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South Asia is a global hotspot of air pollution, harboring 37 of the world's 40 most polluted cities. Sixty percent of its residents inhabit areas characterized by high pollution levels, where concentrations of fine particulate matter (PM2.5) - accountable for chronic respiratory diseases and over two million premature deaths annually in the region - surpass the least stringent air quality standard set by the World Health Organization (WHO). Addressing this problem with fragmented approaches is unlikely to yield significant results, as air pollution extends beyond geographical boundaries. Even if fully executed, existing policy measures will only offer partial relief in diminishing PM2.5 concentrations in South Asia. This study aims to identify and map air pollution hotspots in South Asia in terms of concentration and exposure, understand the various sources of pollution in hotspot areas, and help categorize policy actions and interventions based on a systematic analysis of costs and benefits using the GAINS modeling framework. A large variety of emission sources contribute to PM2.5 pollution in ambient air therefore, effective air quality management needs to balance measures across these sources. Our results reveal that the current environmental policies will decouple emissions from economic growth, however, will not be sufficient to deliver large reductions in ambient PM2.5 in the South Asia region. There is scope for further measures beyond current policies that could approach the WHO Interim Targets (35 µg/m3) for PM2.5. Finally, cost-optimal strategies for air quality management can achieve significant cost savings compared to conventional approaches; however, they require cooperation within states, regions and countries in South Asia. Monitoring of the chemical composition of PM2.5 reveals that a significant share of total fine particulate matter in ambient air in South Asia is composed of secondary particles, i.e., particles that are formed in the atmosphere

This dataset contains the parameterization of a no-policy baseline scenario of the global 11-regional MESSAGEix-GLOBIOM integrated assessment model. Regions, time periods, commodities, technologies and relations included in this model are described in a separate repository. The dataset relies on the MESSAGEix modeling framework (Huppmann et al. 2019) and can be imported into MESSAGEix via the read_excel() functionality for which a tutorial is available. After the import the scenario can be solved and modified to create new scenarios. Note that the published scenario as included in the ENGAGE global scenarios dataset has been run with a release candidate of version 3.4.0 of MESSAGEix.

The global anthropogenic emissions of air pollutants including sulfur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3), particulate matter (distinguishing PM2.5, PM10, BC, OC, OM), non-methane volatile organic compounds (VOC), carbon monoxide (CO), and methane (CH4) were developed at CIAM/IIASA[2]. These emission datasets have been produced with the GAINS model[1] (Amann et al., 2011) within work under UNECE Convention on Long-Range Transboundary Air Pollution (LRTAP) and cover the 1990-2050 period. The respective emission datasets consist of 5-yearly global annual and monthly emissions and include two to three scenarios, depending on the version of the dataset: a baseline that is referred to as current legislation case (LRTAP Baseline), a scenarios exploring technical mitigation potential for all pollutant species (LRTAP MTFR), and a scenario that combines climate policy, behavioral changes, and strong technical mitigation of all pollutants (LRTAP LOW) Brief explanation of assumptions used in these three scenarios. The current legislation scenario (LRTAP Baseline) assumes implementation and effective enforcement of all committed energy and environmental policies affecting emissions of air pollutants and greenhouse gases. CIAM has undertaken a review and update of historical data (up to 2020) driving emissions of all species in the GAINS model, drawing on the statistical information from EUROSTAT, International Energy Agency (IEA), UN Food and Agriculture Organization (FAO), as well as reporting of data and emissions to the Center on Emission Inventories and Projections[3]. For the EU27, the energy and agriculture projections are consistent with the objectives of the European Green Deal and Fit for 55 package making EU carbon neutral by 2050; these are consistent with the projections used in the EU third Clean Air Outlook [4]. For West Balkan, Republic of Moldova, Georgia, and Ukraine, a similar set of modelling tools was used as for the EU developing a new consi

Over the last decades, energy and pollution control policies combined with structural changes in the economy decoupled emission trends from economic growth, increasingly also in the developing world. It is found that effective implementation of the presently decided national pollution control regulations should allow further economic growth without major deterioration of ambient air quality, but will not be enough to reduce pollution levels in many world regions. A combination of ambitious policies focusing on pollution controls, energy and climate, agricultural production systems and addressing human consumption habits could drastically improve air quality throughout the world. By 2040, mean population exposure to PM2.5 from anthropogenic sources could be reduced by about 75% relative to 2015 and brought well below the WHO guideline in large areas of the world. While the implementation of the proposed technical measures is likely to be technically feasible in the future, the transformative changes of current practices will require strong political will, supported by a full appreciation of the multiple benefits. Improved air quality would avoid a large share of the current 3–9 million cases of premature deaths annually. At the same time, the measures that deliver clean air would also significantly reduce emissions of greenhouse gases and contribute to multiple UN sustainable development goals.

The global gasoline and diesel fuel vehicle fleets impose substantial impacts on air quality, human health, and climate change. Here we quantify the global radiative forcing and human health impacts of the global gasoline and diesel sectors using the NCAR CESM global chemistry‐climate model for year 2015 emissions from the IIASA GAINS inventory. Net global radiative effects of short‐lived climate forcers (including aerosols, ozone, and methane) from the gasoline and diesel sectors are +13.6 and +9.4 mW m−2, respectively. The annual mean net aerosol contributions to the net radiative effects of gasoline and diesel are −9.6 ± 2.0 and +8.8 ± 5.8 mW m−2. Aerosol indirect effects for the gasoline and diesel road vehicle sectors are −16.6 ± 2.1 and −40.6 ± 4.0 mW m−2. The fractional contributions of short‐lived climate forcers to the total global climate impact including carbon dioxide on the 20‐year time scale are similar, 14.9% and 14.4% for gasoline and diesel, respectively. Global annual total PM2.5‐ and ozone‐induced premature deaths for gasoline and diesel sectors approach 115,000 (95% CI: 69,000–153,600) and 122,100 (95% CI: 78,500–157,500), with corresponding years of life lost of 2.10 (95% CI: 1.23–2.66) and 2.21 (95% CI: 1.47–2.85) million years. Substantial regional variability of premature death rates is found for the diesel sector when the regional health effects are normalized by the annual total regional vehicle distance traveled. Regional premature death rates for the gasoline and diesel sectors, respectively, vary by a factor of eight and two orders of magnitude, with India showing the highest for both gasoline and diesel sectors.

Black carbon (BC) contributes to Arctic climate warming, yet source attributions are inaccurate due to lacking observational constraints and uncertainties in emission inventories. Year-round, isotope-constrained observations reveal strong seasonal variations in BC sources with a consistent and synchronous pattern at all Arctic sites. These sources were dominated by emissions from fossil fuel combustion in the winter and by biomass burning in the summer. The annual mean source of BC to the circum-Arctic was 39 +/- 10% from biomass burning. Comparison of transport-model predictions with the observations showed good agreement for BC concentrations, with larger discrepancies for (fossil/biomass burning) sources. The accuracy of simulated BC concentration, but not of origin, points to misallocations of emissions in the emission inventories. The consistency in seasonal source contributions of BC throughout the Arctic provides strong justification for targeted emission reductions to limit the impact of BC on climate warming in the Arctic and beyond.

Black carbon (BC) contributes to Arctic climate warming, yet source attributions are inaccurate due to lacking observational constraints and uncertainties in emission inventories. Year-round, isotope-constrained observations reveal strong seasonal variations in BC sources with a consistent and synchronous pattern at all Arctic sites. These sources were dominated by emissions from fossil fuel combustion in the winter and by biomass burning in the summer. The annual mean source of BC to the circum-Arctic was 39 ± 10% from biomass burning. Comparison of transport-model predictions with the observations showed good agreement for BC concentrations, with larger discrepancies for (fossil/biomass burning) sources. The accuracy of simulated BC concentration, but not of origin, points to misallocations of emissions in the emission inventories. The consistency in seasonal source contributions of BC throughout the Arctic provides strong justification for targeted emission reductions to limit the impact of BC on climate warming in the Arctic and beyond.

Air pollution is linked with many of the United Nations Sustainable Development Goals. Strategies aiming at the improved air quality interact directly with climate mitigation targets, access to clean energy services, waste management, and other aspects of socio-economic development. Continuation of current policies in the key emitting sectors implies that a number of sustainability goals will likely not be met within the next two decades: emissions of air pollutants would cause 40% more premature deaths from outdoor air pollution than today, carbon emissions would rise globally by 0.4% per year, while nearly two billion people would not have access to clean cooking. This paper examines integrated policies to put the world on track towards three interlinked goals of achieving universal energy access, limiting climate change and reducing air pollution. Scenario analysis suggests that these goals can be attained simultaneously with substantial benefits. By 2040, emissions of main pollutants are projected to drop by 60–80% relative to today, and associated health impacts are quantified at two million avoided deaths from ambient and household air pollution combined. In comparison to costs needed for the decarbonization of global economy, additional investments in air pollution control and access to clean fuels are very modest against major societal gains. However, holistic and systemic policy assessment is required to avoid potential trade-offs.

Ambient air pollution in India contributes to negative health impacts and early death. Ground-based monitors often used to quantify health impacts are located in urban regions, yet approximately 70% of India's population lives in rural communities. We simulate high-resolution concentrations of fine particulate matter (PM) and ozone from the regional Community Multi-scale Air Quality model over northern India, including updated estimates of anthropogenic emissions for transportation, residential combustion and location-based industrial and electrical generating emissions in a new anthropogenic emissions inventory. These simulations inform seasonal air quality and health impacts due to anthropogenic emissions, contrasting urban versus rural regions. For our northern India domain, we estimate 463 200 (95% confidence interval: 444 600-482 600) adults die prematurely each year from PM2.5 and that 37 800 (28 500-48 100) adults die prematurely each year from O3. This translates to 5.8 deaths per 10 000 attributable to air pollution out of an annual rate of 72 deaths per 10 000 (8.1% of deaths) using 2010 estimates. We estimate that the majority of premature deaths resulting from PM2.5 and O3 are in rural (383 600) as opposed to urban (117 200) regions, where we define urban as cities and towns with populations of at least 100 000 people. These findings indicate the need for rural monitoring and appropriate health studies to understand and mitigate the effects of ambient air pollution on this population in addition to supporting model evaluation.

We apply the NCAR CAM5-Chem global aerosol–climate model to quantify the net global radiative effects of black and organic carbon aerosols from global and Indian solid fuel cookstove emissions for the year 2010. Our updated assessment accounts for the direct radiative effects, changes to cloud albedo and lifetime (aerosol indirect effect, AIE), impacts on clouds via the vertical temperature profile (semi-direct effect, SDE), and changes in the surface albedo of snow and ice (surface albedo effect). In addition, we provide the first estimate of household solid fuel black carbon emission effects on ice clouds. Anthropogenic emissions are from the IIASA GAINS ECLIPSE V5a inventory. A global dataset of black carbon (BC) and organic aerosol (OA) measurements from surface sites and aerosol optical depth (AOD) from AERONET is used to evaluate the model skill. Compared with observations, the model successfully reproduces the spatial patterns of atmospheric BC and OA concentrations, and agrees with measurements to within a factor of 2. Globally, the simulated AOD agrees well with observations, with normalized mean bias close to zero. However, the model tends to underestimate AOD over India and China by ~ 19 % but overestimate it over Africa by ~ 25 %. Without BC serving as ice nuclei (IN), global and Indian solid fuel cookstove aerosol emissions have a net cooling impact on global climate of −141 ± 4 mW m−2 and −12 ± 4 mW m−2, respectively. The net radiative impacts are dominated by the AIE and SDE mechanisms, which originate from enhanced cloud condensation nuclei concentrations for the formation of liquid and mixed-phase clouds, and a suppression of convective transport of water vapor from the lower troposphere to the upper troposphere/lower stratosphere that in turn leads to reduced ice cloud formation. When BC is allowed to behave as a source of IN, the net global climate impacts of the global and Indian solid fuel cookstove emissions range from −260 to +135 mW m−2 and −

To evaluate uncertainty in the spatial distribution of air emissions over India, we compare satellite and surface observations with simulations from the U.S. Environmental Protection Agency (EPA) Community Multi-Scale Air Quality (CMAQ) model. Seasonally representative simulations were completed for January, April, July, and October 2010 at 36 km × 36 km using anthropogenic emissions from the Greenhouse Gas-Air Pollution Interaction and Synergies (GAINS) model following version 5a of the Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants project (ECLIPSE v5a). We use both tropospheric columns from the Ozone Monitoring Instrument (OMI) and surface observations from the Central Pollution Control Board (CPCB) to closely examine modeled nitrogen dioxide (NO2) biases in urban and rural regions across India. Spatial average evaluation with satellite retrievals indicate a low bias in the modeled tropospheric column (-63.3%), which reflects broad low-biases in majority non-urban regions (-70.1% in rural areas) across the sub-continent to slightly lesser low biases reflected in semi-urban areas (-44.7%), with the threshold between semi-urban and rural defined as 400 people per km2. In contrast, modeled surface NO2 concentrations exhibit a slight high bias of +15.6% when compared to surface CPCB observations predominantly located in urban areas. Conversely, in examining extremely population dense urban regions with more than 5000 people per km2 (dense-urban), we find model overestimates in both the column (+57.8) and at the surface (+131.2%) compared to observations. Based on these results, we find that existing emission fields for India may overestimate urban emissions in densely populated regions and underestimate rural emissions. However, if we rely on model evaluation with predominantly urban surface observations from the CPCB, comparisons reflect model high biases, contradictory to the knowledge gained using satellite observations. Satellites thus s

After completion of the analyses that informed the negotiations on the revised national emission ceilings directive (NECD), important factors have changed. Improved emission inventories, the recent climate and energy policies of the European Union and new source‐oriented emission control regulations have profound implications on further actions to meet the emission reduction requirements of the NECD. Considering the interplay of this new information, this report presents an updated outlook for emissions and air quality in the European Union, and explores the prospects of achieving the WHO guideline values to protect human health and the Union’s long‐term environmental policy objectives on the protection of ecosystems. It is found that, broadly speaking, by 2030 the recent legislation will bring the WHO guidelines for PM2.5 within reach for most areas, while further efforts, especially for agricultural ammonia emissions and PM emissions from residential combustion of solid fuels will be required at hot spots.