Changes In Air Pollutant Concentrations Research Articles

Quantifying the impact of COVID-19 restrictions on air pollution in Ahvaz: a comparative dual-approach assessment of observed against baseline and forecasted criteria air pollutants.

In response to the COVID-19 pandemic, the Iranian government swiftly implemented immediate and decisive measures to control the spread of the infection. This study aims to demonstrate the impact of restriction measure on air pollution, also to highlight the potential variability in results that can arias from different methodological approach. A comprehensive dual-approach assessment was conducted to evaluate the effect of the lockdown measures on criteria air pollutants. Firstly, a traditional approach compared air quality during the pandemic period with baseline conditions from 2013 to 2019. Secondly, observed air pollution values during different periods with varying restrictions in 2020 were compared with expected values. This comprehensive analysis allows for a robust comparison and quantification of the impact of different lockdown measures in Ahvaz. The study revealed significant changes in air pollutant concentrations in Ahvaz during 2020, with variations observed across different pollutants. Notable reductions were observed in O3 levels, particularly in November (-54.44% compared to the baseline) and December (-63.58% compared to expected values). Decreases in CO levels were observed in multiple months, while substantial reductions in PM10 and PM2.5 were observed during various periods. Inconsistencies in the magnitudes and directions of changes were found when comparing baseline and forecasted values. The overall stringency index showed an inverse association with changes in O3, NO2, and CO, with international travel controls and restrictions on internal movement having significant impacts. This study provides valuable insights into the impact of COVID-19 lockdown measures on air pollution in Ahvaz, Iran, using a comprehensive dual-approach assessment. The findings highlight the effectiveness of these measures in reducing specific criteria air pollutants and emphasize the importance of implementing appropriate strategies for air quality management during similar public health emergencies.

Climatic Impact on the Air Pollutant Concentrations Emitted from Gas Stations in Fallujah, Al-Anbar, Western Iraq

The research investigates the influence of climatic elements like temperature, relative humidity, rainfall, evaporation, dust storms, wind direction, and speed on the air pollutant concentrations at gas stations in Fallujah city while examining their impact on humans and the environment. This was achieved by measuring concentrations of CO, NO2, and CH4, suspended particles (TSP, PM10), and Cu, and Pb. It was found that the impact increased in July compared to January, because of the influence of the measured climatic elements. Rising temperatures increase air pollutants' concentrations in the atmosphere, while winds spread them from emission sites to distant locations. Moreover, summer temperatures significantly impacted air pollutant concentrations at the study stations, particularly at Al-Rehab gas station, with the highest recorded temperatures of 48.4°C. Some gas stations also exceeded local environmental limits for TSP, PM10, CO, NO2, and Pb. The study revealed that summer temperatures with wind speeds of 2.1 m/s resulted in the highest air pollutants values, while winter temperatures with wind speeds of 1.9 m/s decreased these values. Higher temperatures increase chemical reactions and temperature inversions, leading to increased pollution concentrations and exacerbating air quality problems in the study area. Strong winds disperse pollutants, reducing concentration in the study area, while low wind speed increases concentrations and stagnation, resulting in poor air quality in the studied sites. The importance of the current study lies in evaluating pollutants in the Fallujah city, determining their causes, the extent of the impact, and contribution of natural and human sources in causing changes in air pollutant concentrations; caused by emission from the source. Moreover, the current study showed its impact on human’s health, and trying to find solutions to limit these effects or reduce their levels to ensure the safety of the environment and health.

Health Impacts of Fine Particulate Matter Shift Due to Urbanization in China.

Rapid urbanization and industrialization have resulted in diverse anthropogenic activities and emissions between urban and non-urban regions, leading to varying levels of exposure to air pollutants and associated health risks. However, endeavors to mitigate air pollution and health benefits have displayed considerable heterogeneity across different regions. Therefore, comprehending the changes in air pollutant concentrations and health impacts within an urbanization context is imperative for promoting environmental equity. This paper uses gross domestic product (GDP)- and population-weighted methods to distinguish anthropogenic emissions from urban and non-urban areas in China and quantified their contributions to fine particulate matter (PM2.5) using the Community Multiscale Air Quality (CMAQ) model in 2010 and 2019. Anthropogenic emissions from urban and non-urban (outside urban) regions decreased by 26 and 44% from 2010 to 2019, respectively, resulting in 31 and 28% reductions of PM2.5 in China. PM2.5-related premature mortality attributed to non-urban and urban anthropogenic emission decreases by 8%. Non-urban anthropogenic activities are the main contributor to PM2.5 (56% in 2010 and 2019) and its associated premature mortality (59%), which also predominantly affects non-urban premature mortality (37-42% in 2010-2019). Population changes increase the proportion of premature mortality in urban populations (7-19%) from 2010 to 2019. This study emphasizes the shift of affected populations due to urbanization and population changes.

Capturing Exposure Disparities with Chemical Transport Models: Evaluating the Suitability of Downscaling Using Land Use Regression.

High resolution exposure surfaces are essential to capture disparities in exposure to traffic-related air pollution in urban areas. In this study, we develop an approach to downscale Chemical Transport Model (CTM) simulations to a hyperlocal level (∼100m) in the Greater Toronto Area (GTA) under three scenarios where emissions from cars, trucks and buses are zeroed out, thus capturing the burden of each transportation mode. This proposed approach statistically fuses CTMs with Land-Use Regression using machine learning techniques. With this proposed downscaling approach, changes in air pollutant concentrations under different scenarios are appropriately captured by downscaling factors that are trained to reflect the spatial distribution of emission reductions. Our validation analysis shows that high-resolution models resulted in better performance than coarse models when compared with observations at reference stations. We used this downscaling approach to assess disparities in exposure to nitrogen dioxide (NO2) for populations composed of renters, low-income households, recent immigrants, and visible minorities. Individuals in all four categories were disproportionately exposed to the burden of cars, trucks, and buses. We conducted this analysis at spatial resolutions of 12, 4, 1 km, and 100 m and observed that disparities were significantly underestimated when using coarse spatial resolutions. This reinforces the need for high-spatial resolution exposure surfaces for environmental justice analyses.

Stochastic dynamics of an SIR model for respiratory diseases coupled air pollutant concentration changes

Industrial development has made air pollution increasingly severe, and many respiratory diseases are closely related to air quality in terms of infection and transmission. In this work, we used the classic stochastic susceptible–infectious–recovered (SIR) model to reflect the spread of respiratory disease, coupled with the diffusion process of air pollutants to the infectious disease model, and we investigated the impact of various environmental noises on the process of disease transmission and air pollutant diffusion. The value of this study lies in two aspects. Mathematically, we define threshold R1s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\mathcal{R}_{1}^{s}$\\end{document} for extinction and threshold R2s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\mathcal{R}_{2}^{s}$\\end{document} for persistence of the disease in the stochastic model (R2s<R1s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\mathcal{R}_{2}^{s}<\\mathcal{R}_{1}^{s}$\\end{document}) when the parameters are constant, and we show that (i) when R1s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\mathcal{R}_{1}^{s}$\\end{document} is less than 1, the disease will go to stochastic extinction; (ii) when R2s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\mathcal{R}_{2}^{s}$\\end{document} is larger than 1, the disease will persist almost surely and the model has a unique ergodic stationary distribution; (iii) when R1s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\mathcal{R}_{1}^{s}$\\end{document} is larger than 1 and R2s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\mathcal{R}_{2}^{s}$\\end{document} is less than 1, the extinction of the disease has randomness, which is demonstrated through numerical experiments. In addition, we derive the exact expression of the probability density function of the stationary distribution by solving the corresponding Fokker–Planck equation under the condition of disease persistence and analyze the effects of random noises on stationary distribution characteristics and the disease extinction. Epidemiologically, the change of the concentration of air pollutants affects the conditions for disease extinction and persistence. The increase in the inflow of pollutants and the increase in the clearance rate have negative and positive impacts on the spread of diseases, respectively. We found that an increase in random noise intensity will increase the variance, reduce the kurtosis of distribution, which is not conducive to predicting and controlling the development status of the disease; however, large random noise intensity can also increase the probability of disease extinction and accelerates disease extinction. We further investigate the dynamic of the stochastic model, assuming that the inflow rate switches between two levels by numerical experiments. The results show that the random noise has a significant impact on disease extinction. The data fitting of the switching model shows that the model can effectively depict the relationship and changes in trends between air pollution and diseases.

Societal Co-benefits of Zero-Emission Vehicles in the Freight Industry.

This study was set in the Greater Toronto and Hamilton Area (GTHA), where commercial vehicle movements were assigned across the road network. Implications for greenhouse gas (GHG) emissions, air quality, and health were examined through an environmental justice lens. Electrification of light-, medium-, and heavy-duty trucks was assessed to identify scenarios associated with the highest benefits for the most disadvantaged communities. Using spatially and temporally resolved commercial vehicle movements and a chemical transport model, changes in air pollutant concentrations under electric truck scenarios were estimated at 1-km2 resolution. Heavy-duty truck electrification reduces ambient black carbon and nitrogen dioxide on average by 10 and 14%, respectively, and GHG emissions by 10.5%. It achieves the highest reduction in premature mortality attributable to fine particulate matter chronic exposure (around 200 cases per year) compared with light- and medium-duty electrification (less than 150 cases each). The burden of all traffic in the GTHA was estimated to be around 600 cases per year. The benefits of electrification accrue primarily in neighborhoods with a high social disadvantage, measured by the Ontario Marginalization Indices, narrowing the disparity of exposure to traffic-related air pollution. Benefits related to heavy-duty truck electrification reflect the adverse impacts of diesel-fueled freight and highlight the co-benefits achieved by electrifying this sector.

Analysing ML Models for AQI Prediction using Meteorological Data

Air is essential for every living being to survive. The modernization and industrialization have led to the rise of pollutants which affect the quality of air directly. Predicting changes in air pollutant concentrations due to human and nature drivers is critical and challenging, particularly in areas with scant data inputs and high variability of parameters. The air quality index (AQI) is an index for reporting air quality on a daily basis. It is a measure of how air pollution affects one's health within a short time period. The purpose of the AQI is to help people know how the local air quality impacts their health. The Environmental Protection Agency (EPA) calculates the AQI for five major air pollutants, for which national air quality standards have been established to safeguard public health. Every country has its own method of calculating the Air Quality Index and the rules for Air Quality Buckets. According to the Central pollution control board, Ministry of Environment, Forests, and climate change of India, the AQI is divided into 6 buckets with the minimum and maximum AQI being 0 and 500 respectively. The buckets are Good, Satisfactory, Moderate, Poor, Very Poor and Severe.

Biological effects of particulate matter samples during the COVID-19 pandemic: a comparison with the pre-lockdown period in Northwest Italy

In 2020, during the COVID-19 pandemic, containment measures were applied inducing potential changes in air pollutant concentrations and thus in air toxicity. This study evaluates the role of restrictions on biological effects of particulate matter (PM) in different Northwest Italy sites: urban background, urban traffic, rural, and incinerator. Daily PM samples collected in 2020 were pooled according to restrictions: January/February (no restrictions), March and April (first lockdown), May/June and July/August/September (low restrictions), October/November/December (second lockdown). The 2019 samples (pre-pandemic period) were pooled as 2020 for comparison. Pools were extracted with organic solvents and extracts were tested to assess cytotoxicity (WST-1 assay) and genotoxicity (comet assay) on BEAS-2B cells, mutagenicity (Ames test) on TA98 and TA100 Salmonella typhimurium strains, and estrogenic activity (gene reporter assay) on MELN cells. Pollutant concentrations were also analyzed (PM10, PM2.5, polycyclic aromatic hydrocarbons). No difference was observed for PM and polycyclic aromatic hydrocarbon concentrations between 2020 and 2019. During lockdown months (2020), PM cytotoxicity/genotoxicity was significantly lower in some sites than during 2019, while considering PM mutagenicity/estrogenic activity some differences were detected but without statistical significance. PM extract effects decreased in some sites during 2020; this may be due to lockdowns that reduced/modified pollutant emissions and may be related also to complex PM origin/formation and to meteorological conditions. In conclusion, the study confirms that PM biological effects cannot be assessed considering only the PM concentration and suggests to include a battery of bioassay for air quality monitoring in order to protect human health from air pollution effects.Graphical

Impact of traffic on air pollution in a mid-sized urban city during COVID-19 lockdowns.

In this study, we evaluated the changes in air pollutant concentrations around Milwaukee, WI, during and after lockdown due to the COVID-19 pandemic for a period of 126days. Measurements of particulate matter (PM1, PM2.5, and PM10), NH3, H2S, and O3 + NO2, were made on a 74-km route of arterial and highway roads from April to August 2020 using a Sniffer 4D sensor mounted to a vehicle. Traffic volume during measurement periods were estimated from smartphone-based traffic data. From lockdown (March 24, 2020-June 11, 2020) to post-lockdown (June 12, 2020-August 26, 2020) median traffic volume increased roughly 30-84%, depending upon the road type. In addition, increases in mean concentrations of NH3 (277%), PM (220-307%), and O3 + NO2 (28%) were also observed. For both traffic and air pollutants, abrupt changes in the data were observed mid-June, shortly after lockdown measures were lifted in Milwaukee County. Indeed, traffic was able to explain up to 57% of PM, 47% of NH3, and 42% of O3 + NO2 variance in pollutant concentrations on arterial and highway road segments. Two arterial roads that did not have statistically significant changes in traffic patterns during the lockdown exhibited no statistically significant trends between traffic and air quality parameters. This study demonstrated that COVID-19 lockdowns in Milwaukee, WI, caused significant decreases in traffic, which in turn had a direct impact on air pollutants. It also highlights the need for traffic volume and air quality data at relevant spatial and temporal scales for accurately assessing source apportionment of combustion-based air pollutants, which cannot be captured with typical ground-based sensor systems.

Comprehensive Evaluation of Environmental Air Quality Based on the Entropy Weights and Concentration Variation Trends of Pollutants

The comprehensive index method has difficulties in evaluating the influence of air pollutant concentration changes on ambient air quality. Thus, a comprehensive evaluation method based on pollutant entropy weights and trend-regulating factors is proposed. According to the information entropy rates of 6 pollutants, the single entropy weight index is proposed by integrating it with the single-quality index, which reflects pollutant variations in evaluation periods. The Spearman’s rank correlation coefficient between the pollutant and Air Quality Index (AQI) is defined as the trend-regulating factor, which indicates the correlations between pollutants and improvements or retrogressions in ambient air quality. The covariance is used to determine the variation trend of ambient air quality, which decides the positive or negative of trend-regulating factor. This method is used to study the ambient air quality rates in 10 cities of Shaanxi Province from 2017 to 2022. The trends of air quality improvements vary among the central, northern, and southern cities. The central cities have more spaces for air quality improvements in terms of PM2.5 and O3. Although prevention efforts have reduced the impacts of pollutants, PM2.5 is still the key factor affecting improvements in ambient air quality in most cities in winter. Additionally, the O3 pollution in summer was not controlled effectively. The contribution to air pollution of O3 increased, on the contrary with the improvement in air quality. The coordinated control of PM2.5 and O3 is still an important method of ambient air quality improvement.

Air Quality and Traffic Trends in Cincinnati, Ohio during the COVID-19 Pandemic

During 2020, the stay-at-home order mandated in response to the COVID-19 pandemic led to significant changes in traffic volumes in Cincinnati, OH. Air pollutant concentrations (PM2.5, black carbon (BC), carbon monoxide (CO), nitrogen dioxide (NO2), nitrogen dioxide (NOX), and ozone (O3)) monitored at two ground monitoring sites in the city of Cincinnati were analyzed intra-annually in 2020 to quantify if the stay-at-home order impacted air quality. Interannual analyses were also conducted to evaluate differences in 2020 data versus historical years (2016–2019). Traffic volume data were also analyzed, where it was observed that, compared to pre-pandemic 2020, total traffic counts decreased by up to 26.41% during Ohio’s stay-at-home order, while heavy-duty vehicle traffic increased by up to 26.95% during the latter half of 2020. Statistical analysis indicated nonuniform changes in air pollutant concentrations at both sites throughout 2020. During the lockdown period at the central monitoring site, PM2.5 increased by 9%, while NO2 decreased by 30% compared to pre-pandemic concentrations in 2020. For BC and CO, there were no significant changes.

Time-Series Monitoring of Dust-Proof Nets Covering Urban Construction Waste by Multispectral Images in Zhengzhou, China

Rapid urbanization has produced a huge amount of construction waste. The operations and consequences of construction and demolition can lead to windblown dust problems, profoundly affecting the living environment of residents. Fortunately, dust-proof nets have been used in construction sites to reduce and prevent pollution by fine particles such as dust, so it is important to monitor and evaluate their efficacy. In this study, Earth observation techniques were used for the extraction and monitoring of solid waste and dust-proof nets. In order to fully perceive the validity and necessity of dust-proof nets for urban air health, we conducted a case study in Zhengzhou, China. We explored the potential of multispectral remote sensing available for monitoring urban construction waste and proposed a multi-layer classification method to identify construction waste and dust-proof nets based on Landsat-8 OLI and Sentinel-2 MSI data, with an average identification accuracy and Kappa coefficient of 96.27% and 0.93 for construction waste in the study area from 2015 to 2020, respectively. In addition, our study revealed the driving factors and impact of temporal variations in regional construction waste areas and dust-proof nets coverage. The results indicate the classification can track municipal solid waste management and changes in air pollutant concentrations and is useful for achieving SDG 11.6 (reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management). This study has the potential to monitor construction waste and dust-proof nets, paving the way for better urban environmental governance and surveillance actions in the future, especially involving big data.

Modeling the impacts of land use/land cover change on meteorology and air quality during 2000–2018 in the Yangtze River Delta region, China

The land use/land cover (LULC) change in the fast-developing city clusters of China exhibits impacts on both the meteorology and air quality. However, this effect, especially in the Yangtze River Delta (YRD), has not been well quantified. In this study, the LULC data are extracted from Landsat satellite imageries for year 2000 and 2018 for the YRD region. The Weather Research and Forecasting with Chemistry (WRF/Chem) model is applied to investigate the impact of historical LULC change on regional meteorology and air pollution over the YRD region during the past two decades. Two simulation scenarios are performed with two sets of LULC data to represent the pre-urbanization (LULC of year 2000) and the most recent urban pattern (LULC of year 2018). Results indicate that rapid urbanization leads to an increase of monthly mean 2-m temperature by 0.4–2.1 °C but decrease of the 10-m wind speed by 0.5–1.3 m/s in urban areas; the maximum increase of daytime planetary boundary layer height (PBLH) in July and November is 289 and 132 m, respectively. Affected by favorable changes in the meteorological conditions due to LULC change, the PM2.5 concentrations in most urban areas show a decreasing trend, especially during the nighttime in summer. On the contrary, surface ozone (O3) concentration in urban areas has increased by 7.2–9.8 ppb in summer and 1.9–2.1 ppb in winter. Changes in O3 concentration are inversely proportional to changes in NOx and the spatial distribution of PM2.5. Areas with higher O3 concentration are consistent with areas of higher temperature and lower wind speed. Our findings reveal that LULC changes during the past years bring observable changes in air pollutant concentrations, which should not be neglected in the YRD region regarding air quality trends as well as policy evaluations under the warming threat.

The Multi-Time Scale Changes in Air Pollutant Concentrations and Its Mechanism before and during the COVID-19 Periods: A Case Study from Guiyang, Guizhou Province

The lockdown during the coronavirus disease 2019 (COVID-19) pandemic provides a scarce opportunity to assess the efficiency of air pollution mitigation. Herein, the monitoring data of air pollutants were thoroughly analyzed together with meteorological parameters to explore the impact of human activity on the multi-time scale changes of air pollutant concentrations in Guiyang city, located in Southwest China. The results show that the COVID-19 lockdown had different effects on the criteria air pollutants, i.e., PM2.5 (diameter ≤ 2.5 μm), PM10 (diameter ≤ 10 μm), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) concentrations. The lockdown caused a significant drop in NO2 concentration. During the first-level lockdown period, the NO2 concentration declined sharply by 8.41 μg·m−3 (45.68%). The decrease in NO concentration caused the “titration effect” to weaken, leading to a sharp increase in O3 concentration. Although human activities resumed partially and the “titration effect” enhanced certainly during the second-level lockdown period, the meteorological conditions became more conducive to the formation of O3 by photochemical reactions. Atmosphere oxidation was enhanced to promote the generation of secondary aerosols through gas–particle transitions, thus compensating for the reduced primary emission of PM2.5. The implication of this study is that the appropriate air pollution control policies must be initiated to suppress the secondary generation of both PM2.5 and O3.

Investigation of the impacts of urban vegetation loss on the ecosystem service of air pollution mitigation in Karaj metropolis, Iran.

The present study aims to investigate the relationship between reduced air pollution and ecosystem services in Karaj metropolis, Iran. To the end, the trends in the concentrations of O3, NO2, CO, SO2, PM10, and PM2.5 as the main atmospheric pollutants of Karaj were studied. Five time series models of autoregressive (AR), moving average (MA), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA) were used to predict changes in air pollutant concentrations. Air pollution zoning is conducted via ArcGIS10.3 by using spline tension interpolation method. Then, normalized difference vegetation index (NDVI) was obtained from Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images to analyze vegetation dynamics as an index of ecosystem functioning. NDVI thresholds were selected to present guidelines for qualitative and quantitative changes in green cover and were divided into five different categories. Based on the results, AR (1) and ARIMA (1,2,1) were recognized as appropriate models for predicting the concentration of air pollutants in the study area. A decrease in very dense vegetation coverage and increase in poor vegetation areas, followed by an increase in air pollution, revealed that the loss of urban green coverage and decreased ecosystem services were positively related. Furthermore, the expansion of urban lands toward the north and the west from the baseline to future condition led to great changes in the land cover and losses in vegetation along these axes, which finally resulted in increased air pollution in these areas. Thus, the results of this study can be directly used in decision-making in the area of air pollution.

Modelling public health improvements as a result of air pollution control policies in the UK over four decades—1970 to 2010

In much of the industrialised world, policy interventions to address the challenges of wide-spread air pollution as resulting from development and economic progress in the 2nd half of the 20th century have overall led to reductions in air pollution levels and related health effects since the 1970s. While overall improvements towards reducing health effects from ambient air pollution are recorded, comprehensive and consistent assessments of the long-term impact of policy interventions are still scarce. In this paper, we conduct a model assessment over a 40 year period of air pollution in the UK. In order to correct for the short and longer term variability of meteorological factors contributing to trends in ambient concentrations of priority air pollutants (nitrogen dioxide, sulphur dioxide, fine particulate matter and ozone), we use a fixed meteorological year for all model simulations. Hence, the modelled changes in air pollutant concentrations and related health effects are solely a function of the changes in emissions since 1970. These changes in emissions are primarily driven by policy interventions, ranging from phasing out of specific fuels or substances, to regulating the use of chemicals and driving the development of cleaner, more efficient technologies. Over the 40 year period, UK attributable mortality due to exposure to PM2.5 and NO2 have declined by 56% and 44% respectively, while ozone attributable respiratory mortality increased by 17% over the same period (however, with a slight decrease by 14% between 2000 and 2010).

Impact of winter droughts on air pollution over Southwest China

The meteorological causes of winter droughts over Southwest China (SWC) have been widely investigated in recent years; however, little information is available on the impact of these droughts on air pollution. This study (1) characterized and quantified the impact of winter droughts on air pollution over SWC and (2) investigated the atmospheric teleconnections associated with the winter droughts in this region using air pollution monitoring data, routinely observed meteorological data, and National Centers for Environmental Prediction/National Center for Atmospheric Research and ERA-Interim reanalysis data. The main results are as follows: (1) A surface high pressure system together with a weak descent in the middle troposphere was the main cause of the SWC drought in December 2017. (2) It has been found that precipitation, the number of precipitation days and the atmospheric boundary layer height all decreased during the droughts, resulting in unfavorable conditions for the dispersion of air pollutants. (3) The concentrations of PM2.5 and PM10 were much higher during dry periods than those during non-dry periods over SWC, especially in the Sichuan Basin of the SWC. (4) WRF-Chem simulations reproduced the observed changes in air pollutant concentrations between dry and non-dry conditions. (5) Atmospheric teleconnections associated with the winter droughts in SWC were negative phases of the conventional Eurasian teleconnection, Eastern Atlantic/Western Russia pattern, Arctic Oscillation and North Atlantic Oscillation, and a La Niña event. Overall, this study provides scientific support for the long-term potential and accurate short-term predictions of air pollution in SWC.

Hourly Profiles of Air Pollution Variation in Selected Cities, Towns and Villages in Poland

Concentration of air pollution in urbanized and agricultural areas is related to the activity of various economy sectors (the so-called SNAP categories). Therefore, the change in the emission of pollutants by an anthropogenic source should result in a change in the air pollution level in the selected area. To better understand the nature of changes in air pollution concentration in urban and agricultural areas, an analysis of data of five-year (2012-2016) air quality measurements, carried out at selected automatic air quality monitoring stations in Poland, Europe. The data came from stations located in 5 regions (Lower Silesia, Greater Poland, Lodz, Masovia and Lublin) in central Poland. The average hourly concentrations in selected areas in cities, towns and villages were compared with the hourly emission factors of power generation sector (SNAP1), residential and commercial combustion sector (SNAP2) and road transport sector (SNAP7). The hourly profiles of air pollution level were expressed by means of the “imission factor”, as analogous to the hourly profiles of the “emission factor” that is being used in the LOTOS-EUROS and the CHIMERE chemistry-transport models.

HTAP2 multi-model estimates of premature human mortality due to intercontinental transport of air pollution and emission sectors.

Ambient air pollution from ozone and fine particulate matter is associated with premature mortality. As emissions from one continent influence air quality over others, changes in emissions can also influence human health on other continents. We estimate global air pollution-related premature mortality from exposure to PM2.5 and ozone, and the avoided deaths from 20% anthropogenic emission reductions from six source regions, North America (NAM), Europe (EUR), South Asia (SAS), East Asia (EAS), Russia/Belarus/Ukraine (RBU) and the Middle East (MDE), three global emission sectors, Power and Industry (PIN), Ground Transportation (TRN) and Residential (RES) and one global domain (GLO), using an ensemble of global chemical transport model simulations coordinated by the second phase of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP2), and epidemiologically-derived concentration-response functions. We build on results from previous studies of the TF-HTAP by using improved atmospheric models driven by new estimates of 2010 anthropogenic emissions (excluding methane), with more source and receptor regions, new consideration of source sector impacts, and new epidemiological mortality functions. We estimate 290,000 (95% CI: 30,000, 600,000) premature O3-related deaths and 2.8 million (0.5 million, 4.6 million) PM2.5-related premature deaths globally for the baseline year 2010. While 20% emission reductions from one region generally lead to more avoided deaths within the source region than outside, reducing emissions from MDE and RBU can avoid more O3-related deaths outside of these regions than within, and reducing MDE emissions also avoids more PM2.5-related deaths outside of MDE than within. Our findings that most avoided O3-related deaths from emission reductions in NAM and EUR occur outside of those regions contrast with those of previous studies, while estimates of PM2.5-related deaths from NAM, EUR, SAS and EAS emission reductions agree well. In addition, EUR, MDE and RBU have more avoided O3-related deaths from reducing foreign emissions than from domestic reductions. For six regional emission reductions, the total avoided extra-regional mortality is estimated as 6,000 (-3,400, 15,500) deaths/year and 25,100 (8,200, 35,800) deaths/year through changes in O3 and PM2.5, respectively. Interregional transport of air pollutants leads to more deaths through changes in PM2.5 than in O3, even though O3 is transported more on interregional scales, since PM2.5 has a stronger influence on mortality. For NAM and EUR, our estimates of avoided mortality from regional and extra-regional emission reductions are comparable to those estimated by regional models for these same experiments. In sectoral emission reductions, TRN emissions account for the greatest fraction (26-53% of global emission reduction) of O3-related premature deaths in most regions, in agreement with previous studies, except for EAS (58%) and RBU (38%) where PIN emissions dominate. In contrast, PIN emission reductions have the greatest fraction (38-78% of global emission reduction) of PM2.5-related deaths in most regions, except for SAS (45%) where RES emission dominates, which differs with previous studies in which RES emissions dominate global health impacts. The spread of air pollutant concentration changes across models contributes most to the overall uncertainty in estimated avoided deaths, highlighting the uncertainty in results based on a single model. Despite uncertainties, the health benefits of reduced intercontinental air pollution transport suggest that international cooperation may be desirable to mitigate pollution transported over long distances.

Atmospheric mercury species measurements across the Western Mediterranean region: Behaviour and variability during a 2015 research cruise campaign

In the framework of the ongoing MEDOCEANOR measurements program, an oceanographic cruise campaign was carried out during summer 2015 in the Western sector of Mediterranean Sea basin, on-board the research vessel ”Minerva Uno” of the Italian National Research Council (CNR). The overall goal was to investigate the dynamic patterns of mercury in the Marine Boundary Layer (MBL) and the main factors affecting mercury behaviour at both coastal and offshore locations. The mean concentrations of the recorded Hg species were 1.6 ± 0.5 ngm−3, 11.8 ± 15.0 pgm−3, and 2.4 ± 1.1 pgm−3, respectively for GEM, GOM, and PBM. Moreover, during the measurement period typical fair-weather conditions of the Mediterranean summer were encountered with high levels of solar radiation and temperature that favoured photochemical reactions. Atmospheric pollutants such as ozone, sulphur oxides and nitrogen oxides and other meteorological parameters were in addition recorded and jointly discussed with selected mercury events in terms of their spatio-temporal variations. Changes in air pollutant concentrations were also argued in the light of their likely influencing sources, among which, anthropogenic activities, such as the mercury cell chlor-alkali complex in Tuscany, Italy, and natural influence, like volcanic ashes, detected around the Aeolian area and the in-situ production of reactive gaseous mercury within the Marine Boundary Layer.