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Under the Paris Agreement, India has pledged that 40 percent of its electricity generating capacity will come from non-fossil-fuel sources by the year 2030; however, this pledge does not limit total coal-fired generating capacity. As of 2019, planned increases in coal-fired capacity totaled 95 gigawatts--46 percent of installed coal-fired capacity in 2018. In this paper, we estimate the carbon dioxide benefits and health co-benefits of not building these plants. We also estimate the mortality impacts of the 2018 stock of coal-fired power plants and use it to calculate the tax on electricity generation from coal that would internalize these damages.

The source apportionment study in Hyderabad listed transportation, industries, and waste burning as critical sources of particulate matter (PM) pollution in the city. The studiespresented in this papers cover the time period of 2010 and 2018 for the Greater Hyderabad Municipal Corporation region, accounting for PM10 (PM size Last update on the emissions inventory, modeled annual and monthly source contributionsis also available as part of the India-APnA city program. Resource material for emissions and energy analysis is available here.

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

Bengaluru - capital of the state of Karnataka is the original “Silicon Valley” of India. In this paper, we present a comprehensive snapshot of the state of air quality in Bengaluru, along with an emissions inventory for the pollutants necessary for chemical transport modeling at 0.01° grid resolution (approximately 1-km), for an urban airshed covering 60 × 60 grids (4300 km2). For 2015, emission estimates for the city are 31,300 tons of PM2.5, 67,100 tons of PM10, 5300 tons of SO2, 56,900 tons of NOx, 335,550 tons of CO, and 83,500 tons of NMVOCs. Overall, transport is the key emission source for Bengaluru - vehicle exhaust and on-road dust resuspension account for a combined 56% and 70% of total PM2.5 and PM10 emissions; followed by industries (17.8% including the brick kilns), open waste burning (11.0%), and domestic cooking, heating, and lighting (6.5%), in case of PM2.5. We conducted particulate pollution source apportionment of local and non-local sources, using WRF meteorological model and CAMx chemical transport modeling system. A comparison of range of 24-hr average modeled PM2.5 concentrations (36.5 ± 9.0 μg/m3) and monitored PM2.5 concentrations (32.3 ± 24.2 μg/m3) by month, shows that the model catches the quantitative ranges and qualitative trends. The modeled source contributions highlight the vehicle exhaust (28%) and dust (including on-road resuspended dust and construction activities) (23%), and open waste burning (14%), as the key air pollution sources. Unless there is an aggressive strategy to improve urban planning and public transport options, pollutant emissions under the business as usual scenario are expected to increase at least 50% in 2030 and doubling the urban area with PM2.5 annual averages above the national ambient standard of 40 μg/m3.

We examine the health implications of electricity generation from the 2018 stock of coal-fired power plants in India, as well as the health impacts of the expansion in coal-fired generation capacity expected to occur by 2030. We estimate emissions of SO(2), NO(X), and particulate matter 2.5 μm (PM(2.5)) for each plant and use a chemical transport model to estimate the impact of power plant emissions on ambient PM(2.5). Concentration-response functions from the 2019 Global Burden of Disease (GBD) are used to project the impacts of changes in PM(2.5) on mortality. Current plus planned plants will contribute, on average, 13% of ambient PM(2.5) in India. This reflects large absolute contributions to PM(2.5) in central India and parts of the Indo-Gangetic plain (up to 20 μg/m(3)). In the south of India, coal-fired power plants account for 20–25% of ambient PM(2.5). We estimate 112,000 deaths are attributable annually to current plus planned coal-fired power plants. Not building planned plants would avoid at least 844,000 premature deaths over the life of these plants. Imposing a tax on electricity that reflects these local health benefits would incentivize the adoption of renewable energy.

There is weak institutional capacity to address air pollution in an integrated manner, because there is little in the way of an organized knowledge base that may help support pollution control planning. In this paper, we present a repository of resource links with information directly relevant for energy, emissions, and pollution analysis in India covering (1) Official national and state level portals; guidelines, acts, and rules documents (2) Ambient air quality monitoring (3) Satellite retrievals and tools (4) Global and regional health impact analysis and tools (5) Compiled statistics, maps, and other geo-spatial databases (6) Compiled databases on energy, emissions, meteorology, and reanalysis fields (7) Compiled statistics on Indian energy sectors. The resource links in the paper were last accessed in January 2021.

National Clean Air Programme (NCAP), launched in 2019, is India's flagship program for better air quality in 122 cities. This review evaluates the scientific, legislative, financial, and institutional framework of the 102 publicly available clean air action plans submitted under NCAP. We assessed the robustness of the plans using the background information on pollution sources and their contribution; legal backing for the clean air action plan; cost of measures; and existing institutional accountability regime. We used the tally chart method for estimating city-specific and sector-specific mitigation measures, the number of actions under the purview of various implementation agencies, and the number of institutional, physical, and promotional interventions in the plans. Transport and road dust together cover 50% of action points, followed by interventions for the industries. Domestic cooking and heating is mentioned as a source only in 42 plans for a total of 2% of the action points. Institutional nature of the interventions was observed in 74% of the action points, using the language “overseeing, planning, proposing, preparing, investigating, identifying, ensuring, strengthening, training, studying, and engaging”. We also identified the plans that contained information on source contributions, an outline of financial requirements for executing the plans and measures for mitigating pollution from regional sources. Only 25% of the plans integrated information on the relative source contributions to formulate control strategies. Even fewer plans outlined the financial requirements for executing the plans. The institutional and administrative arrangements for ensuring inter-departmental and regional alignment in air pollution mitigation strategies are absent. We close the review with recommendations which include mandating regular updates for emission and pollution loads, granting greater fiscal autonomy for Urban Local Bodies to maintain the infrastructure necessary for sustaining air quality benefits, and moving from city-centric to airshed-centric air quality management.

Indo-Gangetic Plain (IGP) experienced an intensive air pollution episode during November 2017. Weather Research and Forecasting model with Chemistry (WRF-Chem), a coupled meteorology–chemistry model, was used to simulate this episode. In order to capture PM2.5 peaks, we modified input chemical boundary conditions and biomass burning emissions. CAM-Chem and MERRA-2 global models provided gaseous and aerosol chemical boundary conditions, respectively. We also incorporated VIIRS active fire points to fill missing fire emissions in FINN and scaled by a factor of seven for an 8-days period. Evaluations against various observations indicated the model captured the temporal trend very well although missed the peaks on Nov. 7th, 8th, and 10th. Modeled aerosol composition in Delhi showed Secondary Inorganic Aerosols (SIA) and Secondary Organic Aerosols (SOA) comprised 30 % and 27 % of total PM2.5 concentration, respectively, during November, with a modeled OC / BC ratio of 2.72. Back trajectories showed agricultural fires in Punjab were the major source for extremely polluted days in Delhi. Furthermore, high concentrations above the boundary layers in vertical profiles suggested either the plume rise in the model released the emissions too high, or the model did not mix the smoke down fast enough. Results also showed long-range transported dusts did not affect Delhi's air quality during the episode. Spatial plots showed averaged Aerosol Optical Depth (AOD) of 0.58 (± 0.4) over November. The model AODs were biased high over central India and low over eastern IGP, indicating improving emissions in eastern IGP can significantly improve the air quality predictions. We also found high ozone concentrations over the domain, which indicates ozone should be considered in future air quality management strategies alongside particulate matters.

Summary: Background: The association of air pollution with multiple adverse health outcomes is becoming well established, but its negative economic impact is less well appreciated. It is important to elucidate this impact for the states of India. Methods: We estimated exposure to ambient particulate matter pollution, household air pollution, and ambient ozone pollution, and their attributable deaths and disability-adjusted life-years in every state of India as part of the Global Burden of Disease Study (GBD) 2019. We estimated the economic impact of air pollution as the cost of lost output due to premature deaths and morbidity attributable to air pollution for every state of India, using the cost-of-illness method. Findings: 1·67 million (95% uncertainty interval 1·42–1·92) deaths were attributable to air pollution in India in 2019, accounting for 17·8% (15·8–19·5) of the total deaths in the country. The majority of these deaths were from ambient particulate matter pollution (0·98 million [0·77–1·19]) and household air pollution (0·61 million [0·39–0·86]). The death rate due to household air pollution decreased by 64·2% (52·2–74·2) from 1990 to 2019, while that due to ambient particulate matter pollution increased by 115·3% (28·3–344·4) and that due to ambient ozone pollution increased by 139·2% (96·5–195·8). Lost output from premature deaths and morbidity attributable to air pollution accounted for economic losses of US$28·8 billion (21·4–37·4) and $8·0 billion (5·9–10·3), respectively, in India in 2019. This total loss of $36·8 billion (27·4–47·7) was 1·36% of India's gross domestic product (GDP). The economic loss as a proportion of the state GDP varied 3·2 times between the states, ranging from 0·67% (0·47–0·91) to 2·15% (1·60–2·77), and was highest in the low per-capita GDP states of Uttar Pradesh, Bihar, Rajasthan, Madhya Pradesh, and Chhattisgarh. Delhi had the highest per-capita economic loss due to air pollution, followed by Haryana in 2019, with 5·4 times variation across all states. Interpretation: The high burden of death and disease due to air pollution and its associated substantial adverse economic impact from loss of output could impede India's aspiration to be a $5 trillion economy by 2024. Successful reduction of air pollution in India through state-specific strategies would lead to substantial benefits for both the health of the population and the economy. Funding: UN Environment Programme; Bill & Melinda Gates Foundation; and Indian Council of Medical Research, Department of Health Research, Ministry of Health and Family Welfare, Government of India.

Delhi, with a population of 22 million (1.6% of national total) is one of the most polluted capital cities in the world. Nearly 50% of the published literature in India focus on air pollution in Delhi. However, air pollution impacts are not limited only to the capital city. Yet, there is little information and attempt to quantify these impacts for Tier 1 and 2 cities, even though they account for >30% of India's population. To remedy this vacuum of information, the Air Pollution knowledge Assessments (APnA) city program deliberately focuses on 20 Indian cities, other than Delhi. We established baseline multi-pollutant high-resolution emissions inventory, after collating information from multiple resources detailed in this paper, which was used to estimate spatial concentrations of key pollutants across city's urban airshed using WRF-CAMx chemical transport modeling system. The inventory includes anthropogenic sources, such as transport (road, rail, ship, and aviation), large scale power generation (from coal, diesel, and gas power plants), small scale power generation (from diesel generator sets for household use, commercial use, and agricultural water pumping), small and medium scale industries, dust (road resuspension and construction), domestic (cooking, heating, and lighting), open waste burning, and open fires and non-anthropogenic sources, such as sea salt, dust storms, biogenic, and lightning. The emissions inventory is currently in use for 3-day advance air quality forecasting for public release on an on-going basis. Using meteorological parameters and big data like gridded speed maps from google, the emissions inventory is dynamically updated. The results from this research will be valuable to local and national policy makers - especially the information on source contributions to air pollution.

In Chhattisgarh, Raipur-Durg-Bhilai (RDB) tri-city area hosts the new administrative capital of the state, interconnected by an expressway forming the industrial corridor and is one of the largest steel manufacturing hubs in India. Between 1998 and 2016, the satellite and global model data derived concentrations show a 50% increase in the overall PM2.5 pollution in the region. The average PM10 concentration measured at commercial, industrial, and residential monitoring stations is 125 ± 52 µg m–3 in 2015. None of the stations currently measure PM2.5. The annual average PM10 concentrations in 2011 is 175 ± 110 µg m–3, which translates to 28% improvement in 5 years. A multiple pollutant emissions inventory was established for this urban airshed (extending 60 km × 30 km), with annual estimates of 41,500 tons of PM2.5, 59,650 tons of PM10, 7,600 tons of SO2, 67,000 tons of NOx, 163,300 tons of CO, 118,150 tons of NMVOCs, and 3.1 million tons of CO2 for 2015, and further projected to 2030 under business as usual conditions. The ambient source contributions were calculated using WRF-CAMx chemical transport modeling system, highlighting the heavy industries (mostly steel) (23%), followed by transport (including on road dust) (29%), domestic cooking and heating (12%), open waste burning (6%), as the key air pollution sources in the urban area. The city has an estimated 26% of the ambient annual PM2.5 pollution originating outside the urban airshed - this contribution is mostly coming from the coal-fired power plants, large (metal and non-metal processing) industries, and brick kilns located outside the urban airshed and seasonal open biomass fires.

Air pollution is a persistent and well-established public health problem in India: emissions from coal-fired power plants have been associated with over 80,000 premature deaths in 2015. Premature deaths could rise by four to five times this number by 2050 without additional pollution controls. We site a model 500 MW coal-fired electricity generating unit at eight locations in India and examine the benefits and costs of retrofitting the plant with a flue-gas desulfurization unit to reduce sulfur dioxide emissions. We quantify the mortality benefits associated with the reduction in sulfates (fine particles) and value these benefits using estimates of the value per statistical life transferred to India from high income countries. The net benefits of scrubbing vary widely by location, reflecting differences in the size of the exposed population. They are highest at locations in the densely populated north of India, which are also among the poorest states in the country.

Air quality remains a significant environmental health challenge in India, and large sections of the population live in areas with poor ambient air quality. This article presents a summary of the regulatory monitoring landscape in India, and includes a discussion on measurement methods and other available government data on air pollution. Coarse particulate matter (PM10) concentration data from the national regulatory monitoring network for 12 years (2004–2015) were systematically analyzed to determine broad trends. Less than 1% of all PM10 measurements (11 out of 4789) were found to meet the annual average WHO Air Quality Guideline (20 μg/m3), while 19% of the locations were in compliance with the Indian air quality standards for PM10 (60 μg/m3). Further efforts are necessary to improve measurement coverage and quality including the use of hybrid monitoring systems, harmonized approaches for sampling and data analysis, and easier data accessibility.

Power from coal-fired thermal power plants (TPPs) represents a large percentage of the electricity generated in India. As the demand increases, expansion of the coal-fired TPPs is the most likely scenario, which will lead to an array of environmental and health impacts. The proposed projects in India net a generation capacity of 300 GW through 2030. With limited emission control regulations in place, this will increase the number of health impacts—some from direct particulate matter (PM) emissions and some from secondary PM, especially due to the chemical transformation of sulfur emissions. The WRF-CAMx chemical transport modeling system was utilized to study the impact of these emissions from the planned coal-fired TPPs. The additional 300 GW of projects will result in 3-times the coal consumption and at least 2-times the health impacts (premature mortality and asthma attacks), compared to those estimated for the operational TPPs. The technology to control all criteria pollutant emissions, which could reduce the health impacts linked to ambient PM2.5 from the coal-fired TPPs by as much as 50%, is widely available, and the only barrier to implementing these solutions is the lack of a stricter timeline.

Exposures to ambient and household fine-particulate matter (PM2.5) together are among the largest single causes of premature mortality in India according to the Global Burden of Disease Studies (GBD). Several recent investigations have estimated that household emissions are the largest contributor to ambient PM2.5 exposure in the country. Using satellite-derived district-level PM2.5 exposure and an Eulerian photochemical dispersion model CAMx (Comprehensive Air Quality Model with Extensions), we estimate the benefit in terms of population exposure of mitigating household sources--biomass for cooking, space- and water-heating, and kerosene for lighting. Complete mitigation of emissions from only these household sources would reduce India-wide, population-weighted average annual ambient PM2.5 exposure by 17.5, 11.9, and 1.3%, respectively. Using GBD methods, this translates into reductions in Indian premature mortality of 6.6, 5.5, and 0.6%. If PM2.5 emissions from all household sources are completely mitigated, 103 (of 597) additional districts (187 million people) would meet the Indian annual air-quality standard (40 μg m-3) compared with baseline (2015) when 246 districts (398 million people) met the standard. At 38 μg m-3, after complete mitigation of household sources, compared with 55.1 μg m-3 at baseline, the mean annual national population-based concentration would meet the standard, although highly polluted areas, such as Delhi, would remain out of attainment. Our results support expansion of programs designed to promote clean household fuels and rural electrification to achieve improved air quality at regional scales, which also has substantial additional health benefits from directly reducing household air pollution exposures.

The Gridpoint Statistical Interpolation (GSI) Three-Dimensional Variational (3DVAR) data assimilation system is extended to treat the MOSAIC aerosol model in WRF-Chem, and to be capable of assimilating surface PM2.5 concentrations. The coupled GSI-WRF-Chem system is applied to reproduce aerosol levels over China during an extremely polluted winter month, January 2013. After assimilating surface PM2.5 concentrations, the correlation coefficients between observations and model results averaged over the assimilated sites are improved from 0.67 to 0.94. At nonassimilated sites, improvements (higher correlation coefficients and lower mean bias errors (MBE) and root-mean-square errors (RMSE)) are also found in PM2.5, PM10, and AOD predictions. Using the constrained aerosol fields, we estimate that the PM2.5 concentrations in January 2013 might have caused 7550 premature deaths in Jing-Jin-Ji areas, which are 2% higher than the estimates using unconstrained aerosol fields. We also estimate that the daytime month...

Non-methane volatile organic compounds (NMVOCs) are important precursors to reactions producing tropospheric ozone and secondary organic aerosols. The present work uses a detailed technology-linked NMVOC emission database for India, along with a standard mapping method to measured NMVOC profiles, to develop speciated NMVOC emissions, which are aggregated into multiple chemical mechanisms used in chemical transport models. The fully speciated NMVOC emissions inventory with 423 constituent species, was regrouped into model-ready reactivity classes of the RADM2, SAPRC99 and CB-IV chemical mechanisms, and spatially distributed at 25 x 25 km(2) resolution, using source-specific spatial proxies. Emissions were considered from four major sectors, i.e. industry, transport, agriculture and residential and from non-combustion activities (use of solvents and paints). It was found that residential cooking with biomass fuels, followed by agricultural residue burning in fields and on-road transport, were largest contributors to the highest reactivity group of NMVOC emissions from India. The emissions were evaluated using WRF-CAMx simulations, using the SAPRC99 photochemical mechanism, over India for contrasting months of April, July and October 2010. Modelled columnar abundance of NO2, CO and O-3 agreed well with satellite observations both in magnitude and spatial distribution, in the three contrasting months. Evaluation of monthly and spatial differences between model predictions and observations indicates the need for further refinement of the spatial distribution of NOX emissions, spatio-temporal distribution of agricultural residue burning emissions. (C) 2016 Elsevier Ltd. All rights reserved.

Air pollution poses a critical threat to human health with ambient and household air pollution identified as key health risks in India. While there are many studies investigating concentration, composition, and health effects of air pollution, investigators are only beginning to focus on estimating or measuring personal exposure. Further, the relevance of exposures studies from the developed countries in developing countries is uncertain. This review summarizes existing research on exposure to particulate matter (PM) in India, identifies gaps and offers recommendations for future research. There are a limited number of studies focused on exposure to PM and/or associated health effects in India, but it is evident that levels of exposure are much higher than those reported in developed countries. Most studies have focused on coarse aerosols, with a few studies on fine aerosols. Additionally, most studies have focused on a handful of cities, and there are many unknowns in terms of ambient levels of PM as well as personal exposure. Given the high mortality burden associated with air pollution exposure in India, a deeper understanding of ambient pollutant levels as well as source strengths is crucial, both in urban and rural areas. Further, the attention needs to expand beyond the handful large cities that have been studied in detail.

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.

Designing air quality policies that improve public health can benefit from information about air pollution health risks and impacts, which include respiratory and cardiovascular diseases and premature death. Several computer-based tools help automate air pollution health impact assessments and are being used for a variety of contexts. Expanding information gathered for a May 2014 World Health Organization expert meeting, we survey 12 multinational air pollution health impact assessment tools, categorize them according to key technical and operational characteristics, and identify limitations and challenges. Key characteristics include spatial resolution, pollutants and health effect outcomes evaluated, and method for characterizing population exposure, as well as tool format, accessibility, complexity, and degree of peer review and application in policy contexts. While many of the tools use common data sources for concentration-response associations, population, and baseline mortality rates, they vary in the exposure information source, format, and degree of technical complexity. We find that there is an important tradeoff between technical refinement and accessibility for a broad range of applications. Analysts should apply tools that provide the appropriate geographic scope, resolution, and maximum degree of technical rigor for the intended assessment, within resources constraints. A systematic intercomparison of the tools' inputs, assumptions, calculations, and results would be helpful to determine the appropriateness of each for different types of assessment. Future work would benefit from accounting for multiple uncertainty sources and integrating ambient air pollution health impact assessment tools with those addressing other related health risks (e.g., smoking, indoor pollution, climate change, vehicle accidents, physical activity).

Size distributions of particulate matter and twelve constituent elements were measured at a high traffic site in New Delhi, India during winter 2013. While PM was found to be trimodal, individual elements showed varying size distribution patterns. Three key types of size distributions were observed including unimodal with peaks either in the coarse (Al, Si) or fine (Pb) modes, bimodal with peaks in the fine range (S) and multimodal with peaks in accumulation and coarse (Cu, Sb) modes. Elements such as Al, Si and Fe were found to be in predominantly in the coarse range while Cu, Zn, Pb and Sb were found to be in the fine size range. Two modes dominate the size distribution. One is coarse (ca. 3 μm) and contains mainly crustal elements and hence arises from sources such as soil, road dust, construction dust and possible coal fly ash. The other, more intense mode is fine (ca. 0.6 μm) and appears to comprise sulphate and anthropogenic trace metals which have entered the droplet mode through hygroscopic particle growth in the very high humidity conditions of the Delhi winter. A third, less intensive mode ca. at 0.2 μm probably arises from relatively fresh anthropogenic emissions which have not grown into the droplet mode.

PM2.5 pollution in Delhi averaged 150 μg/m3 from 2012 through 2014, which is 15 times higher than the World Health Organization's annual-average guideline. For this setting, we present on-road exposure of PM2.5 concentrations for 11 transport microenvironments along a fixed 8.3-km arterial route, during morning rush hour. The data collection was carried out using a portable TSI DustTrak DRX 8433 aerosol monitor, between January and May (2014). The monthly-average measured ambient concentrations varied from 130 μg/m3 to 250 μg/m3. The on-road PM2.5 concentrations exceeded the ambient measurements by an average of 40% for walking, 10% for cycle, 30% for motorised two wheeler (2W), 30% for open-windowed (OW) car, 30% for auto rickshaw, 20% for air-conditioned as well as for OW bus, 20% for bus stop, and 30% for underground metro station. On the other hand, concentrations were lower by 50% inside air-conditioned (AC) car and 20% inside the metro rail carriage. We find that the percent exceedance for open modes (cycle, auto rickshaw, 2W, OW car, and OW bus) reduces non-linearly with increasing ambient concentration. The reduction is steeper at concentrations lower than 150 μg/m3 than at higher concentrations. After accounting for air inhalation rate and speed of travel, PM2.5 mass uptake per kilometer during cycling is 9 times of AC car, the mode with the lowest exposure. At current level of concentrations, an hour of cycling in Delhi during morning rush-hour period results in PM2.5 dose which is 40% higher than an entire-day dose in cities like Tokyo, London, and New York, where ambient concentrations range from 10 to 20 μg/m3.

In a mega-city like Delhi, vehicle exhaust emissions play a central role for urban air quality management, and measuring these on-road emissions in an environment with mixed fuels, mixed engine sizes, mixed technologies, and mixed usage patterns, is a challenging task, which means a better understanding of the vehicle and the passenger characteristics in the city is necessary. In this paper, we present a series of survey methods – vehicle owners’ interviews at fuel stations, on-road observational studies, and use of data resources like vehicle registrations and pollution under check program, which can be utilized to benchmark parameters linked to an emissions inventory methodology (Activity–Share–Intensity–Factor – ASIF). We present estimates for fuel efficiency, age profiles, annual mileage, and number of in-use vehicles for cars, motorized two-wheelers (2Ws), three-wheeled scooter rickshaws (3Ws), and buses and modeled survival functions for cars and 2Ws using log-logistic distribution function. In addition, using GPS data logging, we evaluated speed distribution of buses, cars, 2Ws and 3Ws, which resulted in evaluating the modal idling times, all of which were utilized to develop a robust vehicle exhaust emissions inventory for Delhi – segregated by vehicle type, fuel type, and age group. The survey methods and analytical techniques are simple and fast to implement, which when replicated, can provide a useful data source to estimate on-road transport emissions in urban areas.

Haze is a serious air pollution problem in China, especially in Beijing and surrounding areas, affecting visibility, public health and regional climate. In this study, the Weather Research and Forecasting-Chemistry (WRF-Chem) model was used to simulate PM2.5 (particulate matters with aerodynamic diameter≤2.5 μm) concentrations during the 2013 severe haze event in Beijing, and health impacts and health-related economic losses were calculated based on model results. Compared with surface monitoring data, the model results reflected pollution concentrations accurately (correlation coefficients between simulated and measured PM2.5 were 0.7, 0.4, 0.5 and 0.6 in Beijing, Tianjin, Xianghe and Xinglong stations, respectively). Health impacts assessments show that the PM2.5 concentrations in January might cause 690 (95% confidence interval (CI): (490, 890)) premature deaths, 45,350 (95% CI: (21,640, 57,860)) acute bronchitis and 23,720 (95% CI: (17,090, 29,710)) asthma cases in Beijing area. Results of the economic losses assessments suggest that the haze in January 2013 might lead to 253.8 (95% CI: (170.2, 331.2)) million US$ losses, accounting for 0.08% (95% CI: (0.05%, 0.1%)) of the total 2013 annual gross domestic product (GDP) of Beijing.

Why open air quality data mattersAir pollution data measured by governments across the world are a public good that can lead to transformational advances in public health when made openly available. Such advances are needed because, according to the WHO, one out of every eight deaths in the world is due to air pollution (WHO 2014). These deaths disproportionately occur in high population density, lower income countries (WHO 2016), which, where data are available, often correspond to regions with higher long-term levels of ambient pollution (Figure 1).

For a 40-year horizon (1990-2030), on-road vehicle exhaust emissions were evaluated, retrospectively and prospectively, for the largest urban agglomeration in India - the Greater Delhi region with a combined population of 22 million in 2011 (Delhi along with Ghaziabad, Noida, Greater Noida, Faridabad and Gurgaon). Emissions of particulate matter, sulfur dioxide, carbon monoxide and volatile organic compounds (VOCs) reached their peak during late 1990s through early 2000s after which they reduced significantly through year 2012. On the other hand, nitrogen oxides (NOx) and carbon dioxide show an increasing trend. The most reduction in emissions between 1998 and 2012 occurred as a result of implementation of four sets of vehicular emission standards, removal of lead, reduction of sulfur content, mandatory retirement of older commercial vehicles, and conversion of diesel and petrol run public transport vehicles to compressed natural gas. In addition, changes in the vehicular technology have also contributed to controlling emissions especially in case of auto-rickshaws and motorized two-wheelers, which changed from two-stroke to four-stroke. The rising trend of NOx along with the presence of VOCs indicates increasing tendency to form ground-level ozone and as a result, smog in the region. We predict that the current regime of vehicle technology, fuel standards, and high growth rate of private vehicles, is likely to nullify all the past emission reductions by the end of 2020s. (C) 2015 Elsevier Ltd. All rights reserved.

Coal-fired power plants, in addition to emitting greenhouse gases, are a major source of local pollution and health damages throughout the world. China, the United States, and other countries that rely on coal for electricity production regulate emissions from coal-fired power plants, primarily for health reasons. In the United States, the 1990 Clean Air Act Amendments caused many power plants to switch to low-sulfur coal or to install flue-gas desulfurization units (FGD units, or scrubbers). Subsequent tightening of sulfur dioxide (SO2) regulations has caused more plants to scrub their emissions. In 2010, power plants with FGD units accounted for 60 percent of the electricity generated from coal in the United States (Schmalensee and Stavins 2013). By 2013, 95 percent of China’s coal-fired generating capacity had been fitted with FGD units (Ministry of Environmental Protection 2014).India, which relies on coal to generate 76 percent of its electricity (CEA 2015), did not regulate SO2 emissions from coal-fired power plants until December 2015. That lack of regulation may have been due, in part, to the low sulfur content of Indian coal (Chikkatur and Sagar 2007). Indian coal is approximately 0.5 percent sulfur by weight, similar to Powder River Basin (PRB) coal in the United States (Lu and others 2013). However, the population exposed to SO2 emissions from power plants in India is much greater than that in the United States, as is the amount of coal burned to generate a kilowatt hour (kWh) of electricity. Recent studies suggest serious health effects associated with SO2 emissions from Indian power plants. Guttikunda and Jawahar (2014) estimate that Indian power plants caused more than 80,000 deaths in 2011; they attribute 30–40 percent of these deaths to SO2. Cropper and others (2012) suggest that as many as 60 percent of the deaths associated with coal-fired power plants in India may be attributable to SO2 emissions rather than to directly emitted particulate matter or oxides of nitrogen (NOx).This chapter analyzes the health benefits and the costs of installing FGD units at each of the 72 coal-fired power plants in India, plants that in 2009 constituted 90 percent of coal-fired generating capacity. We estimate the health benefits of one FGD unit by estimating SO2 emissions from a plant without an FGD unit and then translating those emissions into changes in ambient air quality. This is accomplished using an Eulerian photochemical dispersion model (CAMx) that allows SO2 to form fine sulfate particles (smaller than 2.5 micrometers in diameter [PM2.5]) in the atmosphere. The impacts of PM2.5 on premature mortality are estimated for ischemic heart disease, stroke, lung cancer, chronic obstructive pulmonary disease (COPD), and acute lower respiratory infection (ALRI) using the integrated exposure response (IER) coefficients in Burnett and others (2014). We assume that a scrubber will reduce SO2 emissions by 90 percent. The annual reductions in premature mortality and associated life years lost resulting from use of scrubbers are combined with an estimate of annualized capital and operating costs to compute the cost per statistical life saved and cost per disability-adjusted life year (DALY) averted associated with each FGD unit.Reducing SO2 emissions from coal-fired power plants offers additional benefits that we do not quantify. These include improvements in visibility (which yield aesthetic and recreation benefits) and reduced acidic deposition. Acidic deposition can reduce soil quality (through nutrient leaching), impair timber growth, and harm freshwater ecosystems (USEPA 2011).

Elevated levels of ambient air pollution has been implicated as a major risk factor for morbidities and premature mortality in India, with particularly high concentrations of particulate matter in the Indo-Gangetic plain. High resolution spatiotemporal estimates of such exposures are critical to assess health effects at an individual level. This article retrospectively assesses daily average PM2.5 exposure at 1 km × 1 km grids in Delhi, India from 2010 to 2016, using multiple data sources and ensemble averaging approaches. We used a multi-stage modeling exercise involving satellite data, land use variables, reanalysis based meteorological variables and population density. A calibration regression was used to model PM2.5: PM10 to counter the sparsity of ground monitoring data. The relationship between PM2.5 and its spatiotemporal predictors was modeled using six learners; generalized additive models, elastic net, support vector regressions, random forests, neural networks and extreme gradient boosting. Subsequently, these predictions were combined under a generalized additive model framework using a tensor product based spatial smoothing. Overall cross-validated prediction accuracy of the model was 80% over the study period with high spatial model accuracy and predicted annual average concentrations ranging from 87 to 138 μg/m3. Annual average root mean squared errors for the ensemble averaged predictions were in the range 39.7–62.7 μg/m3 with prediction bias ranging between 4.6 and 11.2 μg/m3. In addition, tree based learners such as random forests and extreme gradient boosting outperformed other algorithms. Our findings indicate important seasonal and geographical differences in particulate matter concentrations within Delhi over a significant period of time, with meteorological and land use features that discriminate most and least polluted regions. This exposure assessment can be used to estimate dose response relationships more accurately over a wide range of particulate matter concentrations.

The presence of land sea breezes is advantageous to Chennai and Vishakhapatnam. With most industrial and power plant emissions dispersed to the sea, their overall impact on the urban air quality is lessened. However, the same is not true for the diffused emissions, such as the vehicle exhaust, domestic cooking, open waste burning, and road dust, which are steadily increasing. The annual averages for 2012 in Chennai are 121.5 ± 45.5, 12.1 ± 3.5, and 20.8 ± 7.0 and in Vishakhapatnam are 70.4 ± 29.7, 18.9 ± 14.4, and 15.6 ± 6.3, for PM10, SO2, and NO2 respectively. All the concentrations are reported in micrograms per cubic millimeter. In this paper, we present sector-specific emissions inventory for particulate and gaseous pollutants, which is spatially disaggregated at 0.01° resolution, suitable for atmospheric dispersion modeling. For the urban airshed, the ambient particulate concentrations were modeled using the ATMoS dispersion model, which when overlaid on gridded population, resulted in estimated 4,850 and 1,250 premature deaths and 390,000 and 110,000 asthma attacks in year 2012, for the Greater Chennai and the Greater Vishakhapatnam regions, respectively. The total emissions are also projected to 2030. Under the current growth rates and policy assumptions, the pollution levels are likely to further increase, if the expected changes in the industrial energy efficiency, environmental regulations in the power plants, and fuel standards for the vehicles are not introduced as planned.

In India, of the 210 GW electricity generation capacity, 66% is derived from coal, with planned additions of 76 GW and 93 GW during the 12th and the 13th five year plans, respectively. Atmospheric emissions from the coal-fired power plants are responsible for a large burden on human health. In 2010–11, 111 plants with an installed capacity of 121 GW, consumed 503 million tons of coal, and generated an estimated 580 ktons of particulates with diameter less than 2.5 μm (PM2.5), 2100 ktons of sulfur dioxides, 2000 ktons of nitrogen oxides, 1100 ktons of carbon monoxide, 100 ktons of volatile organic compounds, and 665 million tons of carbon dioxide. These emissions resulted in an estimated 80,000 to 115,000 premature deaths and 20.0 million asthma cases from exposure to PM2.5 pollution, which cost the public and the government an estimated INR 16,000 to 23,000 crores (USD 3.2 to 4.6 billion). The emissions were estimated for the individual plants and the atmospheric modeling was conducted using CAMx chemical transport model, coupled with plume rise functions and hourly meteorology. The analysis shows that aggressive pollution control regulations such as mandating flue gas desulfurization, introduction and tightening of emission standards for all criteria pollutants, and updating procedures for environment impact assessments, are imperative for regional clean air and to reduce health impacts. For example, a mandate for installation of flue gas desulfurization systems for the operational 111 plants could reduce the PM2.5 concentrations by 30–40% by eliminating the formation of the secondary sulfates and nitrates.

Road transport in India plays a vital role in our growing economy. Given an aggressive vehicle sales outlook through 2030, in order to maintain a balance between the energy demand, growing on-road emissions, and overall air quality in the cities, there is a need to implement and enforce Bharat-5 standards (equivalent of Euro-V) nationwide by 2015. Any delay in its implementation or even staggered implementation of the standards will result in a delayed response for improving air quality in the Indian cities.

India faces one of the highest disease burdens from air pollution in the world, with an estimated 100% of the population living in areas with PM2.5 concentrations above the World Health Organization Guideline (10 μg/m3 annual average). With development and population growth, increases in ambient air pollution are anticipated. Combined with an aging population and increasing burden of chronic diseases, ambient air pollution will remain a concern for India well into the current century. Air quality measurements make critical contributions to the identification and prioritization of sources and locations of greatest concern, benchmarking against standards and guidelines, and in the evaluation of effectiveness of actions to reduce emissions. We compare the density of India's monitoring network with that of comparator countries and find large differences. For example, given the ~200 PM2.5 monitoring sites in operation during the 2010–2016 period, we find that India's monitor density of ~0.14 monitors/million persons (1 monitor for every 6.8 million people) is well below that of other highly populated countries such as China (1.2 monitors/million persons), the USA (3.4 monitors/million persons), Japan (0.5 monitors/million persons), Brazil (1.8) and most European countries (2–3 monitors/million persons). To address these gaps between India and monitor densities of comparator countries will require 1600–4000 monitors (1.2–3 monitors/million persons) at an estimated capital (annual operating) cost of US $212–540 ($106–270) million. Even at these densities, only relatively basic information on common air pollutants at high temporal, but limited spatial, resolution would be available. Small-scale variability in air pollution levels within urban areas would not be well-characterized, nor would there be information on chemical constituents useful for evaluating and improving simulations and forecasts, or for characterizing source contributions. As a sufficiently dense traditional network is developed over time, the potential for an integrated monitoring framework to serve as a near-term complement to a traditional network is assessed. In this design, a smaller number of traditional monitoring sites would be linked to a single advanced surface monitoring station in each of ~11 airsheds identified as a minimal number for India. These sites would combine measurement of chemical speciation of particulate matter with measurements of aerosol scatter and aerosol optical depth to link measurements with global and regional satellite-based estimates. In turn, the advanced and traditional sites could serve as calibration nodes for low-cost sensor networks designed to complement periodic mobile monitoring campaigns and/or land use regression models, to provide high spatial and temporal resolution. Such a framework could be established at a substantially reduced cost relative to that of a traditional networks, subject to specific design and complexity considerations. The same general approach may also be applicable to the many other countries with limited or no air quality monitoring and where estimates suggest air quality is a concern.

The global burden of disease study estimated 695,000 premature deaths in 2010 due to continued exposure to outdoor particulate matter and ozone pollution for India. By 2030, the expected growth in many of the sectors (industries, residential, transportation, power generation, and construction) will result in an increase in pollution related health impacts for most cities. The available information on urban air pollution, their sources, and the potential of various interventions to control pollution, should help us propose a cleaner path to 2030. In this paper, we present an overview of the emission sources and control options for better air quality in Indian cities, with a particular focus on interventions like urban public transportation facilities; travel demand management; emission regulations for power plants; clean technology for brick kilns; management of road dust; and waste management to control open waste burning. Also included is a broader discussion on key institutional measures, like public awareness and scientific studies, necessary for building an effective air quality management plan in Indian cities.

The source apportionment study in Hyderabad listed transportation, industries, and waste burning as critical sources of particulate matter (PM) pollution in the city. In this paper, we present sector-specific emissions for 2010–2011 for the Greater Hyderabad Municipal Corporation region, accounting for 42,600 t of PM10 (PM size

A total of 1,000 kilns producing 3.5 billion bricks and consuming 0.85 million tons of coal per year resulted in an estimated 2,200 to 4,000 premature deaths and 0.2 to 0.5 million asthma attacks per year in the Greater Dhaka region. In this paper, the emission reductions and health cost savings are presented for moving to cleaner brick manufacturing technologies for the districts of Gazipur, Savar, Dhamrai, Rupganj, Manikganj, Kaliganj, and Narayanganj. A summary of various technologies and feasibility of these technologies based on lessons learnt from the pilots is discussed. We explored three “what-if” scenarios through 2020 for better energy efficiency, lower coal consumption, and lower emission rates, under which the total health cost savings are estimated to range between USD12 million annually for short-term implementation and up to 55 million annually for long-term implementation. Between 2015 and 2020, the cumulative health cost savings could range between USD126 and 234 million, which clearly outweigh any cost of capital investment necessary for the technology change. An improvement in energy efficiency will result in USD1.8 to 3.0 million per year in coal savings, which will accrue to the kiln owners collectively, and these savings will pay back the capital investment within 3–4 years, in addition to the health cost savings for the city inhabitants. Hence, the entrepreneurs have all the social, environmental, and economic incentives to adopt cleaner technologies. A major gap at the regulatory level is in building awareness for the entrepreneurs and setting up an incentive structure to implement this transition, which is being addressed by an advisory committee by the Government of Bangladesh responsible for the revision of the Brick Burning Act of 1989 and related legislations.

Black carbon aerosol plays a unique and important role in Earth's climate system. Black carbon is a type of carbonaceous material with a unique combination of physical properties. This assessment provides an evaluation of black-carbon climate forcing that is comprehensive in its inclusion of all known and relevant processes and that is quantitative in providing best estimates and uncertainties of the main forcing terms: direct solar absorption; influence on liquid, mixed phase, and ice clouds; and deposition on snow and ice. These effects are calculated with climate models, but when possible, they are evaluated with both microphysical measurements and field observations. Predominant sources are combustion related, namely, fossil fuels for transportation, solid fuels for industrial and residential uses, and open burning of biomass. Total global emissions of black carbon using bottom-up inventory methods are 7500 Gg yr−1 in the year 2000 with an uncertainty range of 2000 to 29000. However, global atmospheric absorption attributable to black carbon is too low in many models and should be increased by a factor of almost 3. After this scaling, the best estimate for the industrial-era (1750 to 2005) direct radiative forcing of atmospheric black carbon is +0.71 W m−2 with 90% uncertainty bounds of (+0.08, +1.27) W m−2. Total direct forcing by all black carbon sources, without subtracting the preindustrial background, is estimated as +0.88 (+0.17, +1.48) W m−2. Direct radiative forcing alone does not capture important rapid adjustment mechanisms. A framework is described and used for quantifying climate forcings, including rapid adjustments. The best estimate of industrial-era climate forcing of black carbon through all forcing mechanisms, including clouds and cryosphere forcing, is +1.1 W m−2 with 90% uncertainty bounds of +0.17 to +2.1 W m−2. Thus, there is a very high probability that black carbon emissions, independent of co-emitted species, have a positive forcing and warm the climate. We estimate that black carbon, with a total climate forcing of +1.1 W m−2, is the second most important human emission in terms of its climate forcing in the present-day atmosphere; only carbon dioxide is estimated to have a greater forcing. Sources that emit black carbon also emit other short-lived species that may either cool or warm climate. Climate forcings from co-emitted species are estimated and used in the framework described herein. When the principal effects of short-lived co-emissions, including cooling agents such as sulfur dioxide, are included in net forcing, energy-related sources (fossil fuel and biofuel) have an industrial-era climate forcing of +0.22 (−0.50 to +1.08) W m−2 during the first year after emission. For a few of these sources, such as diesel engines and possibly residential biofuels, warming is strong enough that eliminating all short-lived emissions from these sources would reduce net climate forcing (i.e., produce cooling). When open burning emissions, which emit high levels of organic matter, are included in the total, the best estimate of net industrial-era climate forcing by all short-lived species from black-carbon-rich sources becomes slightly negative (−0.06 W m−2 with 90% uncertainty bounds of −1.45 to +1.29 W m−2). The uncertainties in net climate forcing from black-carbon-rich sources are substantial, largely due to lack of knowledge about cloud interactions with both black carbon and co-emitted organic carbon. In prioritizing potential black-carbon mitigation actions, non-science factors, such as technical feasibility, costs, policy design, and implementation feasibility play important roles. The major sources of black carbon are presently in different stages with regard to the feasibility for near-term mitigation. This assessment, by evaluating the large number and complexity of the associated physical and radiative processes in black-carbon climate forcing, sets a baseline from which to improve future climate forcing estimates.

The World Health Organization (WHO) listed the air pollution in Ulaanbaatar (Mongolia) among the top 5 cities with the worst air quality in the world. Air quality in the winter season reaches highs of 750 μg/m3 for daily average fine particulates (PM) due to increased coal combustion and lower mixing heights (

In Delhi and its satellite cities, vehicle exhaust, industries, waste burning, and construction activities account for the bulk of the particulate (PM) pollution, which between 2008 and 2011, averaged 123±87 μg/m3 for PM2.5 and 208±137 μg/m3 for PM10, both exceeding the national annual ambient standards of 40 μg/m3 and 60 μg/m3, respectively.Amulti-sectoral emissions inventory for 2010 was modeled using the ATMoS dispersion model and local meteorology to estimate health impacts in terms of premature mortality and morbidity effects. For the observed PM levels in the city, the health impacts analysis estimates 7,350–16,200 premature deaths and 6.0 million asthma attacks per year. For six residential and industrial zones, we also modeled the sector contributions to ambient PM2.5 ranging 16–34% for vehicle exhaust, 20–27% for diffused sources, 14–21% for industries, 3–16% diesel generator sets, and 4–17% brick kilns. Finally, we present some thoughts on technological, institutional, and behavioral interventions that might help the Government of Delhi to develop an action plan encompassing multiple sources and include interventions to reduce health impacts in the future years.

The Gridpoint Statistical Interpolation (GSI) Three-Dimensional Variational (3DVAR) data assimilation system is extended to treat the MOSAIC aerosol model in WRF-Chem, and to be capable of assimilating surface PM

Author(s): Hill, L Drew; Edwards, Rufus; Turner, Jay R; Argo, Yuma D; Olkhanud, Purevdorj B; Odsuren, Munkhtuul; Guttikunda, Sarath; Ochir, Chimedsuren; Smith, Kirk R | Abstract: IntroductionWinter air pollution in Ulaanbaatar, Mongolia is among the worst in the world. The health impacts of policy decisions affecting air pollution exposures in Ulaanbaatar were modeled and evaluated under business as usual and two more-strict alternative emissions pathways through 2024. Previous studies have relied on either outdoor or indoor concentrations to assesses the health risks of air pollution, but the burden is really a function of total exposure. This study combined projections of indoor and outdoor concentrations of PM2.5 with population time-activity estimates to develop trajectories of total age-specific PM2.5 exposure for the Ulaanbaatar population. Indoor PM2.5 contributions from secondhand tobacco smoke (SHS) were estimated in order to fill out total exposures, and changes in population and background disease were modeled. The health impacts were derived using integrated exposure-response curves from the Global Burden of Disease Study.ResultsAnnual average population-weighted PM2.5 exposures at baseline (2014) were estimated at 59 μg/m3. These were dominated by exposures occurring indoors, influenced considerably by infiltrated outdoor pollution. Under current control policies, exposures increased slightly to 60 μg/m3 by 2024; under moderate emissions reductions and under a switch to clean technologies, exposures were reduced from baseline levels by 45% and 80%, respectively. The moderate improvement pathway decreased per capita annual disability-adjusted life year (DALY) and death burdens by approximately 40%. A switch to clean fuels decreased per capita annual DALY and death burdens by about 85% by 2024 with the relative SHS contribution increasing substantially.ConclusionThis study demonstrates a way to combine estimated changes in total exposure, background disease and population levels, and exposure-response functions to project the health impacts of alternative policy pathways. The resulting burden analysis highlights the need for aggressive action, including the elimination of residential coal burning and the reduction of current smoking rates.

The Government of India has recently initiated unprecedented efforts to address the substantial national health burden attributable to ambient and household air pollution. The key first step was the constitution by the Ministry of Health and Family Welfare (MHFW) of an expert committee on air pollution and health. This committee put together a landmark report (MHFW 2015) released earlier this year that outlined targeted actions aimed at providing the largest exposure reductions (and consequent health benefits), instead of traditional approaches to air quality management. India’s health ministry is perhaps the first among low- and middle-income countries to initiate steps that directly address air pollution as a national health concern. Discussions initiated through this process have led to a number of concrete policy actions consistent with these recommendations, the most recent of which is highlighted within the 2016–2017 budget just released by the government: a commitment of at least US$1.5 billion to address household air pollution by providing clean cooking gas to 50 million poor households in the next 3 years (MF 2016). This is an enormous step forward in addressing household air pollution caused by polluting cookfuels. The impetus for the MHFW report came from recent assessments on the burden of disease that highlight the scale of air pollution–related health impacts (Lim et al. 2012, Forouzanfar et al. 2015) and the need to address dual burdens from ambient and household air pollution in the nation (Balakrishnan et al. 2014). Around 1.5 million premature deaths in India—deaths due to a range of acute and chronic health conditions—are attributable annually to the indoor and outdoor exposures of the population to air pollution (IHME 2015). This places air pollution near or at the top of the list of all known risk factors for ill health in the country, above high blood pressure, smoking, child and maternal malnutrition, and risk factors for diabetes (IHME 2015). Although management of the latter risks has long been part of national health programs managed by the MHFW, the report makes a compelling case for a major national cross-ministerial effort directed at air pollution, to be spearheaded by the ministry. We describe here the salient recommendations of the report. The health burden from air pollution comes from outdoor exposures in both urban and rural areas as well as from within households. In fact, approximately 170 million households, primarily in rural areas, are exposed indoors and near the household to pollution resulting from poor combustion of solid fuels such as wood, crop residues, and dung in traditional cookstoves (Bonjour et al. 2013). The result is more premature deaths from this cause (> 0.9 million annually) than in any other country (IHME 2015). Unfortunately, in spite of significant economic development and consequent growth in the use of clean fuels in some populations, the number of people using solid fuels in India does not seem to have changed significantly in the past 30 years (Bonjour et al. 2013), although it now constitutes a smaller fraction of the overall population. Vehicles and power plants—key contributors to urban ambient air pollution—historically have been the only sources to be addressed in national ambient air quality management efforts. The report addresses both household (indoor) and outdoor air pollution in an integrative fashion instead of treating them as separate issues—the first time by a government agency, to our knowledge. This is especially relevant given that household cookstoves also contribute an estimated 25% of ambient air pollution in India (Chafe et al. 2014). Notably and importantly, the report invokes a new paradigm of exposure management instead of concentration management as a national air pollution control strategy, and prioritizes policies and actions accordingly to achieve substantially enhanced cost effectiveness and speed in achieving health benefits. From a health perspective, what matters is not just the absolute volume of emissions but how much of the pollution is breathed in by individuals, defined sometimes as the “intake fraction” (Bennett et al. 2002). With advances in understanding and capabilities that have come from new monitoring technologies, digital data management, remote sensing, and associated modeling, it is now possible to undertake such exposure apportionment in India. Several potential approaches can be used to rank sources that are most proximate to the population (such as stoves, vehicles, and neighborhood trash burning) as highest priority, since these produce the highest exposures per unit emissions. It implies somewhat different strategies for monitoring than have been used in other countries—for example, placing more ambient monitors in rural as well as urban areas to better follow population exposures (Balakrishnan et al. 2014). The report provides a health-based argument for intensifying efforts to control household air pollution and to expand access to truly clean sources of household energy, shifting the focus away from just promoting so-called “improved” technologies that have not proven to be health protective (MNRE 2016). It also promotes a new research agenda to apply the range of modeling, networking, and sensing tools now becoming available to assess in specific places the impact of specific source types on exposure rather than just on concentrations (NRC 2012). The report also highlights that reducing air pollution to achieve health goals will require actions across a range of government agencies, but that it can—and should—be led by health ministries (as has been the case for smoking and sanitation, for example). A clearer recognition of the problem and articulation of possible pollution control strategies can cause other ministries to start taking steps on their own, as has been the case with the Ministry of Petroleum and Natural Gas, which has taken a number of innovative steps to expand access to clean cooking gas to the poor (see, for example, Tripathi et al. 2015). Health ministries can also deploy their own assets to mitigate the impacts of air pollution through appropriate training, treatment, and information dissemination. In India, the MHFW has a million local health workers, tens of thousands of hospitals and clinics, and thousands of institutions that train health professionals, as well as extensive infrastructure through the Rural Health Mission and state governments. Health ministries and medical doctors also retain unique status in most countries as credible sources of information to influence the public, media, and policy makers. All in all, the integrative exposure management–driven approach taken by the MHFW report will not only help address this major problem in an efficient and effective manner, but also serve as a model for other developing countries that likewise need to manage health impacts from a combination of household and outdoor air pollution. The report is pioneering particularly by virtue of its coming from a ministry of health. Ever since the widespread recognition of the hazards of environmental pollution in the 1950s, actions on air pollution have been nearly universally managed by environment agencies. Although health is commonly considered as a criterion for setting regulatory standards, air pollution has been considered neither in the same landscape as all the other important risk factors that affect national health (most of which lie in completely different sectors), nor in the context of the special assets of the health sector that can be brought to bear on air pollution control. Even though air pollution is a major threat, of course, a resource-constrained country such as India has many other health challenges to address as well, and needs to carefully weigh the relative benefits of actions across sectors. With 10 of the dirtiest 20 cities in the world (WHO 2016) and 700 million people caught in the “chulha trap,” i.e., still lacking access to clean fuels for cooking (Smith and Sagar 2014), India is in the throes of a silent health crisis due to air pollution that has become the greatest of any major country in the world both in total and per capita (twice that of China) (IHME 2015). The extreme nature of the situation requires major initiatives and innovative cross-sectoral approaches to organize the monitoring and evaluation of targeted controls. India’s MHFW has set the ball in motion by providing a pioneering framework for addressing air pollution impacts for both rural and urban populations.