India’s coal-heavy electricity system is the world’s third largest and a major emitter of air pollution and greenhouse gas emissions. Consequently, it remains a focus of decarbonization and air pollution control policy. Considerable heterogeneity exists between states in India in terms of electricity demand, generation fuel mix, and emissions. However, no analysis has disentangled the expected, state-level spatial differences and interactions in air pollution mortality under current and future power sector policies in India. We use a reduced-complexity air quality model to evaluate annual PM2.5mortalities associated with electricity production and consumption in each state in India. Furthermore, we test emissions control, carbon tax, and market integration policies to understand how changes in power sector operations affect ambient PM2.5concentrations and associated mortality. We find poorer, coal-dependent states in eastern India disproportionately face the burden of PM2.5mortality from electricity in India by importing deaths. Wealthier, high renewable energy states in western and southern India meanwhile face a lower burden by exporting deaths. This suggests that as these states have adopted more renewable generation, they have shifted their coal generation and associated PM2.5mortality to eastern areas. We also find widespread sulfur emissions control decreases mortality by about 50%. Likewise, increasing carbon taxes in the short term reduces annual mortality by up to 9%. Market reform where generators between states pool to meet demand reduces annual mortality by up to 8%. As India looks to increase renewable energy, implement emissions control regulations, establish a carbon trading market, and move towards further power market integration, our results provide greater spatial detail for a federally structured Indian electricity system.
Energy transitions and decarbonization require rapid changes to a nation’s electricity generation mix. There are many feasible decarbonization pathways for the electricity sector, yet there is vast uncertainty about how these pathways will advance or derail the nation’s energy equality goals. We present a framework for investigating how decarbonization pathways, driven by a least-cost paradigm, will impact air pollution inequality across vulnerable groups (e.g., low-income, minorities) in the US. We find that if no decarbonization policies are implemented, Black and high-poverty communities may be burdened with 0.19–0.22 μg/m3higher PM2.5concentrations than the national average during the energy transition. National mandates requiring more than 80% deployment of renewable or low-carbon technologies achieve equality of air pollution concentrations across all demographic groups. Thus, if least-cost optimization capacity expansion models remain the dominant decision-making paradigm, strict low-carbon or renewable energy technology mandates will have the greatest likelihood of achieving national distributional energy equality. Decarbonization is essential to achieving climate goals, but myopic decarbonization policies that ignore co-pollutants may leave Black and high-poverty communities up to 26–34% higher PM2.5exposure than national averages over the energy transition.
more » « less- Award ID(s):
- 2017789
- NSF-PAR ID:
- 10384596
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
Abstract Background The spatiotemporal variation of observed trace gases (NO 2 , SO 2 , O 3 ) and particulate matter (PM 2.5 , PM 10 ) were investigated over cities of Yangtze River Delta (YRD) region including Nanjing, Hefei, Shanghai and Hangzhou. Furthermore, the characteristics of different pollution episodes, i.e., haze events (visibility < 7 km, relative humidity < 80%, and PM 2.5 > 40 µg/m 3 ) and complex pollution episodes (PM 2.5 > 35 µg/m 3 and O 3 > 160 µg/m 3 ) were studied over the cities of the YRD region. The impact of China clean air action plan on concentration of aerosols and trace gases is examined. The impacts of trans-boundary pollution and different meteorological conditions were also examined. Results The highest annual mean concentrations of PM 2.5 , PM 10 , NO 2 and O 3 were found for 2019 over all the cities. The annual mean concentrations of PM 2.5 , PM 10 , and NO 2 showed continuous declines from 2019 to 2021 due to emission control measures and implementation of the Clean Air Action plan over all the cities of the YRD region. The annual mean O 3 levels showed a decline in 2020 over all the cities of YRD region, which is unprecedented since the beginning of the China’s National environmental monitoring program since 2013. However, a slight increase in annual O 3 was observed in 2021. The highest overall means of PM 2.5 , PM 10 , SO 2 , and NO 2 were observed over Hefei, whereas the highest O 3 levels were found in Nanjing. Despite the strict control measures, PM 2.5 and PM 10 concentrations exceeded the Grade-1 National Ambient Air Quality Standards (NAAQS) and WHO (World Health Organization) guidelines over all the cities of the YRD region. The number of haze days was higher in Hefei and Nanjing, whereas the complex pollution episodes or concurrent occurrence of O 3 and PM 2.5 pollution days were higher in Hangzhou and Shanghai. The in situ data for SO 2 and NO 2 showed strong correlation with Tropospheric Monitoring Instrument (TROPOMI) satellite data. Conclusions Despite the observed reductions in primary pollutants concentrations, the secondary pollutants formation is still a concern for major metropolises. The increase in temperature and lower relative humidity favors the accumulation of O 3 , while low temperature, low wind speeds and lower relative humidity favor the accumulation of primary pollutants. This study depicts different air pollution problems for different cities inside a region. Therefore, there is a dire need to continuous monitoring and analysis of air quality parameters and design city-specific policies and action plans to effectively deal with the metropolitan pollution.more » « less
-
more » « less
Abstract Nearly three billion people in low- and middle-income countries (LMICs) rely on polluting fuels, resulting in millions of avoidable deaths annually. Polluting fuels also emit short-lived climate forcers (SLCFs) and greenhouse gases (GHGs). Liquefied petroleum gas (LPG) and grid-based electricity are scalable alternatives to polluting fuels but have raised climate and health concerns. Here, we compare emissions and climate impacts of a business-as-usual household cooking fuel trajectory to four large-scale transitions to gas and/or grid electricity in 77 LMICs. We account for upstream and end-use emissions from gas and electric cooking, assuming electrical grids evolve according to the 2022 World Energy Outlook’s ‘Stated Policies’ Scenario. We input the emissions into a reduced-complexity climate model to estimate radiative forcing and temperature changes associated with each scenario. We find full transitions to LPG and/or electricity decrease emissions from both well-mixed GHG and SLCFs, resulting in a roughly 5 millikelvin global temperature reduction by 2040. Transitions to LPG and/or electricity also reduce annual emissions of PM2.5by over 6 Mt (99%) by 2040, which would substantially lower health risks from household air pollution. Full transitions to LPG or grid electricity in LMICs improve climate impacts over BAU trajectories.
-
more » « less
Abstract Electric vehicles (EVs) constitute just a fraction of the current U.S. transportation fleet; however, EV market share is surging. EV adoption reduces on-road transportation greenhouse gas emissions by decoupling transportation services from petroleum, but impacts on air quality and public health depend on the nature and location of vehicle usage and electricity generation. Here, we use a regulatory-grade chemical transport model and a vehicle-to-electricity generation unit electricity assignment algorithm to characterize neighborhood-scale (∼1 km) air quality and public health benefits and tradeoffs associated with a multi-modal EV transition. We focus on a Chicago-centric regional domain wherein 30% of the on-road transportation fleet is instantaneously electrified and changes in on-road, refueling, and power plant emissions are considered. We find decreases in annual population-weighted domain mean NO2(−11.83%) and PM2.5(−2.46%) with concentration reductions of up to −5.1 ppb and −0.98
µ g m−3in urban cores. Conversely, annual population-weighted domain mean maximum daily 8 h average ozone (MDA8O3) concentrations increase +0.64%, with notable intra-urban changes of up to +2.3 ppb. Despite mixed pollutant concentration outcomes, we find overall positive public health outcomes, largely driven by NO2concentration reductions that result in outsized mortality rate reductions for people of color, particularly for the Black populations within our domain. -
more » « less
Abstract Ammonia (NH3) emissions from fertilizer application is a highly uncertain input to chemical transport models (CTMs). Reducing such uncertainty is important for improving predictions of ambient NH3and PM2.5concentrations, for regulatory and policy purposes and for exploring linkages of air pollution to human health and ecosystem services. Here, we implement a spatially and temporally resolved inventory of NH3emissions from fertilizers, based on high-resolution crop maps, crop nitrogen demand and a process model, as input to the Comprehensive Air Quality Model with Extensions (CAMx). We also examine sensitivity to grid resolution, by developing inputs at 12 km × 12 km and 4 km × 4 km, for the Corn Belt region in the Midwest United States, where NH3emissions from chemical fertilizer application contributes to approximately 50% of anthropogenic emissions. Resulting predictions of ambient NH3and PM2.5concentrations were compared to predictions developed using the baseline 2011 National Emissions Inventory, and evaluated for closure with ground observations for May 2011. While CAMx consistently underpredicted NH3concentrations for all scenarios, the new emissions inventory reduced bias in ambient NH3concentration by 33% at 4 km × 4 km, and modestly improved predictions of PM2.5, at 12 km × 12 km (correlation coefficients r = 0.57 for PM2.5, 0.88 for PM-NH4, 0.71 for PM-SO4, 0.52 for PM-NO3). Our findings indicate that in spite of controlling for total magnitude of emissions and for meteorology, representation of NH3emissions and choice of grid resolution within CAMx impacts the total magnitude and spatial patterns of predicted ambient NH3and PM2.5concentrations. This further underlines the need for improvements in NH3emission inventories. For future research, our results also point to the need for better understanding of the effect of model spatial resolution with regard to both meteorology and chemistry in CTMs, as grid size becomes finer.
