This paper examines how policies intended to reduce carbon dioxide (CO2) emissions affect air pollution exposure, mortality risk, and monetary benefits across the income distribution in the United States (U.S.). We use an energy system optimization model (ESOM) to translate several climate change mitigation policies into CO2-equivalent emission reductions. The ESOM also tracks emissions of three air pollutants: fine particulate matter, sulfur dioxide, and nitrogen oxides. The AP3 model links changes in emissions of local air pollutants to county-level ambient concentrations, exposure, mortality risk, and monetary damages. We present three central results. First, the monetary benefits from reduced air pollution exposure of the climate policies amount to less than 1% of real per capita income. Second, the monetary benefits are progressively distributed. Specifically, counties with a 10% higher real median income level tend to incur between 5% and 6% lower benefits from the carbon tax, the net zero scenario, and the clean electricity standard in 2030. These estimated elasticities are closer to zero in 2040 and 2050. Third, benefits are distributed progressively in the northeast and the western census regions, and regressively in the Midwest. In the southeast, benefits and income are uncorrelated.

Chronic exposure to fine particulate matter (PM2.5) has been linked to increased mortality rates. Chemical transport models (CTMs) are often used to evaluate PM2.5 regulations in the United States, but are computationally expensive and require significant technical expertise. Reduced-complexity models (RCMs) for PM2.5 are computationally-efficient air quality models designed to estimate ambient PM2.5 concentrations attributable to emissions of PM2.5 and PM2.5-precursors. We evaluate the ability of three widely-used RCMs (AP3, EASIUR, and InMAP) to reproduce changes in PM2.5 concentrations during 1990-2010. Compared to changes in observed, speciated PM2.5 concentrations, RCMs demonstrated strong correlation (R=0.61–0.66) and low mean error (0.84–1.03 μg m-3), performing comparably to a CTM (R=0.72, error=0.74 μg m-3). Most RCM applications assume that concentrations respond linearly to emissions; over the 20-year period investigated, during which most PM2.5 precursor emissions declined ~37–65%, we find only modest evidence of nonlinearities. These findings support the use of RCMs for many policy analysis and other applications for which the slight loss of accuracy compared to CTMs is tolerable, especially given RCM benefits. Common benefits of RCMs include increased computational efficiency and accessibility of air pollution modeling.

The ability to provide speciated and source-resolved PM2.5 estimates make chemical transport models a potentially valuable tool for exposure assessments. However, epidemiological studies often require unbiased estimates, which can be challenging for chemical transport models. We use geographically weighted regression to predict and correct the bias in source-resolved PM2.5 species (elemental carbon, organic aerosol, ammonium, nitrate, and sulfate) across the continental U.S. for 2001 and 2010. The regression models are trained using speciated ground-level monitors from the CSN and IMPROVE networks. A 10-fold cross-validation shows minimal bias across all simulated PM2.5 species (0 – 3%) and improved agreement with ground-level monitors (R2 = 0.53 – 0.97). Corrections also improve the agreement between simulated and observed species mixtures on a fractional basis. The source-resolved exposure estimates developed in this study are suitable for use in health analyses of PM2.5 toxicity.

This study investigates the role of hydrogen as a decarbonization strategy for the iron and steel industry in the United States (U.S.) in the presence of an economy-wide net zero CO2 emissions target. Our analysis shows that hydrogen-based direct reduced iron (H2DRI) provides a cost-effective decarbonization strategy only under a relatively narrow set of conditions. Using today's best estimates of the capital and variable costs of alternative decarbonized iron and steelmaking technologies in a U.S. economy-wide simulation framework, we find that carbon capture technologies can achieve comparable decarbonization levels by 2050 and greater cumulative emissions reductions from iron and steel production at a lower cost. Simulations suggest hydrogen contributes to economy-wide decarbonization, but H2DRI is not the preferred use case for hydrogen in most scenarios. The average abatement cost for U.S. iron and steel production could be as low as $70/tonne CO2 with existing technologies plus carbon capture, while the cost with H2DRI rises to over $500/tonne CO2. We also find that IRA tax credits are insufficient to spur hydrogen use in steelmaking in our model and that a green steel production tax credit would need to be as high as $300/tonne steel to lead to sustained H2DRI use.

While air pollution from most U.S. sources has decreased, emissions from wildland fires have risen. Here, we use an integrated assessment model to estimate that wildfire and prescribed burn smoke caused $200 billion in health damages in 2017, associated with 20,000 premature deaths. Nearly half of this damage came from wildfires, predominantly in the West, with the remainder from prescribed burns, mostly in the Southeast. Our analysis reveals positive correlations between smoke exposure and various social vulnerability measures; however, when also considering smoke susceptibility, these disparities are systematically influenced by age. Senior citizens, who are disproportionately White, represented 16% of the population but incurred 75% of the damages. Nonetheless, within most age groups, Native American and Black communities experienced the greatest damages per capita. Our work highlights the extraordinary and disproportionate effects of the growing threat of fire smoke and calls for targeted, equitable policy solutions for a healthier future.

Pediatric asthma affects roughly 1 in 15 children in the U.S., with significantly higher rates in Black children. Exposure to NO2 contributes to asthma development, but quantitative analysis of pediatric asthma burden attributable to NOx emissions is rarely performed due to a lack of suitable models. This paper describes a new model, Marginal Asthma prevalence from NOx Emissions (MANE), which assesses the pediatric asthma burden from NOx emissions and its distribution across race/ethnicities. We find that emissions in more densely populated areas cause a larger number of pediatric asthma cases and tend to disproportionately impact minority communities. We applied our model to assess the pediatric asthma burden from sources of NOx, finding that diesel heavy-duty vehicles are responsible for approximately 4% of all pediatric asthma cases in the U.S. Additionally, we find that all source sectors considered disproportionately impact children of color, with 65-100% higher per-capita asthma rates in Black children compared to white non-Hispanic children. Finally, we find that emissions in a limited number of urban areas are responsible for a large share of asthma cases, suggesting that local, targeted restrictions on NOx emissions may provide great public health benefit nationally.

Energy system optimization models facilitate analyses on a national or regional scale. However, understanding the impacts of climate policy on specific populations requires a much higher spatial resolution. Here, we link an energy system optimization model to an integrated assessment model via an emission downscaling algorithm, translating air pollution emissions from nine U.S. regions to U.S. counties. We simulate the impacts of six distinct policy scenarios, including a current policy and a 2050 net-zero target, on NOx, SO2, and PM2.5 emissions from on-road transportation and electricity generation. We compare different policies based on their ability to reduce emission exposure and exposure disparity across racial groups, allowing decision-makers to assess the air pollution impacts of various policy instruments more holistically. Modeled policies include a clean electricity standard, an on-road ICE vehicle ban, a carbon tax, and a scenario that reaches net-zero GHG emissions by 2050. While exposure and disparities decrease in all scenarios, our results reveal persistent disparities until at least 2040, particularly for Black non-Hispanic Americans. Our estimates of avoided deaths due to air pollution emphasize the importance of policy timing, showing that thousands of lives can be saved by taking action in the near-term.

Biogenic secondary organic aerosol (bSOA) is a major component of atmospheric particulate matter (PM2.5) in the southeast United States especially during the summer, when emissions of biogenic volatile organic compound (VOCs) are high and emissions from anthropogenic sources enhance the formation of secondary particulate matter. In this work we test the hypothesis whether a chemical transport model (PMCAMx) that includes a detailed description of gas-phase chemistry, secondary organic aerosol (SOA) formation based on the volatility basis set (VBS), and interactions among compounds based on partitioning theory can predict organic aerosol (OA) concentrations that are consistent with the observed changes in OA in response to significant changes in anthropogenic emissions during the summers of 2001 and 2010. The model had good performance for OA for both periods and its predictions were consistent with the observed changes in both the urban and rural areas. The fractional error of OA predictions remained practically the same (0.41 and 0.44 at Chemical Speciation Network (CSN) sites and 0.40 to 0.41 at Interagency Monitoring of Protected Visual Environments (IMPROVE) sites in the summers of 2001 and 2010 respectively) in the two examined periods. The fractional bias of OA predictions increased from 0.10 to 0.22 at CSN sites and decreased from 0 to-0.09 at IMPROVE sites between the two periods. Average predicted bSOA concentrations in the southeast US did not change appreciably from the summer of 2001 to the summer of 2010, while the anthropogenic SOA decreased by 45%. As a result, the biogenic fraction of predicted total OA increased from 0.46 in 2001 to 0.63 in 2010. Partitioning effects due to reduced anthropogenic OA from 2001 resulted in 0.4 mu g m- 3 less biogenic OA on average in the southeast US in the summer of 2010. This was offset by biogenic SOA increases due to higher biogenic vapor emissions in the warmer 2010 summer. Removing the NOx-dependence of SOA formation yields resulted in higher fractional error and fractional bias at both CSN and IMPROVE sites in both summer periods, demonstrating the efficacy of the current formulation of SOA yields. The results of the analysis support the conclusion that a CTM that simulates NOx-dependent SOA chemistry and semivolatile partitioning of SOA material can consistently predict the observed changes in a region rich in biogenic VOCs and SOA.

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 PM 2.5 mortalities 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 PM 2.5 concentrations and associated mortality. We find poorer, coal-dependent states in eastern India disproportionately face the burden of PM 2.5 mortality 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 PM 2.5 mortality 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.

Dataset for use with "Closing the Gap: Achieving U.S. Climate Goals Beyond the Inflation Reduction Act"

People of color disproportionately bear the health impacts of air pollution, making air quality a critical environmental justice issue. However, quantitative analysis of the disproportionate impacts of emissions is rarely done due to a lack of suitable models. Our work develops a high-resolution reduced-complexity model (EASIUR-HR) to evaluate the disproportionate impacts of ground-level primary PM2.5 emissions. Our approach combines a Gaussian plume model for near-source impacts of primary PM2.5 with a previously developed reduced-complexity model, EASIUR, to predict primary PM2.5 concentrations at a spatial resolution of 300 m across the contiguous United States. We find that lowresolution models underpredict important local spatial variation of air pollution exposure to primary PM2.5 emissions, potentially underestimating the contribution of these emissions to national inequality in PM2.5 exposure by more than a factor of 2. We apply EASIUR-HR to analyze the impacts of vehicle electrification on exposure disparities. While such a policy has small aggregate air quality impacts nationally, it reduces exposure disparity for race/ethnic minorities. Our high-resolution RCM for primary PM2.5 emissions (EASIUR-HR) is a new, publicly available tool to assess inequality in air pollution exposure across the United States.

Accurate predictions of source resolved atmospheric PM2.5 concentrations at high resolutions using chemical transport models (CTMs) require expensive CTM simulations and development of high-resolution emissions inventories. We use multiple machine learning (ML) approaches to downscale coarse-resolution (36 x 36 km2) CTM predictions to 1 x 1 km2 spatial resolution. ML predictions include concentrations of the major chemical components of PM2.5 and the contributions of its major emissions sources. Inputs for the ML models include 36 x 36 km2 source resolved CTM predicted concentrations of all PM2.5 components, meteorological data, and several land-use (LU) variables. The output of our ML models is the 1 x 1 km2 source-resolved concentrations of all major PM2.5 components in southwestern Pennsylvania (5184 km2 domain) during February and July 2017. Models were trained and validated using 1 x 1 km2 resolution source-and species-resolved CTM predictions of PM2.5 from recent complementary studies. The best overall performance was found using a random forest (RF) model, where species and source resolved PM2.5 concentrations were reproduced with low normalized mean bias (|NMB| < 0.01). The downscaling model captures the spatial distribution of PM2.5 both by component and source, with some discrepancies when predicting the plumes of large point sources that have long-range impacts. In a test of generalizability to unknown domains, the model differentiates well between areas that are primarily urban, rural, or industrial but faces challenges with the reproduction of the effects of large point sources of PM2.5 when entire quadrants are removed from the training data. The results represent a proof of concept for downscaling low-resolution CTM predictions using native high-resolution CTM predictions in training.

A computational fluid dynamics (CFD) model that solves the steady-state Reynolds-Averaged Navier-Stokes (RANS) equations for buoyant compressible pollution dispersion under different meteorological conditions is developed. A 6.4 km by 6.4 km computational domain over a complex terrain with a height of 1 km above the ground surface is created. Meteorological data from multiple available sources are utilized to obtain boundary conditions of wind speed, air temperature, turbulent kinetic energy (TKE), and its dissipation rate. To evaluate the model, a monitoring network of four anemometers is deployed. Model predictions are compared with measurements of wind speed and the concentration of SO2 emitted by a local coke plant. Comparisons show that the predicted wind speeds are reasonably close to the measured mean wind speeds and the average error is within 10 percent at one location where relative fast wind speeds are recorded. The CFD model also predicts the correct trend of varying wind speeds across multiple sites of different elevations. The model also provides good predictions of SO2 concentrations for multiple cases, considering the complex nature of the terrain and meteorological conditions.

KEYWORDS PM25, air pollution sources, exposure BACKGROUND AND AIM The quantification of the contributions of sources of air pollutants, especially PM2.5, can be used for the improvement of our understanding of PM2.5 health effects. Three-dimensional chemical transport models are well suited to address this problem, since they simulate all the major processes that impact PM2.5 concentrations and transport. In this work, we quantified the changes in the concentration, exposure, composition, and sources of PM2.5 in the US. Significant reductions of emissions of SO2, NOx, VOCs and primary PM have taken place. METHODS We evaluate our understanding of the links between these emissions concentration and exposure changes combining a chemical transport model (PMCAMx) with the Particle Source Apportionment Algorithm. RESULTS Results for 1990, 2001 and 2010 are presented. The 63% reduction in PM2.5 sulfate concentrations from electrical generation units during these 20 years has led to a 60% reduction in PM2.5 sulfate exposure. Also, the reductions in elemental carbon (EC) concentrations from road transport by 72% have led to a reduction of PM2.5 EC exposure of 70%. CONCLUSIONS In 1990 90% of the US population was exposed to PM2.5 concentrations to equal and higher than the suggested annual mean by the WHO, but this reduced to 70% in 2010. These results are the basis for an epidemiological study linking PM2.5 sources in the US and their health effect (Pond et al., 2021). REFERENCES Pond, Z. A., Hernandez, C. S., Adams, P. J., Pandis, S. N., Garcia, G. R., Robinson, L. A., Marshall, J. D., Burnett, R., Skyllakou, K., Rivera, P. G., Karnezi, E., Coleman, C. J., and Pope, A. C.: Cardiopulmonary Mortality and Fine Particulate Air Pollution by Species and Source in a National U.S. Cohort, Env. Science & Tech. Article ASAP, 2021.

The purpose of this study was to estimate cardiopulmonary mortality associations for long-term exposure to PM2.5 species and sources (i.e., components) within the U.S. National Health Interview Survey cohort. Exposures were estimated through a chemical transport model for six species (i.e., elemental carbon (EC), primary organic aerosols (POA), secondary organic aerosols (SOA), sulfate (SO4), ammonium (NH4), nitrate (NO3)) and five sources of PM2.5 (i.e., vehicles, electricity-generating units (EGU), non-EGU industrial sources, biogenic sources (bio), "other" sources). In single-pollutant models, we found positive, significant (p < 0.05) mortality associations for all components, except POA. After adjusting for remaining PM2.5 (total PM2.5 minus component), we found significant mortality associations for EC (hazard ratio (HR) = 1.36; 95% CI [1.12, 1.64]), SOA (HR = 1.11; 95% CI [1.05, 1.17]), and vehicle sources (HR = 1.06; 95% CI [1.03, 1.10]). HRs for EC, SOA, and vehicle sources were significantly larger in comparison to those for remaining PM2.5 (per unit μg/m3). Our findings suggest that cardiopulmonary mortality associations vary by species and source, with evidence that EC, SOA, and vehicle sources are important contributors to the PM2.5 mortality relationship. With further validation, these findings could facilitate targeted pollution regulations that more efficiently reduce air pollution mortality.

Increasing the resolution of chemical transport model (CTM) predictions in urban areas is important to capture sharp spatial gradients in atmospheric pollutant concentrations and better inform air quality and emissions controls policies that protect public health. The chemical transport model PMCAMx (Particulate Matter Comprehensive Air quality Model with Extensions) was used to assess the impact of increasing model resolution on the ability to predict the source-resolved variability and population exposure to PM2.5 at 36×36, 12×12, 4×4, and 1×1 km resolutions over the city of Pittsburgh during typical winter and summer periods (February and July 2017). At the coarse resolution, county-level differences can be observed, while increasing the resolution to 12×12 km resolves the urban–rural gradient. Increasing resolution to 4×4 km resolves large stationary sources such as power plants, and the 1×1 km resolution reveals intra-urban variations and individual roadways within the simulation domain. Regional pollutants that exhibit low spatial variability such as PM2.5 nitrate show modest changes when increasing the resolution beyond 12×12 km. Predominantly local pollutants such as elemental carbon and primary organic aerosol have gradients that can only be resolved at the 1×1 km scale. Contributions from some local sources are enhanced by weighting the average contribution from each source by the population in each grid cell. The average population-weighted PM2.5 concentration does not change significantly with resolution, suggesting that extremely high resolution PM2.5 predictions may not be necessary for effective urban epidemiological analysis at the county level.

Emission factors from Indian electricity remain poorly characterized, despite known spatial and temporal variability. Limited publicly available emissions and generation data at sufficient detail make it difficult to understand the consequences of emissions to climate change and air pollution, potentially missing cost-effective policy designs for the world's third largest power grid. We use reduced-form and full-form power dispatch models to quantify current (2017-2018) and future (2030-2031) marginal CO2, SO2, NOX, and PM2.5 emission factors from Indian power generation. These marginal emissions represent emissions changes due to small changes in demand. For 2017-2018, spatial variability in marginal CO2 emission factors range 3 orders of magnitude across India's states. There is limited seasonal and intraday variability with coal generation likely to meet changes in demand more than half the time in more than half of the states. Assuming the Government of India approximate 2030 targets, the median marginal CO2 emission factor across states decreases by approximately a factor of 2, but emission factors still span 3 orders of magnitude across states. Under 2030-2031 assumptions there is greater seasonal and intraday variability by up to factors of two and four, respectively. Estimates provide emission factors to evaluate interventions such as electric vehicles, increased air conditioning, and energy efficiency.

Emissions of greenhouse gases and air pollutants in India are important contributors to climate change and health damages. This study estimates current emissions from India’s electricity sector and simulates the state-level implications of climate change and air pollution policies. We find that (i) a carbon tax results in little short-term emissions reductions because there is not enough dispatchable lower emission spare capacity to substitute coal; (ii) moving toward regional dispatch markets rather than state-level dispatch decisions will not lead to emissions reductions; (iii) policies that have modest emissions effects at the national level nonetheless have disparate state-level emissions impacts; and (iv) pricing or incentive mechanisms tied to production or consumption will result in markedly different costs to states.

Accurately predicting urban PM2.5 concentrations and composition has proved challenging in the past, partially due to the resolution limitations of computationally intensive chemical transport models (CTMs). Increasing the resolution of PM2.5 predictions is desired to support emissions control policy development and address issues related to environmental justice. A nested grid approach using the CTM PMCAMx-v2.0 was used to predict PM2.5 at increasing resolutions of 36 km × 36 km, 12 km × 12 km, 4 km × 4 km, and 1 km × 1 km for a domain largely consisting of Allegheny County and the city of Pittsburgh in southwestern Pennsylvania, US, during February and July 2017. Performance of the model in reproducing PM2.5 concentrations and composition was evaluated at the finest scale using measurements from regulatory sites as well as a network of low-cost monitors. Novel surrogates were developed to allocate emissions from cooking and on-road traffic sources to the 1 km × 1 km resolution grid. Total PM2.5 mass is reproduced well by the model during the winter period with low fractional error (0.3) and fractional bias (+0.05) when compared to regulatory measurements. Comparison with speciated measurements during this period identified small underpredictions of PM2.5 sulfate, elemental carbon (EC), and organic aerosol (OA) offset by a larger overprediction of PM2.5 nitrate. In the summer period, total PM2.5 mass is underpredicted due to a large underprediction of OA (bias = −1.9 µg m−3, fractional bias = −0.41). In the winter period, the model performs well in reproducing the variability between urban measurements and rural measurements of local pollutants such as EC and OA. This effect is less consistent in the summer period due to a larger fraction of long-range-transported OA. Comparison with total PM2.5 concentration measurements from low-cost sensors showed improvements in performance with increasing resolution. Inconsistencies in PM2.5 nitrate predictions in both periods are believed to be due to errors in partitioning between PM2.5 and PM10 modes and motivate improvements to the treatment of dust particles within the model. The underprediction of summer OA would likely be improved by updates to biogenic secondary organic aerosol (SOA) chemistry within the model, which would result in an increase of long-range transport SOA seen in the inner modeling domain. These improvements are obvious topics for future work towards model improvement. Comparison with regulatory monitors showed that increasing resolution from 36 to 1 km improved both fractional error and fractional bias in both modeling periods. Improvements at all types of measurement locations indicated an improved ability of the model to reproduce urban–rural PM2.5 gradients at higher resolutions.