural, urban, peri-urban, marine, island, airport, or mountain climates. Of the thirty-one, eighteen were identified as peri-urban, with fourteen of these being airports; six were identified as marine or island; four were identified as rural; one as urban was identified; and two were identified as mountain. The two climate stations at Churchill, Manitoba, located near the shores of Hudson Bay, were initially identified as peri-urban. This was re-assessed after adjusting the number of “winter” months used in the metric for identifying marine and island climates (which, for all other analyses, excluded only December, January, and February). For Churchill, to match the sea ice season, the months of November, March, April, and May were also excluded. Then, a strong marine signal was found for both stations. There is a potential to use these thermal metrics to create a sea ice climatology in Hudson Bay, particularly for pre-satellite reconnaissance (1971). Lake Louise and Banff, Alberta, are the first mountain stations to be identified as such outside of British Columbia. Five airport/non-airport pairs are examined to explore the difference between an airport site and a local site uninfluenced by the airport. In two cases, the expected outcome was not realized through the decision tree analysis. Both Jasper and Edmonton Stony Plain were classified as peri-urban. These two locations illustrated the influence of proximity to large highways. In both cases the expected outcome was replaced by peri-urban, reflective of the localized impact of the major highway. This was illustrated in both cases using a time series of the peri-urban metric before and after major highway development, which had statistically significant differences. This speaks to the importance of setting climate stations appropriately away from confounding influences. It also suggests additional metrics to assess the environmental consistency of climate time series. Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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13 pages, 1871 KiB

Open AccessArticle

Association between Short-Term Exposure to Ambient Air Pollution and Mortality from Cardiovascular Diseases in Ulaanbaatar, Mongolia

by Nandin-Erdene Bayart, Krassi Rumchev, Christopher M. Reid, Sylvester Dodzi Nyadanu and Gavin Pereira

Atmosphere 2024, 15(9), 1110; https://doi.org/10.3390/atmos15091110 - 13 Sep 2024

Abstract

Cardiovascular diseases (CVD) are one of the leading causes of death globally, and a major contributor to CVD mortality is ambient air pollution (AAP). This study aimed to evaluate associations between AAP and mortality from CVD, including ischemic heart diseases (IHD) and strokes. [...] Read more.

Cardiovascular diseases (CVD) are one of the leading causes of death globally, and a major contributor to CVD mortality is ambient air pollution (AAP). This study aimed to evaluate associations between AAP and mortality from CVD, including ischemic heart diseases (IHD) and strokes. Data on daily mortality records, six criteria AAP and meteorology in the capital city of Mongolia were collected between 1 January 2016 and 31 December 2022. A time-stratified case-crossover design was analysed with distributed lag conditional Poisson regression to estimate the relative risk of CVD mortality. We found that for each interquartile range increase in PM_2.5, PM₁₀, SO₂ and NO₂ pollutants, the risk of CVD mortality increased by 1.5% (RR = 1.015; 95% CI: 1.005, 1.025), 4.4% (RR = 1.044; 95% CI: 1.029, 1.059), 3.1% (RR = 1.033; 95% CI: 1.015, 1.047) and 4.8% (RR = 1.048; 95% CI: 1.013, 1.085) at lag01, respectively. The association between all pollutants, except O₃, and CVD mortality was higher in subgroups ≥ 65 years and male, during the cold season and after using a new type of coal briquettes. Despite using the new type of coal briquettes, Ulaanbaatar’s ambient air pollution remained higher than the WHO’s guidelines. Based on our findings, we recommend that efforts should be focused on adopting more efficient strategies to reduce the current pollution level. Full article

(This article belongs to the Section Air Quality and Health)

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19 pages, 6634 KiB

Open AccessArticle

New Makeup Air Method through Ceiling for Kitchen Ventilation in Severely Cold Regions and Its Effect on Air Environment

by Xiaoxu Li, Kailiang Huang, Guohui Feng, Guanyu Cao, Ainong Li and Xu Teng

Atmosphere 2024, 15(9), 1109; https://doi.org/10.3390/atmos15091109 - 12 Sep 2024

Abstract

Severely cold weather reduces the willingness of residents to open windows while cooking. This results in an insufficient replenishment of makeup air and a reduction in the range hood discharge capacity. For an effective trade-off between indoor air temperature maintenance and air quality [...] Read more.

Severely cold weather reduces the willingness of residents to open windows while cooking. This results in an insufficient replenishment of makeup air and a reduction in the range hood discharge capacity. For an effective trade-off between indoor air temperature maintenance and air quality aggravation in winter, a new makeup air supply method (ceiling makeup air) was proposed and established both experimentally and numerically. The improvements in the kitchen air environment during cooking were studied through experimental tests and CFD simulations, considering different makeup air arrangements. The results reveal that the ceiling makeup air scheme can significantly reduce the concentration of PM2.5 compared with the cracks makeup air scheme (wherein the kitchen window and door are closed). Moreover, it increased the indoor temperature by over 11.9 °C compared with the open window makeup air scheme. The average relative error between the experimental and simulated data was within 6.1%. Among the considered factors, the size of the air inlet had the largest impact. This was followed by the layout, size, and shape of the ceiling inlets. The ceiling makeup air scheme demonstrated the potential for improving residential kitchen air environments in severely cold regions. Full article

(This article belongs to the Section Biometeorology and Bioclimatology)

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24 pages, 3886 KiB

Open AccessArticle

De-Carbonisation Pathways in Jiangxi Province, China: A Visualisation Based on Panel Data

by Shun Li, Jie Hua and Gaofeng Luo

Atmosphere 2024, 15(9), 1108; https://doi.org/10.3390/atmos15091108 - 11 Sep 2024

Abstract

Environmental degradation remains a pressing global concern, prompting many nations to adopt measures to mitigate carbon emissions. In response to international pressure, China has committed to achieving a carbon peak by 2030 and carbon neutrality by 2060. Despite extensive research on China’s overall [...] Read more.

Environmental degradation remains a pressing global concern, prompting many nations to adopt measures to mitigate carbon emissions. In response to international pressure, China has committed to achieving a carbon peak by 2030 and carbon neutrality by 2060. Despite extensive research on China’s overall carbon emissions, there has been limited focus on individual provinces, particularly less developed provinces. Jiangxi Province, an underdeveloped province in southeastern China, recorded the highest GDP (Gross Domestic Product) growth rate in the country in 2022, and it holds significant potential for carbon emission reduction. This study uses data from Jiangxi Province’s 14th Five-Year Plan and Vision 2035 to create three carbon emission reduction scenarios and predict emissions. The extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology), along with various visualisation techniques, is employed to analyse the impacts of population size, primary electricity application level, GDP per capita, the share of the secondary industry in fixed-asset investment, and the number of civilian automobiles owned on carbon emissions. The study found that there is an inverted U-shaped curve relationship between GDP per capita and carbon emissions, car ownership is not a major driver of carbon emissions, and the development of primary electricity has significant potential as a means for reducing carbon emissions in Jiangxi Province. If strict environmental protection measures are implemented, Jiangxi Province can reach its peak carbon target by 2029, one year ahead of the national target. These findings provide valuable insights for policymakers in Jiangxi Province to ensure that their environmental objectives are met. Full article

(This article belongs to the Special Issue Air Pollution in China (3rd Edition))

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Research framework and procedures. Full article ">Figure 2
Trends in annual carbon dioxide emissions and their influencing factors in Jiangxi Province, 2000–2021. Full article ">Figure 3
Correlation heat map analysis of 6 variables. Full article ">Figure 4
The ridge traces of the model variables, the relationship between R2, and the ridge regression coefficient K. (a) The relationship between the regression values of the variables and the ridge regression coefficient K; (b) the relationship among the model’s goodness-of-fit, R2, and the ridge regression coefficient K. Full article ">Figure 5
Comparison of predicted and actual values from ridge regression. Full article ">Figure 6
Radar diagram of SLA, BAU, and VEH simulation strategies. Full article ">Figure 7
Forecast of carbon dioxide emissions and gross domestic product per capita in Jiangxi Province, (a) represents the projected curve of carbon emissions in Jiangxi Province under the three carbon emission policies, and (b) represents the projected curve of GDP per capita in Jiangxi Province. Full article ">

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