Managing future air quality in megacities: A case study for Delhi

Accepted Manuscript Managing future air quality in megacities: A case study for Delhi Markus Amann, Pallav Purohit, Anil D. Bhanarkar, Imrich Bertok, Jens BorkenKleefeld, Janusz Cofala, Chris Heyes, Gregor Kiesewetter, Zbigniew Klimont, Jun Liu, Dipanjali Majumdar, Binh Nguyen, Peter Rafaj, Padma S. Rao, Robert Sander, Wolfgang Schöpp, Anjali Srivastava, B. Harsh Vardhan PII:

S1352-2310(17)30285-6

DOI:

10.1016/j.atmosenv.2017.04.041

Reference:

AEA 15308

To appear in:

Atmospheric Environment

Received Date: 2 February 2017 Revised Date:

26 April 2017

Accepted Date: 28 April 2017

Please cite this article as: Amann, M., Purohit, P., Bhanarkar, A.D., Bertok, I., Borken-Kleefeld, J., Cofala, J., Heyes, C., Kiesewetter, G., Klimont, Z., Liu, J., Majumdar, D., Nguyen, B., Rafaj, P., Rao, P.S., Sander, R., Schöpp, W., Srivastava, A., Vardhan, B.H., Managing future air quality in megacities: A case study for Delhi, Atmospheric Environment (2017), doi: 10.1016/j.atmosenv.2017.04.041. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Managing future air quality in megacities: A case study for Delhi

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Markus Amanna, Pallav Purohita1, Anil D. Bhanarkarb, Imrich Bertoka, Jens Borken-Kleefelda, Janusz Cofalaa, Chris Heyesa, Gregor Kiesewettera, Zbigniew Klimonta, Jun Liu a, Dipanjali Majumdara, Binh Nguyena, Peter Rafaja, Padma S. Raob, Robert Sandera, Wolfgang Schöppa, Anjali Srivastavab, B. Harsh Vardhanb

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International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361, Laxenburg, Austria b

National Environmental Engineering Research Institute (NEERI), Nehru Marg, Nagpur, 440020, India

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Abstract

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Megacities in Asia rank high in air pollution at the global scale. In many cities, ambient concentrations of fine particulate matter (PM2.5) have been exceeding both the WHO interim targets as well as respective national air quality standards. This paper presents a systems analytical perspective on management options that could efficiently improve air quality at the urban scale, having Delhi as a case study. We employ the newly developed GAINS-City policy analysis framework, consisting of a bottom up emission calculation combined with atmospheric chemistry-transport calculation, to derive innovative insights into the current sources of pollution and their impacts on ambient PM2.5, both from emissions of primary PM as well as precursors of secondary inorganic and organic aerosols. We outline the likely future development of these sources, quantify the related ambient PM2.5 concentrations and health impacts, and explore potential policy interventions that could effectively reduce environmental pollution and resulting health impacts in the coming years. The analysis demonstrates that effective improvement of Delhi’s air quality requires collaboration with neighboring States and must involve sources that are less relevant in industrialized countries. At the same time, many of the policy interventions will have multiple co-benefits on development targets in Delhi and its neighboring States. Outcomes of this study, as well as the modelling tools used herein, are applicable to other urban areas and fast growing metropolitan zones in the emerging Asian regions.

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Keywords: Air pollution, PM2.5, Population exposure, Policy analysis, Health impacts, Co-benefits

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Corresponding author: (Tel: +43-2236807-336; Fax: +43-2236807-533; E-mail: [email protected])

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1. Introduction Indian cities rank high in air pollution at the global scale - predominantly for fine particulate matter (PM2.5) that is associated with serious health impacts. According to the World Health Organization (WHO), four out of the ten cities with highest concentrations are found in India (Figure S1) (WHO, 2016a). While the WHO has established a global guideline value of 10 µg/m3 for the annual mean concentrations of PM2.5, it is estimated that only about one percent of the Indian population has been exposed to less than this level in 2015 (OECD/IEA, 2016). Approximately, 62 percent of the population faced an exposure to annual mean ambient PM2.5 concentrations of more than 35 µg/m3, the Target Level 3 defined by the WHO. The National Ambient Air Quality Standards (NAAQS) of the Indian government set a permissible limit of annual PM2.5 concentrations of 40 µg/m3 (CPCB, 2012), which is exceeded widely in the country (Figure S2).

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These high pollution levels cause significant health impacts; estimates for India for 2010 range between 483,000 and 1,267,000 cases of premature deaths annually from outdoor pollution, and 748,000-1,254,000 cases from indoor household pollution (Forouzanfar et al., 2016, 2015; Lim et al., 2012; WHO, 2016b). While their quantification remains uncertain, it is likely that health impacts impose a significant burden on the economy; a recent estimate of the World Bank suggests for 2013 welfare losses equivalent to 7.7 percent of national GDP (Lim et al., 2012; WHO, 2016b; World Bank, 2016).

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India’s ambitious plans for further economic development imply additional growth in many sectors that are currently important sources of pollution. The current governmental policies outline a continued 7-8 percent growth per year in general economic output (MoF, 2016; OECD, 2015). Even larger growth rates are envisaged for activities that are generally believed to be major sources of pollution. For instance, the sales of domestically manufactured vehicles have increased by at least 15 percent per annum since 2008 (SIAM, 2013), and traffic volumes are expected to grow by 10.5 percent per year in future (MoSPI, 2015). Also for power generation, forecasts suggest a 11.1 percent per year increase in the future (CEA, 2015; CEA, 2016). Thus, unless effective countermeasures to control pollution from these increased activities are taken, ambient air quality is poised to deteriorate further in the future.

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Delhi is one of the largest megacities of South Asia. It is considered among the most polluted megacities of the world (WHO, 2016a) that often violates NAAQS with respect to PM2.5. Since long, the Indian authorities have imposed regulations on important emission sources that should lead to significant improvements in ambient air quality and eventually to compliance with the Indian air quality standards and international guidelines (see Table 1). In December 2015, the Indian Ministry of Environment, Forest and Climate Change (MoEFCC) has notified the revised standards for coal-based thermal power plants in the country, with the primary aim of minimising air pollution (MoEFCC, 2015). From 2017, coal thermal power plants across India will have to cut PM emissions by as much as 40 percent and reduce their water consumption by nearly a third according to the revised standards.

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Ambient air pollution management remains a major policy challenge in Indian megacities, like Delhi, despite the implementation of several policies such as shifting of public transport to Compressed Natural Gas (CNG), converting coal power plants to natural gas (CPCB, 2011). Highly polluting industries and brick kilns were not permitted to operate within the jurisdiction of the National Capital Territory (NCT) of Delhi (Narain and Bell, 2006). Focusing on the NCT of Delhi, this study assesses the likely impacts of conceivable additional regulations and strategies on air quality, population exposure and health outcomes.

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2. Methodology To explore the likely impacts of alternative policy interventions on emission reductions for the NCT of Delhi (Figure S3), we employ a detailed atmospheric chemistry model that quantifies the impacts of emissions of primary PM and PM precursor pollutants from different economic sectors and source regions on ambient concentrations of PM2.5 in Delhi. Computations employ a detailed inventory of PM2.5, SO2, NOx, NH3 and volatile organic compounds (VOCs) in the NCT of Delhi region, which has been developed by the National Environmental Engineering Research Institute (NEERI) based on a wealth of statistical information and local measurements (see: supplementary information).

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Emissions from this inventory are subsequently fed into the Greenhouse gas – Air pollution Interactions and Synergies (GAINS) model that has been developed by the International Institute for Applied Systems Analysis (IIASA) (Amann et al., 2011, 2008; Purohit et al., 2010). The GAINS model is a widely applied policy analysis tool to identify cost-effective policy interventions to reduce health impacts of air pollution while maximizing co-benefits with other policy priorities (Hordijk and Amann, 2007; Reis et al., 2012). GAINS quantifies the implications of alternative pathways of economic development on emissions of PM2.5 and its precursors on a detailed sectoral representation of anthropogenic activity and individual emission control technology application. It estimates the technical potential for emission reductions at the various sources and calculates the associated emission control costs, as well as the contributions from natural sources (e.g., soil dust, secondary organic aerosols, sea salt). Emission factors for each pollutant, sector, fuel, and control technology are taken from the GAINS database and calibrated to reflect Indian conditions. The GAINS version that has been developed for this particular study estimates the resulting ambient concentration fields of primary particles as well as of secondary aerosols, considering emissions within the NCT of Delhi as well as relevant emissions in the rest of India, which are transported in the atmosphere into the city domain (Note that, due to their small size, PM2.5 particles remain in the atmosphere for several days and are transported with the wind during this time). We combine calculations from two atmospheric models: Regional chemistry-transport calculations of PM and precursors on a 0.5° grid are based on the European Monitoring and Evaluation Programme (EMEP) chemistry transport model (Simpson et al., 2012). Following the established methodology for atmospheric dispersion in GAINS (Kiesewetter et al., 2015), linear transfer coefficients have been derived from a set of full year sensitivity simulations using meteorological conditions of 2010 in which emissions of one pollutant (PPM, SO2, NOx, NH3, VOC) from one source region (each Indian state) were reduced by 15% at a time. These transfer coefficients are applied to emissions of all pollutants from source regions outside Delhi, and to PM precursor emissions from Delhi, following the logic that the formation of secondary aerosols takes place on longer spatial and temporal scales and therefore the 0.5° resolution is sufficient.

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Local variation of PM2.5 concentrations within the city is mainly due to primary PM emissions within the city. Consequently, the dispersion of primary PM2.5 emissions from Delhi itself is calculated based on the AERMOD plume dispersion model, a high-resolution steady-state dispersion model designed for short-range (up to 50 km) passive dispersion of air pollutant emissions from sources with different emission characteristics such as point sources, line sources, and area sources (Cimorelli et al., 2005; Perry et al. 2005). The spatial distribution of PM2.5 emissions for Delhi NCT as used in GAINS calculations is shown in Figure S5 of the supplementary material, which is based on spatially explicit sectoral emission distribution patterns developed by NEERI for different source sector groups (Bhanarkar et al., 2017). The model computes subsequent population exposure to PM2.5 at a 2x2 km resolution, and derives indicative estimates of the resulting impacts on premature mortality, both from ambient pollution as well as the exposure to household pollution within homes. Ambient

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concentration calculations and their uncertainties, and the health impact estimation methodology applied in this study are further detailed in Section 7 of the supplement.

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3. Results

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3.1 Understanding of the current situation To explore the scope and effectiveness of potential future policy interventions, the analysis starts from a detailed inventory of currently implemented and decided emission control measures (Table 1). Assuming effective implementation of all these measures, the analysis combines technical features (i.e., emission reduction efficiencies) of each measure with economic activities, emission factors, and meteorological and chemical features of the dispersion of pollutants in the atmosphere. As a result, the analysis closely reproduces not only observations of ambient levels of PM2.5 across the model domain (Figure 1), but also provides information on chemical composition and source attribution of PM2.5 (Figure 2).

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Table 1: Air pollution prevention/control policies in India

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Current status • Air pollution is a serious issue, with most cities in India violating PM2.5 targets; main sources are fuel wood and biomass burning, fuel adulteration, vehicle emissions, large-scale crop residue burning • Electricity sources: Thermal (Coal, Oil, Gas) – 70 percent, Hydro (14 percent), Renewables (wind, bagasse, solar, biomass, waste to power, water mills) - (14 percent), Nuclear (2 percent).

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Current/Planned policies & programs • National Ambient Air Quality Standards (NAAQS) • National Air Quality Index (AQI) • Air (Prevention and Control of Pollution) Act (1981) • Air Quality legislation/programs

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• Industries that have the potential to impact air quality: petroleum refining, chemicals, textiles, steel, cement, mining, basic metal industries, nonmetallic mineral products, etc.

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• 40 percent non-fossil-based power capacity by 2030 (primarily by renewables and nuclear) • Promotion of renewables and energy efficiency • Use of high efficiency Electrostatic precipitator technology in large coalbased power plants (where applicable) • New environmental norms for coal-based thermal power plants2 • To phase-out aged and inefficient subcritical coal thermal power plants • Emission regulations for industries • Promotion of renewable energy investment • Incentives to purchase energy efficient equipment; clean production/installation of pollution control technologies • Other actions at national, sub-national and/or local level to reduce industry

The new standards are aimed at reducing emission of PM10 (0.98 kg/MWh), sulphur dioxide (7.3 kg/MWh) and Oxide of nitrogen (4.8 kg/MWh), which will in turn help in bringing about an improvement in the Ambient Air Quality (AAQ) in and around thermal power plants. The technology employed for the control of the proposed limit of Sulfur Dioxide (SO2) and Nitrogen Oxide (NOx) will also help in control of mercury emission (at about 70-90%) as a co-benefit (MoEFCC, 2015).

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Continuous PM2.5 observations are available from the network maintained by the Indian Central Pollution Control Board (CPCB, 2016a). Here we use monitoring data at four sites (ITO, Anand Vihar, Punjabi Bagh, R.K. Puram) averaged during the period 2012-2014; their locations are visible on the map in Figure 1. Due to India’s monsoon climate the inter-seasonal variation in meteorology and ambient concentrations is expected to be higher than the inter-annual variation, justifying the use of not exactly overlapping periods for comparison with observations.

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emissions (i. e. Finance Act 2015 provides incentives for manufacturing facilities to be set up in rural areas of some states). Transport • Key transport-related air • Vehicle emission limit: Euro III (Euro IV quality challenges: rapid in 11 major cities); Euro VI3 from 2020 onwards growth; a number of older vehicles still in use without • Fuel Sulphur content: 350 ppm (50 ppm catalytic converters; some in 11 major cities) taxis and auto-rickshaws use • Actions to expand, improve and promote adulterated fuel (differential public transport, mass transit and nontaxes encourage this). motorized transport • Ministry of Road Transport & • Shifting of public transport buses from Highways (MoRTH) has diesel to CNG in Delhi decided to leapfrog from BS- • Other transport - related actions: IV to BS-VI emission norms Incentives for production or purchase of directly by 01.04.2020. green vehicles4 (i. e. the electric vehicle incentive program); promotion of biofuels5, ban on used car import, etc. Open burning of • Open burning of agricultural • Legal framework: National Green waste (outdoor) and municipal waste is Tribunal (NGT) has recently directed common way to handle waste; corporations, authorities and state Despite ban, open burning governments to ensure garbage not burned continues. openly; Cities are not complying with the Municipal Solid Waste (Management & Handling) Rules 2000 Open burning of • Major fuels used for cooking • Promotion of off-grid/grid electrification, biomass and space heating: 67 percent cleaner cooking fuels (i. e. LPG, (indoor) households use solid fuels. electricity) and cleaner cook stoves Source: (GoI, 2011; CPCB 2012; Dholakia et al., 2013; UNEP 2015; MNRE 2016; MoEFCC, 2015)

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In January 2016, GoI has decided to leapfrog directly from Euro IV emission norms for petrol and diesel to Euro VI standards. The BS-VI norms will be implemented for new vehicles by April 2020 and for existing vehicles by April 2021 (MoRTH, 2016). 4 The Government of India has set a goal of deploying 6–7 million hybrid and Plug-in electric vehicles (PEVs) on Indian roads by the year 2020 (Sheppard et al., 2016). 5 National Biofuels Policy proposed a target of 20% blending of transportation fuels — diesel and petrol (gasoline) — with bio-diesel and bio-ethanol by 2017 (Purohit and Dhar, 2015).

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Figure 1: Comparison of ambient PM2.5 concentrations in the Delhi model domain: Modelled results for 2015 (field shading) versus observations (circles) for 2012-2014.

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While there have been several studies on chemical composition of SPM and PM10 in Delhi, only few studies are available for PM2.5 (Bisht et al., 2015; Chowdhury et al., 2007; Pant et al., 2015; Sharma et al., 2016). Figure 2 presents chemical composition of PM2.5 from GAINS estimation for 2015 and observations obtained from literature (Bisht et al. (2015): from January to December 2012 in an institutional area (IITM); Pant et al. (2015): from summer (2014) and winter (2013/14) in a traffic hotspot (Mathura road); Sharma et al. (2015): at an urban site of Delhi (CSIR/NPL), India from January, 2013, to December, 2014). The chemical composition of traffic hotspot at Mathura road is enhanced in black carbon (BC) and secondary inorganic aerosols (Pant et al., 2015) relative to the GAINS area average. The institutional site (CSIT/NPL) shows large differences between seasons (122±94.1 µg/m3), and the GAINS modeled value of 138 µg/m3 for this location falls within this range and is likely explained by difference in the modeled metereology. The chemical composition appears to match with the observations. ITO, Anand Vihar and Mathura road are traffic hotspots hence the measured concentrations are above the average. The PM2.5 concentration at the representative residential site R. K. Puram is within the range of 120 µg/m3, that is consistent with the GAINS esimated population weighted PM2.5 concentration in Delhi. In summary, based on the detailed emission inventory, the GAINS model can well reproduce the observations, which gives us confidence that the subsequent projections reasonably well reflect the reality on the ground.

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Figure 2: Chemical composition of PM2.5: Observations (Bisht et al., 2015; CPCB, 2016a; Pant et al., 2015; Sharma et al., 2016) against GAINS model results for 2015

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Most instructive, however, is the source attribution that quantifies contributions to ambient PM2.5 from the different sources. Fighre 3 shows the contributions to ambient PM2.5 concentrations computed for 2015 for the I.T.O station, which is located in the city center close to a road side (left panel) and the contributions to population-weighted PM2.5 exposure in the Delhi NCT (right panel). The x-axis distinguishes the spatial origin of PM2.5, i.e., (i) from natural sources, (ii) pollution originating from the long-range transport of emissions from sources in India outside Delhi, Haryana and Uttar Pradesh, such as Punjab and Rajasthan, as well as Pakistan (“Long-range”), (iii) from emission sources in the surrounding States Haryana and Uttar Pradesh (“HR+UP”), and (iv) from emissions originating from the Delhi NCT region. The y-axis indicates the amounts originating from emissions of the different economic sectors.

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Figure 3: Source contributions to PM2.5 annual concentration and population weighted exposure in 2015; left panel: I.T.O. monitoring station, right panel: population-weighted mean exposure with the same legends in the left panel.

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Strikingly, despite the large size of Delhi NCT with about 18 million inhabitants, only about 40 percent of population-weighted exposure to PM2.5 originates from emission sources within the domain (at traffic hot spots, the share increases to about 55 percent). 60 percent of PM2.5 in ambient air in Delhi is transported into the city from outside, out of which about half comes from the surrounding States Haryana and Uttar Pradesh, one quarter from even more remote anthropogenic sources, and one quarter from natural sources.

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At the ITO traffic hot spot site, about one third of ambient PM2.5 was caused by emissions from road traffic, out of which about 55 percent emerge from exhaust gas (diesel soot) and 45 percent from nonexhaust emissions (road dust, brake and tire wear). On a population-weighted basis, transport emissions of primary PM2.5 contributed about 17 percent to total PM2.5 in ambient air; other important sources include emissions of primary PM2.5 from solid fuel combustion in cook stoves (mainly from the surrounding rural areas) with a 20 percent contribution, 23 percent from secondary inorganic aerosols formed from reactions of SO2 emissions (emitted in power plants and industry) and NOx (from traffic, power plants and industry) with NH3 from agricultural activities, 14 percent from primary PM2.5 emitted at high stacks, 12 percent from primary PM2.5 during the burning of agricultural and municipal waste, and 9 percent from natural sources (soil dust and sea salt).

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It is noteworthy that this source allocation (which is supported by the chemical analysis of PM monitoring) contradicts wide-spread misbeliefs that the majority of air pollution in Indian cities emerges from vehicles (Badami, 2005; Gurjar et al., 2010). While at traffic hot spots vehicles contribute the largest share, still about two-thirds of PM2.5 at such locations originates from other sources. Furthermore, currently only slightly more than half of traffic-related PM2.5 comes from exhaust emissions, while the other fraction is non-exhaust, in particular resuspended road dust, as well as tire and brake wear. As a consequence, policy interventions that focus only on vehicles’ exhaust emissions will have limited impact on PM2.5 concentrations in ambient air, especially when a rapid growth in traffic volumes will cause similar increases in non-exhaust emissions.

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In addition, while air quality at traffic hot-spots is relevant for compliance with ambient air quality standards, actual health impacts of air pollution in the total population are more closely related to the population-weighted exposure to PM2.5. The right panel of Figure 3 clearly indicates that traffic-

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related (exhaust and non-exhaust) emissions make an even smaller contribution to the long-term exposure of the population in the whole city. At the same time, it highlights the importance of emission sources that receive less attention in current emission control strategies for urban air quality, such as the uncontrolled burning of biomass and waste, road dust, as well as SO2 and NOx emissions from large plants and traffic.

3.2 Future trends To explore the likely future development of air quality in Delhi and the effectiveness of alternative policy interventions, we analyze an economic projection provided by NEERI for the year 2030. This projection assumes an annual growth of the population in the Delhi NCT by 2.8 percent, resulting by 2030 in a 50 percent larger population than in 2015. At the same time, economic wealth (expressed as GDP/capita) will improve by 5 percent/year, resulting in an 8 percent annual growth of economic output in the region, i.e., a doubling compared to 2015. Transport demand is assumed to follow the same growth path (Table 2).

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The economic projection is accompanied by an energy forecast that suggests a significant decline in the energy intensity (3.6 percent/year), so that total energy consumption in Delhi will grow by only 4.2 percent by year. Most of the increased energy demand would be supplied by natural gas (+7.3 percent/year) and liquid fuels (+2.9 percent/year), while the consumption of coal and biomass will remain at current levels.

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Table 2: Assumed development of key indicators in Delhi NCT between 2015 and 2030 Change 2015-2030 +50% +110% +200% +200%

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Change 2015-2030 -40% +190% +80% ± 0%

For the regions outside Delhi, we employ the assumption of an economic growth of 6.2 percent per year and the energy projection presented by the International Energy Agency (IEA) in its special report on Energy and Air Quality (IEA, 2016). As a baseline air quality projection, we assume full and efficient implementation of the emission control legislation that is currently in force for Delhi and the neighboring States (see Table 1).

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With these assumptions, population-weighted exposure to PM2.5 in the Delhi region would rise by 2030 by about 9 percent to 132 µg/m3 (Figure 4). The anticipated expansion of transport volumes would cause an even larger growth of PM2.5 concentrations at traffic hot spots (e.g., to 195 µg/m3 at the ITO station, which would then exceed the current Indian Ambient Air Quality Standard (see Table S1 in the supplementary section) by almost a factor of five.

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It is noteworthy that most of the increase emerges from higher emissions in Delhi. The inflow of pollution would stabilize at the 2015 level, essentially due to the new legislation on SO2 emissions from large point sources (MoEFCC, 2015) that affect the formation of secondary inorganic aerosols. While the new legislation for emission sources in Delhi will show significant effect (e.g., Bharat VI for exhaust emissions from mobile sources), their benefit will be compensated by higher non-exhaust emissions that emerge as a consequence of the general growth in transport activities.

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Figure 4: Source contribution to population-weighted PM2.5 in Delhi NCT for the Baseline projection in 2030

3.3 The scope for additional measures in Delhi

As the baseline projection does not indicate progress towards the achievement of the National Air Quality Standards in Delhi, we use the GAINS-Delhi tool to explore the effectiveness of additional emission control strategies.

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An ‘Advanced Technology’ scenario

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As a logical extension of current policies, we analyze an ‘Advanced Technology’ case which assumes that the Delhi government would strengthen measures that rely on technical emission controls, in line with examples widely adopted in many other countries in the world. In particular, such a scenario would further tighten the emission limit values for large point sources for SO2, NOx and PM/TSP. For mobile sources, tighter controls would be introduced for non-road mobile machinery, while emission standards for road vehicles would remain at the Bharat VI level that is already assumed in the baseline. Furthermore, cremation would become electric.

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Effective implementation of these additional requirements by 2030 would reduce population-weighted PM2.5 to 120 µg/m3, i.e., by 10 percent compared to the baseline, and thereby bring it back to the 2015 levels (Figure 5; left panel). However, despite the high ambitions for new technology, emission sources from within the NCT area would still contribute about 50 µg/m3 to population exposure, which in itself already exceeds the current National Ambient Air Quality Standard.

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Figure 5: Source contribution to population-weighted PM2.5 in Delhi NCT in 2030; left panel: ‘Advanced Technology’, right panel ‘Delhi Clean Air’ set of measures applied to Delhi with the same legends in the left panel.

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A ‘Delhi Clean Air’ strategy

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An alternative scenario explores the scope for air quality improvements from regulating emissions from such non-industrial activities. As these sources are less relevant in industrialized countries, they are often overlooked in simplistically drawn lessons from international experience. While there is less experience documented, these sources have been controlled in many countries along their development path, although often not with a focus on air quality management.

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In particular, this ‘Delhi Clean Air’ strategy considers road paving, improved waste management with a ban on open trash burning, fewer fireworks in the city, and promotion of electric barbecues (to replace 50 percent of current BBQ activities). Furthermore, it is assumed that solid fuel use for cook stoves will be phased out in Delhi up to 2030 and replaced by electricity or natural gas.

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Such a strategy would lead to significant improvements in air quality in Delhi, with populationweighted PM2.5 exposure dropping to about 90 µg/m3, i.e., by 30 percent below today’s levels (Figure 5; right panel).

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Although addressing the traditional emission sources that are characteristic for Indian cities will enable a significant improvement in air quality, it will only solve part of the problem as a large share of pollution will remain, imported from the surrounding areas. For the scenario above, 80 percent of particulate matter in Delhi’s air will be imported from outside, so that a further strengthening of regulations within Delhi would have only very limited impact on local air quality. As shown in Figure 5 (right panel), after Delhi would have taken all practical measures, the majority of pollution would be transported from the surrounding States Haryana and Uttar Pradesh. About 40 percent of the import from these areas would result from large point source emissions of SO2 and NOx (forming secondary inorganic aerosols), while the more conventional emission sources account for about 60 percent.

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Thus, following the Western standard model for air quality management, i.e., tightening emission standards for large industrial emission sources and transportation in Haryana, Uttar Pradesh and

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Figure 6: Source contribution to population-weighted PM2.5 in Delhi NCT in 2030; left panel: ‘Advanced Technology’, right panel ‘Delhi Clean Air’ set of measures applied to Delhi, Haryana and Uttar Pradesh with the same legends in the left panel.

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Much larger benefits could be achieved from extending the ‘Delhi Clean Air’ strategy to the three neighbouring States, enhanced by an organized collection and recycling of agricultural waste to substitute coal and oil in the industrial sector, to be accompanied by a ban on agricultural waste burning. Furthermore, it is assumed that biomass for cook stoves will not only be substituted by electricity and natural gas (as in Delhi), but in rural areas also by liquefied petroleum gas (LPG).

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Such a cooperative strategy would have major impacts on air quality in Delhi, and could halve current exposure levels for PM2.5 to around 60 µg/m3 (Figure 6; right panel). In addition, it would also lead to significantly lower pollution levels around Delhi, and thereby deliver even larger benefits in surrounding areas.

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3.5 Benefits from the air quality measures

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The above analysis explored the response of a health-related exposure metric, i.e., populationweighted PM2.5 concentrations in Delhi NCT towards different emission control assumptions. Obviously, both the population distribution and concentrations of PM2.5 in ambient air show significant spatial variation within the model domain. Such distributional aspects are important for an analysis of the compliance with air quality standards, which typically refer to concentrations at specific locations.

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As mentioned above, the GAINS-Delhi model computes concentrations and population exposure with a 2x2 km spatial resolution, to be compared with the corresponding distribution of (today’s and future) population.

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For the year 2015, the analysis reveals that within the Delhi model domain essentially all people faced pollution levels that exceeded the (annual) National Ambient Air Quality Standards of 40 µg/m3. About 2.5 million people were exposed to air that exceeded the NAAQS (see Table S1)) by more than four times (>160 µg/m3), 7.5 million people experienced exceedance between a factor of three and four (i.e., between 120 and 160 µg/m3), and 10 million between 40 and 80 µg/m3. For the baseline

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case in 2030, the group with the highest exposure (>160 µg/m3) would increase to more than 8 million people, and 19 million people (i.e., almost the current population of Delhi) would be exposed to more than 120 µg/m3. Compared to today’s situation, only the joint implementation of the ‘Delhi Clean Air’ scenario in Delhi, Haryana and Uttar Pradesh could reduce total population exposure in absolute terms, i.e., reduce the number of people that face exceedances of the current Indian NAAQS (Figure 7).

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Health impacts Quantifying the current number of premature deaths attributable to air pollution and projecting the future entails considerable uncertainty. The GAINS-Delhi model has implemented the current methodologies that are widely used to estimate the global burden of disease from exposure to ambient and household air pollution around the world, mainly developed by the Global Burden of Disease (GBD) studies (Lim et al., 2012; Burnett et al., 2014; WHO, 2016b; World Bank, 2016). Concentration-response relationships used in individual studies differ considerably, and uncertainties are high. To illustrate the range of outcomes, we quantify premature mortality using both GBD-2010 and GBD-2013 sets of integrated exposure-response relationships, and use their arithmetic mean as central value. Details of the health impact calculation can be found in Section 7 of the supplementary material.

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The GAINS-Delhi model estimates that in 2015 ambient air pollution caused about 8,300 [6,10010,540] cases of premature deaths in Delhi. In the baseline case, this number would increase to about 14,000 [10,150-17,430], a combined effect of the 50 percent growth in population size, population aging, and the higher exposure as a result of higher emissions. While the ‘Advanced Technology’ scenarios would reduce population exposure to some extent, the current health impact methodologies suggest even smaller improvements in health effects, essentially due to the assumptions of declining health effects under high pollution levels as reflected in the non-linear shapes of the integrated exposure response functions used. Even the regional application of the ‘Advanced Technology’ scenario would lead to an increase in the number of premature deaths by about 55 percent compared to 2015, an aspect that will be relevant for the future planning of public health authorities. Larger health benefits are expected for improvements at lower exposure levels, e.g., from the ‘Delhi Clean Air’ scenario (Figure 8). Note that we here quantify health impacts for Delhi only; additional health benefits can be expected in the neighbouring states, particularly when emission reductions are applied there as well.

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Compared to the ‘Advanced Technology’ scenario that does not modify the levels of any economic activities, the ‘Delhi Clean Air’ strategy addresses pollution sources in a more holistic way. It promotes reduction and substitution of most polluting activities by cleaner alternatives without diminishing human welfare through, e.g., energy efficiency improvements, enhanced public transport, use of cleaner fuels for cook stoves, and the replacement of coal with natural gas.

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The ‘Delhi Clean Air’ strategy does not, per se, aim at a reduction of greenhouse gas emissions. While beneficial for air quality purposes, it even adopts certain measures that lead to higher direct CO2 emissions (e.g., replacement of biomass for cook stoves by LPG, natural gas or electricity). At the same time, the analysis accounts for associated changes in the emissions of all greenhouse gases, especially emissions of methane from biomass combustion and waste management. It is noteworthy that, as a side effect of the adopted measures, Delhi’s greenhouse gas emissions in the ‘Delhi Clean Air’ strategy would be more than 20 percent lower than in the baseline case (Figure 9). Co-controls of air pollution and greenhouse gas emissions in Haryana and Uttar Pradesh are even larger due to the ban of agricultural waste burning, an activity that is not relevant in Delhi NCT.

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Figure 9: Air pollutant and greenhouse gas emissions in Delhi; 2015 compared with the baseline projection for 2030 (CLE) and the ‘Delhi Clean Air’ scenario for 2030

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Co-benefits on development targets

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In addition to the co-control of greenhouse gas emissions, measures of the ‘Delhi Clean Air’ strategy would deliver a range of benefits on other development priorities (in Delhi and in its surroundings), which however are not quantified in this initial analysis. A non-exhaustive list includes, inter alia, cost-effective environmental strategies and integrated investment planning6, accelerated infrastructure 6

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development (power grid expansion, loss reduction, enhanced gas distribution networks, waste management practices), time savings from avoided biofuel collection, additional jobs for the manufacturing of clean technologies (e.g., efficient and electric stoves, renewable energy technologies), more efficient land use due to less ash and soot disposal, lower emissions of other toxic substances (mercury, Hg; persistent organic pollutants, POPs) with subsequent health benefits, improved traffic management, improved protection of historical monuments and buildings, enhance gains from tourism, etc.

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4. Conclusions

Poor air quality is a major concern world-wide, with a significant burden on public health. For example, the official data confirms that exposure to PM2.5 is alarmingly high in most Indian cities (CPCB, 2016b), and NAAQS are being continuously violated. In fact, according to the WHO recent urban air quality database, 10 Indian cities have made it to the world’s top 20 worst cities with air pollution (WHO, 2016a). This study explores potential policy interventions that could effectively reduce air pollution in Delhi and resulting health impacts in near future.

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Many emission sources in Delhi and its surroundings contribute to PM2.5 in the city

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It is found that, despite the large size of Delhi NCT with currently about 18 million inhabitants, only about 40 percent of (population-weighted exposure to) PM2.5 originates from local emission sources (at traffic hot spots, the share increases to about 55 percent). Approximately, 60 percent of PM2.5 in ambient air in Delhi is transported into the city from outside. Transport emissions contributed less than 20 percent to total PM2.5 in ambient air; other important sources include biomass fuel combustion in cook stoves (mainly from the surrounding rural areas) with a 20 percent contribution, 23 percent from secondary inorganic aerosols formed from reactions of SO2 and NOx emissions (emitted by power plants, industry and traffic) with NH3 from agricultural activities, 14 percent from dust emitted at high stacks, 12 percent from the burning of agricultural and municipal waste, and 9 percent from natural sources (including soil dust and biogenic emissions).

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This source allocation (which is supported by the chemical analysis of PM monitoring) contradicts wide-spread misbeliefs that the majority of air pollution in cities emerges from vehicles. While at traffic hot spots indeed vehicles contribute the largest share, still about two-thirds of PM2.5 at such locations originates from other sources. Furthermore, currently only slightly more than half of trafficrelated PM2.5 comes from exhaust emissions, while the other fraction is non-exhaust, e.g., road dust, tire and brake wear, etc. As a consequence, policy interventions that focus only on exhaust emissions will have limited impact on PM2.5 concentrations in ambient air, especially when a rapid growth in traffic volumes will cause similar increases in non-exhaust emissions.

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quality and greenhouse gas targets, taking into account regional differences in emission control costs and atmospheric dispersion characteristics) (Amann et al., 2011). However, identification of cost optimal strategies for NCT of Delhi is beyond the scope of the present assessment. We have estimated the cumulative investment from 2016 to 2030 in end-of-pipe technologies is 49% higher than in the current legislation or baseline scenario, at €265 million, rather than €178 million when advanced control measures are implemented in NCT of Delhi only. Measures such as ban on trash and agricultural waste burning, good practice in small scale industrial processes, fewer fireworks in the city, etc. are low hanging fruits. At the same time implementation of advance control measures in small industrial and business facilities, high efficiency PMcontrols in power plants require a large investments.

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The analysis suggests that the increased levels of economic activities following the anticipated economic growth path will counteract the air quality benefits of the ambitious pollution control measures that have been adopted by the authorities. While recent regulations to control emissions at large point sources should stabilize the inflow of pollution from the surrounding areas, the decline in PM exhaust gas emissions from the implementation of the Bharat VI standards in Delhi is likely to be compensated by higher non-exhaust emissions (e.g., road dust, tire and brake wear) that follow the anticipated growth in traffic volumes. As a consequence, it must be expected that (populationweighted) air quality would further deteriorate in the future, despite the adopted policy measures.

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Effective improvements of Delhi’s air quality require collaboration with neighboring States

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The analysis clearly indicates that even the most stringent emission control measures, if restricted to the Delhi NCT area, will not be sufficient to effectively reduce ambient pollution levels in the city and approach the Indian National Ambient Air Quality Standards. The most ambitious scenario for measures in Delhi would reduce ambient levels in the area to about 90 µg/m3. Since in this scenario about 70 µg/m3 of pollution would be imported from outside Delhi, coordinated action with neighboring States is indispensable for any effective improvements of air quality within Delhi (Figure 10). Furthermore, achievement of the National Ambient Air Quality Standards or of the even lower target levels issued by the World Health Organization (WHO) would be facilitated by concerted action to address the long-range transport of pollution from other Indian States and Pakistan.

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Figure 10: Origin of PM2.5 in Delhi in the different scenarios

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Effective interventions must involve sources that are less relevant in industrialized countries

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It will be essential that the relevant authorities address emission sources that are not always prominent in air quality management portfolios of other countries and cities. Major emissions and reduction potentials exist, inter alia, for road paving to reduce road dust emission, a rapid transition to clean cooking fuels within the Delhi NCT and in the neighboring States, and comprehensive collection and management procedures for municipal and agricultural waste, that should enable a ban of all open 17

ACCEPTED MANUSCRIPT burning of waste. Furthermore, authorities need to develop approaches to reduce pollution from cultural behavior and activities, e.g., less charcoal use for barbecues, fewer fireworks within the city, more environmentally friendly forms of cremation, etc. (Figure 11). While such changed practices will be essential for cleaner air in Delhi, they need to be accompanied by strict control of SO2, NOx and PM emissions at large plants in the surrounding area, if the NAAQS are to be achieved.

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Figure 11: Contributions of different source categories to the population-weighted PM2.5 concentrations in Delhi, for the different scenarios

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Many of the policy interventions will have multiple co-benefits on development targets In addition to the co-control of greenhouse gas emissions, measures of the ‘Delhi Clean Air’ strategy would deliver a range of benefits on other development priorities (in Delhi and in its surroundings).

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Acknowledgments

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This study is the outcome of a joint collaborative project between the National Environmental Engineering Research Institute (NEERI), Nagpur, India, and the International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria. The cooperation was funded by the Technology Information Forecasting and Assessment Council (TIFAC) of the Department of Science and Technology of the Government of India, which acts as the Indian National Member Organization of IIASA, and by core funds of the International Institute for Applied Systems Analysis.

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Horino, M., Horita, N., Hosgood, H.D., Hoy, D.G., Hsairi, M., Htet, A.S., Hu, G., Huang, J.J., Husseini, A., Hutchings, S.J., Huybrechts, I., Iburg, K.M., Idrisov, B.T., Ileanu, B.V., Inoue, M., Jacobs, T.A., Jacobsen, K.H., Jahanmehr, N., Jakovljevic, M.B., Jansen, H.A.F.M., Jassal, S.K., Javanbakht, M., Jayaraman, S.P., Jayatilleke, A.U., Jee, S.H., Jeemon, P., Jha, V., Jiang, Y., Jibat, T., Jin, Y., Johnson, C.O., Jonas, J.B., Kabir, Z., Kalkonde, Y., Kamal, R., Kan, H., Karch, A., Karema, C.K., Karimkhani, C., Kasaeian, A., Kaul, A., Kawakami, N., Kazi, D.S., Keiyoro, P.N., Kemmer, L., Kemp, A.H., Kengne, A.P., Keren, A., Kesavachandran, C.N., Khader, Y.S., Khan, A.R., Khan, E.A., Khan, G., Khang, Y.-H., Khatibzadeh, S., Khera, S., Khoja, T.A.M., Khubchandani, J., Kieling, C., Kim, C., Kim, D., Kimokoti, R.W., Kissoon, N., Kivipelto, M., Knibbs, L.D., Kokubo, Y., Kopec, J.A., Koul, P.A., Koyanagi, A., Kravchenko, M., Kromhout, H., Krueger, H., Ku, T., Defo, B.K., Kuchenbecker, R.S., Bicer, B.K., Kuipers, E.J., Kumar, G.A., Kwan, G.F., Lal, D.K., Lalloo, R., Lallukka, T., Lan, Q., Larsson, A., Latif, A.A., Lawrynowicz, A.E.B., Leasher, J.L., Leigh, J., Leung, J., Levi, M., Li, X., Li, Y., Liang, J., Liu, S., Lloyd, B.K., Logroscino, G., Lotufo, P.A., Lunevicius, R., MacIntyre, M., Mahdavi, M., Majdan, M., Majeed, A., Malekzadeh, R., Malta, D.C., Manamo, W.A.A., Mapoma, C.C., Marcenes, W., Martin, R.V., Martinez-Raga, J., Masiye, F., Matsushita, K., Matzopoulos, R., Mayosi, B.M., McGrath, J.J., McKee, M., Meaney, P.A., Medina, C., Mehari, A., Mejia-Rodriguez, F., Mekonnen, A.B., Melaku, Y.A., Memish, Z.A., Mendoza, W., Mensink, G.B.M., Meretoja, A., Meretoja, T.J., Mesfin, Y.M., Mhimbira, F.A., Millear, A., Miller, T.R., Mills, E.J., Mirarefin, M., Misganaw, A., Mock, C.N., Mohammadi, A., Mohammed, S., Mola, G.L.D., Monasta, L., Hernandez, J.C.M., Montico, M., Morawska, L., Mori, R., Mozaffarian, D., Mueller, U.O., Mullany, E., Mumford, J.E., Murthy, G.V.S., Nachega, J.B., Naheed, A., Nangia, V., Nassiri, N., Newton, J.N., Ng, M., Nguyen, Q.L., Nisar, M.I., Pete, P.M.N., Norheim, O.F., Norman, R.E., Norrving, B., Nyakarahuka, L., Obermeyer, C.M., Ogbo, F.A., Oh, I.-H., Oladimeji, O., Olivares, P.R., Olsen, H., Olusanya, B.O., Olusanya, J.O., Opio, J.N., Oren, E., Orozco, R., Ortiz, A., Ota, E., PA, M., Pana, A., Park, E.-K., Parry, C.D., Parsaeian, M., Patel, T., Caicedo, A.J.P., Patil, S.T., Patten, S.B., Patton, G.C., Pearce, N., Pereira, D.M., Perico, N., Pesudovs, K., Petzold, M., Phillips, M.R., Piel, F.B., Pillay, J.D., Plass, D., Polinder, S., Pond, C.D., Pope, C.A., Pope, D., Popova, S., Poulton, R.G., Pourmalek, F., Prasad, N.M., Qorbani, M., Rabiee, R.H.S., Radfar, A., Rafay, A., Rahimi-Movaghar, V., Rahman, M., Rahman, M.H.U., Rahman, S.U., Rai, R.K., Rajsic, S., Raju, M., Ram, U., Rana, S.M., Ranganathan, K., Rao, P., García, C.A.R., Refaat, A.H., Rehm, C.D., Rehm, J., Reinig, N., Remuzzi, G., Resnikoff, S., Ribeiro, A.L., Rivera, J.A., Roba, H.S., Rodriguez, A., Rodriguez-Ramirez, S., Rojas-Rueda, D., Roman, Y., Ronfani, L., Roshandel, G., Rothenbacher, D., Roy, A., Saleh, M.M., Sanabria, J.R., Sanchez-Riera, L., Sanchez-Niño, M.D., Sánchez-Pimienta, T.G., Sandar, L., Santomauro, D.F., Santos, I.S., Sarmiento-Suarez, R., Sartorius, B., Satpathy, M., Savic, M., Sawhney, M., Schmidhuber, J., Schmidt, M.I., Schneider, I.J.C., Schöttker, B., Schutte, A.E., Schwebel, D.C., Scott, J.G., Seedat, S., Sepanlou, S.G., Servan-Mori, E.E., Shaddick, G., Shaheen, A., Shahraz, S., Shaikh, M.A., Levy, T.S., Sharma, R., She, J., Sheikhbahaei, S., Shen, J., Sheth, K.N., Shi, P., Shibuya, K., Shigematsu, M., Shin, M.-J., Shiri, R., Shishani, K., Shiue, I., Shrime, M.G., Sigfusdottir, I.D., Silva, D.A.S., Silveira, D.G.A., Silverberg, J.I., Simard, E.P., Sindi, S., Singh, A., Singh, J.A., Singh, P.K., Slepak, E.L., Soljak, M., Soneji, S., Sorensen, R.J.D., Sposato, L.A., Sreeramareddy, C.T., Stathopoulou, V., Steckling, N., Steel, N., Stein, D.J., Stein, M.B., Stöckl, H., Stranges, S., Stroumpoulis, K., Sunguya, B.F., Swaminathan, S., Sykes, B.L., Szoeke, C.E.I., Tabarés-Seisdedos, R., Takahashi, K., Talongwa, R.T., Tandon, N., Tanne, D., Tavakkoli, M., Taye, B.W., Taylor, H.R., Tedla, B.A., Tefera, W.M., Tegegne, T.K., Tekle, D.Y., Terkawi, A.S., Thakur, J.S., Thomas, B.A., Thomas, M.L., Thomson, A.J., Thorne-Lyman, A.L., Thrift, A.G., Thurston, G.D., Tillmann, T., Tobe-Gai, R., Tobollik, M., Topor-Madry, R., Topouzis, F., Towbin, J.A., Tran, B.X., Dimbuene, Z.T., Tsilimparis, N.,

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ACCEPTED MANUSCRIPT Tura, A.K., Tuzcu, E.M., Tyrovolas, S., Ukwaja, K.N., Undurraga, E.A., Uneke, C.J., Uthman, O.A., van Donkelaar, A., van Os, J., Varakin, Y.Y., Vasankari, T., Veerman, J.L., Venketasubramanian, N., Violante, F.S., Vollset, S.E., Wagner, G.R., Waller, S.G., Wang, J.L., Wang, L., Wang, Y., Weichenthal, S., Weiderpass, E., Weintraub, R.G., Werdecker, A., Westerman, R., Whiteford, H.A., Wijeratne, T., Wiysonge, C.S., Wolfe, C.D.A., Won, S., Woolf, A.D., Wubshet, M., Xavier, D., Xu, G., Yadav, A.K., Yakob, B., Yalew, A.Z., Yano, Y., Yaseri, M., Ye, P., Yip, P., Yonemoto, N., Yoon, S.-J., Younis, M.Z., Yu, C., Zaidi, Z., Zaki, M.E.S., Zhu, J., Zipkin, B., Zodpey, S., Zuhlke, L.J., Murray, C.J.L., 2016. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet 388, 1659–1724. doi:10.1016/S01406736(16)31679-8

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Forouzanfar, M.H., Alexander, L., Anderson, H.R., Bachman, V.F., Biryukov, S., Brauer, M., Burnett, R., Casey, D., Coates, M.M., Cohen, A., Delwiche, K., Estep, K., Frostad, J.J., Kc, A., Kyu, H.H., Moradi-Lakeh, M., Ng, M., Slepak, E.L., Thomas, B.A., Wagner, J., Aasvang, G.M., Abbafati, C., Ozgoren, A.A., Abd-Allah, F., Abera, S.F., Aboyans, V., Abraham, B., Abraham, J.P., Abubakar, I., Abu-Rmeileh, N.M.E., Aburto, T.C., Achoki, T., Adelekan, A., Adofo, K., Adou, A.K., Adsuar, J.C., Afshin, A., Agardh, E.E., Khabouri, M.J.A., Lami, F.H.A., Alam, S.S., Alasfoor, D., Albittar, M.I., Alegretti, M.A., Aleman, A.V., Alemu, Z.A., Alfonso-Cristancho, R., Alhabib, S., Ali, R., Ali, M.K., Alla, F., Allebeck, P., Allen, P.J., Alsharif, U., Alvarez, E., Alvis-Guzman, N., Amankwaa, A.A., Amare, A.T., Ameh, E.A., Ameli, O., Amini, H., Ammar, W., Anderson, B.O., Antonio, C.A.T., Anwari, P., Cunningham, S.A., Arnlöv, J., Arsenijevic, V.S.A., Artaman, A., Asghar, R.J., Assadi, R., Atkins, L.S., Atkinson, C., Avila, M.A., Awuah, B., Badawi, A., Bahit, M.C., Bakfalouni, T., Balakrishnan, K., Balalla, S., Balu, R.K., Banerjee, A., Barber, R.M., Barker-Collo, S.L., Barquera, S., Barregard, L., Barrero, L.H., Barrientos-Gutierrez, T., Basto-Abreu, A.C., Basu, A., Basu, S., Basulaiman, M.O., Ruvalcaba, C.B., Beardsley, J., Bedi, N., Bekele, T., Bell, M.L., Benjet, C., Bennett, D.A., Benzian, H., Bernabé, E., Beyene, T.J., Bhala, N., Bhalla, A., Bhutta, Z.A., Bikbov, B., Abdulhak, A.A.B., Blore, J.D., Blyth, F.M., Bohensky, M.A., Başara, B.B., Borges, G., Bornstein, N.M., Bose, D., Boufous, S., Bourne, R.R., Brainin, M., Brazinova, A., Breitborde, N.J., Brenner, H., Briggs, A.D.M., Broday, D.M., Brooks, P.M., Bruce, N.G., Brugha, T.S., Brunekreef, B., Buchbinder, R., Bui, L.N., Bukhman, G., Bulloch, A.G., Burch, M., Burney, P.G.J., Campos-Nonato, I.R., Campuzano, J.C., Cantoral, A.J., Caravanos, J., Cárdenas, R., Cardis, E., Carpenter, D.O., Caso, V., Castañeda-Orjuela, C.A., Castro, R.E., Catalá-López, F., Cavalleri, F., Çavlin, A., Chadha, V.K., Chang, J., Charlson, F.J., Chen, H., Chen, W., Chen, Z., Chiang, P.P., Chimed-Ochir, O., Chowdhury, R., Christophi, C.A., Chuang, T.-W., Chugh, S.S., Cirillo, M., Claßen, T.K., Colistro, V., Colomar, M., Colquhoun, S.M., Contreras, A.G., Cooper, C., Cooperrider, K., Cooper, L.T., Coresh, J., Courville, K.J., Criqui, M.H., Cuevas-Nasu, L., Damsere-Derry, J., Danawi, H., Dandona, L., Dandona, R., Dargan, P.I., Davis, A., Davitoiu, D.V., Dayama, A., Castro, E.F. de, Cruz-Góngora, V.D. la, Leo, D.D., Lima, G. de, Degenhardt, L., Pozo-Cruz, B. del, Dellavalle, R.P., Deribe, K., Derrett, S., Jarlais, D.C.D., Dessalegn, M., deVeber, G.A., Devries, K.M., Dharmaratne, S.D., Dherani, M.K., Dicker, D., Ding, E.L., Dokova, K., Dorsey, E.R., Driscoll, T.R., Duan, L., Durrani, A.M., Ebel, B.E., Ellenbogen, R.G., Elshrek, Y.M., Endres, M., Ermakov, S.P., Erskine, H.E., Eshrati, B., Esteghamati, A., Fahimi, S., Faraon, E.J.A., Farzadfar, F., Fay, D.F.J., Feigin, V.L., Feigl, A.B., Fereshtehnejad, S.-M., Ferrari, A.J., Ferri, C.P., Flaxman, A.D., Fleming, T.D., Foigt, N., Foreman, K.J., Paleo, U.F., Franklin, R.C., Gabbe, B., Gaffikin, L., Gakidou, E., Gamkrelidze, A., Gankpé, F.G., Gansevoort, R.T., García-Guerra, F.A., Gasana, E., Geleijnse, J.M., Gessner, B.D., Gething,

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P., Gibney, K.B., Gillum, R.F., Ginawi, I.A.M., Giroud, M., Giussani, G., Goenka, S., Goginashvili, K., Dantes, H.G., Gona, P., Cosio, T.G. de, González-Castell, D., Gotay, C.C., Goto, A., Gouda, H.N., Guerrant, R.L., Gugnani, H.C., Guillemin, F., Gunnell, D., Gupta, Rahul, Gupta, Rajeev, Gutiérrez, R.A., Hafezi-Nejad, N., Hagan, H., Hagstromer, M., Halasa, Y.A., Hamadeh, R.R., Hammami, M., Hankey, G.J., Hao, Y., Harb, H.L., Haregu, T.N., Haro, J.M., Havmoeller, R., Hay, S.I., Hedayati, M.T., Heredia-Pi, I.B., Hernandez, L., Heuton, K.R., Heydarpour, P., Hijar, M., Hoek, H.W., Hoffman, H.J., Hornberger, J.C., Hosgood, H.D., Hoy, D.G., Hsairi, M., Hu, G., Hu, H., Huang, C., Huang, J.J., Hubbell, B.J., Huiart, L., Husseini, A., Iannarone, M.L., Iburg, K.M., Idrisov, B.T., Ikeda, N., Innos, K., Inoue, M., Islami, F., Ismayilova, S., Jacobsen, K.H., Jansen, H.A., Jarvis, D.L., Jassal, S.K., Jauregui, A., Jayaraman, S., Jeemon, P., Jensen, P.N., Jha, V., Jiang, F., Jiang, G., Jiang, Y., Jonas, J.B., Juel, K., Kan, H., Roseline, S.S.K., Karam, N.E., Karch, A., Karema, C.K., Karthikeyan, G., Kaul, A., Kawakami, N., Kazi, D.S., Kemp, A.H., Kengne, A.P., Keren, A., Khader, Y.S., Khalifa, S.E.A.H., Khan, E.A., Khang, Y.-H., Khatibzadeh, S., Khonelidze, I., Kieling, C., Kim, D., Kim, S., Kim, Y., Kimokoti, R.W., Kinfu, Y., Kinge, J.M., Kissela, B.M., Kivipelto, M., Knibbs, L.D., Knudsen, A.K., Kokubo, Y., Kose, M.R., Kosen, S., Kraemer, A., Kravchenko, M., Krishnaswami, S., Kromhout, H., Ku, T., Defo, B.K., Bicer, B.K., Kuipers, E.J., Kulkarni, C., Kulkarni, V.S., Kumar, G.A., Kwan, G.F., Lai, T., Balaji, A.L., Lalloo, R., Lallukka, T., Lam, H., Lan, Q., Lansingh, V.C., Larson, H.J., Larsson, A., Laryea, D.O., Lavados, P.M., Lawrynowicz, A.E., Leasher, J.L., Lee, J.-T., Leigh, J., Leung, R., Levi, M., Li, Yichong, Li, Yongmei, Liang, J., Liang, X., Lim, S.S., Lindsay, M.P., Lipshultz, S.E., Liu, S., Liu, Y., Lloyd, B.K., Logroscino, G., London, S.J., Lopez, N., Lortet-Tieulent, J., Lotufo, P.A., Lozano, R., Lunevicius, R., Ma, J., Ma, S., Machado, V.M.P., MacIntyre, M.F., MagisRodriguez, C., Mahdi, A.A., Majdan, M., Malekzadeh, R., Mangalam, S., Mapoma, C.C., Marape, M., Marcenes, W., Margolis, D.J., Margono, C., Marks, G.B., Martin, R.V., Marzan, M.B., Mashal, M.T., Masiye, F., Mason-Jones, A.J., Matsushita, K., Matzopoulos, R., Mayosi, B.M., Mazorodze, T.T., McKay, A.C., McKee, M., McLain, A., Meaney, P.A., Medina, C., Mehndiratta, M.M., Mejia-Rodriguez, F., Mekonnen, W., Melaku, Y.A., Meltzer, M., Memish, Z.A., Mendoza, W., Mensah, G.A., Meretoja, A., Mhimbira, F.A., Micha, R., Miller, T.R., Mills, E.J., Misganaw, A., Mishra, S., Ibrahim, N.M., Mohammad, K.A., Mokdad, A.H., Mola, G.L., Monasta, L., Hernandez, J.C.M., Montico, M., Moore, A.R., Morawska, L., Mori, R., Moschandreas, J., Moturi, W.N., Mozaffarian, D., Mueller, U.O., Mukaigawara, M., Mullany, E.C., Murthy, K.S., Naghavi, M., Nahas, Z., Naheed, A., Naidoo, K.S., Naldi, L., Nand, D., Nangia, V., Narayan, K.V., Nash, D., Neal, B., Nejjari, C., Neupane, S.P., Newton, C.R., Ngalesoni, F.N., Ngirabega, J. de D., Nguyen, G., Nguyen, N.T., Nieuwenhuijsen, M.J., Nisar, M.I., Nogueira, J.R., Nolla, J.M., Nolte, S., Norheim, O.F., Norman, R.E., Norrving, B., Nyakarahuka, L., Oh, I.-H., Ohkubo, T., Olusanya, B.O., Omer, S.B., Opio, J.N., Orozco, R., Pagcatipunan, R.S., Pain, A.W., Pandian, J.D., Panelo, C.I.A., Papachristou, C., Park, E.-K., Parry, C.D., Caicedo, A.J.P., Patten, S.B., Paul, V.K., Pavlin, B.I., Pearce, N., Pedraza, L.S., Pedroza, A., Stokic, L.P., Pekericli, A., Pereira, D.M., Perez-Padilla, R., Perez-Ruiz, F., Perico, N., Perry, S.A.L., Pervaiz, A., Pesudovs, K., Peterson, C.B., Petzold, M., Phillips, M.R., Phua, H.P., Plass, D., Poenaru, D., Polanczyk, G.V., Polinder, S., Pond, C.D., Pope, C.A., Pope, D., Popova, S., Pourmalek, F., Powles, J., Prabhakaran, D., Prasad, N.M., Qato, D.M., Quezada, A.D., Quistberg, D.A.A., Racapé, L., Rafay, A., Rahimi, K., Rahimi-Movaghar, V., Rahman, S.U., Raju, M., Rakovac, I., Rana, S.M., Rao, M., Razavi, H., Reddy, K.S., Refaat, A.H., Rehm, J., Remuzzi, G., Ribeiro, A.L., Riccio, P.M., Richardson, L., Riederer, A., Robinson, M., Roca, A., Rodriguez, A., RojasRueda, D., Romieu, I., Ronfani, L., Room, R., Roy, N., Ruhago, G.M., Rushton, L., Sabin, N., Sacco, R.L., Saha, S., Sahathevan, R., Sahraian, M.A., Salomon, J.A., Salvo, D., Sampson, U.K., Sanabria, J.R., Sanchez, L.M., Sánchez-Pimienta, T.G., Sanchez-Riera, L.,

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ACCEPTED MANUSCRIPT Sandar, L., Santos, I.S., Sapkota, A., Satpathy, M., Saunders, J.E., Sawhney, M., Saylan, M.I., Scarborough, P., Schmidt, J.C., Schneider, I.J.C., Schöttker, B., Schwebel, D.C., Scott, J.G., Seedat, S., Sepanlou, S.G., Serdar, B., Servan-Mori, E.E., Shaddick, G., Shahraz, S., Levy, T.S., Shangguan, S., She, J., Sheikhbahaei, S., Shibuya, K., Shin, H.H., Shinohara, Y., Shiri, R., Shishani, K., Shiue, I., Sigfusdottir, I.D., Silberberg, D.H., Simard, E.P., Sindi, S., Singh, A., Singh, G.M., Singh, J.A., Skirbekk, V., Sliwa, K., Soljak, M., Soneji, S., Søreide, K., Soshnikov, S., Sposato, L.A., Sreeramareddy, C.T., Stapelberg, N.J.C., Stathopoulou, V., Steckling, N., Stein, D.J., Stein, M.B., Stephens, N., Stöckl, H., Straif, K., Stroumpoulis, K., Sturua, L., Sunguya, B.F., Swaminathan, S., Swaroop, M., Sykes, B.L., Tabb, K.M., Takahashi, K., Talongwa, R.T., Tandon, N., Tanne, D., Tanner, M., Tavakkoli, M., Ao, B.J.T., Teixeira, C.M., Rojo, M.M.T., Terkawi, A.S., Texcalac-Sangrador, J.L., Thackway, S.V., Thomson, B., Thorne-Lyman, A.L., Thrift, A.G., Thurston, G.D., Tillmann, T., Tobollik, M., Tonelli, M., Topouzis, F., Towbin, J.A., Toyoshima, H., Traebert, J., Tran, B.X., Trasande, L., Trillini, M., Trujillo, U., Dimbuene, Z.T., Tsilimbaris, M., Tuzcu, E.M., Uchendu, U.S., Ukwaja, K.N., Uzun, S.B., Vijver, S. van de, Dingenen, R.V., Gool, C.H. van, Os, J. van, Varakin, Y.Y., Vasankari, T.J., Vasconcelos, A.M.N., Vavilala, M.S., Veerman, L.J., Velasquez-Melendez, G., Venketasubramanian, N., Vijayakumar, L., Villalpando, S., Violante, F.S., Vlassov, V.V., Vollset, S.E., Wagner, G.R., Waller, S.G., Wallin, M.T., Wan, X., Wang, H., Wang, J., Wang, L., Wang, W., Wang, Y., Warouw, T.S., Watts, C.H., Weichenthal, S., Weiderpass, E., Weintraub, R.G., Werdecker, A., Wessells, K.R., Westerman, R., Whiteford, H.A., Wilkinson, J.D., Williams, H.C., Williams, T.N., Woldeyohannes, S.M., Wolfe, C.D.A., Wong, J.Q., Woolf, A.D., Wright, J.L., Wurtz, B., Xu, G., Yan, L.L., Yang, G., Yano, Y., Ye, P., Yenesew, M., Yentür, G.K., Yip, P., Yonemoto, N., Yoon, S.-J., Younis, M.Z., Younoussi, Z., Yu, C., Zaki, M.E., Zhao, Y., Zheng, Y., Zhou, M., Zhu, J., Zhu, S., Zou, X., Zunt, J.R., Lopez, A.D., Vos, T., Murray, C.J., 2015. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet 386, 2287– 2323. doi:10.1016/S0140-6736(15)00128-2

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ACCEPTED MANUSCRIPT Burnett, R.T., Byers, T.E., Calabria, B., Carapetis, J., Carnahan, E., Chafe, Z., Charlson, F., Chen, H., Chen, J.S., Cheng, A.T.-A., Child, J.C., Cohen, A., Colson, K.E., Cowie, B.C., Darby, S., Darling, S., Davis, A., Degenhardt, L., Dentener, F., Des Jarlais, D.C., Devries, K., Dherani, M., Ding, E.L., Dorsey, E.R., Driscoll, T., Edmond, K., Ali, S.E., Engell, R.E., Erwin, P.J., Fahimi, S., Falder, G., Farzadfar, F., Ferrari, A., Finucane, M.M., Flaxman, S., Fowkes, F.G.R., Freedman, G., Freeman, M.K., Gakidou, E., Ghosh, S., Giovannucci, E., Gmel, G., Graham, K., Grainger, R., Grant, B., Gunnell, D., Gutierrez, H.R., Hall, W., Hoek, H.W., Hogan, A., Hosgood, H.D., Hoy, D., Hu, H., Hubbell, B.J., Hutchings, S.J., Ibeanusi, S.E., Jacklyn, G.L., Jasrasaria, R., Jonas, J.B., Kan, H., Kanis, J.A., Kassebaum, N., Kawakami, N., Khang, Y.-H., Khatibzadeh, S., Khoo, J.-P., Kok, C., Laden, F., Lalloo, R., Lan, Q., Lathlean, T., Leasher, J.L., Leigh, J., Li, Y., Lin, J.K., Lipshultz, S.E., London, S., Lozano, R., Lu, Y., Mak, J., Malekzadeh, R., Mallinger, L., Marcenes, W., March, L., Marks, R., Martin, R., McGale, P., McGrath, J., Mehta, S., Memish, Z.A., Mensah, G.A., Merriman, T.R., Micha, R., Michaud, C., Mishra, V., Hanafiah, K.M., Mokdad, A.A., Morawska, L., Mozaffarian, D., Murphy, T., Naghavi, M., Neal, B., Nelson, P.K., Nolla, J.M., Norman, R., Olives, C., Omer, S.B., Orchard, J., Osborne, R., Ostro, B., Page, A., Pandey, K.D., Parry, C.D., Passmore, E., Patra, J., Pearce, N., Pelizzari, P.M., Petzold, M., Phillips, M.R., Pope, D., Pope, C.A., Powles, J., Rao, M., Razavi, H., Rehfuess, E.A., Rehm, J.T., Ritz, B., Rivara, F.P., Roberts, T., Robinson, C., Rodriguez-Portales, J.A., Romieu, I., Room, R., Rosenfeld, L.C., Roy, A., Rushton, L., Salomon, J.A., Sampson, U., Sanchez-Riera, L., Sanman, E., Sapkota, A., Seedat, S., Shi, P., Shield, K., Shivakoti, R., Singh, G.M., Sleet, D.A., Smith, E., Smith, K.R., Stapelberg, N.J., Steenland, K., Stöckl, H., Stovner, L.J., Straif, K., Straney, L., Thurston, G.D., Tran, J.H., Van Dingenen, R., van Donkelaar, A., Veerman, J.L., Vijayakumar, L., Weintraub, R., Weissman, M.M., White, R.A., Whiteford, H., Wiersma, S.T., Wilkinson, J.D., Williams, H.C., Williams, W., Wilson, N., Woolf, A.D., Yip, P., Zielinski, J.M., Lopez, A.D., Murray, C.J., Ezzati, M., 2012. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet 380, 2224–2260. doi:10.1016/S0140-6736(12)61766-8

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ACCEPTED MANUSCRIPT Pant, P., Shukla, A., Kohl, S.D., Chow, J.C., Watson, J.G., Harrison, R.M., 2015. Characterization of ambient PM2.5 at a pollution hotspot in New Delhi, India and inference of sources. Atmospheric Environment 109, 178–189. doi:10.1016/j.atmosenv.2015.02.074

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Highlights

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Development of GAINS-City policy analysis model for Delhi Nearly 60 percent of pollution in Delhi caused by PM 2.5 originates from outside Improvement of Delhi’s air quality requires collaboration with neighboring States Several policy interventions will have multiple co-benefits on development targets

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