Cause-specific premature death from ambient PM2.5 exposure in India: Estimate adjusted for baseline mortality

Epidemiological trends and age-period-cohort effects on cardiovascular diseases burden attributable to ambient air pollution across BRICS

Long-term exposure to ambient air pollution raises the risk of deaths and morbidity worldwide. From 1990 to 2019, we observed the epidemiological trends and age-period-cohort effects on the cardiovascular diseases (CVD) burden attributable to ambient air pollution across Brazil, Russia, India, China, and South Africa (BRICS). The number of CVD deaths related to ambient particulate matter (PM) pollution increased nearly fivefold in China [5.0% (95% CI 4.7, 5.2)] and India [5.7% (95% CI 5.1, 6.3)] during the study period. The age-standardized CVD deaths and disability-adjusted life years (DALYs) due to ambient PM pollution significantly increased in India and China but decreased in Brazil and Russia. Due to air pollution, the relative risk (RR) of premature CVD mortality (< 70 years) was higher in Russia [RR 12.6 (95% CI 8.7, 17.30)] and India [RR 9.2 (95% CI 7.6, 11.20)]. A higher period risk (2015-2019) for CVD deaths was found in India [RR 1.4 (95% CI 1.4, 1.4)] followed by South Africa [RR 1.3 (95% CI 1.3, 1.3)]. Across the BRICS countries, the RR of CVD mortality markedly decreased from the old birth cohort to young birth cohorts. In conclusion, China and India showed an increasing trend of CVD mortality and morbidity due to ambient PM pollution and higher risk of premature CVD deaths were observed in Russia and India.

Evaluating the sensitivity of mortality attributable to pollution to modeling Choices: A case study for Colorado

We evaluated the sensitivity of estimated PM2.5 and NO2 health impacts to varying key input parameters and assumptions including: 1) the spatial scale at which impacts are estimated, 2) using either a single concentration-response function (CRF) or using racial/ethnic group specific CRFs from the same epidemiologic study, 3) assigning exposure to residents based on home, instead of home and work locations for the state of Colorado. We found that the spatial scale of the analysis influences the magnitude of NO2, but not PM2.5, attributable deaths. Using county-level predictions instead of 1 km2 predictions of NO2 resulted in a lower estimate of mortality attributable to NO2 by ∼ 50 % for all of Colorado for each year between 2000 and 2020. Using an all-population CRF instead of racial/ethnic group specific CRFs results in a 130 % higher estimate of annual mortality attributable for the white population and a 40 % and 80 % lower estimate of mortality attributable to PM2.5 for Black and Hispanic residents, respectively. Using racial/ethnic group specific CRFs did not result in a different estimation of NO2 attributable mortality for white residents, but led to ∼ 50 % lower estimates of mortality for Black residents, and 290 % lower estimate for Hispanic residents. Using NO2 based on home instead of home and workplace locations results in a smaller estimate of annual mortality attributable to NO2 for all of Colorado by 2 % each year and 0.3 % for PM2.5. Our results should be interpreted as an exercise to make methodological recommendations for future health impact assessments of pollution.

The impact of fine particulate matter on chronic obstructive pulmonary disease deaths in Pudong New Area, Shanghai, during a long period of air quality improvement

Chronic obstructive pulmonary disease (COPD) deaths attributed to fine particulate matter (with an aerodynamic equivalent diameter <2.5 μm, PM2.5) exposure are a common global public health concern. Recent improvements in air quality and the corresponding health benefits have received much attention. Thus, we have explored the trends of PM2.5 pollution improvement on COPD deaths during an important period of air pollution control (2008-2017) in Pudong New Area, Shanghai, China. Data, including daily COPD death counts, meteorological variables, and ambient air pollutants, were collected from 2008 to 2017. Generalized additive models were fitted to evaluate the percent change (%) in pollution-related COPD deaths. The results showed that the number of days with daily PM2.5 concentrations <35 μg/m3 increased from 19 days (5.19%) in 2008 to 166 days (45.48%) in 2017, and PM2.5 concentrations >75 μg/m3 decreased from 222 days (60.66%) in 2008 to 33 days (9.04%) in 2017. The associations in the overall period between 2008 and 2017 was significant. In subperiod analysis, each 10 μg/m3 increment in PM2.5 was associated with a percent change (%) of 0.89 (95% confidence interval [CI], 0.37, 1.42) at lag 5 and 0.78 (95% CI, 0.26, 1.30) at lag 6 during 2008-2013. Significant results were also found at lag 0-5 [percent change (%), 1.12 (95% CI, 0.09, 2.17)], lag 0-6 [percent change (%), 1.52 (95% CI, 0.43, 2.62)] and lag 0-7 [percent change (%), 1.72 (95% CI, 0.57, 2.88)] during 2008-2013. By contrast, no significant association was found between 2014 and 2017. In conclusion, the decreased COPD deaths associated with PM2.5 exposure were found, especially after the air quality improvement turning point in 2014.

Spatio-temporal health benefits attributable to PM2.5 reduction in an Indian city

Fine particulate matter (PM_2.5) is linked with a wide spectrum of human health effects and has the highest contribution to total air pollution mortality. This study aims to quantify health benefits of reducing PM_2.5 concentration to World Health Organization standard (annual mean = 10 µg m^-3) for various health endpoints during 2011-2019 period using AirQ+ and BenMAP-CE software packages. Intraurban assessment in Vellore city, India was done by estimating health benefits at ward level. Both software packages estimated annual average all-cause, ischemic heart disease, stroke, and chronic obstructive pulmonary disease health benefits in the range of 919-945, 175-234, 70-152, and 99-175 cases at city level and 15-16, 3-4, 1-3, and 2-3 cases at ward level, respectively. Sensitivity analysis showed that relative risk had a large influence on health benefit estimates. Present study results will play a crucial role in the future air quality and public health policies of Vellore city.

The effect of social determinants of health on severity of microbial keratitis presentation at a tertiary eye care hospital in Southern India

Purpose

Understanding the association between social determinants of health (SDoHs) and microbial keratitis (MK) can inform underlying risk for patients and identify risk factors associated with worse disease, such as presenting visual acuity (VA) and time to initial presentation.

Methods

This was a cross-sectional study was conducted with patients presenting with MK to the cornea clinic at a tertiary care hospital in Madurai, India. Patient demographics, SDoH survey responses, geographic pollution, and clinical features at presentation were collected. Descriptive statistics, univariate analysis, multi-variable linear regression models, and Poisson regression models were utilized.

Results

There were 51 patients evaluated. The mean age was 51.2 years (SD = 13.3); 33.3% were female and 55% did not visit a vision center (VC) prior to presenting to the clinic. The median presenting logarithm of the minimum angle of resolution (logMAR) VA was 1.1 [Snellen 20/240, inter-quartile range (IQR) = 20/80 to 20/4000]. The median time to presentation was 7 days (IQR = 4.5 to 10). The average particulate matter 2.5 (PM2.5) concentration, a measure of air pollution, for the districts from which the patients traveled was 24.3 μg/m3 (SD = 1.6). Age- and sex-adjusted linear regression and Poisson regression results showed that higher levels of PM2.5 were associated with 0.28 worse presenting logMAR VA (Snellen 2.8 lines, P = 0.002). Patients who did not visit a VC had a 100% longer time to presentation compared to those who did (incidence rate ratio = 2.0, 95% confidence interval = 1.3-3.0, P = 0.001).

Conclusion

Patient SDoH and environmental exposures can impact MK presentation. Understanding SDoH is important for public health and policy implications to mitigate eye health disparities in India.

Driving factors behind the continuous increase of long-term PM2.5-attributable health burden in India using the high-resolution global datasets from 2001 to 2020

Air pollution is the fourth leading global risk factor, whereas in India air pollution is reported as the highest risk factor with millions of premature deaths every year. Despite implementation of several air pollution control plans, PM_2.5 levels over India have not noticeably reduced. PM_2.5-associated health burdens in India have increased significantly in past decades. A fine resolution (0·01° × 0·01°) analysis of PM_2.5-attribulable premature deaths (rather than the coarse-level analysis) may elucidate the reason for this increase and inform and effective start-of-the-art state-level and national emission control strategies. This study quantified the spatiotemporal dynamics of PM_2.5-attributable premature deaths from 2001 to 2020 and applied a decomposition analysis to dissect the contribution of various associated parameters, such as PM_2.5 concentration, population distribution and disease-specific baseline death rate. Results show significant spatiotemporal variations of PM_2.5 and associated health burden in India. During the study period, population weighted PM_2.5 value increased from 46.0 to 59.5 μg/m³ and associated non-communicable death increased around 87.6 %, from 1050 [95 % (CI): 880-1210] thousand to 1970 (95 % CI: 1658-2259) thousand. The states of Uttar Pradesh, Bihar, West Bengal, Maharashtra, Rajasthan, and Madhya Pradesh had the highest PM_2.5-attributable deaths. In these states, non-accidental deaths increased from 232.1, 112.7, 81.4, 79.1, 66.3 and 58.5 thousand in 2001 to 424.1, 226.7, 156.2, 154.5, 123.3 and 119.7 thousand in 2020. In per capita population (/10⁵ population), the highest PM_2.5-attributable deaths were observed in Delhi, Uttar Pradesh, Bihar, Haryana and Punjab. Throughout the study period, demographic changes outweighed the health burden and were responsible for ~62.8 % increase of PM_2.5-related non-accidental deaths across India, whereas the change in PM_2.5 concentration influenced only 18.7 %. The change in baseline mortality rate impacts differently for the estimation of disease-specific mortality changes. Our findings suggest more dynamic and comprehensive policies at state-specific level, especially for North India is very indispensable for the overall decrease of PM_2.5-related deaths in India.

A global review of the state of the evidence of household air pollution's contribution to ambient fine particulate matter and their related health impacts

Direct exposure to household fine particulate air pollution (HAP) associated with inefficient combustion of fuels (wood, charcoal, coal, crop residues, kerosene, etc.) for cooking, space-heating, and lighting is estimated to result in 2.3 (1.6-3.1) million premature yearly deaths globally. HAP emitted indoors escapes outdoors and is a leading source of outdoor ambient fine particulate air pollution (AAP) in low- and middle-income countries, often being a larger contributor than well-recognized sources including road transport, industry, coal-fired power plants, brick kilns, and construction dust. We review published scientific studies that model the contribution of HAP to AAP at global and major sub-regional scales. We describe strengths and limitations of the current state of knowledge on HAP's contribution to AAP and the related impact on public health and provide recommendations to improve these estimates. We find that HAP is a dominant source of ambient fine particulate matter (PM2.5) globally - regardless of variations in model types, configurations, and emission inventories used - that contributes approximately 20 % of total global PM2.5 exposure. There are large regional variations: in South Asia, HAP contributes ∼ 30 % of ambient PM2.5, while in high-income North America the fraction is ∼ 7 %. The median estimate indicates that the household contribution to ambient air pollution results in a substantial premature mortality burden globally of about 0.77(0.54-1) million excess deaths, in addition to the 2.3 (1.6-3.1) million deaths from direct HAP exposure. Coordinated global action is required to avert this burden.

A novel mathematical model for estimating the relative risk of mortality attributable to the combined effect of ambient fine particulate matter (PM2.5) and cold ambient temperature

Exposures to ambient fine particulate matter (PM_2.5) and cold ambient temperatures have been identified as important risk factors in contributing towards the global mortality from chronic obstructive pulmonary disease (COPD). Despite China currently being the country with the largest population in the world, previous relative risk (RR) models have considered little or no information from the ambient air pollution related cohort studies in the country. This likely provides a less accurate picture of the trend in air pollution attributable mortality in the country over time. A novel relative risk model called pollutant-temperature exposure (PTE) model is proposed to estimate the RR attributable to the combined effect of air pollution and ambient temperature in a population. In this paper, the pollutant concentration-response curve was extrapolated from the cohort studies in China, whereas the temperature response curve was extracted from a study in Yangtze River Delta (YRD) region. The performance of the PTE model was compared with the integrated exposure-response (IER) model using the data of YRD region, which revealed that the estimated relative risks of the PTE model were noticeably higher than the IER model during the winter season. Furthermore, the predictive ability of the PTE model was validated using actual data of Ningbo city, which showed that the estimated RR using the PTE model with 1-month moving average data showed a good result with the trend of actual COPD mortality, indicated by a lower root mean square error (RMSE = 0.956). By considering the combined effect of ambient air pollutant and ambient temperature, the PTE model is expected to provide more accurate relative risk estimates for the regions with high levels of ambient PM_2.5 and seasonal variation of ambient temperature.

Potential health and economic impacts of shifting manufacturing from China to Indonesia or India

The diversification or decoupling of production chains from China to alternative Asian countries such as India or Indonesia would impact the spatial distribution of anthropogenic emissions, with corresponding economic impacts due to mortality associated with particulate matter exposure. We evaluated these changes using the Community Earth System Model, the Integrated Exposure-Response (IER) model and Willingness To Pay (WTP) method. Significant effects on PM_2.5 related mortality and economic cost for these deaths were seen in many East, Southeast and South Asian countries, particularly those immediately downwind of these three countries. Transferring all of export-related manufacturing to Indonesia resulted in significant mortality decreases in China and South Korea by 78k (5 per 100k) and 1k (2 per 100k) respectively, while Indonesia's mortality significantly increased (73.7k; 29 per 100k), as well as India, Pakistan and Nepal. When production was transferred to India, mortality rates in East Asia show similar changes to the Indonesian scenario, while mortalities in India increased dramatically (87.9k; 6 per 100k), and mortalities in many neighbors of India were also severely increased. Nevertheless, the economic costs for PM_2.5 related mortality were much smaller than national GDP changes in China (0.9 % of GDP vs. 18.3 % of GDP), India (2.7 % of GDP vs. 84.3 % of GDP) or Indonesia (9.4 % of GDP vs. 337 % of GDP) due to shifting all of export-related production lines from China to India or Indonesia. Morally, part of the benefits of economic activity should be used to compensate the neighboring communities where mortality increases occur.

Air pollution-induced health impacts and health economic losses in China driven by US demand exports

Understanding how trade between regions or countries drives the transfer of air pollution has attracted considerable interest recently, but few studies have explored the various transfer pathways or evaluated economic losses due to the health impact of such air pollution. Here, we assess the air pollutant emissions and related health impacts and economic losses in China caused by export trade due to US demand by combining the linked multi-regional input-output (MRIO) model, GEOS-Chem model, integrated exposure-response model, and the willingness to pay method. We show that the air pollutant emissions embedded in China's export due to the US demand reached 5792.38 Kt in 2012 (2.48% of the total), which includes direct exports of intermediate (40.27%) and final (33.61%) products and indirect exports of intermediate products via domestic provinces (16.43%, domestic spillover) and other countries (9.69%, foreign spillover). The resulting increase in PM_2.5 (<2.8 μg m^-3) leads to additional 27,963 deaths in 30 provinces, with a higher death toll in coastal areas and the corresponding economic loss was higher in more developed regions and reached USD 2.08 billion. This study highlights the region-different air pollution and health impacts in China embedded in the US-demand trade, and provides a framework for the analysis of health and economic losses hidden in global trade, particularly between developing and developed countries.

Mathematical modeling in the health risk assessment of air pollution-related disease burden in China: A review

This review paper covers an overview of air pollution-related disease burden in China and a literature review on the previous studies which have recently adopted a mathematical modeling approach to demonstrate the relative risk (RR) of air pollution-related disease burden. The associations between air pollution and disease burden have been explored in the previous studies. Therefore, it is necessary to quantify the impact of long-term exposure to ambient air pollution by using a suitable mathematical model. The most common way of estimating the health risk attributable to air pollution exposure in a population is by employing a concentration-response function, which is often based on the estimation of a RR model. As most of the regions in China are experiencing rapid urbanization and industrialization, the resulting high ambient air pollution is influencing more residents, which also increases the disease burden in the population. The existing RR models, including the integrated exposure-response (IER) model and the global exposure mortality model (GEMM), are critically reviewed to provide an understanding of the current status of mathematical modeling in the air pollution-related health risk assessment. The performances of different RR models in the mortality estimation of disease are also studied and compared in this paper. Furthermore, the limitations of the existing RR models are pointed out and discussed. Consequently, there is a need to develop a more suitable RR model to accurately estimate the disease burden attributable to air pollution in China, which contributes to one of the key steps in the health risk assessment. By using an updated RR model in the health risk assessment, the estimated mortality risk due to the impacts of environment such as air pollution and seasonal temperature variation could provide a more realistic and reliable information regarding the mortality data of the region, which would help the regional and national policymakers for intensifying their efforts on the improvement of air quality and the management of air pollution-related disease burden.

Time Trends of Greenspaces, Air Pollution, and Asthma Prevalence among Children and Adolescents in India

The prevalence of childhood asthma contributes to the global burden of the disease substantially. Air pollution in India has increased. In this study, we examine the associations among greenspaces, air pollution, and asthma prevalence in children and adolescents over a large, diverse population in India. We used state-wide global burden of disease data on asthma from age 0 to 19 years in 2005, 2011, and 2017. For greenspace, we used the normalized differential vegetation index (NDVI), which is the surface reflectance of light during photosynthetic activity. NDVI, air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, and O₃), weather, and socio-demographic factors were included in generalized estimating equation (GEE) models to estimate their associations with childhood asthma prevalence over time. Novel data visualization illustrated the complex spatial distributions. NDVI was associated with asthma prevalence (β = 0.144; 95% CI = 0.10, 0.186; p < 0.0001) for high PM_2.5, along with high levels of both gaseous air pollutants, SO₂, and NO₂ ((β = 0.12; 95% CI = 0.08, 0.16; p < 0.0001) and (β = 0.09; 95% CI = 0.05, 0.13; p < 0.0001)). However, NDVI and high O₃, had a strong negative association with asthma prevalence (β = -0.19; 95% CI = -0.26, -0.11; p < 0.0001). We observed additional effects of the interaction between the NDVI and high concentrations of PM_2.5, PM₁₀, NO₂, and O₃, assuming that these associations share a common pathway, and found interaction effects for asthma prevalence. Given the changing environmental conditions that interplay over geographical characteristics on the prevalence of asthma, further studies may elucidate a better understanding of these complex associations.

Air quality impacts of crop residue burning in India and mitigation alternatives

Crop residue burning contributes to poor air quality and imposes a health burden on India. Despite government bans and other interventions, this practice remains widespread. Here we estimate the impact of changes in agricultural emissions on air quality across India and quantify the potential benefit of district-level actions using an adjoint modeling approach. From 2003 to 2019, we find that agricultural residue burning caused 44,000-98,000 particulate matter exposure-related premature deaths annually, of which Punjab, Haryana, and Uttar Pradesh contribute 67-90%. Due to a combination of relatively high downwind population density, agricultural output, and cultivation of residue-intensive crops, six districts in Punjab alone contribute to 40% of India-wide annual air quality impacts from residue burning. Burning two hours earlier in Punjab alone could avert premature deaths up to 9600 (95% CI: 8000-11,000) each year, valued at 3.2 (95% CI: 0.49-7.3) billion US dollars. Our findings support the use of targeted and potentially low-cost interventions to mitigate crop residue burning in India, pending further research regarding cost-effectiveness and feasibility.

Subnational implications from climate and air pollution policies in India’s electricity sector

Emissions of greenhouse gases and air pollutants in India are important contributors to climate change and health damages. This study estimates current emissions from India’s electricity sector and simulates the state-level implications of climate change and air pollution policies. We find that (i) a carbon tax results in little short-term emissions reductions because there is not enough dispatchable lower emission spare capacity to substitute coal; (ii) moving toward regional dispatch markets rather than state-level dispatch decisions will not lead to emissions reductions; (iii) policies that have modest emissions effects at the national level nonetheless have disparate state-level emissions impacts; and (iv) pricing or incentive mechanisms tied to production or consumption will result in markedly different costs to states.

Quantifying the multiple environmental, health, and economic benefits from the electrification of the Delhi public transport bus fleet, estimating a district-wise near roadway avoided PM2.5 exposure

This study investigates the co-benefits from the utilization of the battery-electric bus (BEB) fleet in the Delhi public transportation system as a part of the Delhi electric vehicles policy 2020. To this aim, an integrated quantitative assessment framework is developed to estimate the expected environmental, health, and economic co-benefits from replacing the currently existing public bus fleet with the new BEBs in Delhi. First, the model estimates the avoided emissions from deploying the BEB fleet, using a detailed battery energy simulation model, considering the impact of the battery capacity loss on the annual operational time (hours of service) of the BEB. The annual operational time of the BEB is greatly affected by its battery degradation, which results in time lost due to charging the battery. This indicates that the annual passenger-kilometer (PKM) delivered by the BEB is less than the regular bus, under the same traveling condition. Second, considering fine particles (PM_2.5) as the most health-harming pollutant, the model calculates the near roadway avoided PM_2.5 exposure in the selected traffic zones of 11 major districts of Delhi, using a Gaussian dispersion model. Third, the near roadway avoided PM_2.5 exposure is further used in a health impact assessment model, which considers concentration-response functions for several diseases to evaluate the public health benefits from introducing the BEB fleet in Delhi. The research findings indicate that, the utilization of the new BEB fleet leads to a 74.67% reduction in the total pollutant emissions from the existing bus fleet in Delhi. The results of the integrated co-benefits assessment reveal a significant reduction in PM_2.5 emissions (44 t/y), leading to avoidance of mortality (1370 cases) and respiratory diseases related hospital admissions (2808 cases), respectively, and an annual savings of about USD 383 million from the avoided mortality and morbidity cases in Delhi.

The dynamics of cardiovascular and respiratory deaths attributed to long-term PM2.5 exposures in global megacities

Exposure to ambient fine particulate matter (PM_2.5) air pollution is a significant driver of premature deaths. We estimate the number of cardiovascular and respiratory (CR) premature deaths attributed to long-term exposure to PM_2.5 in 33 global megacities based on long-term remotely sensed observations from 2000 to 2019. Our analysis uses high-resolution (0.01 degree) PM_2.5 concentration data and cause-specific integrated exposure-response (IER) functions developed for the Global Burden of Disease Project. From 2000 to 2019, PM_2.5-related CR death rates per 1000 people increased in 6 of 33 megacities, decreased in 9, and remained constant in 18 megacities. The increase in PM_2.5-related CR mortality in 11 megacities located in South and East Asia during the period 2000-2019 can be attributed to the increases in PM_2.5 concentrations. All 33 megacities could avoid 30,248 (9 %), 62,989 (20 %), 128,457 (40 %), 198,462 (62 %) and all of the estimated 322,515 CR deaths attributed to PM_2.5 pollution in 2019 if they were to attain the World Health Organization's four interim PM_2.5 targets (IT-1, IT-2, IT-3, and IT-4) and the new air quality guideline (AQG), respectively. Major improvements in air quality are needed to reduce the number of CR deaths attributed to PM_2.5 in South and East Asia, in addition to ny reductions that would likely follow shifts in the population structures of these megacities moving forward.

Can the Indian national ambient air quality standard protect against the hazardous constituents of PM2.5?

Globally, exposure to ambient fine particulate matter (PM_2.5) pollution claims ∼9 million lives, yearly, and a quarter of this deaths occurs in India. Regulation of PM_2.5 pollution in India is based on compliance with its National Ambient Air Quality Standard (NAAQS) of 40 μg/m³, which is eight times the revised global air quality guideline (AQG) of 5 μg/m³. But, whether the NAAQS provides adequate protection against the hazardous components in PM_2.5 is still not clear. Here, we examined the risk to health associated with exposure to PM_2.5-bound polychlorinated biphenyls (PCB), heavy metals and polycyclic aromatic hydrocarbons (PAHs) in an Indian district averaging below the NAAQS. The annual average concentrations of PM_2.5 mass, Σ₂₈PCB and Σ₁₃PAHs were 34 ± 17 μg/m³, 21 ± 12 ng/m³ and 458 ± 246 ng/m³, respectively. Concentrations of As, Cr, Mn and Ni in PM_2.5 surpassed the screening levels for residential air. Substantial level of risks to health were associated with exposure to dioxin-like PCBs (Σ₁₂dlPCB), PAHs, As, Cr and Ni. The hazard index or lifetime cancer risk were 240, or 9 cases per 1000 population, respectively. The estimated risks to health through exposure to hazardous components, except Ni, were greatest in rural areas, having a lower average PM_2.5 concentration, than urban or peri-urban areas, suggesting higher toxicity potential of rural combustion sources. The large disparity between the estimated risk values and the acceptable risk level suggests that it would take a more stringent standard, such as the global AQG, to protect vulnerable populations in India from hazardous components in PM_2.5.

Decadal changes in PM2.5-related health impacts in China from 1990 to 2019 and implications for current and future emission controls

In China, the rapid development of the economy and implementation of multiple emission control policies in recent decades have been accompanied by dramatic changes in air quality. In this study, PM_2.5 concentrations estimated by using MERRA-2 reanalysis data were integrated into the Global Exposure Mortality Model (GEMM) to explore the spatiotemporal variation of nationwide PM_2.5-related premature mortality from 1990 to 2019, and the driving factors behind decadal changes were evaluated. Since 2000, as a result of PM_2.5 pollution, air quality in China has deteriorated substantially, especially in the fast-developing eastern and southern parts. In 2009, the nationwide population-weighted (PW) PM_2.5 concentration peaked at 41.4 μg/m³ (95% confidence interval [CI], 36.7-46.2). Simultaneously, the GEMM results revealed that nationwide PM_2.5-related deaths increased remarkably from 1089 (95% CI, 965-1210) thousand in 1990 to 1795 (1597-1986) thousand in 2009. The implementation of the toughest-ever Air Pollution Prevention and Control Action Plan (APPCAP) in 2013 effectively controlled PM_2.5 pollution in China. By 2018, the nationwide PW PM_2.5 concentration had decreased to 34.0 (29.2-38.9) μg/m³. Dynamic trend prediction revealed that, although the APPCAP achieved substantial health benefits, the policy did not result in further remarkable reductions in PM_2.5-related deaths; in 2019, deaths peaked at 1932 (1716-2140) thousand. PM_2.5-related deaths in 2030 were projected for each of four emission control scenarios. The results of the driving factor analysis and the future projections indicated that the health benefits from improving air quality are likely to be counterbalanced by changes in the population age structure. Because population ageing is becoming more and more rapid in China and the challenge of climate change is increasing, the results of this study imply that policymakers need to implement more stringent measures and set more ambitious emission control targets to reduce nationwide PM_2.5-related premature mortality in the future.

Assessment of PM2.5-related health effects: A comparative study using multiple methods and multi-source data in China

In China, PM_2.5 pollution has caused extensive death and economic loss. Thus, an accurate assessment of the spatial distribution of these losses is crucial for delineating priority areas for air pollution control in China. In this study, we assessed the PM_2.5 exposure-related health effects according to the integrated exposure risk function and non-linear power law (NLP) function in 338 prefecture-level cities in China by utilizing online monitoring data and the PM_2.5 Hindcast Database (PHD). Our results revealed no significant difference between the monitoring data and PHD (p value = 0.66 > 0.05). The number of deaths caused by PM_2.5-related Stroke (cerebrovascular disease), ischemic heart disease, chronic obstructive pulmonary disease, and lung cancer at the national level estimated through the NLP function was 0.27 million (95% CI: 0.06-0.50), 0.23 million (95% CI: 0.08-0.38), 0.31 million (95% CI: 0.04-0.57), and 0.31 million (95% CI: 0.16-0.40), respectively. The total economic cost at the national level in 2016 was approximately US$80.25 billion (95% CI: 24.46-132.25). Based on a comparison of Z statistics, we propose that the evaluation results obtained using the NLP function and monitoring data are accurate. Additionally, according to scenario simulations, Beijing, Chongqing, Tianjin, and other cities should be priority areas for PM_2.5 pollution control to achieve considerable health benefits. Our statistics can help improve the accuracy of PM_2.5-related health effect assessments in China.

Impact of Circular, Waste-Heat Reuse Pathways on PM2.5-Air Quality, CO2 Emissions, and Human Health in India: Comparison with Material Exchange Potential

India is home to 1.3 billion people who are exposed to some of the highest levels of ambient air pollution in the world. In addition, India is one of the fastest-growing carbon-emitting countries. Here, we assess how two strategies to reuse waste-heat from coal-fired power plants and other large sources would impact PM_2.5-air quality, human health, and CO₂ emissions in 2015 and a future year, 2050, using varying levels of policy adoption (current regulations, proposed single-sector policies, and ambitious single-sector strategies). We find that power plant and industrial waste-heat reuse as input to district heating systems (DHSs), a novel, multisector strategy to reduce local biomass burning for heating emissions, can offset 71.3-85.2% of residential heating demand in communities near a power plant (9.3-12.4% of the nationwide heating demand) with the highest benefits observed during winter months in areas with collocated industrial activity and higher residential heating demands (e.g., New Delhi). Utilizing waste-heat to generate electricity via organic Rankine cycles (ORCs) can generate an additional 22 (11% of total coal-fired generating capacity), 41 (8%), 32 (13%), and 6 (5%) GW of electricity capacity in the 2015, 2050-current regulations, 2050-single-sector, and 2050-ambitious-single-sector scenarios, respectively. Emission estimates utilizing these strategies were input to the GEOS-Chem model, and population-weighted, simulated PM_2.5 showed small improvements in the DHS (0.2-0.4%) and ORC (0.3-3.4%) scenarios, where the minimal DHS PM_2.5-benefit is attributed to the small contribution of biomass burning for heating to nationwide PM_2.5 emissions (much of the biomass burning activity is for cooking). The PM_2.5 reductions lead to ∼130-36,000 mortalities per year avoided among the scenarios, with the largest health benefits observed in the ORC scenarios. Nationwide CO₂ emissions reduced <0.04% by DHSs but showed larger reductions using ORCs (1.9-7.4%). Coal fly-ash as material exchange in cement and brick production was assessed, and capacity exists to completely reutilize unused fly-ash toward cement and brick production in each of the scenarios.

Potential hotspot modeling and monitoring of PM2.5 concentration for sustainable environmental health in Maharashtra, India

Modern human civilization has suffered from the disastrous impact of COVID-19, but it teaches us the lesson that the environment can restore its stability without human activity. The Government of India (GOI) has launched many strategies to prevent the situation of COVID-19, including a lockdown that has a great impact on the environment. The present study focuses on the analysis of Particulate Matter 2.5 (PM2.5) concentration levels in pre-locking, lockdown, and unlocking phases across ten major cities of Maharashtra (MH) that were the COVID hotspot of India during the COVID-19 outbreak; phase-wise and year-wise (2018-2020) hotspot analysis, box diagram and line graph methods were used to assess spatial variation in PM2.5 across MH cities. Our study showed that the PM2.5 concentration level was severe at pre-lockdown stage (January-March) and it decreased dramatically at the lockdown stage, later it also increased in its previous position at the unlocking stages, i.e., PM2.5 decreased dramatically (59%) during the lockdown period compared to the pre-lockdown period due to the shutdown of outdoor activities. It returns to its previous position due to the unlocking situation and increases (70%) compared to the lockdown period which illustrated the ups and downs of PM2.5 and ensures the position of different cities in the Air Quality Index (AQI) categories at different times. In the pre-lockdown phase, maximum PM2.5 concentration was in Navi Mumbai (NAV) (358) and Mumbai (MUM) (338), and Pune (PUN) (335) and Nashik NAS (325) subsequently, whereas at the last of the lockdown phase, it becomes Chandrapur (CHN) (82), Nagpur (NAG) (76), and Solapur (SOL) (45) subsequently. Hence, the restoration of the environment during the lockdown phase was temporary rather than permanent. Therefore, our findings propose that several effective policies of government such as relocation of polluting industries, short-term lockdown, odd-even vehicle number, installation of air purifier, and government strict initiatives are needed in making a sustainable environment.

Ammonium Chloride Associated Aerosol Liquid Water Enhances Haze in Delhi, India

The interaction between water vapor and atmospheric aerosol leads to enhancement in aerosol water content, which facilitates haze development, but its concentrations, sources, and impacts remain largely unknown in polluted urban environments. Here, we show that the Indian capital, Delhi, which tops the list of polluted capital cities, also experiences the highest aerosol water yet reported worldwide. This high aerosol water promotes secondary formation of aerosols and worsens air pollution. We report that severe pollution events are commonly associated with high aerosol water which enhances light scattering and reduces visibility by 70%. Strong light scattering also suppresses the boundary layer height on winter mornings in Delhi, inhibiting dispersal of pollutants and further exacerbating morning pollution peaks. We provide evidence that ammonium chloride is the largest contributor to aerosol water in Delhi, making up 40% on average, and we highlight that regulation of chlorine-containing precursors should be considered in mitigation strategies.

Exposure assessment of PM2.5 in temple premises and crematoriums in Kanpur, India

Regular use of incense and earthen lamps in temples leads to the release of particulate matter (PM), airborne flecks, and gaseous pollutants. Similarly, the cremation of dead bodies using timber and other accessories such as incense, organic chemicals containing carbon, and clothes generates air pollutants. It is currently unclear how much emissions and exposure these activities may lead. This work attempts to fill this gap in our understanding by assessing the associated emissions of PM_2.5 and the corresponding exposure. Ten temples and two cremation grounds were considered for the sampling of PM_2.5. The average PM_2.5 concentration at the ten temples and the two crematoriums was found to be 658.30 ± 112.63 µg/m³ and 1043.50 ± 191.63 µg/m³, respectively. The range of real-time PM_2.5 data obtained from the nearest twelve stations located in the vicinity was 113-191 µg/m³. The exposure assessment in terms of deposition dose was carried out using the ICRP model. The maximum and minimum total respiratory deposition dose rate for PM_2.5 for temples was 175.75 µg/min and 101.15 µg/min, respectively. For crematoriums, the maximum and minimum value of same was 252.3 µg/min and 194.31 µg/min, respectively, for an exposure period of 10 min.

Short-term effects of different PM2.5 ranges on daily all-cause mortality in Jinan, China

To examine the effects of different PM_2.5 concentration ranges on daily all-cause mortality, 8768 all-cause deaths were recorded in the database of the Shandong Provincial Hospital Affiliated to Shandong First Medical University. Data of air pollutants (PM_2.5 and O₃) concentration were provided by the Jinan Environment Monitoring Center. The relative risk of all-cause mortality was assessed using a quasi-Poisson regression model after adjusting for confounding factors. The concentrations of PM2.5 were divided into four ranges 0-35 μg/m³; 35-75 μg/m³; 75-115 μg/m³; 115-150 μg/m³. There was no significant relationship between PM2.5 exposure and all-cause deaths in individuals aged < 60 years. However, for individuals aged ≥ 60 years, there was a significant positive association between exposure concentrations and all-cause deaths within the ranges 0-35 μg/m³, 35-75 μg/m³, and 115-150 μg/m³ with a mortality increase of 1.07 (1.01, 1.13), 1.03 (1.00, 1.05), and 1.05 (1.01, 1.08), respectively. When the population aged ≥ 60 years was stratified into gender groups, exposure to PM2.5 in the range 0-35 μg/m³ increased the mortality risk in men but not women. All-cause mortality in women, but not men, increased significantly with exposure to PM2.5 in the ranges of 35-75, 75-115, and 115-150 μg/m³.

A Bayesian Multi-Outcome Analysis of Fine Particulate Matter and Cardiorespiratory Hospitalizations

BACKGROUND

Short-term fine particulate matter (PM2.5) exposure is positively associated with acute cardiovascular and respiratory events. Understanding whether this association varies across specific cardiovascular and respiratory conditions has important biologic, clinical, and public health implications.

METHODS

We conducted a time-stratified case-crossover study of hospitalizations from 2000 through 2014 among United States Medicare beneficiaries aged 65+. The outcomes were hospitalizations with any of 57 cardiovascular and 32 respiratory discharge diagnoses. We estimated associations with two-day moving average PM2.5 as a piecewise linear term with a knot at PM2.5 = 25 g/m3. We used Multi-Outcome Regression with Tree-structured Shrinkage (MOReTreeS) to identify de novo groups of related diseases such that PM2.5 associations are: (1) similar within outcome groups; but (2) different between outcome groups. We adjusted for temperature, humidity, and individual-level characteristics. We introduce an R package, moretrees.

RESULTS

Our dataset included 16,007,293 cardiovascular and 8,690,837 respiratory hospitalizations. Of 57 cardiovascular diseases, 51 were grouped and positively associated with PM2.5. We observed a stronger positive association for heart failure, which formed a separate group. We observed negative associations for groups containing the outcomes other aneurysm and intracranial hemorrhage. Of 32 respiratory outcomes, 31 were grouped and were positively associated with PM2.5. Influenza formed a separate group with a negative association.

CONCLUSIONS

We used a new statistical approach, MOReTreeS, to uncover variation in the association between short-term PM2.5 exposure and hospitalizations for cardiovascular and respiratory causes controlling for patient characteristics, time trends, and environmental confounders.

Health risk and disease burden attributable to long-term global fine-mode particles

Particulate matter 2.5 (PM_2.5) pollution has long been a global environmental problem and still poses a great threat to public health. This study investigates global spatiotemporal variations in PM_2.5 using the newly developed satellite-derived PM_2.5 dataset from 1998 to 2018. An integrated exposure-response (IER) model was employed to examine the characteristics of PM_2.5-related deaths caused by chronic obstructive pulmonary disease (COPD), ischemic heart disease (IHD), lung cancer (LC), and stroke in adults (age≥25), as well as lower respiratory infection (LRI) in children (age≤5). The results showed that high annual PM_2.5 concentrations were observed mainly in East Asia and South Asia. Over the 19-year period, PM_2.5 concentrations constantly decreased in developed regions, but increased in most developing regions. Approximately 84% of the population lived in regions where PM_2.5 concentrations exceeded 10 μg/m³. Meanwhile, the vast majority of the population (>60%) in East and South Asia was consistently exposed to PM_2.5 levels above 35 μg/m³. PM_2.5 exposure was linked to 3.38 (95% UI: 3.05-3.70) million premature deaths globally in 2000, a number that increased to 4.11 (95% UI: 3.55-4.69) million in 2018. Premature deaths related to PM_2.5 accounted for 6.54%-7.79% of the total cause of deaths worldwide, with a peak in 2011. Furthermore, developing regions contributed to the majority (85.95%-95.06%) of PM_2.5-related deaths worldwide, and the three highest-ranking regions were East Asia, South Asia, and Southeast Asia. Globally, IHD and stroke were the two main contributors to total PM_2.5-related deaths, followed by COPD, LC, and LRI.

The Environmental Story During the COVID-19 Lockdown: How Human Activities Affect PM2.5 Concentration in China?

At the end of 2019, the very first COVID-19 coronavirus infection was reported and then it spread across the world just like wildfires. From late January to March 2020, most cities and villages in China were locked down, and consequently, human activities decreased dramatically. This letter presents an "offline learning and online inference" approach to explore the variation of PM2.5 pollution during this period. In the experiments, a deep regression model was trained to establish the complex relationship between remote sensing data and in situ PM2.5 observations, and then the spatially continuous monthly PM2.5 distribution map was simulated using the Google Earth Engine platform. The results reveal that the COVID-19 lockdown truly decreased the PM2.5 pollution with certain hysteresis and the fine particle pollution begins to increase when advancing resumption of work and production gradually.

Valuing burden of premature mortality attributable to air pollution in major million-plus non-attainment cities of India

Accelerating growth due to industrialization and urbanization has improved the Indian economy but simultaneously has deteriorated human health, environment, and ecosystem. In the present study, the associated health risk mortality (age > 25) and welfare loss for the year 2017 due to excess PM_2.5 concentration in ambient air for 31 major million-plus non-attainment cities (NACs) in India is assessed. The cities for the assessment are prioritised based on population and are classified as ‘X’ (> 5 million population) and ‘Y’ (1–5 million population) class cities. Ground-level PM_2.5 concentration retrieved from air quality monitoring stations for the NACs ranged from 33 to 194 µg/m³. Total PM_2.5 attributable premature mortality cases estimated using global exposure mortality model was 80,447 [95% CI 70,094–89,581]. Ischemic health disease was the leading cause of death accounting for 47% of total mortality, followed by chronic obstructive pulmonary disease (COPD-17%), stroke (14.7%), lower respiratory infection (LRI-9.9%) and lung cancer (LC-1.9%). 9.3% of total mortality is due to other non-communicable diseases (NCD-others). 7.3–18.4% of total premature mortality for the NACs is attributed to excess PM_2.5 exposure. The total economic loss of 90,185.6 [95% CI 88,016.4–92,411] million US$ (as of 2017) was assessed due to PM_2.5 mortality using the value of statistical life approach. The highest mortality (economic burden) share of 61.3% (72.7%) and 30.1% (42.7%) was reported for ‘X’ class cities and North India zone respectively. Compared to the base year 2017, an improvement of 1.01% and 0.7% is observed in premature mortality and economic loss respectively for the year 2024 as a result of policy intervention through National Clean Air Action Programme. The improvement among 31 NACs was found inconsistent, which may be due to a uniform targeted policy, which neglects other socio-economic factors such as population, the standard of living, etc. The study highlights the need for these parameters to be incorporated in the action plans to bring in a tailored solution for each NACs for better applicability and improved results of the programme facilitating solutions for the complex problem of air pollution in India.

Modeling Spatial Distribution and Determinant of PM2.5 at Micro-Level Using Geographically Weighted Regression (GWR) to Inform Sustainable Mobility Policies in Campus Based on Evidence from King Abdulaziz University, Jeddah, Saudi Arabia

Air pollution is fatal. Fine particles, such as PM2.5, in ambient air might be the cause of many physical and psychological disorders, including cognitive decline. This is why educational policymakers are adopting sustainable mobility, and other policy measures, to make their campuses carbon-neutral; however, car-dependent cities and their university campuses are still lagging behind in this area. This study attempts to model the spatial heterogeneity and determinants of PM2.5 at the King Abdulaziz University campus in Jeddah, which is ranked first among the Saudi Arabian universities, as well as in the MENA region. We developed four OLS and GWR models of different peak and off-peak periods during weekdays in order to estimate the determinants of the PM2.5 concentration. The number of cars, humidity, temperature, windspeed, distance from trees, and construction sites were the estimators in our analysis. Because of a lack of secondary data at a finer scale, we collected the samples of all dependent and independent variables at 51 locations on the KAU campus. Model selection was based on RSS, log-likelihood, adjusted R2, and AICc, and a modal comparison shows that the GWR variant of Model-2 outperformed the other models. The results of the GWR model demonstrate the geographical variability of the PM2.5 concentration on the KAU campus, to which the volume of car traffic is the key contributor. Hence, we recommend using the results of this study to support the development of a car-free and zero-carbon campus at KAU; furthermore, this study could be exploited by other campuses in Saudi Arabia and the Gulf region.

Exploring short term spatio-temporal pattern of PM2.5 and PM10 and their relationship with meteorological parameters during COVID-19 in Delhi

Present study aims to examine the impact of lockdown on spatio-temporal concentration of PM2.5 and PM10 - categorized and recorded based on its levels during pre-lockdown, lockdown and unlock phases while noting the relationship of these levels with meteorological parameters (temperature, wind speed, relative humidity, rainfall, pressure, sun hour and cloud cover) in Delhi. To aid the study, a comparison was made with the last two years (2018 to 2019), covering the same periods of pre-lockdown, lockdown and unlock phases of 2020. Correlation analysis, linear regression (LR) was used to examine the impact of meteorological parameters on particulate matter (PM) concentrations in Delhi, India. The findings showed that (i) substantial decline of PM concentration in Delhi during lockdown period, (ii) there were substantial seasonal variation of particulate matter concentration in city and (iii) meteorological parameters have close associations with PM concentrations. The findings will help planners and policy makers to understand the impact of air pollutants and meteorological parameters on infectious disease and to adopt effective strategies for future.

Global PM2.5-attributable health burden from 1990 to 2017: Estimates from the Global Burden of disease study 2017

BACKGROUND

Long-term exposure to ambient and household particulate matter (PM2.5) causes death and health loss, and both are the leading risk factor to global disease burden. We assessed spatiotemporal trends of ambient and household PM2.5 attributable burdens across various diseases at the global, regional, and national levels from 1990 to 2017.

METHODS

Data on PM2.5-attributable disease burdens were extracted from the Global Burden of Disease (GBD) study 2017. Numbers and age-standardized rates (ASRs) of deaths, disability-adjusted life years (DALYs) and corresponding estimated annual percentage change (EAPC) were estimated by disease, age, sex, Socio-demographic Index (SDI), locations.

RESULTS

Exposure to PM2.5 contributed to 4.58 million deaths and 142.52 million DALYs globally in 2017, among which ambient PM2.5 contributed to 64.2% deaths and 58.3% DALYs. ASRs of deaths and DALYs in 2017 decreased to 59.62/10⁵ persons with an EAPC of -2.15 (95% CI: 2.21 to -2.09) and 1856.61/10⁵ persons with an EAPC of -2.58 (95% CI: 2.64 to -2.51), respectively compared to those in 1990. Ambient PM2.5-attributable Non-communicable diseases (NCDs) have dominated major concern in middle and low SDI countries especially in South Asia and East Asia, while household PM2.5-attributable lower respiratory infections (LRIs) still caused the largest burden in low SDI countries in Africa and Asia. Those under 5 and over 70 years old had the largest burdens in PM2.5 attributable LRI and NCDs, respectively.

CONCLUSION

In conclusion, ambient PM2.5-attributable NCDs have threatened public health in middle and low SDI countries, while household PM2.5-attributable LRI still caused the largest burden in low SDI countries. More positive strategies should be tailored to reduce PM2.5-attributable burdens considering specific settings globally.

Health benefits of achieving fine particulate matter standards in India - A nationwide assessment

BACKGROUND

Ambient fine particulate matter (PM_2.5) is one of the leading risk factors in India. The elevated levels of PM_2.5 exposure concentration in India are related to higher premature mortality. However, health benefits or avoidable premature mortality by reducing PM_2.5 concentration is uncertain.

OBJECTIVES

Here we simulated the health benefits by assuming the achievement of 1) National Ambient Air Quality Standards of India (PM_2.5 annual average = 40 μg m^-3), 2) National Clean Air Programme policy (30% reduction) and 3) World Health Organization standard (10 μg m^-3).

METHODOLOGY

Using Environmental Benefits Mapping and Analysis Program - Community Edition (BenMAP-CE), the health benefits are estimated at national, state and district levels for various health endpoints viz., all-cause, ischaemic heart disease (IHD), chronic obstructive pulmonary disease (COPD), lung cancer and stroke. PM_2.5 data, concentration-response coefficient, population, and baseline incidence rate are specified as input data in BenMAP-CE.

RESULTS

At the national level, all-cause health benefits in three simulations range from 0.79 to 2.1 million cases during 2019. Similarly, IHD, COPD, lung cancer, and stroke related health benefits are in the range of 0.28-0.68, 0.17-0.39, 0.01-0.03, and 0.14-0.34 million cases, respectively. State-level estimates showed that Uttar Pradesh, Bihar, and West Bengal are having maximum health benefits whereas north-eastern states are found with lowest estimates. Districts such as Allahabad, Lucknow, Muzaffarpur, Patna, and Sultanpur are estimated to have highest health benefits. States and districts with higher PM_2.5 concentration and exposed population are found with maximum health benefits. Among the three simulations, achievement of the World Health Organization standard resulted in highest estimates. Further, the limitations and sensitivity of input parameters used in this study are discussed in detail.

CONCLUSION

Study results highlighted the need for state and district-specific air quality management measures to increase PM_2.5 related health benefits.

Comprehensive study of a long-lasting severe haze in Seoul megacity and its impacts on fine particulate matter and health

A long-lasting severe haze event was observed over the Seoul metropolitan region (SMR: Seoul, Incheon, and Gyeonggi-do), South Korea, in the winter of 2013 (January 12-16). We comprehensively investigated the atmospheric processes affecting particulate matter (PM) distributions during the haze event, as well as its impact on human health in the study area. These analyses were performed based on meteorological and PM observations and numerical modeling, which included the WRF-CMAQ modeling system and the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE). High PM₁₀ concentrations during the haze event were mostly observed in the western and southern parts of the SMR. Significant differences (60-70 μg m^-3) in the mean PM_2.5 concentrations for haze and non-haze days were predicted mainly in the west-northwest areas of SMR. This might be primarily due to the pollutant transport (horizontal and vertical) from large emission sources (e.g., Chinese emissions) and, in part, their local accumulation (by local emissions) under high-pressure conditions and slow-moving air flows (i.e., blocking effect) around SMR. In addition, the enhanced PM_2.5 concentrations in the study area during the haze event led to an increase in the number of premature deaths.

Rapid increase in mortality attributable to PM2.5 exposure in India over 1998-2015

PM_2.5-attributable deaths and years of life lost (YLL) due to specific causes during 1998-2015 in India were estimated using the integrated exposure-response (IER) model. The estimated PM_2.5-mortality in India revealed an annual increasing rate of 2.7% during the study period. Spatially, deaths due to the exposure to ambient PM_2.5 concentrated mostly in populated North India, and four northern states contributed 43% to the national PM_2.5-attributable deaths in 2015. PM_2.5-attributable deaths in India increased by 21% during 1998-2015 due to the changes of PM_2.5 only, and deaths due to lung cancer (LC) revealed the largest sensitivity to increasing ambient PM_2.5. The findings of this study suggest that aggressive air pollution control strategies should be implemented in North India due to their dominant contributions to the current health risks. Moreover, the rapid growth of LC related deaths with increasing ambient PM_2.5 should not be neglected.

Impact of the COVID-19 lockdown on air quality in the Delhi Metropolitan Region

With the rapid spread of COVID-19 related cases globally, national governments took different lockdown approaches to limit the spread of the virus. Among them, the Government of India imposed a complete nationwide lockdown starting on March 25, 2020. This presented a unique opportunity to explore how a complete standstill in regular daily activities might impact the local environment. In this study, we have analyzed the change in the air quality levels stemming from the reduced anthropogenic activities in one of the most polluted cities in the world, the Delhi Metropolitan Region (DMR). We analyzed station-level changes in particulate matter, PM₁₀ and PM_2.5, across the DMR between April 2019 and 2020. The results of our study showed widespread reduction in the levels of both pollutants, with substantial spatial variations. The largest decreases in particulate matter were associated with industrial and commercial areas. Highest levels of PM₁₀ and PM_2.5 were observed near sunrise with little change in the time of maximum between 2019 and 2020. The results of our study highlight the role of anthropogenic activities on the air quality at the local level.

Assessing the Distribution of Air Pollution Health Risks within Cities: A Neighborhood-Scale Analysis Leveraging High-Resolution Data Sets in the Bay Area, California

BACKGROUND

Air pollution-attributable disease burdens reported at global, country, state, or county levels mask potential smaller-scale geographic heterogeneity driven by variation in pollution levels and disease rates. Capturing within-city variation in air pollution health impacts is now possible with high-resolution pollutant concentrations.

OBJECTIVES

We quantified neighborhood-level variation in air pollution health risks, comparing results from highly spatially resolved pollutant and disease rate data sets available for the Bay Area, California.

METHODS

We estimated mortality and morbidity attributable to nitrogen dioxide (NO2), black carbon (BC), and fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] using epidemiologically derived health impact functions. We compared geographic distributions of pollution-attributable risk estimates using concentrations from a) mobile monitoring of NO2 and BC; and b) models predicting annual NO2, BC and PM2.5 concentrations from land-use variables and satellite observations. We also compared results using county vs. census block group (CBG) disease rates.

RESULTS

Estimated pollution-attributable deaths per 100,000 people at the 100-m grid-cell level ranged across the Bay Area by a factor of 38, 4, and 5 for NO2 [mean=30 (95% CI: 9, 50)], BC [mean=2 (95% CI: 1, 2)], and PM2.5, [mean=49 (95% CI: 33, 64)]. Applying concentrations from mobile monitoring and land-use regression (LUR) models in Oakland neighborhoods yielded similar spatial patterns of estimated grid-cell-level NO2-attributable mortality rates. Mobile monitoring concentrations captured more heterogeneity [mobile monitoring mean=64 (95% CI: 19, 107) deaths per 100,000 people; LUR mean=101 (95% CI: 30, 167)]. Using CBG-level disease rates instead of county-level disease rates resulted in 15% larger attributable mortality rates for both NO2 and PM2.5, with more spatial heterogeneity at the grid-cell-level [NO2 CBG mean=41 deaths per 100,000 people (95% CI: 12, 68); NO2 county mean=38 (95% CI: 11, 64); PM2.5 CBG mean=59 (95% CI: 40, 77); and PM2.5 county mean=55 (95% CI: 37, 71)].

DISCUSSION

Air pollutant-attributable health burdens varied substantially between neighborhoods, driven by spatial variation in pollutant concentrations and disease rates. https://doi.org/10.1289/EHP7679.

Improved air quality during COVID-19 at an urban megacity over the Indo-Gangetic Basin: From stringent to relaxed lockdown phases

The enforced lockdown amid COVID-19 pandemic eased anthropogenic activities across India. The satellite-derived aerosol optical depth (AOD) and absorption AOD showed a significant reduction of ~30% over the Indo-Gangetic Basin (IGB) in north India during the lockdown period in 2020 with respect to the previous year 2019, when no such lockdown was in effect. Further, near-surface air pollutants were investigated at an urban megacity Delhi during 01 March to 31 May 2020. Except O₃, a drastic reduction in PM₁₀, PM_2.5, NO, NO₂ and CO concentrations were observed by ~58%, 47%, 76%, 68% and 58%, respectively during the lockdown period of 2020 as compared to 2019. While, O₃ was low in the initial phase and gradually increased with progression of lockdown phases, the mean O₃ during the entire lockdown period was nearly similar in both the years. Though, all the measured pollutants showed significant reduction during the entire lockdown, a phase-wise enhancement, associated with the conditional relaxations was observed in their concentrations. Thus, the present results may help, not only to assess the impact of outbreak on air quality, but also in designing the mitigation policies in urban megacities in more efficient ways to combat the air pollution problems.

Light absorption enhancement of particulate matters and their source apportionment over the Asian continental outflow site and South Yellow Sea

Light absorption enhancement of black carbon due to the aerosol mixing states is an important parameterization for climate modeling, while emission source contributions to the enhancement factor are unclear. An intensive campaign was conducted simultaneously at a China coastal site (Qingdao city) and maritime sites (South Yellow Sea, SYS) in August and Nov to Dec 2018. The absorption enhancement (E_MAC) of the black carbon was calculated using a two-step solvent dissolution protocol and found 1.96 ± 0.68, 1.64 ± 0.38, and 2.40 ± 0.76 for Qingdao summer (QS), Qingdao autumn (QA), and SYS, respectively. Positive matrix factorization (PMF) model identified six sources of PM_2.5 and E_MAC, which were secondary aerosol (with contribution 27.9% and 29.2%), coal combustion (24.9% and 20.2%), industrial emissions (15.2% and 25.4%), sea salt (6.9% and 9.6%), vehicle emissions (12.1% and 10.9%), and soil dust (13.0% and 4.7%), respectively. These sources increased the absorption of black carbon by a factor of 1.25 ± 0.11 (secondary aerosol), 1.21 ± 0.20 (industrial emissions), 1.17 ± 0.08 (coal combustion), 1.09 ± 0.07 (vehicle emissions), 1.08 ± 0.17 (sea salt), and 1.04 ± 0.10 (soil dust). Based on the correlation between PM and E_MAC source contributions, we estimated that secondary aerosols, industrial emissions, and coal combustion contributed to 74.8% of absorption enhancement at a regional scale in China. The source apportionment for E_MAC offers a new diagnosis for each source regarding aerosol forcing simulation which inputs from the individual emission sector.

Air Quality and Environmental Injustice in India: Connecting Particulate Pollution to Social Disadvantages

While air pollution levels in India are amongst the highest in the world, the link between exposure to air pollution and social disadvantages has not been systematically examined. Using a distributive environmental justice framework, this study connects fine particulate matter (PM2.5) concentration data derived from satellite observations, a global chemical transport model, and ground-based measurements to district level socio-demographic information from the 2011 Census of India. The research objectives are to determine if annual average PM2.5 concentrations (2010) and recent increases in average PM2.5 concentrations (2010-2016) are unequally distributed with respect to socially disadvantaged population and household groups, after controlling for relevant contextual factors and spatial clustering. Overall, more than 85% of people and households in India reside in districts where international air quality standards for PM2.5 are exceeded. Although PM2.5 concentration levels are significantly higher in more urbanized districts located predominantly in northern India, recent increases have occurred in less urbanized areas located mainly in southern and central India. Multivariable statistical analysis indicated: (1) higher PM2.5 concentration in districts with higher percentages of Scheduled Castes (SCs), young children, and households in poor condition residence and without toilets; and (2) higher PM2.5 increases in less urbanized districts with higher percentages of SCs, females, children, people with disabilities, and households with no toilets. These findings thus highlight the need to consider the role of air pollution in exacerbating the consequences of social disadvantages in India.

COVID-19 lockdown induced air pollution reduction over India: A lesson for future air pollution mitigation strategies

Air pollution is one of the biggest problems worldwide and needs to be addressed potentially with the implementation of updated stringent policies and legislative laws. The nationwide lockdown imposed to prevent the COVID-19 outbreak, has given us a unique opportunity to understand the contribution of anthropogenic emissions to the total atmospheric pollutant burden on a global as well as regional scale. Thus, in the present study, we try to investigate the impact of COVID-19 induced lockdown on common ambient air pollutants (i.e., PM_2.5, NO₂, and SO₂) concentration over 22 cities in India using in-situ measurement under a network of Centre Pollution and Control Board (CPCB). A significant reduction in the mean mass concentration of all the studied air pollutants (i.e., PM_2.5, NO₂, and SO₂) (nearly 10–70%) is found during different phases of lockdown which reached within the National Ambient Air Quality Standard (i.e., NAAQS). The reduction in studied air pollutants is more prominent during the first phase of lockdown (mainly NO₂) which could be due to the complete shutdown of industrial activities. The outcome of the present study will be helpful for policymakers to design cost-effective and accurate air pollution mitigation strategies for the development of a sustainable environment. The study also suggests that well-planned short-term and periodical lockdown could be an alternative effective tool of air pollution mitigation.

Avoiding high ozone pollution in Delhi, India

Quantify the influence of aerosol light extinction on surface ozone photochemistry, highlight controlling VOC for improving air quality in Delhi.

Biodegradable CA/CPB electrospun nanofibers for efficient retention of airborne nanoparticles

The increase of the industrialization process brought the growth of pollutant emissions into the atmosphere. At the same time, the demand for advances in aerosol filtration is evolving towards more sustainable technologies. Electrospinning is gaining notoriety, once it enables to produce polymeric nanofibers with different additives and also the obtaining of small pore sizes and fiber diameters; desirable features for air filtration materials. Therefore, this work aims to evaluate the filtration performance of cellulose acetate (CA) nanofibers and cationic surfactant cetylpyridinium bromide (CPB) produced by electrospinning technique for retention of aerosol nanoparticles. The pressure drop and collection efficiency measurements of sodium chloride (NaCl) aerosol particles (diameters from 7 to 300 nm) were performed using Scanning Mobility Particle Sizer (SMPS). The average diameter of the electrospun nanofibers used was 239 nm, ranging from 113 to 398 nm. Experimental results indicated that the nanofibers showed good permeability (10^-11 m²) and high-efficiency filtration for aerosol nanoparticles (about 100 %), which can include black carbon (BC) and the new coronavirus. The pressure drop was 1.8 kPa at 1.6 cm s^-1, which is similar to reported for some high-efficiency nanofiber filters. In addition, it also retains BC particles present in air, which was about 90 % for 375 nm and about 60 % for the 880 nm wavelength. Finally, this research provided information for future designs of indoor air filters and filter media for facial masks with renewable, non-toxic biodegradable, and potential antibacterial characteristics.

Short and Long Term Exposure to Ambient Air Pollution and Impact on Health in India: A Systematic Review

Health effects attributable to short-term and long-term ambient air pollution (AAP) exposure in Indian population are less understood. This study evaluates the effect of short time and long-term exposure to AAP on respiratory morbidity, mortality and premature mortality for the exposed population. A total of 59 studies are reviewed to examine the effects of short-term exposure (n = 23); long-term exposure (n = 18) and premature mortality (n = 18). Short-term exposures to ambient pollutants have strong associations between COPD, respiratory illnesses and higher rates of hospital admission or visit. The long-term effects of AAP, associated with deficit lung function, asthma, heart attack, cardiovascular mortality and premature mortality have received much attention. Particulate matter (PM_2.5 and PM₁₀) is primarily responsible for respiratory health problems. Out of 18 literature reviewed on premature mortality, most (12 of 18) studies have statistically significant associations between AAP exposure and increased premature mortality risk.

A Satellite-Based High-Resolution (1-km) Ambient PM2.5 Database for India over Two Decades (2000–2019): Applications for Air Quality Management

Fine particulate matter (PM2.5) is a major criteria pollutant affecting the environment, health and climate. In India where ground-based measurements of PM2.5 is scarce, it is important to have a long-term database at a high spatial resolution for an efficient air quality management plan. Here we develop and present a high-resolution (1-km) ambient PM2.5 database spanning two decades (2000–2019) for India. We convert aerosol optical depth from Moderate Resolution Imaging Spectroradiometer (MODIS) retrieved by Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm to surface PM2.5 using a dynamic scaling factor from Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data. The satellite-derived daily (24-h average) and annual PM2.5 show a R2 of 0.8 and 0.97 and root mean square error of 25.7 and 7.2 μg/m3, respectively against surface measurements from the Central Pollution Control Board India network. Population-weighted 20-year averaged PM2.5 over India is 57.3 μg/m3 (5–95 percentile ranges: 16.8–86.9) with a larger increase observed in the present decade (2010–2019) than in the previous decade (2000 to 2009). Poor air quality across the urban–rural transact suggests that this is a regional scale problem, a fact that is often neglected. The database is freely disseminated through a web portal ‘satellite-based application for air quality monitoring and management at a national scale’ (SAANS) for air quality management, epidemiological research and mass awareness.

Calculation Methods of Emission Factors and Emissions of Fugitive Particulate Matter in South Korean Construction Sites

Recently, efforts to effectively reduce particulate matter by identifying its sources and trends have become necessary due to the sustained damage it has caused in East Asia. In the case of South Korea, damage due to fugitive dust generated at construction sites in densely populated downtown areas is significant, and particulate matter in such fugitive dust directly influences the health of nearby residents and construction workers. Accordingly, the purpose of the present study was to develop a method for calculating emission factors for PM10 and PM2.5 emission amounts in the fugitive dust generated in construction sites and to derive emission amount trends for major variables to predict the amounts of generated particulate matter. To this end, South Korean emission factors for PM10 and PM2.5 for different construction equipment and activities that generate fugitive dust were derived and a method for calculating the amount of particulate matter using the derived emission factors was proposed. In addition, the calculated total emissions using these factors were compared to those calculated using construction site fugitive dust equations developed for the United States, Europe, and South Korea, and the trend analysis of total emissions according to the major emission factor variables was conducted.

Outdoor air pollution in India is not only an urban problem

Urban outdoor air pollution in the developing world, mostly due to particulate matter with diameters smaller than 2.5 µm (PM2.5), has been highlighted in recent years. It leads to millions of premature deaths. Outdoor air pollution has also been viewed mostly as an urban problem. We use satellite-derived demarcations to parse India's population into urban and nonurban regions, which agrees with the census data. We also use the satellite-derived surface PM2.5 levels to calculate the health impacts in the urban and nonurban regions. We show that outdoor air pollution is just as severe in nonurban regions as in the urban regions of India, with implications to monitoring, regulations, health, and policy.

Tradeoffs between air pollution mitigation and meteorological response in India

To curb the staggering health burden attributed to air pollution, the sustainable solution for India would be to reduce emissions in future. Here we project ambient fine particulate matter (PM_2.5) exposure in India for the year 2030 under two contrasting air pollution emission pathways for two different climate scenarios based on Representative Concentration Pathways (RCP4.5 and RCP8.5). All-India average PM_2.5 is expected to increase from 41.4 ± 26.5 μg m^-3 in 2010 to 61.1 ± 40.8 and 58.2 ± 37.5 μg m^-3 in 2030 under RCP8.5 and RCP4.5 scenarios, respectively if India follows the current legislation (baseline) emission pathway. In contrast, ambient PM_2.5 in 2030 would be 40.2 ± 27.5 (for RCP8.5) and 39.2 ± 25.4 (for RCP4.5) μg m^-3 following the short-lived climate pollutant (SLCP) mitigation emission pathway. We find that the lower PM_2.5 in the mitigation pathway (34.2% and 32.6%, respectively for RCP8.5 and RCP4.5 relative to the baseline emission pathway) would come at a cost of 0.3-0.5 °C additional warming due to the direct impact of aerosols. The premature mortality burden attributable to ambient PM_2.5 exposure is expected to rise from 2010 to 2030, but 381,790 (5-95% confidence interval, CI 275,620-514,600) deaths can be averted following the mitigation emission pathway relative to the baseline emission pathway. Therefore, we conclude that given the expected large health benefit, the mitigation emission pathway is a reasonable tradeoff for India despite the meteorological response. However, India needs to act more aggressively as the World Health Organization (WHO) annual air quality guideline (10 µg m^-3) would remain far off.

The changing PM2.5 dynamics of global megacities based on long-term remotely sensed observations

Satellite observations show that the rapid urbanization and emergence of megacities with 10 million or more residents have raised PM2.5 concentrations across the globe during the past few decades. This study examines PM2.5 dynamics for the 33 cities included on the UN list of megacities published in 2018. These megacities were classified into densely (>1500 residents per km²), moderately (300-1500 residents per km²) and sparsely (<300 residents per km²) populated areas to examine the effect of human population density on PM2.5 concentrations in these areas during the period 1998-2016. We found that: (1) the higher population density areas experienced higher PM2.5 concentrations; and (2) the megacities with high PM2.5 concentrations in these areas had higher concentrations than those in the moderately and sparsely populated areas of other megacities as well. The numbers of residents experiencing poor air quality is substantial: approximately 452 and 163 million experienced average annual PM2.5 levels exceeding 10 and 35 μg/m³, respectively in 2016. We also examined PM2.5 trends during the past 18 years and predict that high PM2.5 levels will likely continue in many of these megacities in the future without substantial changes in their economies and/or pollution abatement practices. There will be more megacities in the highest PM2.5 pollution class and the number of megacities in the lowest PM2.5 pollution class will likely not change. Finally, we analyzed how the PM2.5 pollution burden varies geographically and ranked the 33 megacities in terms of PM2.5 pollution in 2016. The most polluted regions are China, India, and South Asia and the least polluted regions are Europe and Japan. None of the 33 megacities currently fall in the WHO's PM2.5 attainment class (<10 μg/m³) while 9 megacities fall into the PM2.5 non-attainment class (>35 μg/m³). In 2016, the least polluted megacity was New York and most polluted megacity was Delhi whose average annual PM2.5 concentration of 110 μg/m³ is nearly three times the WHO's non-attainment threshold.

Can We Vacuum Our Air Pollution Problem Using Smog Towers?

In November 2019, the Supreme Court of India issued a notification to all the states in the National Capital Region of Delhi to install smog towers for clean air and allocated INR 36 crores (~USD 5.2 million) for a pilot. Can we vacuum our air pollution problem using smog towers? The short answer is “no”. Atmospheric science defines the air pollution problem as (a) a dynamic situation where the air is moving at various speeds with no boundaries and (b) a complex mixture of chemical compounds constantly forming and transforming into other compounds. With no boundaries, it is unscientific to assume that one can trap air, clean it, and release into the same atmosphere simultaneously. In this paper, we outline the basics of atmospheric science to describe why the idea of vacuuming outdoor air pollution is unrealistic, and the long view on air quality management in Indian cities.