The present study attempts to explore and compare the seasonal variability in chemical composition and contributions of different sources of fine and coarse fractions of aerosols (PM2.5 and PM10) in ...Delhi, India from January 2013 to December 2016. The annual average concentrations of PM2.5 and PM10 were 131 ± 79 μg m−3 (range: 17–417 μg m−3) and 238 ± 106 μg m−3 (range: 34–537 μg m−3), respectively. PM2.5 and PM10 samples were chemically characterized to assess their chemical components i.e. organic carbon (OC), elemental carbon (EC), water soluble inorganic ionic components (WSICs) and heavy and trace elements and then used for estimation of enrichment factors (EFs) and applied positive matrix factorization (PMF5) model to evaluate their prominent sources on seasonal basis in Delhi. PMF identified eight major sources i.e. Secondary nitrate (SN), secondary sulphate (SS), vehicular emissions (VE), biomass burning (BB), soil dust (SD), fossil fuel combustion (FFC), sodium and magnesium salts (SMS) and industrial emissions (IE). Total carbon contributes ∼28% to the total PM2.5 concentration and 24% to the total PM10 concentration and followed the similar seasonality pattern. SN and SS followed opposite seasonal pattern, where SN was higher during colder seasons while SS was greater during warm seasons. The seasonal differences in VE contributions were not very striking as it prevails evidently most of year. Emissions from BB is one of the major sources in Delhi with larger contribution during winter and post monsoon seasons due to stable meteorological conditions and aggrandized biomass burning (agriculture residue burning in and around the regions; mainly Punjab and Haryana) and domestic heating during the season. Conditional Bivariate Probability Function (CBPF) plots revealed that the maximum concentrations of PM2.5 and PM10 were carried by north westerly winds (north-western Indo Gangetic Plains of India).
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•Simultaneous sampling of PM2.5 and PM10 was carried out for 4 years (2013–2016).•Seasonal variations in composition and sources of PM2.5 and PM10 are studied in Delhi.•Secondary inorganic aerosol accounts for 21% of PM10 and 27% of PM2.5 mass with contrasting seasonal variations.•Traffic emission contributes greatly to PM10 while biomass burning to PM2.5, both being maximum in winters.•Maximum concentrations of PM2.5 and PM10 were coming from North West direction of Delhi (CBPF plots).
The present work explores the temporal and seasonal variabilities in composition and contributions of different sources to fine and coarse fractions of particulate matter over Delhi.
Few studies have estimated historical exposures to PM10 at a national scale in China using satellite-based aerosol optical depth (AOD). Also, long-term trends have not been investigated.
In this ...study, daily concentrations of PM10 over China during the past 12 years were estimated with the most recent ground monitoring data, AOD, land use information, weather data and a machine learning approach.
Daily measurements of PM10 during 2014–2016 were collected from 1479 sites in China. Two types of Moderate Resolution Imaging Spectroradiometer (MODIS) AOD data, land use information, and weather data were downloaded and merged. A random forests model (non-parametric machine learning algorithms) and two traditional regression models were developed and their predictive abilities were compared. The best model was applied to estimate daily concentrations of PM10 across China during 2005–2016 at 0.1⁰ (≈10 km).
Cross-validation showed our random forests model explained 78% of daily variability of PM10 root mean squared prediction error (RMSE) = 31.5 μg/m3. When aggregated into monthly and annual averages, the models captured 82% (RMSE = 19.3 μg/m3) and 81% (RMSE = 14.4 μg/m3) of the variability. The random forests model showed much higher predictive ability and lower bias than the other two regression models. Based on the predictions of random forests model, around one-third of China experienced with PM10 pollution exceeding Grade Ⅱ National Ambient Air Quality Standard (>70 μg/m3) in China during the past 12 years. The highest levels of estimated PM10 were present in the Taklamakan Desert of Xinjiang and Beijing-Tianjin metropolitan region, while the lowest were observed in Tibet, Yunnan and Hainan. Overall, the PM10 level in China peaked in 2006 and 2007, and declined since 2008.
This is the first study to estimate historical PM10 pollution using satellite-based AOD data in China with random forests model. The results can be applied to investigate the long-term health effects of PM10 in China.
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•Random forests can be successfully used to predict levels of PM10 with AOD.•The random forests model explained 78% of daily variability of PM10.•One-third of China experienced with high PM10 pollution during the past 12 years.•The highest levels of estimated PM10 were present in the Taklamakan Desert.
Random forests can be successfully used to predict levels of PM10 with AOD. The random forests model explained 78% of daily variability of PM10.
In December 2019, a novel disease, coronavirus disease 19 (COVID-19), emerged in Wuhan, People’s Republic of China. COVID-19 is caused by a novel coronavirus (SARS-CoV-2) presumed to have jumped ...species from another mammal to humans. This virus has caused a rapidly spreading global pandemic. To date, over 300,000 cases of COVID-19 have been reported in England and over 40,000 patients have died. While progress has been achieved in managing this disease, the factors in addition to age that affect the severity and mortality of COVID-19 have not been clearly identified. Recent studies of COVID-19 in several countries identified links between air pollution and death rates. Here, we explored potential links between major fossil fuel-related air pollutants and SARS-CoV-2 mortality in England. We compared current SARS-CoV-2 cases and deaths from public databases to both regional and subregional air pollution data monitored at multiple sites across England. After controlling for population density, age and median income, we show positive relationships between air pollutant concentrations, particularly nitrogen oxides, and COVID-19 mortality and infectivity. Using detailed UK Biobank data, we further show that PM2.5 was a major contributor to COVID-19 cases in England, as an increase of 1 m3 in the long-term average of PM2.5 was associated with a 12% increase in COVID-19 cases. The relationship between air pollution and COVID-19 withstands variations in the temporal scale of assessments (single-year vs 5-year average) and remains significant after adjusting for socioeconomic, demographic and health-related variables. We conclude that a small increase in air pollution leads to a large increase in the COVID-19 infectivity and mortality rate in England. This study provides a framework to guide both health and emissions policies in countries affected by this pandemic.
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•Regional levels of NO2, NO and O3 correlate with number of COVID-19 deaths.•Sub-regional NOx levels are associated with increased COVID-19 deaths and cases.•Levels of PM and NOx map increased number of COVID-19 infections in the UK Biobank.
•Concentrations of PM and gaseous pollutants in 31 Chinese cities (from 286 sites) were analyzed.•Concentration levels of PMs were significantly different in various cities.•The correlations between ...PMs and NO2, SO2 were moderate.•The correlation between PMs and CO was instable and that between PMs and O3 was weak.
The variations of mass concentrations of PM2.5, PM10, SO2, NO2, CO, and O3 in 31 Chinese provincial capital cities were analyzed based on data from 286 monitoring sites obtained between March 22, 2013 and March 31, 2014. By comparing the pollutant concentrations over this length of time, the characteristics of the monthly variations of mass concentrations of air pollutants were determined. We used the Pearson correlation coefficient to establish the relationship between PM2.5, PM10, and the gas pollutants. The results revealed significant differences in the concentration levels of air pollutants and in the variations between the different cities. The Pearson correlation coefficients between PMs and NO2 and SO2 were either high or moderate (PM2.5 with NO2: r=0.256–0.688, mean r=0.498; PM10 with NO2: r=0.169–0.713, mean r=0.493; PM2.5 with SO2: r=0.232–0.693, mean r=0.449; PM10 with SO2: r=0.131–0.669, mean r=0.403). The correlation between PMs and CO was diverse (PM2.5: r=0.156–0.721, mean r=0.437; PM10: r=0.06–0.67, mean r=0.380). The correlation between PMs and O3 was either weak or uncorrelated (PM2.5: r=−0.35 to 0.089, mean r=−0.164; PM10: r=−0.279 to 0.078, mean r=−0.127), except in Haikou (PM2.5: r=0.500; PM10: r=0.509).
Exposure to particulate matter (PM10) can induce respiratory diseases that are closely related to bronchial hyperresponsiveness. However, the involved mechanism remains to be fully elucidated. This ...study aimed to demonstrate the effects of PM10 on the acetylcholine muscarinic 3 receptor (CHRM3) expression and the role of the ERK1/2 pathway in rat bronchial smooth muscle. A whole-body PM10 exposure system was used to stimulate bronchial hyperresponsiveness in rats for 2 and 4 months, accompanied by MEK1/2 inhibitor U0126 injection. The whole-body plethysmography system and myography were used to detect the pulmonary and bronchoconstrictor function, respectively. The mRNA and protein levels were determined by Western blotting, qPCR, and immunofluorescence. Enzyme-linked immunosorbent assay was used to detect the inflammatory cytokines. Compared with the filtered air group, 4 months of PM10 exposure significantly increased CHRM3-mediated pulmonary function and bronchial constriction, elevated CHRM3 mRNA and protein expression levels on bronchial smooth muscle, then induced bronchial hyperreactivity. Additionally, 4 months of PM10 exposure caused an increase in ERK1/2 phosphorylation and increased the secretion of inflammatory factors in bronchoalveolar lavage fluid. Treatment with the MEK1/2 inhibitor, U0126 inhibited the PM10 exposure-induced phosphorylation of the ERK1/2 pathway, thereby reducing the PM10 exposure-induced upregulation of CHRM3 in bronchial smooth muscle and CHRM3-mediated bronchoconstriction. U0126 could rescue PM10 exposure-induced pathological changes in the bronchus. In conclusion, PM10 exposure can induce bronchial hyperresponsiveness in rats by upregulating CHRM3, and the ERK1/2 pathway may be involved in this process. These findings could reveal a potential therapeutic target for air pollution induced respiratory diseases.
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•PM10 exposure can cause pulmonary dysfunction in rats.•PM10 can upregulate CHRM3, thereby induce bronchial hyperresponsiveness.•U0126 can rescue bronchial hyperresponsiveness caused by PM10-upregualted CHRM3.•U0126 can decrease the bronchial pathological of PM10 exposure rats.
In order to establish a regional database on natural radioactivity, a series of measurements of 713 atmospheric aerosol samples collected on filters over a two-year period (2018–2019) in center of ...Beijing, northeastern China have been performed to analyze 7Be and 210Pb activity concentrations. The mean activity concentrations of 7Be and 210Pb were found to be 7.10 ± 2.44 mBq m−3 and 2.93 ± 1.52 mBq m−3, respectively. Both the radionuclides exhibited strong seasonal variations, with maximum concentration of 7Be occurring in the spring and that of 210Pb in the winter. The concentration of both the radionuclides was minimum in the rainy summer. Higher 7Be concentration in the spring was mainly caused by the stratosphere to troposphere exchange. Higher 210Pb concentration during winter was maybe attributed to the combustion processes in heating systems and the ingression of continental air masses resulted from winds originating from northwest. The dependence of the activity concentrations of 7Be and 210Pb with meteorological parameters such as rainfall, temperature, and humidity was studied through linear correlation analysis. Statistically significant negative correlations were observed between 7Be and 210Pb activity concentrations with rainfall, respectively, which suggested that the removal mechanisms of these two radionuclides were similar. Lead-210 showed statistically significant correlations with temperature, humidity and PM10. A comparison of the data obtained in the present study for Beijing with the northern hemisphere literature values of 7Be and 210Pb in the atmospheric aerosols showed that the values were smaller than the ones observed in the present study. Overall, the study provides an improved understanding of the temporal variability and correlation of 7Be and 210Pb concentrations in the atmosphere in center of Beijing, northeastern China.
•Regional database on7Be and 210Pb activity concentrations in the atmospheric aerosols in center of Beijing was established.•Rainfall is suggested as the major removal means for 7Be and 210Pb from the atmosphere.•Causal factors for the temporal variability of7Be and 210Pb activity concentrations were identified.•Correlations of temperature, relative humidity and PM10 with 7Be and 210Pb were explored.•Correlation between 7Be and 210Pb activity concentrations was studied.
An extended study on the oxidative potential (OP) of PM10 particles collected from December 2014 to October 2015 at a peninsular site of the Central Mediterranean basin has been performed. PM10 ...particles have been selected to better account for all different aged/fresh particle types. Two acellular assays, i.e., the dithiothreitol (DTT) and ascorbic acid (AA) methods, were used to measure the OP of PM10 particles chemically speciated by more than 40 species.
DTT and AA assays provide close mean values of volume normalized OPV responses, with similar variability range, i.e., mean OPDTTV = 0.24 ± 0.12 nmolDTT min−1 m−3 and mean OPAAV = 0.29 ± 0.18 nmolAA min−1 m−3. Also mass normalized OPm responses are similar for both assays, with mean value close to 0.008 nmol min−1 μg−1.
The measured OPDTTV and OPAAV are correlated with several inorganic species, namely ions and metals, and with organic/elemental carbon. The discrimination of the data according seasonality, i.e., Autumn-Winter (AW, October–March) and Spring-Summer (SS, April–September) days, shows a clear seasonal trend of correlation coefficients. In AW, OPDTTV is strongly correlated with nss-K+ and nss-Ca2+, in addition to Ba, Cd, Ce, Cr, Cu, Fe, and Mn (traffic-related metals) and with EC, OC, and POC associated with the traffic exhaust source and/or with the combustion including biomass-burning source. Otherwise, OPDTTV of SS samples is correlated only with NH4+, Cu, EC, OC, and POC.
The OPAAV of AW samples is well correlated with Ba, Ce, Cr, Cu, Fe, Mn, nss-K+, EC, OC, and POC, which are related with traffic and/or combustion emissions. Conversely, in SS, OPAAV is mainly correlated with NH4+, nss-K+, nss-Mg2+, nss-Ca2+, nss-SO42−, Cu, Mn, P, Pb, and oxalate, that are species related to secondary aerosols and resuspended soil from vehicular traffic and/or transported Saharan dust.
These findings point the importance of both organic components and transition metals to PM oxidative properties, and also suggest that synergistic/antagonistic interactions and cross-correlations between the PM redox-active components are likely responsible for the seasonal variation of the AA and DTT assay response. The inter-correlation among all analysed species has been investigated to explain contrasting results and the negative correlations between OP values and some chemical species.
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•Oxidative potential is assessed for PM10 collected at a Central Mediterranean Site.•Two cell-free assays yield similar OPDTT and OPAA responses.•Association of OPAAV and OPDTTV with PM chemical components varies with seasons.•Metals and primary organic carbon are the main responsible for PM-induced OP.
Cell-free methods assessed the oxidative potential of particulate matter collected at a Central Mediterranean Site and the results were correlated with PM10 chemical composition.
Air pollution epidemiological studies increasingly rely on high-resolution exposure prediction models. However, to date, few models of this type exist for use in China.
We produced a national ...land-use regression model (LUR) to estimate monthly average PM2.5, PM10 and NO2 from 2014 to 2016 in China.
We developed a spatiotemporal semi-parametric model using generalized additive mixed models. A variety of predictor variables were included in model: time varying meteorological data, high resolution land cover data from Globaland30, satellite measures of aerosol optical depth, and Geographic Information System (GIS)-derived predictors. We assessed model performance with two cross-validation (CV) approaches, including hold-out CV, and 10-fold CV.
Over 22,000 monthly observations at 1382 monitoring locations were included to estimate the air pollution exposure. The time-varying spatial terms explained 87%, 71%, and 69% of variability with a hold-out cross-validated R2 of 0.85, 0.62, and 0.62 for PM2.5, PM10 and NO2 models, respectively. Models show that meteorological variables, population density, elevation, distance to road, and land cover types were important predictors for air pollution exposure.
we have developed a new nationwide model to estimate residence-level air pollution exposures, which can be used in studies of the chronic adverse effects of air pollution.
•We developed national scale spatiotemporal land use models to estimate monthly PM2.5, PM10 and NO2 concentrations in China.•For the PM2.5, PM10 and NO2 models, predictors explained 87%, 71%, and 69% of variability of the pollutant distributions.•Meteorological, AOD, and GIS-derived covariates were important predictors for air pollution exposure.
Exposure to air pollution is of great concern for public health although studies on the associations between exposure estimates and personal exposure are limited and somewhat inconsistent. The aim of ...this study was to quantify the associations between personal nitrogen oxides (NOx), ozone (O3) and particulate matter (PM10) exposure levels and ambient levels, and the impact of climate and time spent outdoors in two cities in Sweden. Subjects (n = 65) from two Swedish cities participated in the study. The study protocol included personal exposure measurements at three occasions, or waves. Personal exposure measurements were performed for NOx and O3 for 24 h and PM10 for 24 h, and the participants kept an activity diary. Stationary monitoring stations provided hourly data of NOx, O3 and PM, as well as data on air temperature and relative humidity. Data were analysed using mixed linear models with the subject-id as a random effect and stationary exposure and covariates as fixed effects. Personal exposure levels of NOx, O3 and PM10 were significantly associated with levels measured at air pollution monitoring stations. The associations persisted after adjusting for temperature, relative humidity, city and wave, but the modelled estimates were slightly attenuated from 2.4% (95% CI 1.8–2.9) to 2.0% (0.97–2.94%) for NOx, from 3.7% (95% CI 3.1–4.4) to 2.1% (95% CI 1.1–2.9%) for O3 and from 2.6% (95% 0.9–4.2%) to 1.3% (95% CI − 1.5–4.0) for PM10. After adding covariates, the degree of explanation offered by the model (coefficient of determination, or R2) did not change for NOx (0.64 to 0.63) but increased from 0.46 to 0.63 for O3, and from 0.38 to 0.43 for PM10. Personal exposure to NOx, O3 and PM has moderate to good association with levels measured at urban background sites. The results indicate that stationary measurements are valid as measure of exposure in environmental health risk assessments, especially if they can be refined using activity diaries and meteorological data. Approximately 50–70% of the variation of the personal exposure was explained by the stationary measurement, implying occurrence of misclassification in studies using more crude exposure metrics, potentially leading to underestimates of the effects of exposure to ambient air pollution.
Particulate matter (PM) exposure has been linked to adverse health effects by numerous studies. Therefore, governments have been heavily incentivising the market to switch to electric passenger cars ...in order to reduce air pollution. However, this literature review suggests that electric vehicles may not reduce levels of PM as much as expected, because of their relatively high weight. By analysing the existing literature on non-exhaust emissions of different vehicle categories, this review found that there is a positive relationship between weight and non-exhaust PM emission factors. In addition, electric vehicles (EVs) were found to be 24% heavier than equivalent internal combustion engine vehicles (ICEVs). As a result, total PM10 emissions from EVs were found to be equal to those of modern ICEVs. PM2.5 emissions were only 1–3% lower for EVs compared to modern ICEVs. Therefore, it could be concluded that the increased popularity of electric vehicles will likely not have a great effect on PM levels. Non-exhaust emissions already account for over 90% of PM10 and 85% of PM2.5 emissions from traffic. These proportions will continue to increase as exhaust standards improve and average vehicle weight increases. Future policy should consequently focus on setting standards for non-exhaust emissions and encouraging weight reduction of all vehicles to significantly reduce PM emissions from traffic.
•A positive relationship exists between vehicle weight and non-exhaust emissions.•Electric vehicles are 24% heavier than their conventional counterparts.•Electric vehicle PM emissions are comparable to those of conventional vehicles.•Non-exhaust sources account for 90% of PM10 and 85% of PM2.5 from traffic.•Future policy should focus on reducing vehicle weight.
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