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Atmosphere
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Air Quality and Health in the Mediterranean
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Air Quality and Health in the Mediterranean

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality and Health".

Deadline for manuscript submissions: closed (5 February 2021) | Viewed by 33272

Special Issue Editors

Dr. Francesca Costabile

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Guest Editor
Institute of Atmospheric Sciences and Climate (ISAC), National Research Council (CNR), Via Fosso del Cavaliere 100, 00133 Rome, Italy
Interests: air quality; atmospheric aerosol; health effects; characterization of ultrafine particles; combustion generated aerosol and urban areas; black carbon and carbonaceous aerosol, and relevant toxicology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tareq Hussein

E-Mail Website
Guest Editor
1. Department of Physics, School of Science, University of Jordan, Amman 11942, Jordan
2. Institute for Atmospheric and Earth System Research (INAR / Physics), University of Helsinki, PL 64, FI-00014 Helsinki, Finland
Interests: atmospheric and environmental sciences; air pollution; urban and indoor air quality; dynamics and physical characterization of aerosol particles; emissions and fate of atmospheric aerosols, dry deposition; exposure; modeling, analytical, and numerical methods
Special Issues, Collections and Topics in MDPI journals
Dr. Lorenzo Massimi

E-Mail Website
Guest Editor
Department of Environmental Biology, Sapienza University of Rome, P. le Aldo Moro, 5, 00185 Rome, Italy
Interests: particulate matter; chemical composition; air pollutant distribution; spatial distribution; seasonal variation; indoor/outdoor concentration; chemical fractionation; source tracer; source apportionment; receptor modeling; PMF; oxidative potential; oxidative stress; biomonitoring; element; environmental exposure
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The objective of this Special Issue is to provide an interdisciplinary forum for discussions of our current state of knowledge about the interplay between air quality, human health, and the associated risks in the Mediterranean. This is one of the most controversial topics in current research. The Mediterranean region is affected by frequent dust episodes (originating from the Sahara region and crossing from south to north) and anthropogenic pollution (originating from Southern Europe and crossing from north to south). Therefore, air pollution in the Mediterranean region has complex physical-chemical characteristics for aerosols.

Air pollution is one of the leading environmental risk factors for human health globally, especially with regard to ambient fine particular matter, ozone, and some non-criteria pollutants that are considered to have the highest toxicity, such as metals, organics, black carbon, allergens, and their partitioning in both fine and ultrafine aerosol particles. The assessment of the associated risk, especially regarding the impact to the lungs, the circulatory system, and the brain, is still far from being understood. Despite extraordinary advances, a growing number of challenges remain. An emerging consensus suggests that the time has come for science to establish novel interdisciplinary research partnerships based on cross-sectoral collaborations between different areas of expertise, such as air quality, aerosol science and technology, emission research, meteorology, climatology, toxicology, epidemiology, governance, and risk management. Significant scientific evidence must be obtained to guide the development of new recommendations, policies, and legislation. Rethinking science is necessary to meet today’s priorities.

Dr. Francesca Costabile
Prof. Tareq Hussein
Dr. Lorenzo Massimi
Guest Editors

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Keywords

  • aerosol
  • ultrafine particles
  • toxicity
  • epidemiology
  • black carbon
  • sand and dust storm (SDS)

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19 pages, 7229 KiB  
Article
The Historical Trend of Air Pollution and Its Impact on Human Health in Campania Region (Italy)
by Domenico Toscano and Fabio Murena
Atmosphere 2021, 12(5), 553; https://doi.org/10.3390/atmos12050553 - 25 Apr 2021
Cited by 7 | Viewed by 3505
Abstract
The Campania region covers an area of about 13,590 km2 with 5.8 million residents. The area suffers from several environmental issues due to urbanization, the presence of industries, wastewater treatment, and solid waste management concerns. Air pollution is one of the most [...] Read more.
The Campania region covers an area of about 13,590 km2 with 5.8 million residents. The area suffers from several environmental issues due to urbanization, the presence of industries, wastewater treatment, and solid waste management concerns. Air pollution is one of the most relevant environmental troubles in the Campania region, frequently exceeding the limit values established by European directives. In this paper, airborne pollutant concentration data measured by the regional air quality network from 2003 to 2019 are collected to individuate the historical trends of nitrogen dioxide (NO2), coarse and fine particulate matter with aerodynamic diameters smaller than 10 μm (PM10) and 2.5 μm (PM2.5), and ozone (O3) through the analysis of the number of exceedances of limit values per year and the annual average concentration. Information on spatial variability and the effect of the receptor category is obtained by lumping together data belonging to the same province or category. To obtain information on the general air quality rather than on single pollutants, the European Air Quality Index (EU-AQI) is also evaluated. A special focus is dedicated to the effect of deep street canyons on air quality, since they are very common in the urban areas in Campania. Finally, the impact of air pollution from 2003 to 2019 on human health is also analyzed using the software AIRQ+. Full article
(This article belongs to the Special Issue Air Quality and Health in the Mediterranean)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>A map of air quality monitoring stations in Campania in 2019.</p> Full article ">Figure 2
<p>The influence of the efficiency criteria on the percentage of valid data.</p> Full article ">Figure 3
<p>Average NO<sub>2</sub> values per province. (<b>a</b>): the number of exceedances of the 1-h limit value of 200 μg/m<sup>3</sup>; (<b>b</b>): the annual concentration values.</p> Full article ">Figure 4
<p>Average PM10 values per province. (<b>a</b>): the number of exceedances of the 24-h limit value of 50 μg/m<sup>3</sup>; (<b>b</b>): the annual average concentration values.</p> Full article ">Figure 5
<p>Average PM2.5 values per province. (<b>Up</b>): the 99th percentile of 24-h concentrations; (<b>bottom</b>): the annual average concentration values.</p> Full article ">Figure 6
<p>O<sub>3</sub>: the number of exceedances beyond the limit of 120 μg/m<sup>3</sup> (Directive 2008/50/EC).</p> Full article ">Figure 7
<p>The historical trend of EU-AQI in provinces of the Campania region.</p> Full article ">Figure 8
<p>The impact on human health. Estimates of the attributable proportions percentages for NO<sub>2</sub> (<b>up</b>), PM10 (<b>middle</b>), and PM2.5 (<b>bottom</b>).</p> Full article ">Figure 9
<p>The average annual concentrations per station category: NO<sub>2</sub> (<b>up</b>); PM10 (<b>middle</b>); PM2.5 (<b>bottom</b>).</p> Full article ">Figure 9 Cont.
<p>The average annual concentrations per station category: NO<sub>2</sub> (<b>up</b>); PM10 (<b>middle</b>); PM2.5 (<b>bottom</b>).</p> Full article ">Figure 10
<p>The number of exceedances of the O<sub>3</sub> limit according to the EU threshold of 120 μg/m<sup>3</sup>.</p> Full article ">Figure 11
<p>The percentage of days in which the maximum daily sub-index is met in industrial–background (<b>up</b>) and traffic (<b>bottom</b>) stations.</p> Full article ">Figure 12
<p>The historical trends of fleets of four-wheel vehicles in Campania.</p> Full article ">Figure 13
<p>Maps of the annual averages for 2019. NO<sub>2</sub> (<b>up</b>); PM10 (<b>middle</b>); O<sub>3</sub> (<b>bottom</b>).</p> Full article ">Figure 13 Cont.
<p>Maps of the annual averages for 2019. NO<sub>2</sub> (<b>up</b>); PM10 (<b>middle</b>); O<sub>3</sub> (<b>bottom</b>).</p> Full article ">
18 pages, 8446 KiB  
Article
Influence of Meteorological Conditions and Aerosol Properties on the COVID-19 Contamination of the Population in Coastal and Continental Areas in France: Study of Offshore and Onshore Winds
by Jacques Piazzola, William Bruch, Christelle Desnues, Philippe Parent, Christophe Yohia and Elisa Canepa
Atmosphere 2021, 12(4), 523; https://doi.org/10.3390/atmos12040523 - 20 Apr 2021
Cited by 14 | Viewed by 3245
Abstract
Human behaviors probably represent the most important causes of the SARS-Cov-2 virus propagation. However, the role of virus transport by aerosols—and therefore the influence of atmospheric conditions (temperature, humidity, type and concentration of aerosols)—on the spread of the epidemic remains an open and [...] Read more.
Human behaviors probably represent the most important causes of the SARS-Cov-2 virus propagation. However, the role of virus transport by aerosols—and therefore the influence of atmospheric conditions (temperature, humidity, type and concentration of aerosols)—on the spread of the epidemic remains an open and still debated question. This work aims to study whether or not the meteorological conditions related to the different aerosol properties in continental and coastal urbanized areas might influence the atmospheric transport of the SARS-Cov-2 virus. Our analysis focuses on the lockdown period to reduce the differences in the social behavior and highlight those of the weather conditions. As an example, we investigated the contamination cases during March 2020 in two specific French areas located in both continental and coastal areas with regard to the meteorological conditions and the corresponding aerosol properties, the optical depth (AOD) and the Angstrom exponent provided by the AERONET network. The results show that the analysis of aerosol ground-based data can be of interest to assess a virus survey. We found that moderate to strong onshore winds occurring in coastal regions and inducing humid environment and large sea-spray production episodes coincides with smaller COVID-19 contamination rates. We assume that the coagulation of SARS-Cov-2 viral particles with hygroscopic salty sea-spray aerosols might tend to inhibit its viral infectivity via possible reaction with NaCl, especially in high relative humidity environments typical of maritime sites. Full article
(This article belongs to the Special Issue Air Quality and Health in the Mediterranean)
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Figure 1

Figure 1
<p>(<b>Left</b>): Number of deaths per 100000 residents by French departments. The circles denote the capital cities of the regions investigated: 1 = Nantes, 2 = Paris. (<b>Right</b>): population density of France. Images modified from <a href="https://en.wikipedia.org/wiki/COVID-19_pandemic_in_France" target="_blank">https://en.wikipedia.org/wiki/COVID-19_pandemic_in_France</a> (accessed on 19 February 2021) and <a href="https://en.wikipedia.org/wiki/Demographics_of_France" target="_blank">https://en.wikipedia.org/wiki/Demographics_of_France</a> (accessed on 19 February 2021).</p> Full article ">Figure 2
<p>Number of deaths for COVID-19 per 100,000 residents by department in Paris (red line) and Loire-Atlantique (blue line). See <a href="https://www.gouvernement.fr/info-coronavirus/carte-et-donnees" target="_blank">https://www.gouvernement.fr/info-coronavirus/carte-et-donnees</a> (accessed on 19 February 2021).</p> Full article ">Figure 3
<p>Wind rose recorded in Le Croisic in March April 2020. The wind speed intervals encountered during the campaign are reported above the graphic.</p> Full article ">Figure 4
<p>Time series of both the wind speed (the blue line) and direction (the red line) over March, and April 2020 in (<b>a</b>) the SEM-REV station located in Le Croisic near Nantes and (<b>b</b>) at the station of the Montsouris park in Paris. Above the <a href="#atmosphere-12-00523-f004" class="html-fig">Figure 4</a>a is reported the onshore and offshore wind episodes that occur in the coastal site.</p> Full article ">Figure 5
<p>Calculated air mass back-trajectories in Nantes (French Atlantic shoreline) for March 2020.</p> Full article ">Figure 5 Cont.
<p>Calculated air mass back-trajectories in Nantes (French Atlantic shoreline) for March 2020.</p> Full article ">Figure 6
<p>Calculated air mass back-trajectories in Paris for March 2020.</p> Full article ">Figure 6 Cont.
<p>Calculated air mass back-trajectories in Paris for March 2020.</p> Full article ">Figure 7
<p>Temporal survey of the Angstrom coefficient (<b>left</b>) and the AOD (<b>right</b>) in Le Croisic (Nantes region) in March 2020. The arrow indicates the date of the occurrence of offshore wind conditions.</p> Full article ">Figure 8
<p>Temporal survey of the Angstrom coefficient (<b>left</b>) and the AOD (<b>right</b>) in Paris in March 2020.</p> Full article ">Figure 9
<p>An example of aerosol size distributions typical of the coastal zone issued from measurements conducted on the island of Porquerolles by Van Eijk et al. (2011). As shown, the aerosol concentration <span class="html-italic">dN/dr</span> can be fitted by the sum of five lognormal functions centered on radii of 0.01, 0.03, 0.24, 2 and 10 µm. (the black line). The dashed lines indicate the size intervals dealing with the different aerosol sources found in the coastal zone. The arrows indicate the expected size of SARS-CoV2 (around 100 nm).</p> Full article ">Figure 10
<p>SEM image of a mixing sea-spray-soot as sampled on the Mediterranean coast using a Dekati impactor. The red square denotes a salt crystal, while the black circles show soot. The photograph is issued from aerosol samples acquired on the island of Porquerolles during the MATRAC experiments (Piazzola et al., 2020) [<a href="#B61-atmosphere-12-00523" class="html-bibr">61</a>].</p> Full article ">Figure 11
<p>Variation of the relative humidity versus wind speed for an onshore wind direction (marine air mass episode). The data were recorded on the island of Porquerolles by Piazzola (personal communication). The black line fits the data.</p> Full article ">
28 pages, 9169 KiB  
Article
The Atmospheric Aerosol over Western Greece-Six Years of Aerosol Observations at the Navarino Environmental Observatory
by Hans-Christen Hansson, Peter Tunved, Radovan Krejci, Eyal Freud, Nikos Kalivitis, Tabea Hennig, Giorgos Maneas and Evangelos Gerasopoulos
Atmosphere 2021, 12(4), 445; https://doi.org/10.3390/atmos12040445 - 31 Mar 2021
Cited by 4 | Viewed by 2524
Abstract
The Eastern Mediterranean is a highly populated area with air quality problems. It is also where climate change is already noticed by higher temperatures and s changing precipitation pattern. The anthropogenic aerosol affects health and changing concentrations and properties of the atmospheric aerosol [...] Read more.
The Eastern Mediterranean is a highly populated area with air quality problems. It is also where climate change is already noticed by higher temperatures and s changing precipitation pattern. The anthropogenic aerosol affects health and changing concentrations and properties of the atmospheric aerosol affect radiation balance and clouds. Continuous long-term observations are essential in assessing the influence of anthropogenic aerosols on climate and health. We present six years of observations from Navarino Environmental Observatory (NEO), a new station located at the south west tip of Peloponnese, Greece. The two sites at NEO, were evaluated to show the influence of the local meteorology and to assess the general background aerosol possible. It was found that the background aerosol was originated from aged European aerosols and was strongly influenced by biomass burning, fossil fuel combustion, and industry. When subsiding into the boundary layer, local sources contributed in the air masses moving south. Mesoscale meteorology determined the diurnal variation of aerosol properties such as mass and number by means of typical sea breeze circulation, giving rise to pronounced morning and evening peaks in pollutant levels. While synoptic scale meteorology, mainly large-scale air mass transport and precipitation, strongly influenced the seasonality of the aerosol properties. Full article
(This article belongs to the Special Issue Air Quality and Health in the Mediterranean)
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Figure 1

Figure 1
<p>Map <b>A</b> showing Messina, the southwest region of Peloponnese (mark with a red square in Map <b>B</b>), in the south west of Greece. NEO is situated at the coast about 11 km north the nearest city Pylos. The Methoni site is about 11 km south Pylos.</p> Full article ">Figure 2
<p>Top panel; Transport source area characteristics, last 240 h, for the warm period, May–September (<b>A</b>), cold period November–March (<b>B</b>) and transition months April and October (<b>C</b>). Bottom panel; Transport average altitude characteristics, last 240 h, for the warm period (<b>D</b>), cold period (<b>E</b>) and transition months (<b>F</b>).</p> Full article ">Figure 3
<p>(<b>a</b>,<b>b</b>) a/Local wind roses for Navarino (left panel) and b/Methoni (right panel). Navarino 2011–2013 and Methoni 2014–2016.</p> Full article ">Figure 4
<p>Local wind roses for Methoni (<b>A</b>,<b>C</b>,<b>E</b>,<b>G</b>) and Navarino (<b>B</b>,<b>D</b>,<b>F</b>,<b>H</b>). The warm season daytime is on the top row while night time is 2nd row. The wind roses for the cold season daytime is 3rd while nighttime is on the 4th row.</p> Full article ">Figure 5
<p>(<b>a</b>) The monthly frequency of local wind directions per hour at Methoni, November 2013 to December 2016. (<b>b</b>) The monthly frequency of local wind directions per hour at Navarino, April 2011 to October 2013. (<b>c</b>) Monthly median hourly local wind speed for Navarino, April 2011 to October 2013 and Methoni, November 2013 to December 2016, with the 15 to 85 percentile range indicated by the vertical bars. The grey parts show the period when sun is below horizon.</p> Full article ">Figure 5 Cont.
<p>(<b>a</b>) The monthly frequency of local wind directions per hour at Methoni, November 2013 to December 2016. (<b>b</b>) The monthly frequency of local wind directions per hour at Navarino, April 2011 to October 2013. (<b>c</b>) Monthly median hourly local wind speed for Navarino, April 2011 to October 2013 and Methoni, November 2013 to December 2016, with the 15 to 85 percentile range indicated by the vertical bars. The grey parts show the period when sun is below horizon.</p> Full article ">Figure 6
<p>Monthly precipitation at Methoni, Peloponnese (data source: Hellenic National Meteorological Service-HNMS).</p> Full article ">Figure 7
<p>The diurnal variation of the total particle number concentrations at Navarino (observations for the period April 2011–September 2013) and at Methoni (observations for the period November 2013–December 2016). The lines indicate the median values while the error bars indicate the inter-quartile range.</p> Full article ">Figure 8
<p>Diurnal variation of number concentration of nuclei, Aitken and accumulation modes for the cold period November–March at Navarino (observations for the period April 2011–September 2013) and at Methoni (observations for the period November 2013–December 2016). The dotted curves indicate the median values while the error bars indicate the inter-quartile range.</p> Full article ">Figure 9
<p>Diurnal variation of number concentration of nuclei, Aitken and accumulation modes for the warm period May–September (observations for the period April 2011–September 2013) and at Methoni (observations for the period November 2013–December 2016). The dotted curves indicate the median values while the error bars indicate the inter-quartile range.</p> Full article ">Figure 10
<p>Monthly particle number in the different modes of all measurements between 9 and 15 UTC measured at Navarino and Methoni. The dotted curves indicate the median values while the error bars indicate the inter-quartile range.</p> Full article ">Figure 11
<p>Monthly mean size distributions of measurements between 9 and 15 UTC at Navarino (in red) and Methoni (in blue). The numbers in each frame represents the total amount of hourly average size distributions used in the analysis.</p> Full article ">Figure 12
<p>The median of daytime size distribution of cluster 1 to 6 (in blue). The red curve indicate overall median size distribution as reference. The number of members in each cluster is given in the title of each subplot.</p> Full article ">Figure 13
<p>The relative frequency of occurrences of observations belonging to cluster 1–6.</p> Full article ">Figure 14
<p>The transport probability, i.e., the probability an air mass trajectory has passed through a grid cell for cluster 1–6.</p> Full article ">Figure 15
<p>Average altitude for the transport path of the air mass related to cluster 1–6.</p> Full article ">Figure 16
<p>Mean precipitation along the transport of cluster 1 to 6.</p> Full article ">Figure 17
<p>Integrated number concentration over different size and the relation to accumulated precipitation integrated for the last 120 h of transport.</p> Full article ">Figure A1
<p>Seasonal averages for aerosol number size distributions observed at NAVARINO and Methoni, respectively. Colored areas indicate 25–75th percentile ranges and dashed line represent median.</p> Full article ">
19 pages, 10137 KiB  
Article
Representativeness of Carbon Dioxide Fluxes Measured by Eddy Covariance over a Mediterranean Urban District with Equipment Setup Restrictions
by Gianfranco Rana, Nicola Martinelli, Daniela Famulari, Francesco Pezzati, Cristina Muschitiello and Rossana Monica Ferrara
Atmosphere 2021, 12(2), 197; https://doi.org/10.3390/atmos12020197 - 1 Feb 2021
Cited by 6 | Viewed by 2498
Abstract
The CO2 fluxes measured by the eddy covariance technique (EC) are presented for a district of the urban area of Bari (Italy). The applicability of the EC method was satisfied even though the measurements were taken at a limited height. The CO [...] Read more.
The CO2 fluxes measured by the eddy covariance technique (EC) are presented for a district of the urban area of Bari (Italy). The applicability of the EC method was satisfied even though the measurements were taken at a limited height. The CO2 fluxes are representative of an area with public offices and schools, the university campus, green areas, and busy roads with intensive traffic during school and office times. The measurements were carried out in March–June, covering late winter, characterized by huge vehicle traffic and domestic heating, until late spring, characterized by reduced activities for schools and the university. The source area was determined as a function of atmospheric stability, for data with the ratio between measurement-height/buildings-height in the range of 1.3–1.5. The measured CO2 fluxes were compared to gas consumption values. The results show that the district is a strong source of CO2 during the winter. Emissions were drastically reduced (−82%) after the heating was switched off, and a further decrease in CO2 emissions (−50%) occurred with the reduction of school activities, partly due to the mitigating effect of green areas with large trees in the area. Full article
(This article belongs to the Special Issue Air Quality and Health in the Mediterranean)
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Figure 1
<p>The city of Bari (Apulia, Southern Italy), on the Adriatic Sea. The eddy covariance measurement tower was set up on the rooftop of the CREA-AA building (Agriculture and Environment Center).</p> Full article ">Figure 2
<p>The area used for the footprint morphology analysis located around the measurement point: A circle of 500 m radius, divided in 324 cells of 50 m per sectors of 10°. The colored polygons on the map indicate the campus area (purple), the public buildings (yellow), and the schools (red).</p> Full article ">Figure 3
<p>(<b>a</b>) Weather patterns during the experiment in terms of daily minimum and maximum air temperature, relative humidity, global radiation, and precipitation (courtesy of Associazione Consorzi di Difesa della Puglia, Italy); (<b>b</b>) wind direction during the morning and afternoon as percentage of values per 10° sectors.</p> Full article ">Figure 4
<p>Normalized averaged cospectra (ratio of cospectra over covariance) of sensible heat fluxes, Co<span class="html-italic">(w,T</span>), and of CO<sub>2</sub>, Co(<span class="html-italic">w</span>, <span class="html-italic">CO<sub>2</sub></span>) over a 4 h period (from 11:00 to 15:00, with <span class="html-italic">U</span> from 2.15 to 2.85 m s<sup>−1</sup> and <span class="html-italic">z/L</span> ranging from −0.203 to −0.00469).</p> Full article ">Figure 5
<p>Values of <span class="html-italic">λ<sub>p</sub></span> (upper panel) and <span class="html-italic">λ<sub>f</sub></span> (lower panel) calculated for the 324 cells around the measurement point.</p> Full article ">Figure 6
<p>Color map of mean building height (<span class="html-italic">H<sub>b</sub></span>) in the 324 cells around the measurement point. The ratio between measurement height and building height (<span class="html-italic">z<sub>m</sub>/H<sub>b</sub></span>) as mean of all cell in a sector of 10° is represented by the blue line (values on the radius), with the reference 1.5 value traced in red for comparison.</p> Full article ">Figure 7
<p>Maps of the sources area in unstable (upper panel) and neutral (lower panel) atmospheric conditions: The yellow lines are the footprint extensions (isopleths representing the different percentages: 20%, 40%, 60%, 80%, 100% of the flux footprint), while the red line represents the footprint peak, all for each 10° sector.</p> Full article ">Figure 8
<p>Percentage frequency distribution of CO<sub>2</sub> fluxes in the 10° sectors for runs in unstable (left) and neutral (right) conditions, respectively. CO<sub>2</sub> fluxes are divided in uptake (white), moderate emission (grey), and higher emission (black).</p> Full article ">Figure 9
<p>Daily patterns of mean CO<sub>2</sub> fluxes, total gas consumption (courtesy of Amgas Srl Bari), and mean air temperature. The bars on the CO<sub>2</sub> fluxes refer to the mean random error.</p> Full article ">Figure 10
<p>Relationship between weekly mean of CO<sub>2</sub> fluxes and gas consumption (courtesy of Amgas Srl Bari). The error bars represent the standard deviations for the gas consumption and the mean random errors for the CO<sub>2</sub> fluxes.</p> Full article ">Figure 11
<p>Weekly course derived from daily CO<sub>2</sub> fluxes from Sunday to Saturday in March (heating ON, dark grey box) and April (heating OFF, light grey box).</p> Full article ">Figure 12
<p>Mean daily course of CO<sub>2</sub> fluxes in three periods: Winter (domestic heating and school activities, on the left panel), early spring (without heating and with school activities, center panel), late spring (without heating and school activities, right panel). In the small corner panels, the cumulated CO<sub>2</sub> fluxes for each period are shown. The standard deviations are around 11.7, 4.3, and 1.0 μmol m<sup>−2</sup> s<sup>−1</sup> for the left, center, and right panel, respectively.</p> Full article ">Figure A1
<p>Relative frequency distribution of the absolute random errors at an hourly scale for the sensible heat H.</p> Full article ">Figure A2
<p>Relative frequency distribution of the absolute random errors at an hourly scale for the CO<sub>2</sub> flux.</p> Full article ">
18 pages, 5789 KiB  
Article
Estimation of Particulate Matter Contributions from Desert Outbreaks in Mediterranean Countries (2015–2018) Using the Time Series Clustering Method
by Álvaro Gómez-Losada and José C. M. Pires
Atmosphere 2021, 12(1), 5; https://doi.org/10.3390/atmos12010005 - 23 Dec 2020
Cited by 1 | Viewed by 2676
Abstract
North African dust intrusions can contribute to exceedances of the European PM10 and PM2.5 limit values and World Health Organisation standards, diminishing air quality, and increased mortality and morbidity at higher concentrations. In this study, the contribution of North African dust [...] Read more.
North African dust intrusions can contribute to exceedances of the European PM10 and PM2.5 limit values and World Health Organisation standards, diminishing air quality, and increased mortality and morbidity at higher concentrations. In this study, the contribution of North African dust in Mediterranean countries was estimated using the time series clustering method. This method combines the non-parametric approach of Hidden Markov Models for studying time series, and the definition of different air pollution profiles (regimes of concentration). Using this approach, PM10 and PM2.5 time series obtained at background monitoring stations from seven countries were analysed from 2015 to 2018. The average characteristic contributions to PM10 were estimated as 11.6 ± 10.3 µg·m−3 (Bosnia and Herzegovina), 8.8 ± 7.5 µg·m−3 (Spain), 7.0 ± 6.2 µg·m−3 (France), 8.1 ± 5.9 µg·m−3 (Croatia), 7.5 ± 5.5 µg·m−3 (Italy), 8.1 ± 7.0 µg·m−3 (Portugal), and 17.0 ± 9.8 µg·m−3 (Turkey). For PM2.5, estimated contributions were 4.1 ± 3.5 µg·m−3 (Spain), 6.0 ± 4.8 µg·m−3 (France), 9.1 ± 6.4 µg·m−3 (Croatia), 5.2 ± 3.8 µg·m−3 (Italy), 6.0 ± 4.4 µg·m−3 (Portugal), and 9.0 ± 5.6 µg·m−3 (Turkey). The observed PM2.5/PM10 ratios were between 0.36 and 0.69, and their seasonal variation was characterised, presenting higher values in colder months. Principal component analysis enabled the association of background sites based on their estimated PM10 and PM2.5 pollution profiles. Full article
(This article belongs to the Special Issue Air Quality and Health in the Mediterranean)
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Figure 1

Figure 1
<p>Time series (TS) clustering method showing the four pollution profiles (regimes) detected in the PM<sub>10</sub> TS from the ES1517 Spanish station (2017). First to fourth pollution regimes are represented in blue, green, orange, and red, respectively. (<b>A</b>) TS observations (grey). Below TS, each observation is associated with a cluster. Separately, this grouping is represented above the TS. Horizontal lines represent the average values of each cluster (µ<sub>1</sub>, µ<sub>2</sub>, µ<sub>3</sub>, µ<sub>4</sub>). (<b>B</b>) The estimated density of the mixture of distributions (black line) is superimposed onto the histogram of TS data (grey line). Components of the mixture are represented for each cluster (coloured shadows). (<b>C</b>) Distribution of values of each TS pollution profile is represented as box-whisker plots.</p> Full article ">Figure 2
<p>Distribution of the general evolution of PM<sub>2.5</sub>/PM<sub>10</sub> ratios in studied countries as box-whisker plots (2015–2018). As a reference, medians between countries are joined, and a dotted horizontal line is added at 0.5 ratio.</p> Full article ">Figure 3
<p>Monthly distribution of PM<sub>2.5</sub>/PM<sub>10</sub> ratios as in <a href="#atmosphere-12-00005-f002" class="html-fig">Figure 2</a>. As a reference, countries exceeding the 0.65 ratio in 50% of the data are indicated by a red median, otherwise in blue. ES—Spain; FR—France; HR—Croatia; IT—Italy; PT—Portugal; TR—Turkey.</p> Full article ">Figure 4
<p>PM<sub>10</sub> (<b>A</b>) and PM<sub>2.5</sub> (<b>B</b>) pollution profiles from monitoring sites by countries (2015–2018). First to fourth pollution profiles are shown in blue, green, orange, and red, respectively. ES—Spain; FR—France; HR—Croatia; IT—Italy; PT—Portugal; TR—Turkey.</p> Full article ">Figure 5
<p>Estimation of annual average dust desert contributions to PM<sub>10</sub> (<b>A</b>) and PM<sub>2.5</sub> (<b>B</b>) from 2015 to 2018. Anthropogenic contributions and characteristic and severe contributions by deserts are indicated in green, orange, and red colours, respectively. BA—Bosnia and Herzegovina; ES—Spain; FR—France; HR—Croatia; IT—Italy; PT—Portugal; TR—Turkey. Uncertainty is indicated by means of vertical bars. In A, severe contributions (in red) refer to the right <span class="html-italic">Y</span>-axis.</p> Full article ">Figure 6
<p>Evolution of the PM<sub>10</sub> concentration by regimes and hour of the day. Panels in rows describe the analysis for a single TS. Grey arrows indicate slight fluctuations in concentrations. ES—Spain; FR—France; HR—Croatia; IT—Italy; PT—Portugal; TR—Turkey.</p> Full article ">Figure 7
<p>Evolution of the PM<sub>2.5</sub> concentration by regimes and hour of the day as in <a href="#atmosphere-12-00005-f004" class="html-fig">Figure 4</a>.</p> Full article ">Figure 8
<p>PCA biplot analysis for PM<sub>10</sub> (2015–2018), showing the relative position of monitoring stations by countries concerning the principal components. Four biplot vectors (in red) represent the four regimes of pollution as variables (1—first regime to 4—fourth regime). The 95% confidence interval ellipses are represented with a different colour for each country. BA—Bosnia and Herzegovina; ES—Spain; FR—France; HR—Croatia; IT—Italy; PT—Portugal; TR—Turkey.</p> Full article ">Figure 9
<p>PCA biplot analysis for PM<sub>2.5</sub> (2015–2018), showing the relative position of monitoring stations by countries concerning the principal components. BA—Bosnia and Herzegovina; ES—Spain; FR—France; HR—Croatia; IT—Italy; PT—Portugal; TR—Turkey.</p> Full article ">
13 pages, 1801 KiB  
Article
Chemometric Study of the Correlation between Human Exposure to Benzene and PAHs and Urinary Excretion of Oxidative Stress Biomarkers
by Flavia Buonaurio, Enrico Paci, Daniela Pigini, Federico Marini, Lisa Bauleo, Carla Ancona and Giovanna Tranfo
Atmosphere 2020, 11(12), 1341; https://doi.org/10.3390/atmos11121341 - 11 Dec 2020
Cited by 4 | Viewed by 2154
Abstract
Urban air contains benzene and polycyclic aromatic hydrocarbons (PAHs) which have carcinogenic properties. The objective of this paper is to study the correlation of exposure biomarkers with biomarkers of nucleic acid oxidation also considering smoking. In 322 subjects, seven urinary dose biomarkers were [...] Read more.
Urban air contains benzene and polycyclic aromatic hydrocarbons (PAHs) which have carcinogenic properties. The objective of this paper is to study the correlation of exposure biomarkers with biomarkers of nucleic acid oxidation also considering smoking. In 322 subjects, seven urinary dose biomarkers were analyzed for benzene, pyrene, nitropyrene, benzo[a]pyrene, and naphthalene exposure, and four effect biomarkers for nucleic acid and protein oxidative stress. Chemometrics was applied in order to investigate the existence of a synergistic effect for the exposure to the mixture and the contribution of active smoking. There is a significant difference between nicotine, benzene and PAH exposure biomarker concentrations of smokers and non-smokers, but the difference is not statistically significant for oxidative stress biomarkers. The PAH biomarkers are those which best correlate with all the oxidative stress biomarkers. Results suggest that 8-Oxo-7,8-dihydroguanine and protein nitro-oxidation 3-nitrotyrosine are the most sensitive biomarkers for the exposure to the urban pollutant mixtures and that a synergic effect of the mixtures exists. All the oxidative stress biomarkers studied drive the increase in the oxidative stress biomarkers in the subjects having higher exposures. Chemometrics proved to be a powerful method for the interpretation of human biomonitoring data. Full article
(This article belongs to the Special Issue Air Quality and Health in the Mediterranean)
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<p>Distribution of log<sub>10</sub>-concentration values of urinary cotinine in 322 subjects.</p> Full article ">Figure 2
<p>Results of ComDim modeling. (<b>a</b>) Projection of the samples (scores) on the first two common components (CCs) of the model; the variable loadings associated to the same common components are displayed in panels (<b>b</b>) and (<b>c</b>) for exposure and oxidative stress biomarkers, respectively.</p> Full article ">Figure 3
<p>Score plot resulting from the ComDim analysis (same as <a href="#atmosphere-11-01341-f002" class="html-fig">Figure 2</a>a), colored according to smoker/non-smoker status defined based on urinary cotinine levels.</p> Full article ">Figure 4
<p>Results of multiple linear regression (MLR) modeling between individual oxidative stress biomarkers and the set of exposure biomarkers for the selected subset of 47 individuals with the highest level of exposure: plots of cross-validated predictions vs. observed values.</p> Full article ">
17 pages, 9084 KiB  
Article
Association between the Concentration and the Elemental Composition of Outdoor PM2.5 and Respiratory Diseases in Schoolchildren: A Multicenter Study in the Mediterranean Area
by Christopher Zammit, David Bilocca, Silvia Ruggieri, Gaspare Drago, Cinzia Perrino, Silvia Canepari, Martin Balzan, Stephen Montefort, Giovanni Viegi, Fabio Cibella and on behalf of the RESPIRA Collaborative Project Group
Atmosphere 2020, 11(12), 1290; https://doi.org/10.3390/atmos11121290 - 29 Nov 2020
Cited by 5 | Viewed by 2585 | Correction
Abstract
Abstract: Exposure to outdoor air pollution has been shown to increase asthma symptoms. We assessed the potential role of particulate matter with aerodynamic diameter <2.5 μm (PM2.5) on respiratory condition in schoolchildren in the south Mediterranean area. A total of [...] Read more.
Abstract: Exposure to outdoor air pollution has been shown to increase asthma symptoms. We assessed the potential role of particulate matter with aerodynamic diameter <2.5 μm (PM2.5) on respiratory condition in schoolchildren in the south Mediterranean area. A total of 2400 children aged 11–14 years were recruited, and data on their symptoms were collected through an ISAAC (International Study of Asthma and Allergies in Childhood)-based questionnaire. Outdoor PM2.5 was collected for 48 consecutive hours in the schoolyards of their schools and selected residential outdoor areas. The levels of PM2.5 were measured, along with its elemental composition. The incidence of an acute respiratory illness within the first 2 years of life was higher amongst Sicilian children when compared to Maltese children (29.7% vs. 13.5% respectively, p < 0.0001). Malta had a significantly higher prevalence of doctor‐diagnosed asthma, when compared to Sicily (18.0% Malta vs. 7.5% Sicily, p <0.0001). Similarly, current asthma (7.8% vs. 2.9%, p < 0.0001) and use of asthma medication in the last 12 months (12.1% vs. 4.9%, p < 0.0001) were more frequent amongst Maltese children. Total median PM2.5 was 12.9 μg/m3 in Sicily and 17.9 μg/m3 in Malta. PM2.5 levels were highest in the Maltese urban town of Hamrun (23.6 μg/m3), while lowest in the rural Sicilian town of Niscemi (10.9 μg/m3, p < 0.0001). Hamrun also exhibited the highest levels of nickel, vanadium, lead, zinc, antimony, and manganese, whilst the Sicilian city of Gela had the highest levels of cadmium, and the highest level of PM2.5 when compared to rural Sicily. Elevated levels of PM2.5 were positively associated with the prevalence of doctor diagnosed asthma (odds ratio (OR) 1.05), current asthma (OR 1.06), and use of asthma medication (OR 1.06). All elements in PM2.5 showed increased OR for doctor diagnosed asthma, while higher concentrations of Cd and Mn were associated with higher prevalence of rhinitis. Full article
(This article belongs to the Special Issue Air Quality and Health in the Mediterranean)
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<p>The “RESPIRA” Project involved four communities of the Health District of Gela (red circles in panel <b>A</b>) and four communities in Malta (red circles in panel <b>B</b>) in the south Mediterranean area (upper panel).</p> Full article ">Figure 2
<p>Distribution of outdoor total particulate matter with aerodynamic diameter &lt;2.5 μm (PM<sub>2.5</sub>) measures in each community. Boxplot bars indicate (from the bottom to the top) 10th, 25th, 50th (median), 75th, and 90th percentiles. Values below 10th and above 90th percentiles are plotted as circles. The <span class="html-italic">p</span>-value was computed using the Kruskal–Wallis test.</p> Full article ">Figure 3
<p>Distribution of nickel concentration in outdoor PM<sub>2.5</sub> in each community. Boxplot bars indicate (from the bottom to the top) 10th, 25th, 50th (median), 75th, and 90th percentiles. Values below 10th and above 90th percentiles are plotted as circles. The overall <span class="html-italic">p</span>-value was computed using the Kruskal-Wallis test.</p> Full article ">Figure 4
<p>Distribution of vanadium concentration in outdoor PM<sub>2.5</sub> in each community. Boxplot bars indicate (from the bottom to the top) 10th, 25th, 50th (median), 75th, and 90th percentiles. Values below 10th and above 90th percentiles are plotted as circles. The overall <span class="html-italic">p</span>-value was computed using the Kruskal-Wallis test.</p> Full article ">Figure 5
<p>Distribution of lead concentration in outdoor PM<sub>2.5</sub> in each community. Boxplot bars indicate (from the bottom to the top) 10th, 25th, 50th (median), 75th, and 90th percentiles. Values below 10th and above 90th percentiles are plotted as circles. The overall <span class="html-italic">p</span>-value was computed using the Kruskal-Wallis test.</p> Full article ">Figure 6
<p>Schematic representation of the relationships between &gt; prevalence (and 95% confidence interval) of doctor diagnosis of asthma, current asthma, use of medicine for asthma in the last 12 months, and rhinitis in the last 12 months, and outdoor Vanadium concentration (mean and 95% confidence interval) per each community (Cos/Zej: Cospicua/Zejtun).</p> Full article ">Figure 7
<p>Pearson’s correlation matrix among PM<sub>2.5</sub> and its evaluated elemental components. For each intersection, the <span class="html-italic">R<sup>2</sup></span> and relevant <span class="html-italic">p</span>-values are indicated.</p> Full article ">
17 pages, 2327 KiB  
Article
Regional Inhaled Deposited Dose of Indoor Combustion-Generated Aerosols in Jordanian Urban Homes
by Tareq Hussein, Brandon E. Boor and Jakob Löndahl
Atmosphere 2020, 11(11), 1150; https://doi.org/10.3390/atmos11111150 - 25 Oct 2020
Cited by 11 | Viewed by 2290
Abstract
Indoor combustion processes associated with cooking, heating, and smoking are a major source of aerosols in Jordanian dwellings. To evaluate human exposure to combustion-generated aerosols in Jordanian indoor environments, regional inhaled deposited dose rates of indoor aerosols (10 nm to 25 µm) were [...] Read more.
Indoor combustion processes associated with cooking, heating, and smoking are a major source of aerosols in Jordanian dwellings. To evaluate human exposure to combustion-generated aerosols in Jordanian indoor environments, regional inhaled deposited dose rates of indoor aerosols (10 nm to 25 µm) were determined for different scenarios for adult occupants. The inhaled deposited dose rate provides an estimate of the number or mass of inhaled aerosol that deposits in each region of the respiratory system per unit time. In general, sub-micron particle number (PN1) dose rates ranged from 109 to 1012 particles/h, fine particle mass (PM2.5) dose rates ranged from 3 to 216 µg/h, and coarse particle mass (PM10) dose rates ranged from 30 to 1600 µg/h. Dose rates were found to be dependent on the type and intensity of indoor combustion processes documented in the home. Dose rates were highest during cooking activities using a natural gas stove, heating via natural gas and kerosene, and smoking (shisha/tobacco). The relative fraction of the total dose rate received in the head airways, tracheobronchial, and alveolar regions varied among the documented indoor combustion (and non-combustion) activities. The significant fraction of sub-100 nm particles produced during the indoor combustion processes resulted in high particle number dose rates for the alveolar region. Suggested approaches for reducing indoor aerosol dose rates in Jordanian dwellings include a reduction in the prevalence of indoor combustion sources, use of extraction hoods to remove combustion products, and improved ventilation/filtration in residential buildings. Full article
(This article belongs to the Special Issue Air Quality and Health in the Mediterranean)
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<p>Indoor particle number and mass concentrations in Jordanian homes during background periods: (<b>a</b>) mean and (<b>b</b>) median.</p> Full article ">Figure 2
<p>Mean indoor particle number size distributions during background periods for each home. The legend refers to the home ID.</p> Full article ">Figure 3
<p>Indoor particle number and mass concentrations in Jordanian homes for background periods (BG) and the categorized indoor activities (TYPEs I, II, III, IV): (<b>a</b>) mean and (<b>b</b>) median.</p> Full article ">Figure 4
<p>Mean particle number size distributions during selected indoor activities categorized by activity type: (<b>a</b>) Type I (non-combustion), (<b>b</b>) TYPE II (intensive cooking with different heating types), (<b>c</b>) TYPE III (combustion: heating and cooking), and (<b>d</b>) TYPE IV (combustion: heating, cooking, and smoking). Heating type: natural gas heater (NG), kerosene heater (K), central heating system (C), and air conditioning split unit (AC). Smoking type: shisha (SH) and tobacco smoking (TS). Cooking was reported on either a stove (natural gas) or using non-combustion appliances (i.e., water jug heater, microwave, etc.); the cooking intensity was indicated. The legend refers to the home ID and indoor activities.</p> Full article ">Figure 5
<p>Regional inhaled deposited dose rates calculated for different activities during indoor background conditions for: (<b>a</b>) sub-micron particle number concentrations (PN<sub>1</sub>) and (<b>b</b>,<b>c</b>) particle mass concentrations (PM<sub>2.5</sub> and PM<sub>10</sub>). The color legend is: blue—head airways (head), red—tracheobronchial (TB), and gray—alveolar (Alv). Exposure is based on mean concentrations. Note that yardwork is assumed to be equivalent to housework and running is equivalent to indoor exercising.</p> Full article ">Figure 6
<p>Regional inhaled deposited dose rates calculated for each activity type (TYPEs I, II, III, IV) and background conditions for: (<b>a</b>) sub-micron particle number concentrations (PN<sub>1</sub>) and (<b>b</b>,<b>c</b>) particle mass concentrations (PM<sub>2.5</sub> and PM<sub>10</sub>). The color legend is: blue—yardwork equivalent activities, yellow—walking activities, and red—sitting and resting.</p> Full article ">

Review

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18 pages, 1222 KiB  
Review
Airborne Aerosols and Human Health: Leapfrogging from Mass Concentration to Oxidative Potential
by Carolina Molina, Richard Toro A., Carlos A. Manzano, Silvia Canepari, Lorenzo Massimi and Manuel. A. Leiva-Guzmán
Atmosphere 2020, 11(9), 917; https://doi.org/10.3390/atmos11090917 - 28 Aug 2020
Cited by 44 | Viewed by 6923
Abstract
The mass concentration of atmospheric particulate matter (PM) has been systematically used in epidemiological studies as an indicator of exposure to air pollutants, connecting PM concentrations with a wide variety of human health effects. However, these effects can be hardly explained by using [...] Read more.
The mass concentration of atmospheric particulate matter (PM) has been systematically used in epidemiological studies as an indicator of exposure to air pollutants, connecting PM concentrations with a wide variety of human health effects. However, these effects can be hardly explained by using one single parameter, especially because PM is formed by a complex mixture of chemicals. Current research has shown that many of these adverse health effects can be derived from the oxidative stress caused by the deposition of PM in the lungs. The oxidative potential (OP) of the PM, related to the presence of transition metals and organic compounds that can induce the production of reactive oxygen and nitrogen species (ROS/RNS), could be a parameter to evaluate these effects. Therefore, estimating the OP of atmospheric PM would allow us to evaluate and integrate the toxic potential of PM into a unique parameter, which is related to emission sources, size distribution and/or chemical composition. However, the association between PM and particle-induced toxicity is still largely unknown. In this commentary article, we analyze how this new paradigm could help to deal with some unanswered questions related to the impact of atmospheric PM over human health. Full article
(This article belongs to the Special Issue Air Quality and Health in the Mediterranean)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Schematic mechanistic pathways of particulate matter (PM) producing oxidative stress and inflammatory response.</p> Full article ">Figure 2
<p>Conceptual an integrated research across the exposure-risk assessment-risk management.</p> Full article ">

Other

Jump to: Research, Review

2 pages, 412 KiB  
Correction
Correction: Zammit et al. Association between the Concentration and the Elemental Composition of Outdoor PM2.5 and Respiratory Diseases in Schoolchildren: A Multicenter Study in the Mediterranean Area. Atmosphere 2020, 11, 1290
by Christopher Zammit, David Bilocca, Silvia Ruggieri, Gaspare Drago, Cinzia Perrino, Silvia Canepari, Martin Balzan, Stephen Montefort, Giovanni Viegi, Fabio Cibella and on behalf of the RESPIRA Collaborative Project Group
Atmosphere 2021, 12(6), 706; https://doi.org/10.3390/atmos12060706 - 30 May 2021
Viewed by 2125
Abstract
In the original article [...] Full article
(This article belongs to the Special Issue Air Quality and Health in the Mediterranean)
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Figure 1

Figure 1
<p>Schematic representation of the relationships between prevalence (and 95% confidence interval) of doctor diagnosis of asthma, current asthma, use of medicine for asthma in the last 12 months, and rhinitis in the last 12 months, and outdoor vanadium concentration (mean and 95% confidence interval) per each community (Cos/Zej: Cospicua/Zejtun).</p> Full article ">
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