Air pollution health research priorities for India: Perspectives of the Indo-U.S. Communities of Researchers

Air quality and health co-benefits of climate change mitigation and adaptation actions by 2030: an interdisciplinary modeling study in Ahmedabad, India

Climate change-driven temperature increases worsen air quality in places where coal combustion powers electricity for air conditioning. Climate solutions that substitute clean and renewable energy in place of polluting coal and promote adaptation to warming through reflective cool roofs can reduce cooling energy demand in buildings, lower power sector carbon emissions, and improve air quality and health. We investigate the air quality and health co-benefits of climate solutions in Ahmedabad, India-a city where air pollution levels exceed national health-based standards-through an interdisciplinary modeling approach. Using a 2018 baseline, we quantify changes in fine particulate matter (PM2.5) air pollution and all-cause mortality in 2030 from increasing renewable energy use (mitigation) and expanding Ahmedabad's cool roofs heat resilience program (adaptation). We apply local demographic and health data and compare a 2030 mitigation and adaptation (M&A) scenario to a 2030 business-as-usual (BAU) scenario (without climate change response actions), each relative to 2018 pollution levels. We estimate that the 2030 BAU scenario results in an increase of PM2.5 air pollution of 4.13 µg m-3 from 2018 compared to a 0.11 µg m-3 decline from 2018 under the 2030 M&A scenario. Reduced PM2.5 air pollution under 2030 M&A results in 1216-1414 fewer premature all-cause deaths annually compared to 2030 BAU. Achievement of National Clean Air Programme, National Ambient Air Quality Standards, or World Health Organization annual PM2.5 Air Quality Guideline targets in 2030 results in up to 6510, 9047, or 17 369 fewer annual deaths, respectively, relative to 2030 BAU. This comprehensive modeling method is adaptable to estimate local air quality and health co-benefits in other settings by integrating climate, energy, cooling, land cover, air pollution, and health data. Our findings demonstrate that city-level climate change response policies can achieve substantial air quality and health co-benefits. Such work can inform public discourse on the near-term health benefits of mitigation and adaptation.

Change in the air due to the coronavirus outbreak in four major cities of India: What do the statistics say?

The onset of the novel Coronavirus (COVID-19) has impacted all sectors of society. To avoid the rapid spread of this virus, the Government of India imposed a nationwide lockdown in four phases. Lockdown, due to COVID-19 pandemic, resulted a decline in pollution in India in general and in dense cities in particular. Data on key air quality indicators were collected, imputed, and compiled for the period 1st August 2018 to 31st May 2020 for India's four megacities, namely Delhi, Mumbai, Kolkata, and Hyderabad. Autoregressive integrated moving average (ARIMA) model and machine learning technique e.g. Artificial Neural Network (ANN) with the inclusion of lockdown dummy in both the models have been applied to examine the impact of anthropogenic activity on air quality parameters. The number of indicators having significant lockdown dummy are six (PM_2.5, PM₁₀, NOx, CO, benzene, and AQI), five (PM_2.5, PM₁₀, NOx, SO₂ and benzene), five (PM₁₀, NOx, CO, benzene and AQI) and three (PM_2.5, PM₁₀, and AQI) for Delhi, Kolkata, Mumbai and Hyderabad respectively. It was also observed that the prediction accuracy significantly improved when a lockdown dummy was incorporated. The highest reduction in Mean Absolute Percentage Error (MAPE) is found for CO in Hyderabad (28.98%) followed by the NOx in Delhi (28.55%). Overall, it can be concluded that there is a significant decline in the value of air quality parameters in the lockdown period as compared to the same time phase in the previous year. Insights from the COVID-19 pandemic will help to achieve significant improvement in ambient air quality while keeping economic growth in mind.

High-Resolution PM2.5 Emissions and Associated Health Impact Inequalities in an Indian District

Health and livelihood impacts from ambient air pollution among populations in developing countries are disproportional. These disparities are often overlooked due to a lack of information on microlevel emission data, especially in smaller cities and rural areas. The current work in an Indian district, Saharanpur, proposes the use of novel data sets to estimate microlevel emissions from air-polluting infrastructure sectors in urban and rural areas for use in pollutant transport models. Health impacts estimated based on the surface PM_2.5 concentration suggest that the rate of premature deaths is 158 (95% CI: 122-163) and 143 (95% CI: 65-151) deaths per 100 000 people in urban and rural areas, respectively. Sixty-eight percent of the 6372 (95% CI: 3321-6987) annual premature deaths occurs in rural areas. Depicting higher contribution-exposure disparities among socioeconomic groups, the study observed that compared to their contribution to air pollution, low socioeconomic status (SES) groups in the region experience 6,7, 7, and 26% more premature deaths from PM_2.5 exposure for industries, household cooking fuel burning, open waste burning, and transportation, respectively. The majority of disability-adjusted life years (DALYs) in the study domain are observed in economically weaker worker categories. Reduced income due to the loss of these life years will significantly impact these groups due to their dependence on daily wages for basic life necessities. Microlevel pollution mitigation policies with a focus on these inequalities are critical for promoting environmental equity and justice.

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.

A Pilot Study to Quantify Volatile Organic Compounds and Their Sources Inside and Outside Homes in Urban India in Summer and Winter during Normal Daily Activities

Indian cities have some of the poorest air quality globally but volatile organic compounds (VOCs)—many of which adversely affect health—and their indoor sources remain understudied in India. In this pilot study we quantified hundreds of VOCs inside and outside 26 homes in Ahmedabad and Gandhinagar, Gujarat, in May 2019 and in January 2020. We sampled in the morning and afternoon/evening to capture temporal variability. Total indoor VOCs were measured at higher concentrations in winter (327.0 ± 224.2 µgm−3) than summer (150.1 ± 121.0 µgm−3) and exceeded those measured outdoors. Using variable reduction techniques, we identified potential sources of compounds (cooking, plastics [with an emphasis on plasticizers], consumer products, siloxanes [as used in the production of consumer products], vehicles). Contributions differed by season and between homes. In May, when temperatures were high, plastics contributed substantially to indoor pollution (mean of 42% contribution to total VOCs) as compared to in January (mean of 4%). Indoor cooking and consumer products contributed on average 29% and 10% to all VOCs indoors in January and 16% and 4% in May. Siloxane sources contributed <4% to any home during either season. Cooking contributed substantially to outdoor VOCs (on average 18% in January and 11% in May) and vehicle-related sources accounted for up to 84% of VOCs in some samples. Overall, results indicate a strong seasonal dependence of indoor VOC concentrations and sources, underscoring the need to better understand factors driving health-harming pollutants inside homes to facilitate exposure reductions.

High resolution vehicular exhaust and non-exhaust emission analysis of urban-rural district of India

Air quality deterioration due to vehicular emissions in smaller Indian cities and rural areas remains unacknowledged, even though the situation is alarmingly similar to megacities. The resulting lack of knowledge on travel behavior and vehicle characteristics impacts accuracy of emission studies in these regions. This study uses a novel approach and appropriate primary and secondary data sets to allocate vehicular activities (vehicle population and vehicle kilometer travelled) and associated emissions at a high spatial resolution for estimation and dispersion analysis of vehicular exhaust and non-exhaust PM_2.5 emission in an Indian urban-rural landscape. The study indicates that using approaches that do not allocate vehicles kilometers travelled to areas of their expected travel results in underestimating the percent share of PM_2.5 emissions from rural roads and motorways while overestimating overall PM_2.5 emissions. Particulate matter resuspension is the dominant form of PM_2.5 emissions from the vehicular sector on all road types, constituting an even higher fraction on rural roads. Two-wheelers contribute a high fraction of PM_2.5 emissions (exhaust and non-exhaust combined), followed by heavy commercial vehicles and four-wheelers on urban roads. Light commercial vehicles, especially agricultural tractors dominate these emissions on rural roads. PM_2.5 hotspots are prevalent in urban areas, but several rural areas also experience heavy particulate matter concentrations. Thus, vehicle movement incorporation results in more accurate emission estimation, especially in an urban-rural landscape.

Short-term effects of air pollution on respiratory diseases among young children in Wuhan city, China

BACKGROUND

The high risks for childhood respiratory diseases are associated with exposure to ambient air pollution. However, there are few studies that have explored the association between air pollution exposure and respiratory diseases among young children (particularly aged 0-2 years) based on the entire population in a megalopolis.

METHODS

Daily hospital admission records were obtained from 54 municipal hospitals in Wuhan city, China. We included all children (aged 0-2 years) hospitalized with respiratory diseases between January 2017 and December 2018. Individual air pollution exposure assessment was used in Land Use Regression model and inverse distance weighted. Case-crossover design and conditional logistic regression models were adopted to estimate the hospitalization risk associated with air pollutants.

RESULTS

We identified 62,425 hospitalizations due to respiratory diseases, of which 36,295 were pneumonia. Particulate matter with an aerodynamic diameter less than 2.5 μm (PM_2.5) and nitrogen dioxide (NO₂) were significantly associated with respiratory diseases and pneumonia. ORs of pneumonia were 1.0179 (95% CI 1.0097-1.0260) for PM_2.5 and 1.0131 (95% CI 1.0042-1.0220) for NO₂ at lag 0-7 days. Subgroup analysis suggested that NO₂, Ozone (O₃) and sulfur dioxide (SO₂) only showed effects on pneumonia hospitalizations on male patients, but PM_2.5 had effects on patients of both genders. Except O₃, all pollutants were strongly associated with pneumonia in cold season. In addition, children who aged elder months and who were in central urban areas had a higher hospitalization risk.

CONCLUSIONS

Air pollution is associated with higher hospitalization risk for respiratory diseases, especially pneumonia, among young children, and the risk is related to gender, month age, season and residential location.

Source profiling of air pollution and its association with acute respiratory infections in the Himalayan-bound region of India

The studies related to air pollutants and their association with human health over the mountainous region are of utmost importance and are sparse especially over the Himalayan region of India. The linkages between various atmospheric variables and clinically validated data have been done using various datasets procured from satellite, model reanalysis, and surface observations during 2013-2017. Aerosol optical depth, air temperature, and wind speed are significantly related (p < 0.001) to the incidence of acute respiratory infections with its peak during winter. Model-derived particulate matter (PM_2.5) shows high contributions of black carbon, organic carbon, and sulfate during winter. The wind roses show the passage of winds from the south-west and southern side of the region. Back trajectory density plot along with bivariate polar plot analyses have shown that most of the winds coming from the western side are taking a southward direction before reaching the study area and may be bringing pollutants from the Indo-Gangetic Plain and other surrounding regions. Our study shows that the accumulation of pollutants in the Himalayan valley is owing to the meteorological stability with significant local emissions from burning of biomass and biofuels along with long-range and mid-range transport during the winter season that significantly correlated with the incidence of acute respiratory infections in the region.

Daily nonaccidental mortality associated with short-term PM2.5 exposures in Delhi, India

Supplemental Digital Content is available in the text.

Ambient particulate matter of aerodynamic diameter less than 2.5 microns PM_2.5) levels in Delhi routinely exceed World Health Organization (WHO) guidelines and Indian National Ambient Air Quality Standards (NAAQS) for acceptable levels of daily exposure. Only a handful of studies have examined the short-term mortality effects of PM in India, with none from Delhi examining the contribution of PM_2.5.

Using Task Farming to Optimise a Street-Scale Resolution Air Quality Model of the West Midlands (UK)

High resolution air quality models combining emissions, chemical processes, dispersion and dynamical treatments are necessary to develop effective policies for clean air in urban environments, but can have high computational demand. We demonstrate the application of task farming to reduce runtime for ADMS-Urban, a quasi-Gaussian plume air dispersion model. The model represents the full range of source types (point, road and grid sources) occurring in an urban area at high resolution. Here, we implement and evaluate the option to automatically split up a large model domain into smaller sub-regions, each of which can then be executed concurrently on multiple cores of a HPC or across a PC network, a technique known as task farming. The approach has been tested for a large model domain covering the West Midlands, UK (902 km2), as part of modelling work in the WM-Air (West Midlands Air Quality Improvement Programme) project. Compared to the measurement data, overall, the model performs well. Air quality maps for annual/subset averages and percentiles are generated. For this air quality modelling application of task farming, the optimisation process has reduced weeks of model execution time to approximately 35 h for a single model configuration of annual calculations.

Association Between Air Pollution and COVID-19 Pandemic: An Investigation in Mumbai, India

Spatial hot spots of COVID-19 infections and fatalities are observed at places exposed to high levels of air pollution across many countries. This study empirically investigates the relationship between exposure to air pollutants that is, sulfur dioxide, nitrogen dioxide, and particulate matter (SO2, NO2, and PM10) and COVID-19 infection at the smallest administrative level (ward) of Mumbai City in India. The paper explores two hypotheses: COVID-19 infection is associated with air pollution; the pollutants act as determinants of COVID-19 deaths. Kriging is used to assess the spatial variations of air quality using pollution data, while information on COVID-19 are retrieved from the database of Mumbai municipality. Annual average of PM10 in Mumbai over the past 3 years is much higher than the WHO specified standard across all wards; further, suburbs are more exposed to SO2, and NO2 pollution. Bivariate local indicator of spatial autocorrelation finds significant positive relation between pollution and COVID-19 infected cases in certain suburban wards. Spatial Auto Regressive models suggest that COVID-19 death in Mumbai is distinctly associated with higher exposure to NO2, population density and number of waste water drains. If specific pollutants along with other factors play considerable role in COVID-19 infection, it has strong implications for any mitigation strategy development with an objective to curtail the spreading of the respiratory disease. These findings, first of its kind in India, could prove to be significant pointers toward disease alleviation and better urban living.

Association Between Air Pollution and COVID-19 Pandemic: An Investigation in Mumbai, India

Spatial hot spots of COVID-19 infections and fatalities are observed at places exposed to high levels of air pollution across many countries. This study empirically investigates the relationship between exposure to air pollutants that is, sulfur dioxide, nitrogen dioxide, and particulate matter (SO₂, NO₂, and PM₁₀) and COVID-19 infection at the smallest administrative level (ward) of Mumbai City in India. The paper explores two hypotheses: COVID-19 infection is associated with air pollution; the pollutants act as determinants of COVID-19 deaths. Kriging is used to assess the spatial variations of air quality using pollution data, while information on COVID-19 are retrieved from the database of Mumbai municipality. Annual average of PM₁₀ in Mumbai over the past 3 years is much higher than the WHO specified standard across all wards; further, suburbs are more exposed to SO₂, and NO₂ pollution. Bivariate local indicator of spatial autocorrelation finds significant positive relation between pollution and COVID-19 infected cases in certain suburban wards. Spatial Auto Regressive models suggest that COVID-19 death in Mumbai is distinctly associated with higher exposure to NO₂, population density and number of waste water drains. If specific pollutants along with other factors play considerable role in COVID-19 infection, it has strong implications for any mitigation strategy development with an objective to curtail the spreading of the respiratory disease. These findings, first of its kind in India, could prove to be significant pointers toward disease alleviation and better urban living.

Assessment of air pollution status during COVID-19 lockdown (March-May 2020) over Bangalore City in India

The coronavirus disease 2019 (COVID-19), which became a global pandemic by March 2020, forced almost all countries over the world to impose the lockdown as a measure of social distancing to control the spread of infection. India also strictly implemented a countrywide lockdown, starting from 24 March to 12 May 2020. This measure resulted in the reduction of the sources of air pollution in general: industrial, commercial, and vehicular pollution in particular, with visible improvement in ambient air quality. In this study, the impact of COVID-19 lockdown on the ambient concentration of air pollutants over the city of Bangalore (India) is assessed using Continuous Ambient Air Quality Measurement (CAAQM) data from 10 monitoring stations spread across the city. The data was obtained from Central Pollution Control Board (CPCB) and Karnataka State Pollution Control Board (KSPCB). The analysis of the relative changes in the ambient concentration of six major air pollutants (NO, NO₂, NO_X, PM_2.5, O₃, and SO₂) has been carried out for two periods: March-May 2020 (COVID-19 lockdown) and the corresponding period of 2019 during when there was no lockdown. The analysis revealed significant reduction in the concentration of ambient air pollutants at both daily and monthly intervals. This can be attributed to the reduction in sources of emission; vehicular traffic, industrial, and other activities. The average reduction in the concentration of NO, NO₂, NO_X, PM_2.5, and O₃ between 01 March and 12 May 2020 was found to be 63%, 48%, 48%, 18%, and 23% respectively when compared to the same period in 2019. Similarly, the comparative analysis of pollutant concentrations between pre-lockdown (01-23 March 2020) and lockdown (24 March-12 May 2020) periods has shown a huge reduction in the ambient concentration of air pollutants, 47.3% (NO), 49% (NO₂), 49% (NO_X), 10% (SO₂), 37.7% (PM_2.5), and 15.6% (O₃), resulting in improved air quality over Bangalore during the COVID-19 lockdown period. It is shown that the strict lockdown resulted in a significant reduction in the pollution levels. Such lockdowns may be useful as emergency intervention strategies to control air pollution in megacities when ambient air quality deteriorates dangerously.

Acute respiratory response to individual particle exposure (PM1.0, PM2.5 and PM10) in the elderly with and without chronic respiratory diseases

Limited data were on the acute respiratory responses in the elderly in response to personal exposure of particulate matter (PM). In order to evaluate the changes of airway inflammation and pulmonary functions in the elderly in response to individual exposure of particles (PM_1.0, PM_2.5 and PM₁₀), we analyzed 43 elderly subjects with either asthma, chronic obstructive pulmonary disease (COPD) or Asthma COPD Overlap (ACO) and 40 age-matched subjects without asthma nor COPD in an urban community in Shanghai, China. Data were collected at the baseline and in 6 follow-ups from August 2016 to December 2018, once every 3 months except for the last twice with a 6-month interval. In each follow-up, pulmonary functions, fractional exhaled nitric oxide (FeNO), 7-day continuous personal exposure to airborne particles were measured. Multivariate linear mixed effect regression models were applied to investigate the quantitative changes of pulmonary functions and FeNO in two respective groups. The results showed that on average 4.7 follow-up visits were completed in each participant. In subjects with CRDs, an inter-quartile range (IQR) increase of personal exposure to PM_1.0, PM_2.5 and PM₁₀ was significantly associated with an average increase of FeNO(Lag1) of 6.7 ppb (95%CI 1.2, 9.9 ppb), 6.2 ppb (95%CI 1.5, 12.0 ppb) and 5.6 ppb (95%CI 1.5, 11.0 ppb), respectively, and an average decrease of FEV1(Lag2) of -3.6 L (95%CI -6.0, -1.1 L), -3.6 L (95%CI -6.4, -0.8 L) and -3.2 L (95%CI -5.8, -0.6 L), respectively, in the single-pollutant model. These associations remained consistent in the two-pollutant models adjusting for gaseous air pollutants. Stratified analysis showed that subjects with lower BMI, females and non-allergies were more sensitive to particle exposure. No robust significant effects were observed in the subjects without CRDs. Our study provided data on the susceptibility of the elderly with CRDs to particle exposure of PM_1.0 and PM_2.5, and the modification effects by BMI, gender and history of allergies.

Exceedances and trends of particulate matter (PM2.5) in five Indian megacities

Fine particulate matter (PM_2.5) is the leading environmental risk factor that requires regular monitoring and analysis for effective air quality management. This work presents the variability, trend, and exceedance analysis of PM_2.5 measured at US Embassy and Consulate in five Indian megacities (Chennai, Kolkata, Hyderabad, Mumbai, and New Delhi) for six years (2014-2019). Among all cities, Delhi is found to be the most polluted city followed by Kolkata, Mumbai, Hyderabad, and Chennai. The trend analysis for six years for five megacities suggests a statistically significant decreasing trend ranging from 1.5 to 4.19 μg/m³ (2%-8%) per year. Distinct diurnal, seasonal, and monthly variations are observed in the five cities due to the different site locations and local meteorology. All cities show the highest and lowest concentrations in the winter and monsoon months respectively except for Chennai which observed the lowest levels in April. All the cities consistently show morning peaks (~08: 00-10:00 h) and the lowest level in late afternoon hours (~15:00-16:00 h). We found that the PM_2.5 levels in the cities exceed WHO standards and Indian NAAQS for 50% and 33% of days in a year except for Chennai. Delhi is found to have more than 200 days of exceedances in a year and experiences an average 15 number of episodes per year when the level exceeds the Indian NAAQS. The trends in the exceedance with a varying threshold (20-380 μg/m³) suggest that not only is the annual mean PM_2.5 decreasing in Delhi but also the number of exceedances is decreasing. This decrease can be attributed to the recent policies and regulations implemented in Delhi and other cities for the abatement of air pollution. However, stricter compliance of the National Clean Air Program (NCAP) policies can further accelerate the reduction of the pollution levels.

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.

Scientometric analysis: identification of research trends for ozone as an air pollutant for 2011-2019

Ground level ozone is a major air pollutant with known toxic effects on humans. The research field is well established with many scientists from developed and developing countries contributing original research articles. Strict regulations for ozone air pollution are being implemented worldwide based on supporting scientific literature. In this scientometric analysis, we have analyzed the research trends in the field of ozone air pollution during 2011-2019. The collected SCOPUS data was analyzed using common scientometric analysis methods for known indicators to identify top ten rankings and scientific collaborations important for the field. Our result demonstrates that the USA is leading the field as USEPA and American regulatory authorities have funded most of the research. Two scientists, Russell A.G. and Schwartz J., working in American institutions, are leading with the most publications. Our assessment of ozone and PM together shows a significant impact on research direction in the last years to accommodate the study of both air pollutants together. In addition, we have analyzed the possible disease trends in the field for the last 3 years and identified that cardiovascular system, nervous system, and diabetes are upcoming disease areas that would be studied in the coming future.

Building capacity for air pollution epidemiology in India

Air pollution represents a major public health threat in India affecting 19% of the world's population at extreme levels. Despite this, research in India lags behind in large part due to a lack of comprehensive air pollution exposure assessment that can be used in conjunction with health data to investigate health effects. Our vision is to provide a consortium to rapidly expand the evidence base of the multiple effects of ambient air pollution. We intend to leapfrog current limitations of exposure assessment by developing a machine-learned satellite-informed spatiotemporal model to estimate daily levels of ambient fine particulate matter measuring less than 2.5 µm (PM2.5) at a fine spatial scale across all of India. To catalyze health effects research on an unprecedented scale, we will make the output from this model publicly available. In addition, we will also apply these PM2.5 estimates to study the health outcomes of greatest public health importance in India, including cardiovascular diseases, chronic obstructive pulmonary disease, pregnancy (and birth) outcomes, and cognitive development and/or decline. Thus, our efforts will directly generate actionable new evidence on the myriad effects of air pollution on health that can inform policy decisions, while providing a comprehensive and publicly available resource for future studies on both exposure and health effects. In this commentary, we discuss the motivation, rationale, and vision for our consortium and a path forward for reducing the enormous burden of disease from air pollution in India.

A framework for PM2.5 constituents-based (including PAHs) emission inventory and source toxicity for priority controls: A case study of Delhi, India

A simple mass-based emission inventory (EI) of PM_2.5 alone does not provide the information on the toxicity of the sources, as not all PM_2.5 particles are equally toxic. The PM_2.5 EI should have three inter-linked versions (i) mass-based, (ii) constituent-based and (iii) source toxicity-based. A framework (applied to the city of Delhi) to prepare constituent and source toxicity-based EI was developed. Mass emission of twelve sources was estimated for 89 constituents. The USEPA's CompTox database was used to estimate threshold concentration for the constituents of PM_2.5 for carcinogenic, chronic and acute health effects. A product of mass emission of the constituent and inverse of its threshold concentration provides an assessment of toxicity of the source. Toxicity was not linearly associated with the mass emission. Road dust, vehicles, coal, dung, wood and coal power plant showed the highest toxicity as presence of metals Cr, Co, Cd, and As make these sources disproportionately more toxic. Among PAHs, Dibenzo (ah)anthracene, showed the highest cancer risk with its 98% emission from vehicles. The soft options replacing wood, crop, coal and dung with LPG, elimination of diesel power generation, burning of waste were simple and effective measures to reduce chronic toxicity by about 40%.

Mitigation pathways towards national ambient air quality standards in India

Exposure to ambient particulate matter is a leading risk factor for environmental public health in India. While Indian authorities implemented several measures to reduce emissions from the power, industry and transportation sectors over the last years, such strategies appear to be insufficient to reduce the ambient fine particulate matter (PM_2.5) concentration below the Indian National Ambient Air Quality Standard (NAAQS) of 40 μg/m³ across the country. This study explores pathways towards achieving the NAAQS in India in the context of the dynamics of social and economic development. In addition, to inform action at the subnational levels in India, we estimate the exposure to ambient air pollution in the current legislations and alternative policy scenarios based on simulations with the GAINS integrated assessment model. The analysis reveals that in many of the Indian States emission sources that are outside of their immediate jurisdictions make the dominating contributions to (population-weighted) ambient pollution levels of PM_2.5. Consequently, most of the States cannot achieve significant improvements in their air quality and population exposure on their own without emission reductions in the surrounding regions, and any cost-effective strategy requires regionally coordinated approaches. Advanced technical emission control measures could provide NAAQS-compliant air quality for 60% of the Indian population. However, if combined with national sustainable development strategies, an additional 25% population will be provided with clean air, which appears to be a significant co-benefit on air quality (totaling 85%).

Modeling the Impact of an Indoor Air Filter on Air Pollution Exposure Reduction and Associated Mortality in Urban Delhi Household

Indoor exposure to fine particulate matter (PM_2.5) is a prominent health concern. However, few studies have examined the effectiveness of long-term use of indoor air filters for reduction of PM_2.5 exposure and associated decrease in adverse health impacts in urban India. We conducted 20 simulations of yearlong personal exposure to PM_2.5 in urban Delhi using the National Institute of Standards and Technology's CONTAM program (NIST, Gaithersburg, MD, USA). Simulation scenarios were developed to examine different air filter efficiencies, use schedules, and the influence of a smoker at home. We quantified associated mortality reductions with Household Air Pollution Intervention Tool (HAPIT, University of California, Berkeley, CA, USA). Without an air filter, we estimated an annual mean PM_2.5 personal exposure of 103 µg/m³ (95% Confidence Interval (CI): 93, 112) and 137 µg/m³ (95% CI: 125, 149) for households without and with a smoker, respectively. All day use of a high-efficiency particle air (HEPA) filter would reduce personal PM_2.5 exposure to 29 µg/m³ and 30 µg/m³, respectively. The reduced personal PM_2.5 exposure from air filter use is associated with 8-37% reduction in mortality attributable to PM_2.5 pollution in Delhi. The findings of this study indicate that air filter may provide significant improvements in indoor air quality and result in health benefits.

Urban environment as an independent predictor of insulin resistance in a South Asian population

Background

Developing countries, such as India, are experiencing rapid urbanization, which may have a major impact on the environment: including worsening air and water quality, noise and the problems of waste disposal. We used health data from an ongoing cohort study based in southern India to examine the relationship between the urban environment and homeostasis model assessment of insulin resistance (HOMA-IR).

Methods

We utilized three metrics of urbanization: distance from urban center; population density in the India Census; and satellite-based land cover. Restricted to participants without diabetes (N = 6350); we built logistic regression models adjusted for traditional risk factors to test the association between urban environment and HOMA-IR.

Results

In adjusted models, residing within 0–20 km of the urban center was associated with an odds ratio for HOMA-IR of 1.79 (95% CI 1.39, 2.29) for females and 2.30 (95% CI 1.64, 3.22) for males compared to residing in the furthest 61–80 km distance group. Similar statistically significant results were identified using the other metrics.

Conclusions

We identified associations between urban environment and HOMA-IR in a cohort of adults. These associations were robust using various metrics of urbanization and adjustment for individual predictors. Our results are of public health concern due to the global movement of large numbers of people from rural to urban areas and the already large burden of diabetes.

Electronic supplementary material

The online version of this article (10.1186/s12942-019-0169-9) contains supplementary material, which is available to authorized users.

Applying Integrated Exposure-Response Functions to PM2.5 Pollution in India

Fine particulate matter (PM2.5, diameter ≤2.5 μm) is implicated as the most health-damaging air pollutant. Large cohort studies of chronic exposure to PM2.5 and mortality risk are largely confined to areas with low to moderate ambient PM2.5 concentrations and posit log-linear exposure-response functions. However, levels of PM2.5 in developing countries such as India are typically much higher, causing unknown health effects. Integrated exposure-response functions for high PM2.5 exposures encompassing risk estimates from ambient air, secondhand smoke, and active smoking exposures have been posited. We apply these functions to estimate the future cause-specific mortality risks associated with population-weighted ambient PM2.5 exposures in India in 2030 using Greenhouse Gas-Air Pollution Interactions and Synergies (GAINS) model projections. The loss in statistical life expectancy (SLE) is calculated based on risk estimates and baseline mortality rates. Losses in SLE are aggregated and weighted using national age-adjusted, cause-specific mortality rates. 2030 PM2.5 pollution in India reaches an annual mean of 74 μg/m³, nearly eight times the corresponding World Health Organization air quality guideline. The national average loss in SLE is 32.5 months (95% Confidence Interval (CI): 29.7⁻35.2, regional range: 8.5⁻42.0), compared to an average of 53.7 months (95% CI: 46.3⁻61.1) using methods currently applied in GAINS. Results indicate wide regional variation in health impacts, and these methods may still underestimate the total health burden caused by PM2.5 exposures due to model assumptions on minimum age thresholds of pollution effects and a limited subset of health endpoints analyzed. Application of the revised exposure-response functions suggests that the most polluted areas in India will reap major health benefits only with substantial improvements in air quality.