scholarly journals Role of the atmospheric pollution in the Covid-19 outbreak risk in Italy

2020 ◽  
Author(s):  
Daniele Fattorini ◽  
Francesco Regoli
Keyword(s):  
Air Quality ◽  
Atmospheric Pollution ◽  
Inflammatory Responses ◽  
Integrated Approach ◽  
Atmospheric Pollutants ◽  
Quality Data ◽  
Geographical Differences ◽  
Italian Regions ◽  
Italian Provinces

AbstractBackgroundAfter the initial outbreak in China, the diffusion in Italy of SARS-CoV-2 is exhibiting a clear regional trend with Northern areas being the most affected in terms of both frequency and severity of cases. Among multiple factors possibly involved in such geographical differences, a role has been hypothesized for atmospheric pollution.ObjectivesWe provide additional evidence on the possible influence of air quality, particularly in terms of chronicity of exposure on the spread viral infection in Italian regions.MethodsActual data on to COVID-19 outbreak in Italian provinces and corresponding long-term air quality evaluations, were obtained from Italian and European agencies, elaborated and tested for possible interactions.DiscussionOur elaborations reveal that, beside concentrations, the chronicity of exposure may influence the anomalous variability of SARS-CoV-2 in Italy. Data on distribution of atmospheric pollutants (NO2, O3, PM2.5 and PM10) in Italian regions during the last 4 years, days exceeding regulatory limits, and years of the last decade (2010-2019) in which the limits have been exceeded for at least 35 days, confirmed that Northern Italy has been constantly exposed to chronic air pollution. Long-term air-quality data significantly correlated with cases of Covid-19 in up to 71 Italian provinces (updated 6 April) providing further evidence that chronic exposure to atmospheric contamination may represent a favourable context for the spread of the virus. Pro-inflammatory responses and high incidence of respiratory and cardiac affections are well known, while the capability of this coronavirus to bind particulate matters remains to be established. Atmospheric and environmental pollution should be considered as part of an integrated approach for sustainable development, human health protection and prevention of epidemic spreads.

scholarly journals The Diamond League athletic series: does the air quality sparkle?

2021 ◽  
Author(s):  
James R. Hodgson ◽  
Lee Chapman ◽  
Francis D. Pope
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Performance Metrics ◽  
Urban Air Pollution ◽  
Quality Data ◽  
Performance Variation ◽  
Short And Long Term ◽  
Respiratory Conditions ◽  
Race Performance

AbstractUrban air pollution can have negative short- and long-term impacts on health, including cardiovascular, neurological, immune system and developmental damage. The irritant qualities of pollutants such as ozone (O3), nitrogen dioxide (NO2) and particulate matter (PM) can cause respiratory and cardiovascular distress, which can be heightened during physical activity and particularly so for those with respiratory conditions such as asthma. Previously, research has only examined marathon run outcomes or running under laboratory settings. This study focuses on elite 5-km athletes performing in international events at nine locations. Local meteorological and air quality data are used in conjunction with race performance metrics from the Diamond League Athletics series to determine the extent to which elite competitors are influenced during maximal sustained efforts in real-world conditions. The findings from this study suggest that local meteorological variables (temperature, wind speed and relative humidity) and air quality (ozone and particulate matter) have an impact on athletic performance. Variation between finishing times at different race locations can also be explained by the local meteorology and air quality conditions seen during races.

Temporal patterns and trends of surface ozone concentrations over Portugal

2021 ◽  
Author(s):  
Carla Gama ◽  
Alexandra Monteiro ◽  
Myriam Lopes ◽  
Ana Isabel Miranda
Keyword(s):  
Air Quality ◽  
Iberian Peninsula ◽  
Human Health ◽  
Temporal Variability ◽  
Urban Areas ◽  
Surface Ozone ◽  
Quality Data ◽  
Ozone Concentrations ◽  
Long Term Study

<p>Tropospheric ozone (O<sub>3</sub>) is a critical pollutant over the Mediterranean countries, including Portugal, due to systematic exceedances to the thresholds for the protection of human health. Due to the location of Portugal, on the Atlantic coast at the south-west point of Europe, the observed O<sub>3</sub> concentrations are very much influenced not only by local and regional production but also by northern mid-latitudes background concentrations. Ozone trends in the Iberian Peninsula were previously analysed by Monteiro et al. (2012), based on 10-years of O<sub>3</sub> observations. Nevertheless, only two of the eleven background monitoring stations analysed in that study are located in Portugal and these two stations are located in Porto and Lisbon urban areas. Although during pollution events O<sub>3</sub> levels in urban areas may be high enough to affect human health, the highest concentrations are found in rural locations downwind from the urban and industrialized areas, rather than in cities. This happens because close to the sources (e.g., in urban areas) freshly emitted NO locally scavenges O<sub>3</sub>. A long-term study of the spatial and temporal variability and trends of the ozone concentrations over Portugal is missing, aiming to answer the following questions:</p><p>-           What is the temporal variability of ozone concentrations?</p><p>-           Which trends can we find in observations?</p><p>-           How were the ozone spring maxima concentrations affected by the COVID-19 lockdown during spring 2020?</p><p>In this presentation, these questions will be answered based on the statistical analysis of O<sub>3</sub> concentrations recorded within the national air quality monitoring network between 2005 and 2020 (16 years). The variability of the surface ozone concentrations over Portugal, on the timescales from diurnal to annual, will be presented and discussed, taking into account the physical and chemical processes that control that variability. Using the TheilSen function from the OpenAir package for R (Carslaw and Ropkins 2012), which quantifies monotonic trends and calculates the associated p-value through bootstrap simulations, O<sub>3</sub> concentration long-term trends will be estimated for the different regions and environments (e.g., rural, urban).  Moreover, taking advantage of the unique situation provided by the COVID-19 lockdown during spring 2020, when the government imposed mandatory confinement and citizens movement restriction, leading to a reduction in traffic-related atmospheric emissions, the role of these emissions on ozone levels during the spring period will be studied and presented.</p><p> </p><p>Carslaw and Ropkins, 2012. Openair—an R package for air quality data analysis. Environ. Model. Softw. 27-28,52-61. https://doi.org/10.1016/j.envsoft.2011.09.008</p><p>Monteiro et al., 2012. Trends in ozone concentrations in the Iberian Peninsula by quantile regression and clustering. Atmos. Environ. 56, 184-193. https://doi.org/10.1016/j.atmosenv.2012.03.069</p>

Remote sensing detection of atmospheric pollutants using lidar, sodar and correlation with air quality data in an industrial area

2011 ◽  
Author(s):  
Juliana Steffens ◽  
Renata F. da Costa ◽  
Eduardo Landulfo ◽  
Roberto Guardani ◽  
Paulo F. Moreira, Jr. ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Quality ◽  
Industrial Area ◽  
Atmospheric Pollutants ◽  
Quality Data ◽  
Air Quality Data

scholarly journals Trends of inorganic and organic aerosols and precursor gases in Europe: insights from the EURODELTA multi-model experiment over the 1990–2010 period

2019 ◽  
Author(s):  
Giancarlo Ciarelli ◽  
Mark R. Theobald ◽  
Marta G. Vivanco ◽  
Matthias Beekmann ◽  
Wenche Aas ◽  
...  
Keyword(s):  
Air Quality ◽  
Comprehensive Evaluation ◽  
Quality Data ◽  
Relative Reduction ◽  
Emission Data ◽  
Chemical Aging ◽  
Modeling Experiment ◽  
Phase Precursor ◽  
Total Ammonium

Abstract. In the framework of the EURODELTA-Trends (EDT) modeling experiment, several chemical transport models (CTMs) were applied for the 1990–2010 period to investigate air quality changes in Europe as well as the capability of the models to reproduce observed long-term air quality trends. Five CTMs have provided modeled air quality data for twenty-one continuous years in Europe using emission scenarios prepared by IIASA/GAINS and corresponding year-by-year meteorology derived from ERA-interim global reanalysis. For this study, long-term observations of particle sulfate (SO42−), total nitrate (TNO3), total ammonium (TNHx) as well as sulfur dioxide (SO2) and nitrogen dioxide (NO2) for multiple sites in Europe were used to validate the model results. The trends analysis was performed for the full twenty-one years (referred to as PT), but also for two 11-year sub-periods: 1990–2000 (referred to as P1) and 2000–2010 (referred to as P2). The experiment revealed that the models were able to reproduce the faster decline in observed SO2 concentrations during the first decade, i.e. 1990–2000, with a 64–76 % mean relative reduction in SO2 concentrations indicated by the EDT experiment (range of all the models) versus an 82 % mean relative reduction in observed concentrations. During the second decade, P2, the models estimated a mean relative reduction in SO2 concentrations of about 34–54 %, which was also in line with that observed (47 %). Comparisons of observed and modeled NO2 trends revealed a mean relative decrease of 25 % and between 19–23 % (range of all the models) during the P1 period, and 12 % and between 22–26 % (range of all the models) during the P2 period, respectively. Comparisons of observed and modeled trends in SO42− concentrations during the P1 period indicated that the models were able to reproduce the observed trends at most of the sites, with a 42–54 % mean relative reduction indicated by the EDT experiment (range of all models) versus a 57 % mean relative reduction in observed concentrations, and with good performances also during the P2 and PT periods. Moreover, especially during the P1 period, both modeled and observational data indicated smaller reductions in SO42− concentrations compared with its gas-phase precursor (i.e. SO2), which could be mainly attributed to increased oxidant levels and pH-dependent cloud chemistry. An analysis of the trends in TNO3 concentrations indicated a 28–39 % and 29 % mean relative reduction in TNO3 concentrations for the full period for model data (range of all the models) and observations, respectively. Further analysis of the trends in modeled HNO3 and particle nitrate (NO3−) concentrations revealed that the relative reduction in HNO3 was larger than that for NO3− during the P1 period, which was mainly attributed to an increased availability of “free-ammonia”. By contrast, trends in modeled HNO3 and NO3− concentrations were more comparable during the P2 period. Also, trends of TNHx concentrations were, in general, under-predicted by all models, with worst performance for the P1 period than for P2. Trends in modeled anthropogenic and biogenic secondary organic aerosol (ASOA and BSOA) concentrations together with the trends in available emissions of biogenic volatile organic compounds (BVOCs) were also investigated. A strong decrease in ASOA was indicated by all the models, following the reduction in anthropogenic NMVOCs precursors. Biogenic emission data provided by the modeling teams indicated a few areas with statistically significant increase in isoprene emission and monoterpene emissions during the 1990–2010 period over Fennoscandia and Eastern European regions (i.e. around 14–27 %), which was mainly attributed to the increase of surface temperature. However, the modeled BSOA concentrations did not linearly follow the increase in biogenic emissions. Finally, a comprehensive evaluation against positive matrix factorization (PMF) data, available during the second period (P2) at various European sites, revealed a systematic under-estimation of the modeled SOA fractions of between a factor of 3 to 11, on average, most likely because of missing SOA precursors and formation pathways, with reduced biases for the models that accounted for chemical aging of semi-volatile SOA components in the atmosphere.

Respiratory Symptoms and Spirometric Observations in Relation to Atmospheric Pollutants in a Sample of Urban Population

2000 ◽  
Vol 12 (2) ◽  
pp. 58-64 ◽  
Author(s):  
K. Satish Kumar ◽  
C.E. Prasad ◽  
N. Balakrishna ◽  
K. Visweswara Rao ◽  
P. Uma Maheswara Reddy
Keyword(s):  
Air Quality ◽  
Respiratory Symptoms ◽  
Ambient Air ◽  
Air Pollutant ◽  
Control Measures ◽  
Atmospheric Pollutants ◽  
Quality Data ◽  
Ambient Air Quality ◽  
Group I ◽  
The Mean

The prevalence of respiratory problems and the ventilatory functions in subjects belonging to three sample areas with different levels of pollution was studied to ascertain if there is any association between air pollutant levels and abnormal ventilatory functions. The predominant activity existing in that area served as the basis for stratification of the city into industrial (Group I), commercial (Group II) and residential (Group III) areas. Ambient air quality data of suspended particulate matter SPM, SO2 and NOx of the three sample areas were measured using standard methods. 216 men included in the study were administered the American Thoracic Society - Division of Lung Diseases ATS-DLD respiratory questionnaire, clinically examined and subjected to routine laboratory investigations. Spirometry and salbutamol reversibility tests were performed as per the ATS guidelines 1991. The mean and peak levels of SPM in the commercial area and the peak levels in the residential area were higher than the National Ambient Air Quality Standards (NAAQS). The mean and peak levels of NOx and SO2 in all the three areas were lower than the NAAQS. A high prevalence of ∼ 30-50% of respiratory symptoms was reported in the present study. Respiratory and ventilatory abnormalities were higher in the commercial areas, which are associated with the higher mean and peak levels of SO 2 and the peak levels of NOx. The pollution control measures should also aim at the peak levels of pollutants as they have been shown to exacerbate the respiratory symptoms in the present study. Asia Pac J Public Health 2000;12(2): 58-64

scholarly journals Trends of inorganic and organic aerosols and precursor gases in Europe: insights from the EURODELTA multi-model experiment over the 1990–2010 period

2019 ◽  
Vol 12 (12) ◽  
pp. 4923-4954 ◽  
Author(s):  
Giancarlo Ciarelli ◽  
Mark R. Theobald ◽  
Marta G. Vivanco ◽  
Matthias Beekmann ◽  
Wenche Aas ◽  
...  
Keyword(s):  
Air Quality ◽  
Comprehensive Evaluation ◽  
Quality Data ◽  
Relative Reduction ◽  
International Institute ◽  
Emission Data ◽  
Modeling Experiment ◽  
Phase Precursor ◽  
Total Ammonium

Abstract. In the framework of the EURODELTA-Trends (EDT) modeling experiment, several chemical transport models (CTMs) were applied for the 1990–2010 period to investigate air quality changes in Europe as well as the capability of the models to reproduce observed long-term air quality trends. Five CTMs have provided modeled air quality data for 21 continuous years in Europe using emission scenarios prepared by the International Institute for Applied Systems Analysis/Greenhouse Gas – Air Pollution Interactions and Synergies (IIASA/GAINS) and corresponding year-by-year meteorology derived from ERA-Interim global reanalysis. For this study, long-term observations of particle sulfate (SO42-), total nitrate (TNO3), total ammonium (TNHx) as well as sulfur dioxide (SO2) and nitrogen dioxide (NO2) for multiple sites in Europe were used to evaluate the model results. The trend analysis was performed for the full 21 years (referred to as PT) but also for two 11-year subperiods: 1990–2000 (referred to as P1) and 2000–2010 (referred to as P2). The experiment revealed that the models were able to reproduce the faster decline in observed SO2 concentrations during the first decade, i.e., 1990–2000, with a 64 %–76 % mean relative reduction in SO2 concentrations indicated by the EDT experiment (range of all the models) versus an 82 % mean relative reduction in observed concentrations. During the second decade (P2), the models estimated a mean relative reduction in SO2 concentrations of about 34 %–54 %, which was also in line with that observed (47 %). Comparisons of observed and modeled NO2 trends revealed a mean relative decrease of 25 % and between 19 % and 23 % (range of all the models) during the P1 period, and 12 % and between 22 % and 26 % (range of all the models) during the P2 period, respectively. Comparisons of observed and modeled trends in SO42- concentrations during the P1 period indicated that the models were able to reproduce the observed trends at most of the sites, with a 42 %–54 % mean relative reduction indicated by the EDT experiment (range of all models) versus a 57 % mean relative reduction in observed concentrations and with good performance also during the P2 and PT periods, even though all the models overpredicted the number of statistically significant decreasing trends during the P2 period. Moreover, especially during the P1 period, both modeled and observational data indicated smaller reductions in SO42- concentrations compared with their gas-phase precursor (i.e., SO2), which could be mainly attributed to increased oxidant levels and pH-dependent cloud chemistry. An analysis of the trends in TNO3 concentrations indicated a 28 %–39 % and 29 % mean relative reduction in TNO3 concentrations for the full period for model data (range of all the models) and observations, respectively. Further analysis of the trends in modeled HNO3 and particle nitrate (NO3-) concentrations revealed that the relative reduction in HNO3 was larger than that for NO3- during the P1 period, which was mainly attributed to an increased availability of “free ammonia”. By contrast, trends in modeled HNO3 and NO3- concentrations were more comparable during the P2 period. Also, trends of TNHx concentrations were, in general, underpredicted by all models, with worse performance for the P1 period than for P2. Trends in modeled anthropogenic and biogenic secondary organic aerosol (ASOA and BSOA) concentrations together with the trends in available emissions of biogenic volatile organic compounds (BVOCs) were also investigated. A strong decrease in ASOA was indicated by all the models, following the reduction in anthropogenic non-methane VOC (NMVOC) precursors. Biogenic emission data provided by the modeling teams indicated a few areas with statistically significant increase in isoprene emissions and monoterpene emissions during the 1990–2010 period over Fennoscandia and eastern European regions (i.e., around 14 %–27 %), which was mainly attributed to the increase of surface temperature. However, the modeled BSOA concentrations did not linearly follow the increase in biogenic emissions. Finally, a comprehensive evaluation against positive matrix factorization (PMF) data, available during the second period (P2) at various European sites, revealed a systematic underestimation of the modeled SOA fractions of a factor of 3 to 11, on average, most likely because of missing SOA precursors and formation pathways, with reduced biases for the models that accounted for chemical aging of semi-volatile SOA components in the atmosphere.

scholarly journals Ambient Air Quality, Pollutant Behavior, and Distribution Pattern in Rabigh City Using an Air Dispersion Model

2021 ◽  
Vol 22 (1) ◽  
pp. 1-12
Author(s):  
Aljahdali Mohammed Othman
Keyword(s):  
Air Quality ◽  
Atmospheric Pollution ◽  
Industrial Development ◽  
Dispersion Model ◽  
Heavy Traffic ◽  
Winter Season ◽  
Ambient Air ◽  
Meteorological Conditions ◽  
Atmospheric Pollutants ◽  
Anthropogenic Sources

The rise in industrial development and modern technology is one of the major causes of atmospheric pollution, which negatively affects human health. In this study, meteorological conditions and atmospheric pollution dispersion in Rabigh city and its catchments were analyzed using measured data and an air quality dispersion model. The Hybrid Single-Particle Lagrangian Integrated Trajectory model was used to simulate the dispersion of atmospheric pollutants. A dataset from 2018 was analyzed to clarify the seasonal distributions of atmospheric pollutant concentrations in Rabigh and other areas (Thuwal and Khulais). A significant variation in atmospheric pollutants was recorded across the seasons, which may be caused by changes in meteorological conditions. Variations in other anthropogenic sources related to high population density or heavy traffic in the nearby road may also be involved in these fluctuations. Predictions indicated that pollutants would impact the Thuwal area (>50 μg m−3) and Khulais (>35 μg m−3) during the winter season and affect Thuwal (>20 μg m−3) and Rabigh (>20 μg m−3) during the fall season. The concentrations of pollutants were mostly negatively correlated with wind speed, except for carbon monoxide. We established variations in the seasonal concentration of pollutants and the effect of meteorological conditions on atmospheric pollutants for the year 2018 in the study area. Policymakers and stakeholders must provide solutions to mitigate the environmental effect of atmospheric pollution in Rabigh city, Thuwal, and Khulais for the health of inhabitants.

scholarly journals Assessment of changes in concentrations of selected criteria pollutants in the Vaal and Highveld Priority Areas

2019 ◽  
Vol 29 (2) ◽  
Author(s):  
Gregor Feig ◽  
Rebecca M. Garland ◽  
Seneca Naidoo ◽  
Amukelani Maluleke ◽  
Marna Van der Merwe
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Ambient Air ◽  
Atmospheric Pollutants ◽  
Ambient Air Pollution ◽  
Priority Area ◽  
Priority Areas ◽  
Management Interventions ◽  
Ambient Concentrations

Ambient air pollution has important impacts on a variety of environmental issues, particularly on human health and ecosystem processes. A key tool for understanding the impacts of atmospheric pollution is through the long term measurement of the ambient concentrations of criteria atmospheric pollutants. Monitoring of ambient pollution concentrations has been conducted in the National Air Quality Priority areas since 2009. During this time period, significant changes in the management of air pollution have occurred, including the adoption of the ambient air quality standards, and the implementation of section 21 emission standards. This paper examines the long term evolution of ambient concentrations for PM, SO2 in the Vaal Triangle Airshed Priority Area 2007-2017 and Highveld Priority Area. These trends will be evaluated against the implementation of management interventions and highlight the variation in the measured concentrations and emerging areas of concern.  

MOPGA/Make Air Quality Great Again: AfriqAir and solution-oriented approaches to improving air quality in the Global South

2021 ◽  
Author(s):  
Michael R Giordano ◽  
Julien Bahino ◽  
Matthias Beekmann ◽  
Ramachandran Subramanian ◽  
Keyword(s):  
Air Quality ◽  
Quality Data ◽  
Pollution Levels ◽  
Premature Deaths ◽  
Network Building ◽  
Global Organization ◽  
Data Gap ◽  
Set Up ◽  
The Cost

<div> <div> <p>Air pollution is responsible for seven million premature deaths each year, linked to numerous cardiovascular and other diseases. Both monitoring pollution levels and identifying sources is necessary to reduce overall exposure. Many parts of Africa suffer from extreme pollution levels, but the cost of traditional air quality monitoring leads to a significant data gap, which also hinders the development of local capacity to do these tasks. In order to overcome these obstacles, the “Make Air Quality Great Again” (MAQGA) project was funded by the French Agence nationale de la recherché (ANR) under the MOPGA program. The MAQGA project in turn set up the AfriqAir consortium, a global organization that brings together air quality scientists and researchers interested in using air quality data to tackle air quality problems in Africa. Now entering its third year of existence, the consortium has made real strides in increasing the number of air quality monitors in Africa as well as building capacity with local researchers and partners across the continent. This presentation will provide a recap of what the consortium has achieved with ANR and MOPGA support, how we have persevered through the COVID-19 pandemic, and our plans for the immediate and long-term futures. This presentation will cover the scientific gains made by connecting African air quality researchers as well as the successes aided by the network building that AfriqAir has facilitated. </p> </div> </div>

scholarly journals Dynamic analysis of particulate pollution in haze in Harbin city, Northeast China

2021 ◽  
Vol 13 (1) ◽  
pp. 1656-1667
Author(s):  
Lei Wang ◽  
Jiarong Deng ◽  
Lijin Yang ◽  
Tianrun Yu ◽  
Yunlong Yao ◽  
...  
Keyword(s):  
Air Quality ◽  
Dynamic Analysis ◽  
Single Particle ◽  
Atmospheric Pollutants ◽  
Quality Data ◽  
Aerodynamic Diameter ◽  
Air Mass ◽  
Pollution Concentration ◽  
Trajectory Model ◽  
Air Quality Data

Abstract Based on the air quality data of Harbin in winter from 2015 to 2017, the national winter straw combustion data from 2016 to 2017 and the mixed single particle Lagrange comprehensive track model, Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT), dynamic analysis of Harbin’s winter air quality status and influencing factors. The air quality data were analyzed; it was found that the main pollutants in winter in Harbin were SO2, NO2, PM10 (particles with aerodynamic diameter ≤10 µm), and PM2.5 (particles with aerodynamic diameter ≤2.5 µm); the annual air pollution situation deteriorated sharply from November and continued until March of the following year. Through the research on straw fire prevention points in Harbin, the spatial pattern characteristics and causes of persistent haze in Harbin from 2015 to 2017 were dynamically analyzed. Combining the backward trajectory model to trace the source and trend of air mass in pollution, it is found that the air mass trend is consistent with the distribution of straw-burning points. The research results show that (1) during the winter from 2015 to 2017, the overall air quality situation in Harbin improved, the number of serious pollution days decreased year by year, and the main atmospheric pollutants were PM10 and PM2.5. In October and November, the pollution concentration peaked, and after December, the pollution concentration showed a downward trend until the next spring reached the valley and (2) the most obvious time of haze in Harbin is from November to December, and it is concluded that haze events are closely related to the large number of pollutants caused by the burning of straw around Harbin, and because the northwest monsoon climate affects the air quality, the transportation of fine particles caused by the burning of straw in winter in the surrounding areas of Harbin is the main cause of serious pollution in Harbin.

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