Annual Average PM2 Research Articles

Long-term exposure to ambient PM2·5, active commuting, and farming activity and cardiovascular disease risk in adults in China: a prospective cohort study

Increased physical activity is associated with a reduced risk of cardiovascular disease, but outdoor physical activity can be accompanied by increased inhalation of fine particulate matter (PM2·5). The extent to which long-term exposure to PM2·5 can offset the cardiovascular benefits of physical activity is unknown. We aimed to evaluate whether the associations between active commuting or farming activity and incident risks of cerebrovascular disease and ischaemic heart disease were consistent between populations with different ambient PM2·5 exposures. We did a prospective cohort study using data from people aged 30-79 years without cardiovascular disease at baseline from the China Kadoorie Biobank (CKB). Active commuting and farming activity were assessed at baseline using questionnaires. A high-resolution (1 × 1 km) satellite-based model was used to estimate annual average PM2·5 exposure during the study period. Participants were stratified according to PM2·5 exposure (54 μg/m3 or greater vs less than 54 μg/m3). Hazard ratios (HRs) and 95% CIs for incident cerebrovascular disease and ischaemic heart disease by active commuting and farming activity were estimated using Cox proportional hazard models. Effect modifications by PM2·5 exposure were tested by likelihood ratio tests. Analyses were restricted to the period from Jan 1, 2005, to Dec 31, 2017. Between June 25, 2004, and July 15, 2008, 512 725 people were enrolled in the CKB cohort. 322 399 eligible participants completed the baseline survey and were included in the analysis of active commuting (118 274 non-farmers and 204 125 farmers). Among 204 125 farmers, 2985 reported no farming time and 201 140 were included in the farming activity analysis. During a median follow-up of 11 years, 39 514 cerebrovascular disease cases and 22 313 ischaemic heart disease cases were newly identified. Among non-farmers with exposure to annual average PM2·5 concentrations of less than 54 μg/m3, increased active commuting was associated with lower risks of cerebrovascular disease (highest active commuting vs lowest active commuting HR 0·70, 95% CI 0·65-0·76) and ischaemic heart disease (0·60, 0·54-0·66). However, among non-farmers with exposure to annual average PM2·5 concentrations of 54 μg/m3 or greater, there was no association between active commuting and cerebrovascular disease or ischaemic heart disease. Among farmers with exposure to annual average PM2·5 concentrations of less than 54 μg/m3, increased active commuting (highest active commuting vs lowest active commuting HR 0·77, 95% CI 0·63-0·93) and increased farming activity (highest activity vs lowest activity HR 0·85, 95% CI 0·79-0·92) were both associated with a lower cerebrovascular disease risk. However, among farmers with exposure to annual average PM2·5 concentrations of 54 μg/m3 or greater, increases in active commuting (highest active commuting vs lowest active commuting HR 1·12, 95% CI 1·05-1·19) and farming activity (highest activity vs lowest activity HR 1·18, 95% CI 1·09-1·28) were associated with an elevated cerebrovascular disease risk. The above associations differed significantly between PM2·5 strata (all interaction p values <0·0001). For participants with long-term exposure to higher ambient PM2·5 concentrations, the cardiovascular benefits of active commuting and farming activity were significantly attenuated. Higher levels of active commuting and farming activity even increased the cerebrovascular disease risk among farmers with exposure to annual average PM2·5 concentrations of 54 μg/m3 or greater. National Natural Science Foundation of China, National Key Research and Development Program of China, Kadoorie Charitable Foundation, UK Wellcome Trust.

The spatial and temporal variation of fine particulate matter pollution in Ethiopia: Data from the Atmospheric Composition Analysis Group (1998-2019).

Evidence suggests ambient fine particulate matter (PM2.5) is a risk factor for cardiovascular diseases, lung cancer morbidity and mortality, and all-cause mortality. Countries that implement strong policies are able to reduce ambient PM2.5 concentration. In Ethiopia, however, PM2.5 monitoring stations, laboratory technicians, and equipment are staggeringly limited. In this study, the spatial and temporal variation of PM2.5 in Ethiopia was assessed. Satellite-based PM2.5 estimates, from the year 1998 to 2019, by Atmospheric Composition Analysis Group (ACAG) at a spatial resolution of 0.01° X 0.01° was used. The annual mean PM2.5 concentration for all administrative regions and zones in Ethiopia was extracted. The average mean from the twenty-two years was also calculated. The trend of PM2.5 concentration was graphed and quantitatively calculated using the Mann-Kendall test. The slope of the change over time was estimated using the Theil-Sen slope. At the zonal administration level, for the average annual mean, spatial dependency using univariate Global Moran's I and clustering and outlier tests using Anselin Local Moran's were performed. The country's average annual mean PM2.5 concentration was 17 μgm-3. The Afar region had the highest concentration, 27.9 μgm-3. The Mann-Kendall S was positive and significant at p<0.001. The spatial distribution of satellite-based ambient PM2.5 concentration was non-random. Significant highest value clustering of ambient total PM2.5 concentration exists in the Afar, Eastern Tigray, and Eastern and Southeastern Amhara while the significant lowest value dispersing was observed in the Southern Oromia and Somali region. At the national and regional levels, the annual mean ambient PM2.5 concentration is beyond the World Health Organization (WHO)-recommended level. The ambient PM2.5 concentration distribution is spatially dependent and significantly clustered in space. Installation of additional ground-based PM2.5 monitoring devices, particularly in regions where PM2.5 concentration is higher, is recommended. Validating satellite-based PM2.5 data with ground-based measurements in the country is also advised.

A cross-sectional analysis of ambient fine particulate matter (PM2.5) exposure and haemoglobin levels in children aged under 5 years living in 36 countries

Low haemoglobin (Hb) concentrations and anaemia in children have adverse effects on development and functioning, some of which may have consequences in later life. Exposure to ambient air pollution is reported to be associated with anaemia, but there is little evidence specific to low- and middle-income countries (LMICs), where childhood anaemia prevalence is greatest. We aimed to determine if long-term ambient fine particulate matter (≤2.5 μm in aerodynamic diameter [PM2.5]) exposure was associated with Hb levels and the prevalence of anaemia in children aged <5 years living in 36 LMICs. We used Demographic and Health Survey data, collected between 2010 and 2019, which included blood Hb measurements. Satellite-derived estimates of annual average PM2.5 was the main exposure variable, which was linked to children's area of residence. Anaemia was defined according to standard World Health Organization guidelines (Hb < 11 g/dL). The association of PM2.5 with Hb levels and anaemia prevalence was examined using multivariable linear and logistic regression models, respectively. We examined whether the effects of ambient PM2.5 were modified by a child's sex and age, household wealth index, and urban/rural place of residence. Models were adjusted for relevant covariates, including other outdoor pollutants and household cooking fuel. The study included 154,443 children, of which 89,904 (58.2%) were anaemic. The country-level prevalence of anaemia ranged from 15.8% to 87.9%. Mean PM2.5 exposure was 33.0 (±21.6) μg/m3. The adjusted model showed that a 10 μg/m3 increase in annual PM2.5 concentration was associated with greater odds of anaemia (OR = 1.098 95% CI: 1.087, 1.109). The same increase in PM2.5 was associated with a decrease in average Hb levels of 0.075 g/dL (95% CI: 0.081, 0.068). There was evidence of effect modification by household wealth index and place of residence, with greater adverse effects in children from lower wealth quintiles and children in rural areas. Exposure to annual PM2.5 was cross-sectionally associated with decreased blood Hb levels, and greater risk of anaemia, in children aged <5 years living in 36 LMICs.

PM2.5 and Dementia in a Low Exposure Setting: The Influence of Odor Identification Ability and APOE.

Growing evidence show that long term exposure to air pollution increases the risk of dementia. The aim of this study was to investigate associations between PM2.5 exposure and dementia in a low exposure area, and to investigate the role of olfaction and the APOE ɛ4 allele in these associations. Data were drawn from the Betula project, a longitudinal study on aging, memory, and dementia in Sweden. Odor identification ability was assessed using the Scandinavian Odor Identification Test (SOIT). Annual mean PM2.5 concentrations were obtained from a dispersion-model and matched at the participants' residential address. Proportional hazard regression was used to calculate hazard ratios. Of 1,846 participants, 348 developed dementia during the 21-year follow-up period. The average annual mean PM2.5 exposure at baseline was 6.77μg/m3, which is 1.77μg/m3 above the WHO definition of clean air. In a fully adjusted model (adjusted for age, sex, APOE, SOIT, cardiovascular diseases and risk factors, and education) each 1μg/m3 difference in annual mean PM2.5-concentration was associated with a hazard ratio of 1.23 for dementia (95% CI: 1.01-1.50). Analyses stratified by APOE status (ɛ4 carriers versus non-carriers), and odor identification ability (high versus low), showed associations only for ɛ4 carriers, and for low performance on odor identification ability. PM2.5 was associated with an increased risk of dementia in this low pollution setting. The associations between PM2.5 and dementia seemed stronger in APOE carriers and those with below average odor identification ability.

A new scheme of PM2.5 and O3 control strategies with the integration of SOM, GA and WRF-CAMx

Previous air pollution control strategies didn't pay enough attention to regional collaboration and the spatial response sensitivities, resulting in limited control effects in China. This study proposed an effective PM2.5 and O3 control strategy scheme with the integration of Self-Organizing Map (SOM), Genetic Algorithm (GA) and WRF-CAMx, emphasizing regional collaborative control and the strengthening of control in sensitive areas. This scheme embodies the idea of hierarchical management and spatial-temporally differentiated management, with SOM identifying the collaborative subregions, GA providing the optimized subregion-level priority of precursor emission reductions, and WRF-CAMx providing response sensitivities for grid-level priority of precursor emission reductions. With Beijing-Tianjin-Hebei and the surrounding area (BTHSA, “2 + 26” cities) as the case study area, the optimized strategy required that regions along Taihang Mountains strengthen the emission reductions of all precursors in PM2.5-dominant seasons, and strengthen VOCs reductions but moderate NOx reductions in O3-dominant season. The spatiotemporally differentiated control strategy, without additional emission reduction burdens than the 14th Five-Year Plan proposed, reduced the average annual PM2.5 and MDA8 O3 concentrations in 28 cities by 3.2%–8.2% and 3.9%–9.7% respectively in comparison with non-differential control strategies, with the most prominent optimization effects occurring in the heavily polluted seasons (6.9%–18.0% for PM2.5 and 3.3%–14.2% for MDA8 O3, respectively). This study proposed an effective scheme for the collaborative control of PM2.5 and O3 in BTHSA, and shows important methodological implications for other regions suffering from similar air quality problems.

Association between PM2.5 and hypertension among the floating population in China: a cross-sectional study

ABSTRACT Few studies have investigated the association between PM2.5 and hypertension among floating populations. We therefore examined the relationship using binary logistic regression. Each grade of increment in the annual average PM2.5 (grade one: ≤15 µg/m3; grade two: 15–25 µg/m3; grade three: 25–35 µg/m3 [Excluding 25]; grade four: ≥35 µg/m3) was associated with an increased risk of hypertension (odds ratio [OR] = 1.081, 95% confidence interval (CI): 1.034–1.129). Among the female floating population (OR = 1.114, 95% CI: 1.030–1.204), those with education level of primary school and below (OR = 1.140, 95% CI: 1.058–1.229), construction workers (OR = 1.228, 95% CI: 1.058–1.426), and those living in the eastern region of China (OR = 1.241, 95% CI: 1.145–1.346) were more vulnerable to PM2.5. These results indicate that PM2.5 is positively associated with hypertension in floating populations. Floating populations who are female, less educated, construction workers, and living in the eastern region of China are more vulnerable to the adverse impacts of PM2.5.

Estimating high-spatial-resolution daily PM2.5 mass concentration from satellite top-of-atmosphere reflectance based on an improved random forest model

The aerosol optical depth (AOD) has been widely used to estimate ground PM2.5 mass concentration in the field of remote sensing, but the current AOD retrieval results always contained invalid AOD values due to the limitations of different algorithms, which is not conducive to obtaining daily estimations of PM2.5 mass concentration. This study used the top-of-atmosphere (TOA) reflectance to establish a relationship with ground PM2.5 mass concentration instead of using traditional AOD product data. We developed an improved multiple-factor random forest model (IMFRFM) by integrating TOA reflectance, meteorological data, and land-use data to estimate high-spatial-resolution (1-km × 1-km) daily PM2.5 mass concentration in the Pearl River Delta (PRD) region. The simulation results showed that our model had excellent performance (test-R2 = 0.88, RMSE = 9.20 μg/m³, and MAE = 2.48 μg/m³) with city-based and time-based cross-validation (CV) test-R2 values of 0.82 and 0.88, respectively. The four-year (2017–2020) seasonal results showed that the PM2.5 mass concentration of the PRD region exhibited obvious seasonality, with the highest values in winter and lowest values in summer. The four-year annual results showed that the PM2.5 mass concentration in the PRD region decreased year by year. The annual average PM2.5 mass concentration in most cities in the PRD region were below 40 μg/m³, indicating that the atmospheric environment of the PRD region was gradually improving.

Emissions, meteorological and climate impacts on PM2.5 levels in Southern California using a generalized additive model: Historic trends and future estimates

Annual fine particulate matter (PM2.5) mass concentrations in the South Coast Air Basin (SoCAB) of California decreased from around 30 μg/m3 to 11 μg/m3 between 2000 and 2013 but rose from 11 μg/m3 to 13 μg/m3 between 2014 and 2018, raising important questions about the effectiveness of ongoing emission control policies. A two-step generalized additive model (GAM)-least squares approach was developed to explore the effects of emissions, large-scale climate events and meteorological factors on daily PM2.5 mass concentrations from 2000 to 2019 to quantitatively link impacts of emissions and meteorological on PM2.5 and to assess factors leading to the increase. The GAM had an R2 = 0.99 and root mean square error (RMSE) = 0.7 μg/m3 for the annual average PM2.5 concentrations. The two-step method had an R2 = 0.93 and RMSE = 4.07 μg/m3 for the 98th percentile 24-hr average PM2.5 concentrations. Variations in both emissions and relative humidity were of high importance compared with other included factors. Interactions of NH3 emissions with NOx and SO2 emissions, which lead to ammonium nitrate and sulfate aerosol formation, were the most important factors. Meteorological effects on PM2.5 explained the majority of the daily PM2.5 fluctuations. Emission changes (increases in SO2 and PM2.5) led to increases in predicted PM2.5 between 2014 and 2018. Predicted future PM2.5, using projected emissions and meteorological data from model simulations of representative concentration pathway (RCP) scenarios, are around 12 μg/m3 (annual) and 30 μg/m3 (98th percentile daily), which are both close to the current National Ambient Air Quality Standards (NAAQS) for PM2.5. Meteorological impacts on the predicted PM2.5 in future years lead to variations of ±2 μg/m3 for the annual average and ±5 μg/m3 for the 98th percentile daily level. Future climate changes lead to a probable year-to-year variation that will let PM2.5 levels in some years exceed the standard.

Spatio-Temporal Distribution Characteristics and Drivers of PM2.5 Pollution in Henan Province, Central China, before and during the COVID-19 Epidemic.

PM2.5 is the main cause of haze pollution, and studying its spatio-temporal distribution and driving factors can provide a scientific basis for prevention and control policies. Therefore, this study uses air quality monitoring information and socioeconomic data before and during the COVID-19 outbreak in 18 prefecture-level cities in Henan Province from 2017 to 2020, using spatial autocorrelation analysis, ArcGIS mapping, and the spatial autocorrelation analysis. ArcGIS mapping and the Durbin model were used to reveal the characteristics of PM2.5 pollution in Henan Province in terms of spatial and temporal distribution characteristics and analyze its causes. The results show that: (1) The annual average PM2.5 concentration in Henan Province fluctuates, but decreases from 2017 to 2020, and is higher in the north and lower in the south. (2) The PM2.5 concentrations in Henan Province in 2017-2020 are positively autocorrelated spatially, with an obvious spatial spillover effect. Areas characterized by a high concentration saw an increase between 2017 and 2019, and a decrease in 2020; values in low-concentration areas remained stable, and the spatial range showed a decreasing trend. (3) The coefficients of socio-economic factors that increased the PM2.5 concentration were construction output value > industrial electricity consumption > energy intensity; those with negative effects were: environmental regulation > green space coverage ratio > population density. Lastly, PM2.5 concentrations were negatively correlated with precipitation and temperature, and positively correlated with humidity. Traffic and production restrictions during the COVID-19 epidemic also improved air quality.

Exposure to PM2.5 and PM10 pollution and the risk of respiratory diseases in upper Silesia inhabitants

Background: Multiple studies confirm that PM2.5 and PM10 can affect lung growth and development in children and adolescents, the number of medical visits, and hospital emergency admissions due to asthma, respiratory symptoms, and upper and lower respiratory tract disorders. Objective: The objective of this study was the measurement-based assessment for determining whether the concentrations of PM10 and PM2.5 are within admissible limits or exceeded in the Upper Silesia urban area (Silesia province) and to examine the relationship between PM concentrations and the number of admissions in Primary Health Care due to respiratory diseases like asthma and chronic bronchitis for children, adolescents and adults. Material and methods: The data provided by the Voivodship Inspectorate for Environmental Protection in Katowice, National Health Fund - Silesian Department, and the District Sanitary-Epidemiological Station in Katowice for the period 2012-2017 was used in the statistical analysis. Results: The increase in the number of excessive levels of average daily PM10 concentration was observed in almost all measurement stations, both for the acceptable level, information, and alarm level. The increase in average annual PM2.5 concentrations was also observed. The results showed an increased prevalence of respiratory diseases (ICD-Diagnosis Code 10-CM: J017, J030, J034, J038, J040) during the study period in 14 of 20 cities of Silesia province, consistently higher for children than adults. Conclusions: In Silesia province, the air quality is poor and has deteriorated over the last few years. Exposure to ambient PM2.5 and PM10 was significantly associated with the development of respiratory tract diseases in children and adults.

Air Purifier Intervention to Remove Indoor PM2.5 in Urban China: A Cost-Effectiveness and Health Inequality Impact Study.

Using air purifiers is an intervention to reduce exposure to fine particulate matter (PM2.5) for health benefits. We performed a comprehensive simulation in urban China to estimate the cost-effectiveness of long-term use of air purifiers to remove indoor PM2.5 from indoor and ambient air pollution in five intervention scenarios (S1-S5), where the indoor PM2.5 targets were 35, 25, 15, 10, and 5 μg/m3, respectively. In scenarios S1 to S5, 5221 (95% uncertainty interval: 3886-6091), 6178 (4554-7242), 8599 (6255-10,109), 11,006 (7962-13,013), and 14,990 (10,888-17,610) thousand disability-adjusted-life-years (DALYs) can be avoided at the cost of 201 (199-204), 240 (238-243), 364 (360-369), 522 (515-530), and 921 (905-939) billion Chinese Yuan (CNY), respectively. A high disparity in per capita health benefits and costs was observed by city, which expanded with the decrease of the indoor PM2.5 target. The net benefits of using purifiers in cities varied across scenarios. Cities with a lower ratio of annual average outdoor PM2.5 concentration to gross domestic product (GDP) per capita tended to achieve higher net benefits in the scenario with a lower indoor PM2.5 target. Controlling ambient PM2.5 pollution and developing the economy can reduce the inequality in air purifier use across China.

Concentration, source apportionment and human health risk assessment of elements in PM2.5 at Agra, India

The present study determined the concentration, seasonal variation, source identification and the human health risk of elements (V, Cr, Mn, Fe, Ni, Cu, Zn, As, Cd, Pb and Si) in fine particulate matter (PM2.5). Samples of PM2.5 were collected at the urban site (Bhagwan Talkies) of Agra, India during January to December 2021. The result showed that the average annual PM2.5 concentration at the urban site was 144.32 ± 57.18 μg m−3 which exceeded the air quality guidelines. Seasonal variation of PM2.5 shows high concentration in winter followed by post-monsoon, summer and monsoon. Among the elements analysed, Si showed the highest concentration and V had the lowest concentration. The concentration of Cr, Ni, As and Cd exceeded the WHO limit, whereas V, Mn and Pb concentrations were below the WHO limit. The significant seasonal variations of elements were observed during the study period. An enrichment factor (EF) was applied to assess the level of the contamination. The possible sources of elements were determined using principal component analysis (PCA). The non-carcinogenic risks for children and adults due to Ni and Cu were higher than the safe level (= 1). The HI values for studied metals for both children and adults were 7.02. The carcinogenic risks for both children and adults due to Pb while for children due to Cd were lower than 1 × 10−6. The carcinogenic risks for other studied metals were higher than 1 × 10−6. The total carcinogenic risks for adults and children were higher than the acceptable limit (1 × 10−6).

Air pollution a hidden scourge to cardiovascular health: insight from top polluted cities of Pakistan.

Dear Editor, A range of disorders impacting the cardiovascular system, which includes the heart and blood vessels, are referred to as cardiovascular diseases (CVDs). This mainly includes coronary artery disease, coronary heart disease, cerebrovascular disease, peripheral artery disease, and aortic atherosclerosis1. According to the WHO, 17.9 million deaths were due to CVDs in 2019, representing 32% of global deaths2. There are nine determining modifiable risk factors of CVD, namely smoking, diabetes mellitus, hypertension, obesity, psychosocial factors, unhealthy diet, lack of exercise, alcohol consumption, and apoB/apoA1 ratio3. Air pollution (AP) is known to be another risk factor and is associated with premature deaths in CVD patients in primarily low- and middle-income countries4. Furthermore, new research indicates that prolonged exposure to AP may raise the mortality risk of CVD patients5,6. It has been shown in a few studies that AP increases the chance of developing CVDs such as myocardial infarction, ischemic heart disease, cardiac arrhythmia, heart failure, or cardiac arrest5,6. Particulate matter measuring 2.5 µm in diameter or less (PM2.5) and particulate matter measuring 10 µm in diameter or less (PM10) concentration are used to determine AP. As per WHO 2021 Air Quality guidelines the highest recommended average annual emission level for PM2.5 was 5 μg/m3 and for PM10 was 15 μg/m37. A study by Choi et al.5 showed, in patients, a higher concentration of PM10 was associated with an increased risk of death due to CVD. Furthermore, a study was done by Pope and colleagues, showing a statistical association between PM2.5 and overall cardiovascular mortality. The risk of myocardial infarction, ischemic heart disease, arrhythmia, heart failure, or cardiac arrest death was increased in polluted areas6. Recently Pakistan ranked as the third most polluted country globally based on an annual average PM2.5 concentration in 2021, by the WHO guidelines. 2021 average PM2.5 concentration in Pakistan is 13.4 times the WHO annual air quality guidelines7,8. The US Air Quality Index declared Lahore an unhealthy city for sensitive groups to live in8. According to WHO data from 2016, about 19% of all deaths in Pakistan were due to heart diseases. However, in only 3 years, a 29% increase occurred in deaths caused by CVDs9. This shows a dire need to take measures to mitigate AP and prevent an increase in cardiovascular patients and cardiovascular mortality in the country. CVD patients need multifactorial interventions, especially more intensive measures to modify individual cardiovascular risk factors with diet, drugs, exercise, weight management, complete smoking cessation, and avoidance of secondhand smoke, or combinations thereof10. A concerted effort should be made by the government to educate healthcare providers and at-risk patients alike about the potential health hazards of elevated AP levels through awareness campaigns. Real-time air quality information should be available in local and national media for people as implemented by the United States Environmental Protection Agency which provides air quality forecasts and reports for particle pollution available in different cities10. This will inform people to wear masks more often in areas badly affected by AP. Furthermore, government authorities should set stringent National Air Quality standards to lower PM2.5 concentration and take measures to reduce such as but not limited to, restricting open burning of waste and regulations to mitigate emissions from vehicles and factories. The government should make policies to reduce the use of fossil fuels for energy and replace it with increased construction and use of solar power and hydropower plants. Pakistan needs to protect its vulnerable population from AP-associated CVDs, otherwise, the death rate through CVDs could increase from the already appalling 29% in the near future. HIGHLIGHTS Multiple studies have associated air pollution (AP) with an increased risk of CVDs and CVD-associated mortality. The mortality rate in Pakistan has seen a 29% increase over three years due to CVDs. This article calls out the Government and respective authorities to take precautionary measures as constantly striking AP may appall the CVD associated mortality in the near future. Ethical approval Not applicable. Sources of funding None. Authors’ contribution S.T.R. and A.I. conceived the idea. S.K., U.N., H.u.H., I.U., and M.S.A. collected the data. S.T.R. and M.S.A. analyzed and interpreted the data. I.U. and S.T.R. did write up of the manuscript. M.S.A. reviewed and revised the manuscript for intellectual content critically. All authors approved the final version of the manuscript. Conflicts of interest disclosure The authors declare that they have no financial conflict of interest with regard to the content of this report. Research registration unique identifying number (UIN) None. Guarantor Muhammad Sohaib Asghar. Provenance and peer review Externally peer reviewed, not commissioned.

What Is Polluting Delhi’s Air? A Review from 1990 to 2022

Delhi’s annual average PM2.5 concentration in 2021–22 was 100 μg/m3—20 times more than the WHO guideline of 5 μg/m3. This is an improvement compared to the limited information available for the pre-CNG-conversion era (~30%), immediately before and after 2010 CWG (~28%), and the mid-2010s (~20%). These changes are a result of continuous technical and economic interventions interlaced with judicial engagement in various sectors. Still, Delhi is ranked the most polluted capital city in the world. Delhi’s air quality is a major social and political concern in India, often with questions regarding its severity and primary sources, and despite several studies on the topic, there is limited consensus on source contributions. This paper offers insight by reviewing the influence of Delhi’s urban growth since 1990 on pollution levels and sources and the evolution of technical, institutional, and legal measures to control emissions in the National Capital Region of Delhi.

Chemical composition and potential sources of PM2.5 in Hanoi

The chemical composition of PM2.5 was monitored simultaneously at two sites, one in a general area of the city center and one at a roadside, in Hanoi, Vietnam, during August 2019–July 2020 using 220 daily (24 h) filter samples. PM mass, water soluble ions, trace elements, organic and elemental carbon and sugar anhydrides were measured. The annual average PM2.5 concentrations, 49 and 46 μg m−3 at the traffic and the general urban site, respectively, exceeded the national (25 μg m−3) and 2021 WHO limit values (5 μg m−3). Daily PM2.5 concentrations were the highest in winter when stagnant meteorological conditions prevailed. On average, half of the resolved mass was organic matter, of which about 40% was attributable to biomass burning, most likely rice straw field burning and domestic fuel combustion. One third of PM2.5 was secondary inorganic aerosol which was dominated by sulphate hence indicating a high contribution of stationary sources like coal combustion. The elemental carbon level was higher at the traffic site, except in April 2020 during the COVID-19 restrictions. Zinc was the most common trace element with high daily variations and large differences between the sites, and it often peaked with Cd, Cl− and Pb indicating contribution of industrial sources and/or coal combustion. The highest zinc concentrations appeared on a few days and likely originated from open burning of municipal solid waste. It appeared that scattered open waste and biomass burning, as well as coal combustion, are important sources causing spikes of PM2.5 pollution in Hanoi above the general levels caused by routine industrial and traffic sources, especially during stagnant winter days. Source contributions were further studied with positive matrix factorization producing six source factors: traffic (12%), local secondary inorganic aerosol (SIA, 18%), biomass burning (19%), industry (9%), long-range transported SIA (25%) and dust (17%).

Spatial Differentiation of PM2.5 Concentration and Analysis of Atmospheric Health Patterns in the Xiamen-Zhangzhou-QuanZhou Urban Agglomeration.

Exploring the spatial differentiation of PM2.5 concentrations in typical urban agglomerations and analyzing their atmospheric health patterns are necessary for building high-quality urban agglomerations. Taking the Xiamen-Zhangzhou-Quanzhou urban agglomeration as an example, and based on exploratory data analysis and mathematical statistics, we explore the PM2.5 spatial distribution patterns and characteristics and use hierarchical analysis to construct an atmospheric health evaluation system consisting of exposure-response degree, regional vulnerability, and regional adaptation, and then identify the spatial differentiation characteristics and critical causes of the atmospheric health pattern. This study shows the following: (1) The average annual PM2.5 value of the area in 2020 was 19.16 μg/m3, which was lower than China's mean annual quality concentration limit, and the overall performance was clean. (2) The spatial distribution patterns of the components of the atmospheric health evaluation system are different, with the overall cleanliness benefit showing a "north-central-south depression, the rest of the region is mixed," the regional vulnerability showing a coastal to inland decay, and the regional adaptability showing a "high north, low south, high east, low west" spatial divergence pattern. (3) The high-value area of the air health pattern of the area is an "F-shaped" spatial distribution; the low-value area shows a pattern of "north-middle-south" peaks standing side by side. The assessment of health patterns in the aforementioned areas can provide theoretical references for pollution prevention and control and the construction of healthy cities.

Abstract WMP98: Associations Between Long-term Co, No 2 , O 3 , Pm 2.5 , And So 2 Exposure And 30-day All-cause Hospital Readmission Among Stroke Patients In The Us: 2014-2015

Objective: There are known associations between long-term air pollution exposure and greater stroke incidence, morbidity, and mortality; however, there is little research on the association of pollutant exposure with poststroke hospital readmission. Methods: We assessed associations of average annual CO, NO 2 , O 3 , PM 2.5 , and SO 2 exposure with 30-day all-cause hospital readmission in US fee-for-service Medicare beneficiaries age ≥65 years hospitalized for ischemic stroke in 2014-2015. We fit Cox models to assess 30-day readmission as a function of these pollutants, adjusted for patient and hospital factors and temperature. We repeated the models stratified by performance of the treating hospital on the Centers for Medicare &amp; Medicaid Services (CMS) risk-standardized 30-day all-cause readmission for stroke measure to determine if the results were independent of performance: Low (CMS rate for hospital &lt;25th percentile of national rate), High (&gt;75 th percentile), and Middle (all others). Results: There were 448148 patients discharged with stroke in 2014-2015 of whom 12.5% were readmitted. Average 2-year CO, O 3 , PM 2.5 , and SO 2 values during the study were below national standards. In adjusted analyses, each 1 standard deviation increase in average annual NO 2 and SO 2 exposure was associated with a 3.6% (95% CI 2.9%-4.4%) and a 2.0% (95% CI 1.1%-3.0%) increased readmission risk within 30-days, respectively ( Table ). Associations between long-term air pollution exposure and increased readmissions persisted across performance categories. Conclusion: Long-term air pollution exposure was associated with increased 30-day readmissions after stroke at pollutant levels below national standards across hospital performance categories. Additional research is needed to determine whether improvements in air quality lead to reductions in poststroke hospital readmissions.

Aerosol radiative feedback enhances particulate pollution over India: A process understanding

In this study, we investigate the role of aerosol direct radiative feedback on regional meteorological changes and subsequent distribution of air pollutant with the focus on particulate matter PM2.5 and ozone (O3) over India. WRF-Chem simulations have been applied for Baseline case with both aerosol direct and indirect effects and another simulation including only aerosol indirect effects. The aerosol direct radiative feedback on the meteorology and air quality were investigated by taking the differences between these two simulations. A comprehensive model evaluation showed the model had the capacity to reproduce the observations and well captured the temporal and spatial variation in meteorological and aerosol fields. We found that the reduction in incoming shortwave solar radiation, temperature at 2 m and planetary boundary layer height annually and across all the season due to aerosol radiation interaction (ARI). The concentration of PM2.5 and gaseous precursors like SO2 and NO2 showed enhancements, while O3 concentration mostly showed a reduction. Spatial and annual average PM2.5 concentration was increased by +6%, SO2 concentration was increased by +1.4%, NO2 concentration was increased by +3.4%, and O3 concentration was reduced by −1.4%. Largest regional increases of 40% in particulate pollution induced by ARI occurred over north-western Indian region. The increase in PM2.5 concentration was highly related to stabilisation induced through meteorological variables and increases in primary aerosol concentration. The decrease in O3 concentration was highly related increases in NO2 and SO2 concentration. In the most polluted regions of India, ARI significantly enhance surface-level particulate pollution and related premature mortality, of particular concern considering likely future increasing trends. This study highlights the importance of inclusion and better representations of ARI and for implementing effective mitigation plans.

What Factors Dominate the Change of PM2.5 in the World from 2000 to 2019? A Study from Multi-Source Data.

As the threat to human life and health from fine particulate matter (PM2.5) increases globally, the life and health problems caused by environmental pollution are also of increasing concern. Understanding past trends in PM2.5 and exploring the drivers of PM2.5 are important tools for addressing the life-threatening health problems caused by PM2.5. In this study, we calculated the change in annual average global PM2.5 concentrations from 2000 to 2020 using the Theil-Sen median trend analysis method and reveal spatial and temporal trends in PM2.5 concentrations over twenty-one years. The qualitative and quantitative effects of different drivers on PM2.5 concentrations in 2020 were explored from natural and socioeconomic perspectives using a multi-scale geographically weighted regression model. The results show that there is significant spatial heterogeneity in trends in PM2.5 concentration, with significant decreases in PM2.5 concentrations mainly in developed regions, such as the United States, Canada, Japan and the European Union countries, and conversely, significant increases in PM2.5 in developing regions, such as Africa, the Middle East and India. In addition, in regions with more advanced science and technology and urban management, PM2.5 concentrations are more evenly influenced by various factors, with a more negative influence. In contrast, regions at the rapid development stage usually continue their economic development at the cost of the environment, and under a high intensity of human activity. Increased temperature is known as the most important factor for the increase in PM2.5 concentration, while an increase in NDVI can play an important role in the reduction in PM2.5 concentration. This suggests that countries can achieve good air quality goals by setting a reasonable development path.

Source apportionment and potential source regions of size-resolved particulate matter at a heavily polluted industrial city in the Indo-Gangetic Plain

This study presents results from size-resolved particulate matter (PM) source apportionment in Ghaziabad, an industrial city adjoining New Delhi in the Indo Gangetic Plain (IGP). A full-year of measurement of PM2.5 and PM10 along with their chemical constituents was performed at two locations in the city. The annual average PM2.5 and PM10 concentrations were ∼150±85 μgm−3 and ∼270±140 μgm−3, respectively, with a ratio of 0.55, indicating the considerable influence of dust. USEPA PMF5 was applied to a combination of ions, elements, and carbon fractions of both PM2.5 and PM10. The model resolved eight factors, including combustion aerosol, secondary aerosol, vehicular emissions, resuspended dust I, resuspended dust II, brick manufacturing, copper smelter, and mixed metal processing. These factors were common to both sites and PM fractions, but with different contributions. Annually, the major contributors to PM2.5 were vehicular emissions (∼21–23%), secondary aerosol (20%), and combustion aerosol (∼17–19%) and to PM10, they were dust (∼44–54%) and combustion aerosol (∼23%). When daily average PM2.5dailyavg>90 μgm−3, brick processing, vehicular emissions, and secondary aerosols were found to be contributing to ∼60% of the PM2.5 loading. However, when PM10dailyavg>250 μgm−3, combustion aerosol, and resuspended dust (I and II) were found to be contributing to ∼70% of the PM10 loading. The likely PM source locations were local as well as regional, with the brick kilns and biomass combustion in the region and states in the upper IGP, namely Punjab, Haryana, and Delhi, being important source regions. Also, specifically for PM10, local construction activity and long-range transport of aged dust from the Arabian desert through western India were found to be the source regions. The results of this study suggested that lowering the contribution of combustion sources, including vehicles, industry, and brick kilns can significantly lower the PM2.5 loading by lowering primary particle loading and secondary particle generation. Controlling local dust re-suspension can lower PM10 mass loading, enhancing local air quality.