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Iran plans to build the first coal-fired power plant in 2025 as part of its electricity generation development program. Coal-fired power plants are the main anthropogenic source of mercury emissions. According to Article 8 of the Minamata Convention on Mercury, all Parties need to set targets to control mercury emissions from these sources. No studies have been carried out to estimate mercury emissions from future development plans for coal-fired power plants in Iran. Therefore, the main objectives of this research are to estimate mercury emissions from this power plant and evaluate the best available technologies to control mercury emissions. In the first step, to achieve these objectives, mercury emissions under 13 scenarios were estimated based on the interactive process optimization guidance model and experimental data. To predict ground-level mercury concentrations in the second step, a Gaussian atmosphere dispersion model coupled with a mesoscale numerical weather prediction model was applied. Finally, the hazard quotient was used to assess inhalation risks. The results show that hourly mercury emissions, mercury concentration, and mercury emission factor are estimated to be 0.672 g/h, 0.2 μg/Nm3, and 0.45 kg/TWh, respectively, when circulating fluidized bed boilers equipped with the in-furnace desulfurization process and the cold-side electrostatic precipitator unit are utilized as the best available technology for mercury emission control. In conclusion, the hazard quotient indicates that ground-level mercury concentrations are improbable to induce inhalation risks for the community living within a 30 km radius from this power plant. [ABSTRACT FROM AUTHOR]

This study introduces a novel hybrid model combining Bayesian Stochastic Partial Differential Equations (SPDE) with deep learning, specifically Convolutional Neural Networks (CNN) and Deep Feedforward Neural Networks (DFFNN), to predict PM2.5 concentrations. Traditional models often fail to account for non-linear relationships and complex spatial dependencies, critical in urban settings. By integrating SPDE's spatial-temporal structure with neural networks' capacity for non-linearity, our model significantly outperforms standalone methods. Accurately predicting air pollution supports sustainable public health strategies and targeted interventions, which are critical for mitigating the adverse health effects of PM2.5, particularly in urban areas heavily impacted by climate change. The hybrid model was applied to the Pleasant Run Airshed in Indianapolis, Indiana, utilizing a comprehensive dataset that included PM2.5 sensor data, meteorological variables, and land-use information. By combining SPDE's ability to model spatial-temporal structures with the adaptive power of neural networks, the model achieved a high level of predictive accuracy, significantly outperforming standalone methods. Additionally, the model's interpretability was enhanced through the use of SHAP (Shapley Additive Explanations) values, which provided insights into the contribution of each variable to the model's predictions. This framework holds the potential for improving air quality monitoring and supports more targeted public health interventions and policy-making efforts. [ABSTRACT FROM AUTHOR]

This study presents an improved wet scavenging process for particles in air quality modeling, focusing on the Korean Peninsula. New equations were incorporated into the air quality chemical transport model (CTM) to enhance the simulation of particulate matter (PM) concentrations. The modified air quality CTM module, utilizing size-dependent scavenging formulas, was applied to simulate air quality for April 2018, a month characterized by significant precipitation. Results showed that the modified model produced more accurate predictions of PM10 and PM2.5 concentrations compared to the original air quality CTM model. The maximum monthly average differences were 5.46 µg/m3 for PM10 and 2.87 µg/m3 for PM2.5, with pronounced improvements in high-concentration regions. Time-series analyses for Seoul and Busan demonstrated better agreement between modeled and observed values. Spatial distribution comparisons revealed enhanced accuracy, particularly in metropolitan areas. This study highlights the importance of incorporating region-specific, size-dependent wet scavenging processes in air quality models. The improved model shows promise for more accurate air quality predictions, potentially benefiting environmental management and policy-making in the region. Future research should focus on integrating more empirical data to further refine the wet scavenging process in air quality modeling. [ABSTRACT FROM AUTHOR]

Various types of energetic fuel combustion processes emit dangerous pollutants into the air, including aerosol particles, marked as PM10. Routine air quality monitoring includes determining the PM10 concentration as one of the basic measurements. At some air monitoring stations, the PM10 measurement is supplemented by the simultaneous determination of the concentration of PM2.5 as a finer fraction of suspended particles. Since the PM2.5 fraction has a significant share in the PM10 fraction, the concentrations of both types of particles should be strongly correlated, and the concentrations of one of these fractions can be used to model the concentrations of the other fraction. The aim of the study was to assess the error of predicting PM2.5 concentration using PM10 concentration as the main predictor. The analyzed daily concentrations were measured at 11 different monitoring stations in Poland and covered the period 2010–2021. MLP (multilayer perceptron) artificial neural networks were used to approximate the daily PM2.5 concentrations. PM10 concentrations and time variables were tested as predictors in neural networks. Several different prediction errors were taken as measures of modeling quality. Depending on the monitoring station, in models with one PM10 predictor, the RMSE error values were in the range of 2.31–6.86 μg/m3. After taking into account the second predictor D (date), the corresponding RMSE errors were lower and were in the range of 2.06–5.54 μg/m3. Our research aimed to find models that were as simple and universal as possible. In our models, the main predictor is the PM10 concentration; therefore, the only condition to be met is monitoring the measurement of PM10 concentrations. We showed that models trained at other air monitoring stations, so-called foreign models, can be successfully used to approximate PM2.5 concentrations at another station. [ABSTRACT FROM AUTHOR]

In this study, the levels of sulfur dioxide (SO2) and nitrogen dioxide (NO2) were measured indoors and outdoors using passive samplers in Tymar village (20 homes), an industrial area, and Haji Wsu (15 homes), a non-industrial region, in the summer and the winter seasons. In comparison to Haji Wsu village, the results showed that Tymar village had higher and more significant mean SO2 and NO2 concentrations indoors and outdoors throughout both the summer and winter seasons. The mean outdoor concentration of SO2 was the highest in summer, while the mean indoor NO2 concentration was the highest in winter in both areas. The ratio of NO2 indoors to outdoors was larger than one throughout the winter at both sites. Additionally, the performance of machine learning (ML) approaches: multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) were compared in predicting indoor SO2 concentrations in both the industrial and non-industrial areas. Factor analysis (FA) was conducted on different indoor and outdoor meteorological and air quality parameters, and the resulting factors were employed as inputs to train the models. Cross-validation was applied to ensure reliable and robust model evaluation. RF showed the best predictive ability in the prediction of indoor SO2 for the training set (RMSE = 2.108, MAE = 1.780, and R2 = 0.956) and for the unseen test set (RMSE = 4.469, MAE = 3.728, and R2 = 0.779) values compared to other studied models. As a result, it was observed that the RF model could successfully approach the nonlinear relationship between indoor SO2 and input parameters and provide valuable insights to reduce exposure to this harmful pollutant. [ABSTRACT FROM AUTHOR]

Emission inventories (EIs) are vital for air quality modeling. Specific research goals often require modifying EIs from diverse data sources, demanding significant code development. In this study, we utilized and further developed the High Elective Resolution Modeling Emission System version three for Global and Regional domains (HERMESv3_gr). This user-friendly processing system was adapted for generating EIs compatible with the Chemistry Transport Model Tracel Model 5 Massive Parallel (TM5-MP). The results indicate that HERMESv3_gr is capable of generating EIs with negligible biases ( 10 − 7 relative differences) for TM5-MP, showcasing its effectiveness. We applied HERMESv3_gr to integrate the EI Regional Emission inventory in Asia (REAS) into the global EI Community Emission Data System (CEDS). Comparison of model results using CEDS alone and the integrated EI against measurement data for various pollutants globally revealed small improvements for carbon monoxide (1%) ethane (1–2%), and nitrogen oxide (2%) and larger for propane (5–7%). Ozone in the northern hemisphere improved by about 2% while in the southern hemisphere improvements of 5% could be observed. Our findings highlight the importance of carefully considering the effects of EI integration for accurate air quality modeling. [ABSTRACT FROM AUTHOR]

In this paper we set up a modeling chain to study the impact of different urban planning scenarios on air quality and ultimately the exposure of the population. The analysis relates to the intensity of the polluting activities associated with each scenario, as well as their environmental and health impact. The implementation of a 2030 prospective scenario on Ile-de-France allows us to assess the magnitude of the leverage effect of the actions recommended in the regional master plan. The objective is to quantify the importance of emission reductions, but also the gain in terms of exposure to pollutants, which can be obtained when we transcribe into the model the implementation of regulatory texts on the metropolis of Greater Paris. The results allow us to debate the paradox between reducing emissions and increasing the exposure created by situations of high urban densification.

Taichung Power Plant (TPP) is Taiwan's largest coal-fired power plant and is considered a major source of air pollution. During periods of deteriorating air quality, it is often required to reduce the load to reduce emissions. However, frequent power load shedding not only requires cost but also requires safety considerations. Therefore, it is necessary to explore the role that thermal power plant emissions play in air pollution in Taiwan. This study employed the Community Multiscale Air Quality modeling system with the brute-force method to analyze the PM2.5 concentration contributed by TPP. The results showed that among the various air basins in Taiwan, the Yun-Chi-Nan air basin (YCNAB), located to the south of TPP, was most severely affected by TPP's emissions, with an annual average affected concentration of 1.0 µg m−3 (3.3%). However, when serious PM2.5 pollution events (daily concentration > 70 µg/m3) occurred due to low wind speeds, the Central Taiwan air basin (CTAB), where TPP is located, became the area most severely affected by TPP's emissions. The low wind speed was caused by the interaction between the easterly wind field around Taiwan and Taiwan's north–south mountain ranges. When this happens, TPP's emissions would have a greater impact on the PM2.5 concentration at nearby stations in the CTAB and YCNAB, up to about 11%. Overall, on pollution days caused by low wind speeds, the largest TPP load reduction (40%) still had a certain effect as an emergency measure to improve the high PM2.5 pollution in central and southern Taiwan. [ABSTRACT FROM AUTHOR]

We developed an artificial neural network as an air quality model and estimated the scope of the climate change impact on future (until 2064) summertime trends of hourly ozone concentrations at an urban air quality station in Tehran, Iran. Our developed scenarios assume that present-time emissions conditions of ozone precursors will remain constant in the future. Therefore, only the climate change impact on future ozone concentrations is investigated in this study. General Circulation Model (GCM) projections indicate more favorable climate conditions for ozone formation over the study area in the future: the surface temperature increases over all months of the year, solar radiation increases, and precipitation decreases in future summers, and summertime daily maximum temperature increases about 1.2∘C to 3∘C until 2064. In the scenario based on present-time ozone conditions in the 2012 summer without any exceedances, the summertime exceedance days of the 8-hr ozone standard are projected to increase in the future by about 4.2 days in the short term and about 12.3 days in the mid-term. Similarly, in the scenario based on present-time ozone conditions in the 2010 summer with 58 days of exceedance from the 8-hr ozone standard, exceedances are projected to increase by about 4.5 days in the short term and about 14.1 days in the mid-term. Moreover, the number of Unhealthy and Very Unhealthy days in the 8-hr Air Quality Index (AQI) is also projected to increase based on pollution scenarios of both summers. [ABSTRACT FROM AUTHOR]

Epidemiologic studies have found associations between fine particulate matter (PM2.5) exposure and adverse health effects using exposure models that incorporate monitoring data and other relevant information. Here, we use nine PM2.5 concentration models (i.e., exposure models) that span a wide range of methods to investigate i) PM2.5 concentrations in 2011, ii) potential changes in PM2.5 concentrations between 2011 and 2028 due to on-the-books regulations, and iii) PM2.5 exposure for the U.S. population and four racial/ethnic groups. The exposure models included two geophysical chemical transport models (CTMs), two interpolation methods, a satellite-derived aerosol optical depth-based method, a Bayesian statistical regression model, and three data-rich machine learning methods. We focused on annual predictions that were regridded to 12-km resolution over the conterminous U.S., but also considered 1-km predictions in sensitivity analyses. The exposure models predicted broadly consistent PM2.5 concentrations, with relatively high concentrations on average over the eastern U.S. and greater variability in the western U.S. However, differences in national concentration distributions (median standard deviation: 1.00 μg m-3) and spatial distributions over urban areas were evident. Further exploration of these differences and their implications for specific applications would be valuable. PM2.5 concentrations were estimated to decrease by about 1 μg m-3 on average due to modeled emission changes between 2011 and 2028, with decreases of more than 3 μg m-3 in areas with relatively high 2011 concentrations that were projected to experience relatively large emission reductions. Agreement among models was closer for population-weighted than uniformly weighted averages across the domain. About 50% of the population was estimated to experience PM2.5 concentrations less than 10 μg m-3 in 2011 and PM2.5 improvements of about 2 μg m-3 due to modeled emission changes between 2011 and 2028. Two inequality metrics were used to characterize differences in exposure among the four racial/ethnic groups. The metrics generally yielded consistent information and suggest that the modeled emission reductions between 2011 and 2028 would reduce absolute exposure inequality on average.

Air pollution is a significant global problem that affects human health and the natural environment. This research article aims to provide a comprehensive review of the impacts of air pollution on health and ecosystems and present various analytical techniques used for its measurement and modeling. The study uses a systematic review method to identify and evaluate research articles that deal with air pollution, its impacts on health and ecosystems, analytical techniques, and modeling that are only available in English and published between 2012 and 2023. The findings outlined in this article contribute to an enhanced understanding of the detrimental effects of air pollution and provide insights into effective monitoring and mitigation strategies. In conclusion, by implementing effective policies, adopting clean technologies, promoting sustainable practices, and raising awareness, we can mitigate the impacts of air pollution and create a healthier and more sustainable future. [ABSTRACT FROM AUTHOR]

Herein, we present the initial application of the NOAA-EPA Atmosphere-Chemistry Coupler (NACC) in the cloud ("NACC-Cloud", version 1), which processes NOAA's operational Global Forecast System version 16 (GFSv16) meteorology on-demand and produces model-ready meteorological files needed to drive U.S. EPA's Community Multiscale Air Quality (CMAQ) model. NACC is adapted from the U.S. EPA's Meteorology-Chemistry Interface Processor version 5 (MCIPv5) and is used as the primary model coupler in the current operational NWS/NOAA air quality forecasting model. The development and use of NACC-Cloud in this work are critical to provide the scientific community streamlined access to NOAA's operational GFSv16 data and user-defined processing and download of model-ready, meteorological input for any regional CMAQ domain worldwide. The NACC-Cloud system was implemented on the Amazon® Web Services High-Performance Computing platform, and results from this work show that the NACC-Cloud system is immediately beneficial to the air quality modeling community worldwide. [ABSTRACT FROM AUTHOR]

The average PM10 daily levels over the urban area of Thessaloniki, Greece, usually exceed the air quality limits and therefore the improved PM chemical composition and air quality modeling results that will facilitate the design of the most appropriate mitigation measures (e.g., limitations in wood combustion for heating purposes) are essential. The air quality modeling system WRF-CAMx was applied over a 2 × 2 km2 horizontal resolution grid covering the greater area of Thessaloniki for the year 2015, when Greece was still confronting the consequences of the financial crisis. The output hourly surface concentrations of twelve PM species at three sites of different environmental type characterization in the city of Thessaloniki were temporally and spatially analyzed. Carbonaceous aerosols (organic and elemental) are the major contributor to total PM10 levels during winter representing a 35–40% share. During summer, mineral aerosols (excluding dust) distribute by up to 48% to total PM10 levels, being the major contributor attributed to road traffic. PM species, during winter, increase in the morning and in the afternoon mainly due to road transport and residential heating, respectively, in addition with the unfavorable meteorological conditions. An underestimation of the primary organic carbon aerosol levels during winter is identified. The application of the modeling system using a different speciation profile for the fine particles emissions from residential heating based on observational data instead of the CAMS emissions profile revealed an improvement in the simulated OC/EC values for which a 50% increase was identified compared to the base run. [ABSTRACT FROM AUTHOR]

Volatile organic compounds (VOCs) have crucial influences on atmospheric chemistry. Accurate quantification of the VOC emissions is critical for air pollution research, especially when applying to air quality models. However, current bottom‐up emission inventories have biases, making observational constraints of VOC emissions necessary. We conducted concurrent VOC measurements in the Pearl River Delta (PRD) region during the summer of 2018 and found large discrepancies in the spatiotemporal variations of VOCs between observations and model simulations when using the priori VOC emission inventory (Multi‐resolution Emission Inventory for China). The normalized biases of total VOC concentrations ranged from −55% to 85% across the PRD cities in the study period. To improve the simulations, we constrained the anthropogenic VOC emissions based on their measured concentrations. The observation‐constrained VOC emissions showed clear diurnal variations and resolved the spatially‐concentrated priori emissions by reducing the high emissions by 15%–36% in the central PRD cities while elevating the sparse emissions in other cities. After employing the observation‐constrained VOC emissions, the model better reproduced the spatiotemporal variations of VOCs in the PRD region, alleviating the biases to −13%–13%. Furthermore, simulations of peak ozone (O3) concentrations were amended to reduce the mean normalized bias by 5%–12% on high O3 days. Our work has effectively combined VOC field measurements with air quality modeling to achieve better simulations of VOCs and O3. Besides, the observational‐constrained emissions are flexible for studying short‐term emission changes and their subsequent impacts on air quality. Plain Language Summary: Air quality models are important tools for studying the physical and chemical processes of air pollution. Model simulations rely heavily on the input emission profiles. Despite efforts to establish accurate and model‐ready emission inventories, biases persist, which further affects model results. Volatile organic compounds (VOCs) emitted from various anthropogenic sources are key precursors to ozone (O3) and secondary organic aerosols. In this study, we constructed an observational constraint to validate current VOC emission inventories against observations in nine cities. Constrained emission profiles revealed more pronounced spatiotemporal variations in VOC emissions across cities. Furthermore, since VOC emissions were constrained by observations, air quality model simulations of VOCs and O3 were significantly improved and more consistent with observations in terms of chemical composition, diurnal variation, and spatial variation. Our proposed approach is efficient and flexible in updating VOC emissions and is suitable for air quality modeling. The improved simulations of VOCs and O3 provide scientific support for further studies on the impacts of emission changes on air pollution and pollution control strategies. Key Points: Discrepancies found in spatiotemporal variations of volatile organic compounds (VOCs) between field measurements and model simulationsAnthropogenic VOC emissions in the Pearl River Delta region quantitatively estimated through observationsSimulations of spatiotemporal variations of VOCs and ozone improved by observation‐constrained emissions [ABSTRACT FROM AUTHOR]

Featured Application: The results presented in the article can be used to optimize activities in the field of road traffic management to improve air quality in urban street canyons. Constantly changing vehicle stock, modification of road infrastructure, and other conditions result in a need to update the knowledge on the effectiveness of individual traffic management strategies, which could form the basis for actions taken by local authorities to improve air quality in crowded city centers, especially in street canyons. The article presents research results that evaluate the theoretical effects of introducing select traffic reorganization scenarios in the example of four street canyons located in Krakow (Poland) that are different in terms of vehicle traffic volume and canyon geometry. These scenarios were based on a reduction in the average traffic speed, road capacity or the admission of cars meeting certain exhaust emission standards. The authors estimated changes in emissions of nitrogen oxides (NO, NO2 and total NOx) and particulate matter (PM10 and PM2.5) as well as investigated the effect of these changes on air quality in the canyons using the Operational Street Pollution Model (OSPM). Significant effects in terms of improving air quality were identified only in scenarios based on a significant reduction in traffic volume and the elimination of passenger cars and light commercial vehicles with internal combustion engines that did not meet the requirements of the Euro 4, Euro 5 or Euro 6 emission standards. For these scenarios, depending on the variant and canyon analyzed, the emission reduction was achieved at a level of approximately 36–66% for NO, 28–77% for NO2, 35–67% for NOx and 44–78% for both PM10 and PM2.5. The expected effect of improving air quality in individual street canyons for these substances was 15–44%, 5–14%, 11–36% and 3–14%, respectively. The differences obtained in the percentage reduction of emissions and pollutant concentrations in the air were the result of a relatively high background of pollutants that suppress the achieved effect of improving air quality to a large extent. [ABSTRACT FROM AUTHOR]

• A two-year hourly V d database was developed across North Americas using community dry deposition models. • Median (mean) V d of 45 gaseous species are in the range of 0.018–1.37 cm s−1 (0.05–1.43 cm s−1) across North America. • Median (mean) V d of PM 2.5 is 0.18 (0.20) cm s−1 while that of PM 2.5-10 doubles that of PM 2.5. The dry deposition process refers to the flux loss of an atmospheric pollutant due to uptake of the pollutant by the earth's surfaces. Dry deposition flux of a chemical species is typically calculated as the product of its surface-layer concentration and its dry deposition velocity (V d). Field measurement based V d data are very scarce or do not exist for many chemical species considered in chemistry transport models. In the present study, gaseous and particulate dry deposition schemes were applied to generate a database of hourly V d for 45 gaseous species and three particle size ranges for two years (2016–2017) at a 15 km by 15 km horizontal resolution across North America. Hourly V d of the 45 gaseous species ranged from < 0.001 to 4.6 cm/sec across the whole domain, with chemical species-dependent median (mean) values being in the range of 0.018–1.37 cm/sec (0.05–1.43 cm/sec). The spatial distributions of the two-year average V d showed values higher than 1–3 cm/sec for those soluble and reactive species over certain land types. Soluble species have the highest V d over water surfaces, while insoluble but reactive species have the highest V d over forests. Hourly V d of PM 2.5 across the whole domain ranged from 0.039 to 0.75 cm/sec with median (mean) value of 0.18 (0.20) cm s−1, while the mean V d for PM 2.5–10 is twice that of PM 2.5. Uncertainties in the modeled V d are typically on the order of a factor of 2.0 or larger, which needs to be considered when applying the dataset in other studies. [Display omitted]. [ABSTRACT FROM AUTHOR]

Kao-Ping Air Basin (KPAB) is a heavy industrial area, and also the first and only air quality total quantity control district in Taiwan. Pollutant emission offsetting is an important tool to reduce pollution source emissions and improve air quality in the total quantity control district. In this study, an air quality model was employed to evaluate the sensitivity of SOX, NOX, and primary PM2.5 emissions from point, mobile, and fugitive sources on PM2.5 concentrations in KPAB. The findings show that the emission offset ratios of mobile PM2.5-to-point PM2.5 and fugitive PM2.5-to-point PM2.5 were both greater than one in urban areas (1.3 and 2.0, respectively) and both less than one in non-urban areas. The offset ratios of point SOX-to-point PM2.5 and point NOX-to-point PM2.5 were significantly greater than one, especially those in urban areas (20 and 60, respectively) were higher than those in non-urban areas by more than 2–4 times. No matter whether in urban or non-urban areas, the offset ratio of mobile NOX-to-point NOX was close to one, and the offset ratios of point NOX-to-point PM2.5 and mobile NOX-to-point PM2.5 were similar. The above findings were closely related to the proximity of point sources to densely populated urban areas in KPAB. [ABSTRACT FROM AUTHOR]

As the transportation sector continues to decarbonize through electrification, there is growing interest in quantifying potential tradeoffs in air pollution and health impacts due to potential excess emissions from the power sector. This study investigates air pollution and health impacts of policy-driven changes in the transportation sector and the associated power generation demand in the Northeast and Mid-Atlantic United States. Five illustrative scenarios were designed to capture the effects of different policies under the first mandatory market-based program to reduce greenhouse gases in the US power sector (Regional Greenhouse Gas Initiative—RGGI) and the Transportation and Climate Initiative (TCI). Considering future power generation with new renewable energy investments to meet demands from decarbonized transportation, the scenarios were framed using: 1. 2030 reference cases for both sectors and a hybrid TCI portfolio, 2. Departure from the reference cases defined by Pennsylvania included or not in RGGI, and 3. Power grid emissions estimated under clean energy standard (CES) policy and hybrid TCI portfolio. While the cross-sectoral policy effect on domain-wide concentrations is modest (max ΔPM _2.5 ∼ 0.06 μ g m ^3 , ΔNO _2 ∼ 0.3 ppbv, ΔO _3 ∼ 0.15 ppbv), substantial increases in Ohio and West Virginia were attributed to Pennsylvania joining RGGI. With CES enacted and Pennsylvania in RGGI, significant reductions are seen in average concentrations (max ΔPM _2.5 ∼ 1.2 μ g m ^3 , ΔNO _2 ∼ 1.1 ppbv, ΔO _3 ∼ 1.7 ppbv) except for Louisiana and Mississippi with corresponding disbenefits. When focusing exclusively on emissions reductions from transportation, the hybrid TCI portfolio had health benefits of 530 avoided adult deaths, and 46 000 avoided asthma exacerbations. With a ‘business as usual’ power grid, these benefits remain comparable and are mainly driven by NO _2 , followed by PM _2.5 and O _3 . However, if Pennsylvania joins RGGI, total health benefits and spatial distribution change substantially, with a large portion of adverse health impacts moving from TCI states to Ohio and West Virginia. The overall monetized impact of a CES scenario can substantially exceed the estimated average range of 66–69 Billion US$, depending on the interaction with transportation decarbonization strategies and other drivers of exposure.

The United States Environmental Protection Agency (US EPA) has developed a set of annual North American emissions data for multiple air pollutants across 18 broad source categories for 2002 through 2017. The sixteen new annual emissions inventories were developed using consistent input data and methods across all years. When a consistent method or tool was not available for a source category, emissions were estimated by scaling data from the EPA's 2017 National Emissions Inventory with scaling factors based on activity data and/or emissions control information. The emissions datasets are designed to support regional air quality modeling for a wide variety of human health and ecological applications. The data were developed to support simulations of the EPA's Community Multiscale Air Quality model but can also be used by other regional scale air quality models. The emissions data are one component of EPA's Air Quality Time Series Project which also includes air quality modeling inputs (meteorology, initial conditions, boundary conditions) and outputs (e.g., ozone, PM2.5 and constituent species, wet and dry deposition) for the Conterminous US at a 12 km horizontal grid spacing.

Malaysia has been facing transboundary haze events repeatedly, in which the air contains extremely high particulate matter, particularly PM10, which affects human health and the environment. Therefore, it is crucial to understand the characteristics of PM10 concentration and develop a reliable PM10 forecasting model for early information and warning alerts to the responsible parties in order for them to mitigate and plan precautionary measures during such events. This study aims to analyze PM10 variation and investigate the performance of quantile regression in predicting the next-day, the next two days, and the next three days of PM10 levels during a high particulate event. Hourly secondary data of trace gases and the weather parameters at Pasir Gudang, Melaka, and Petaling Jaya during historical haze events in 1997, 2005, 2013, and 2015. The Pearson correlation was calculated to find the correlation between PM10 level and other parameters. Moderate correlated parameters (r > 0.3) with PM10 concentration were used to develop a Pearson–QR model with percentiles of 0.25, 0.50, and 0.75 and were compared using quantile regression (QR) and multiple linear regression (MLR). Several performance indicators, namely mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R2), and index of agreement (IA), were calculated to evaluate and compare the performances of the predictive model. The highest daily average of PM10 concentration was monitored in Melaka within the range of 69.7 and 83.3 µg/m3. CO and temperature were the most significant parameters associated with PM10 level during haze conditions. Quantile regression at p = 0.75 shows high efficiency in predicting PM10 level during haze events, especially for the short-term prediction in Melaka and Petaling Jaya, with an R2 value of >0.85. Thus, the QR model has high potential to be developed as an effective method for forecasting air pollutant levels, especially during unusual atmospheric conditions when the overall mean of the air pollutant level is not suitable for use as a model. [ABSTRACT FROM AUTHOR]

In preparation for the Fourth Industrial Revolution (IR 4.0) in Malaysia, the government envisions a path to environmental sustainability and an improvement in air quality. Air quality measurements were initiated in different backgrounds including urban, suburban, industrial and rural to detect any significant changes in air quality parameters. Due to the dynamic nature of the weather, geographical location and anthropogenic sources, many uncertainties must be considered when dealing with air pollution data. In recent years, the Bayesian approach to fitting statistical models has gained more popularity due to its alternative modelling strategy that accounted for uncertainties for all air quality parameters. Therefore, this study aims to evaluate the performance of Bayesian Model Averaging (BMA) in predicting the next-day PM10 concentration in Peninsular Malaysia. A case study utilized seventeen years' worth of air quality monitoring data from nine (9) monitoring stations located in Peninsular Malaysia, using eight air quality parameters, i.e., PM10, NO2, SO2, CO, O3, temperature, relative humidity and wind speed. The performances of the next-day PM10 prediction were calculated using five models' performance evaluators, namely Coefficient of Determination (R2), Index of Agreement (IA), Kling-Gupta efficiency (KGE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The BMA models indicate that relative humidity, wind speed and PM10 contributed the most to the prediction model for the majority of stations with (R2 = 0.752 at Pasir Gudang monitoring station), (R2 = 0.749 at Larkin monitoring station), (R2 = 0.703 at Kota Bharu monitoring station), (R2 = 0.696 at Kangar monitoring station) and (R2 = 0.692 at Jerantut monitoring station), respectively. Furthermore, the BMA models demonstrated a good prediction model performance, with IA ranging from 0.84 to 0.91, R2 ranging from 0.64 to 0.75 and KGE ranging from 0.61 to 0.74 for all monitoring stations. According to the results of the investigation, BMA should be utilised in research and forecasting operations pertaining to environmental issues such as air pollution. From this study, BMA is recommended as one of the prediction tools for forecasting air pollution concentration, especially particulate matter level. [ABSTRACT FROM AUTHOR]

The biggest problem facing air protection in Poland is the high levels of suspended particular matter concentrations. Air monitoring reports show that air quality standards, related to PM10 and PM2.5 concentrations, are exceeded every year in many Polish cities. The PM2.5 aerosol fraction is particularly dangerous to human and animal health. Therefore, monitoring the level of PM2.5 concentration should be considered particularly important. Unfortunately, most monitoring stations in Poland do not measure this dust fraction. However, almost all stations are equipped with analyzers measuring PM10 concentrations. PM2.5 is a fine fraction of PM10, and there is a strong correlation between the concentrations of these two types of suspended dust. This relationship can be used to determine the concentration of PM2.5. The main purpose of this analysis was to assess the accuracy of PM2.5 concentration prediction using PM10 concentrations. The analysis was carried out on the basis of long-term hourly data recorded at several monitoring stations in Poland. Artificial neural networks in the form of a multilayer perceptron were used to model PM2.5 concentrations. [ABSTRACT FROM AUTHOR]

Haze episode in Malaysia typically takes place during the dry monsoon season. As a result, high concentration of atmospheric particles was recorded primarily brought by transboundary air pollution from the neighbour country. Therefore, this study aims to evaluate and compare the level of particulate matter (PM10) at urban-industrial areas during the episodic haze episodes in Malaysia. Hourly PM10 concentration with the concentration of gaseous air pollutants such as NOx, NO2, SO2, CO and O3 and meteorological parameters (relative humidity, temperature, wind speed) at urban-industrial areas namely Shah Alam (Selangor), Nilai (Negeri Sembilan), Bukit Rambai (Melaka) and Larkin (Johor), during the haze episode in 1997, 2005, 2013 and 2015 were used for analysis. In this study, spatio-temporal and correlation analysis were used to provide an overview of the distribution pattern and examine the relationships between the gaseous air pollutants and meteorological parameters with PM10 concentration. From the descriptive statistics, it was observed that PM10 level for all study areas were skewed to the right (> + 1) indicating occurrences of extreme events. A significant peak of PM10 concentration for each year of haze events were observed to be started in June or during the southwest monsoon to the inter monsoon in October. The occurrence, duration and impact of 1997 haze was detected to be identical to the 2015 haze event that reached its peak in October. From the correlation analysis, PM10 concentration were strongly correlated to the CO concentration (r > 0.5) during High Particulate Event (HPE). Very weak relationship of PM10 level with meteorological parameters (r < 0.3) were observed. Interestingly, O3 level shows very strong correlation with the meteorological parameters during HPE. The findings provide comprehensive evaluation on PM10 level during the historic haze episodes, thus can help the authorities in developing policies and guidelines to effectively monitor and reduce the negative impact of haze events. [ABSTRACT FROM AUTHOR]

Air quality models are essential tools to meet the United Nations Sustainable Development Goals (UN-SDG) because they are effective in guiding public policies for the management of air pollutant emissions and their impacts on the environment and human health. Despite its importance, Brazil still lacks a guide for choosing and setting air quality models for regulatory purposes. Based on this, the current research aims to assess the combined WRF/CALMET/CALPUFF models for representing SO2 dispersion over non-homogeneous regions as a regulatory model for policies in Brazilian Metropolitan Regions to satisfy the UN-SDG. The combined system was applied to the Rio de Janeiro Metropolitan Area (RJMA), which is known for its physiographic complexity. In the first step, the WRF model was evaluated against surface-observed data. The local circulation was underestimated, while the prevailing observational winds were well represented. In the second step, it was verified that all CALMET three meteorological configurations performed better for the most frequent wind speed classes so that the largest SO2 concentrations errors occurred during light winds. Among the meteorological settings in WRF/CALMET/CALPUFF, the joined use of observed and modeled meteorological data yielded the best results for the dispersion of pollutants. This result emphasizes the relevance of meteorological data composition in complex regions with unsatisfactory monitoring given the inherent limitations of prognostic models and the excessive extrapolation of observed data that can generate distortions of reality. This research concludes with the proposal of the WRF/CALMET/CALPUFF air quality regulatory system as a supporting tool for policies in the Brazilian Metropolitan Regions in the framework of the UN-SDG, particularly in non-homogeneous regions where steady-state Gaussian models are not applicable. [ABSTRACT FROM AUTHOR]

In this work, we identify the current atmospheric sulfur dioxide emissions of the Veracruz port, an important Mexican seaport experiencing rapid growth, and its influence on the surrounding areas. Sulfur dioxide emissions based on port activity, as well as meteorology and air quality simulations, are used to assess the impact. It was found that using marine fuel with low sulfur content reduces emissions by 88%. Atmospheric emission estimates based on the bottom-up methodology range from 3 to 7 Mg/year and can negatively impact air quality up to 3 km downwind. After evaluating different characteristics of vessels in CALPUFF, it was found that maximum sulfur dioxide concentrations ranging between 50 and 88 µg/m3 for a 24-h average occurred 500 m from the port. During 2019, five days had unsatisfactory air quality. The combination of a shallow planetary boundary layer, low wind speed, and large atmospheric emissions significantly degraded local air quality. [ABSTRACT FROM AUTHOR]

Here we present the first simultaneous assimilation of aerosol optical thicknesses (AOTs) over East Asia retrieved from two next‐generation geostationary satellites named Fengyun‐4A (FY‐4A) and Himawari‐8 with an advanced data assimilation system. A new stringent data‐control method is proposed to assimilate only the high quality aerosol products of FY‐4A. The AOTs retrieved from the two geostationary satellites are both comparable to the ground‐based observations over East Asia, and the FY‐4A provides twice the number of the Himawari‐8 AOT retrievals. Validations with independent observations demonstrate the joint assimilation significantly improve the aerosol analysis and forecast skill over East Asia especially in South China, reducing the model biases of 70% and 40%. The aerosol model performances at 95% independent 61 ground‐based sites are more benefited from joint assimilation than single‐sensor assimilation. This demonstrates the high impact and innovative application of joint assimilation technique in the air quality modeling. Plain Language Summary: Fengyun‐4A (FY‐4A) and Himawari‐8, China's and Japan's next‐generation geostationary satellites, can both provide the aerosol monitoring with high temporal‐spatial resolution. However, the coverage areas and sampling numbers from only one geostationary satellite are still insufficient to dramatically advance the aerosol performance. In this study, we present the first application of aerosol optical thicknesses retrieved from FY‐4A and Himawari‐8 to reveal the benefits of joint assimilation on the aerosol forecast and analysis performances over East Asia. Key Points: A new quality‐control method making the best quality of Fengyun‐4A aerosol products for data assimilation is proposedIt is the first attempt to simultaneously assimilate aerosol optical thicknesses (AOTs) from two geostationary satellites with an advanced data assimilation systemJointly assimilating AOTs of two geostationary satellites significantly enhances model performances over East Asia [ABSTRACT FROM AUTHOR]

Ground-level ozone (O3) is a significant source of air pollution, mainly in most urban areas across the globe. Ground-level O3 is not emitted directly into the atmosphere. It results from photo-chemical reactions between precursors and is influenced by weather factors such as temperature. This study investigated the spatial and temporal analysis of ground-level ozone and analyzed the significant anthropogenic precursors and the weather parameters associated with ground-level ozone during daytime and nighttime at three cities in peninsular Malaysia, namely, Kuala Terengganu, Perai, and Seremban from 2016 to 2020. Secondary data were acquired from the Department of Environment (DOE), Malaysia, including hourly data of O3 with trace gases and weather parameters. The secondary data were analyzed using temporal analysis such as descriptive statistics, box plot, and diurnal plot as well as spatial analysis such as contour plot and wind rose diagram. Spearman correlation was used to identify the association of O3 with its precursors and weather parameters. The results show that a higher concentration of O3 during the weekend due to "ozone weekend effects" was pronounced, however, a slightly significant effect was observed in Perai. The two monsoonal seasons in Malaysia had a minimal effect on the study areas except for Kuala Terengganu due to the geographical location. The diurnal pattern of O3 concentration indicates bimodal peaks of O3 precursors during the peak traffic hours in the morning and evening with the highest intensity of O3 precursors detected in Perai. Spearman correlation analysis determined that the variations in O3 concentrations during day and nighttime generally coincide with the influence of nitrogen oxides (NO) and temperature. Lower NO concentration will increase the amount of O3 concentration and an increasing amount of O3 concentration is influenced by the higher temperature of its surroundings. Two predictive models, i.e., linear (multiple linear regression) and nonlinear models (artificial neural network) were developed and evaluated to predict the next day and nighttime O3 levels. ANN resulted in better prediction for all areas with better prediction identified for daytime O3 levels. [ABSTRACT FROM AUTHOR]

In recent years, ground‐level ozone (O3) has been one of the main pollutants hindering air quality compliance in China's large city‐clusters including the Yangtze River Delta (YRD) region. In this work, we utilized the process analysis (PA) and the higher‐order decoupled direct method (HDDM‐3D) tools embedded in the Community Multiscale Air Quality model (CMAQ) to characterize O3 formation and sensitivities to precursors during a typical O3 pollution episode over the YRD region in July 2018. Results indicate that gas‐phase chemistry contributed dominantly to the ground‐level O3 although a significant proportion was chemically produced at the middle and upper boundary layer before reaching the surface via diffusion process. Further analysis of the chemical pathways of O3 and Ox formation provided deep insights into the sensitivities of O3 to its precursors that were consistent with the HDDM results. The first‐order sensitivities of O3 to anthropogenic volatile organic compounds (AVOC) were mainly positive but small, and temporal variations were negligible compared with those to NOx. During the peak O3 time in the afternoon, the first‐ and second‐order sensitivities of O3 to NOx were significantly positive and negative, respectively, suggesting a convex response of O3 to NOx over most areas including Shanghai, Hangzhou, Nanjing and Hefei. These findings further highlighted an accelerated decrease in ground‐level O3 in the afternoon corresponding to continuous decrease of NOx emissions in the afternoon. Therefore, over the YRD region including its metropolises, NOx emission reductions will be more important in reducing the afternoon peak O3 concentration compared with the effect of VOC emission control alone. Plain Language Summary: Ground‐level ozone (O3) is formed primarily from photochemical reactions between volatile organic compounds (VOCs) and nitrogen oxides (NOx). Besides, O3 and other pollutants also frequently undergo various other processes such as vertical/horizontal transport and deposition. These chemical and physical processes cause the complexity of O3 formation and pose challenges to its mitigation. For instance, in the Yangtze River Delta (YRD) region of eastern China, ground‐level O3 has been among the main pollutants hindering air quality compliance. In this study, advanced modeling techniques based on the state‐of‐science community multiscale air quality model were utilized to understand at a regional scale the nonlinear response of O3 to NOx and VOCs, as well as to explore the contributions of these processes to O3 during the pollution episode between 24th and 31th July 2018 over this region. The results emphasized that O3 sensitivity to NOx was high and positive in the afternoon over most areas including the urban cores. This strongly indicates that NOx emission reductions could be an important way to reduce peak O3 and the more the reduction of NOx, the faster the decrease of peak O3. These findings provide important insights into the formulation of policies and regulations to mitigate O3 pollution. Key Points: The O3 chemically produced in the zone above 40 m contributed significantly to the surface O3 through vertical transportO3 sensitivity to NOx was high and positive in the afternoon over most of Yangtze River Delta region, including urban areasControlling NOx emissions is an important way to reduce peak O3, and the more NOx is reduced, the faster the decrease of peak O3 [ABSTRACT FROM AUTHOR]

• NO 2 was modelled in high spatial resolution in an urban area. • Developed a data fusion model using citizen science data for hotspot identification. • Data fusion can address model overestimation or underestimation. • Citizen science raises community awareness of local air quality. • Significant implications for science and environmental health policy. Rapid urbanization has led to many environmental issues, including poor air quality. With urbanization set to continue, there is an urgent need to mitigate air pollution and minimize its adverse health impacts. This study aims to advance urban air quality modelling by integrating a dispersion model output with large-scale citizen science data, collected over a 4-week period by 642 participants in Cork City, Ireland. The dispersion model enabled the identification of major sources of NO 2 air pollution while also addressing gaps in regulatory monitoring efforts. Integrating the diffusion tube data with the dispersion model output, we developed a data fusion model that captured localized fluctuations in air quality, with increases of up to 22μg/m3 observed at major road intersections. The data fusion model provided a more accurate representation of NO 2 concentrations, with estimates within 1.3μg/m3 of the regulatory monitoring measurement at an urban traffic location, an improvement of 11.7μg/m3 from the baseline dispersion model. This enhanced accuracy enabled a more precise assessment of the population exposure to air pollution. The data fusion model showed a higher population exposure to NO 2 compared to the dispersion model, providing valuable insights that can inform environmental health policies aimed at safeguarding public health. [Display omitted] [ABSTRACT FROM AUTHOR]

Macao, a renowned tourist city in the Great Bay Area (GBA), faces the challenge of continuously improving its air quality. In this study, we developed a high-resolution anthropogenic emission inventory for air pollutants in Macao in 2017, tailored for regional-scale numerical modeling. This inventory was created by integrating the best available data including emission factors, statistical data, and Automatic Identification System (AIS) data. The result shows that the emissions of carbon monoxide (CO), nitrogen oxides (NO X), sulfur dioxide (SO 2), volatile organic compounds (VOCs), particulate matter with aerodynamic diameters smaller than 2.5 and 10 μm (PM 2.5 and PM 10 , respectively), and ammonia (NH 3) in Macao were estimated to be 4348.23, 10525.64, 2099.34, 4895.15, 586.00, 1063.72, and 263.85 10^3 kg, respectively, in 2017. On-road mobile sources were the largest contributors to CO emissions, accounting for 61% of the total emissions. NO X emissions mainly originated from ship (47%). Power plant contributed the largest SO 2 emissions (70%). Solvent use accounted for the most VOCs emissions (69%). Dust source was the major emission source of PM 2.5 and PM 10 , contributing 29% and 53% of total emissions, respectively. Sewage treatment was the primary emitter of NH 3 , accounting for 88% of the total emissions. These emissions were then spatially allocated to grid cells with a resolution of 500m × 500m. Ship emissions were concentrated in the Macao-Hong Kong and Macao-Shenzhen shipping channels. Local emissions were concentrated in densely populated areas, including the Macao peninsula and Taipa. Air pollutants were lower in February and the fourth quarter, due to business suspensions during Spring Festival in February and emission reductions from power plant in the fourth quarter. Macao had the highest emission intensity per km2 among GBA cities, potentially causing high exposure concentrations and human health risks. The simulation results from WRF-CMAQ indicate that local emissions have a substantial impact Macao 's air quality. This study provides crucial information on air pollutant emission characteristics in Macao, as well as data support for regional air quality modeling and local pollution emission reduction efforts. [Display omitted] • It is firstly establish high-resolution and model-ready air pollutant emission inventory of Macao. • Macao has the highest emission intensity per km2 in the GBA. • Macao has unique emission characteristics compared to other cities in GBA. [ABSTRACT FROM AUTHOR]

Air pollution has a major impact on human health, especially in cities, and elevated concentrations of PMx are responsible for a large number of premature deaths each year. Therefore, the amount of PM10 in the air is monitored and forecasts are made to predict the air quality. In Poland, mainly deterministic models are used to predict air pollution. Accordingly, research efforts are being made to develop other models to forecast the ambient PM10 levels. The aim of the study was to compare the machine learning models for predicting PM10 levels in the air in the city of Lublin. The following machine learning models were used: Linear regression (LR), K-Nearest Neighbors Regression (KNNR), Support Vector Machine (SVM), Regression Trees (RT), Gaussian Process Regression Models (GPR), Artificial Neural Network (ANN) and Long Short-Term Memory network (LSTM). The collected data for three consecutive years (January 2017 to December 2019) were used to develop the models. In total, 19 parameters, covering meteorological variables and concentrations of several chemical species, were explored as potential predictors of PM10. The data used to build the models did not take into account the seasons. The algorithms achieved the following R2 values: 0.8 for LR, 0.79 for KNNR, 0.82 for SVM, 0.77 for RT, 0.89, 0.90 for ANN and 0.81 for LSTM. Research has shown that the selection of a machine learning model has a large impact on the quality of the results. In this research, the ANN model performed slightly better than other models. Then, an ANN was used to train a network with five output neurons to predict the approximate level of PM10 at different time points (PM level at a given time, after 1 h, after 6 h, after 12 h and after 24 h). The results showed that the developed and tuned ANN model is appropriate (R = 0.89). The model created in this way can be used to determine the risk of exceeding the PM10 alert level and to inform about the air quality in the region. [ABSTRACT FROM AUTHOR]

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Ground-level ozone (O3) is one of the most significant forms of air pollution around the world due to its ability to cause adverse effects on human health and environment. Understanding the variation and association of O3 level with its precursors and weather parameters is important for developing precise forecasting models that are needed for mitigation planning and early warning purposes. In this study, hourly air pollution data (O3, CO, NO2, PM10, NmHC, SO2) and weather parameters (relative humidity, temperature, UVB, wind speed and wind direction) covering a ten year period (2003–2012) in the selected urban areas in Malaysia were analyzed. The main aim of this research was to model O3 level in the band of greatest solar radiation with its precursors and meteorology parameters using the proposed predictive models. Six predictive models were developed which are Multiple Linear Regression (MLR), Feed-Forward Neural Network (FFANN), Radial Basis Function (RBFANN), and the three modified models, namely Principal Component Regression (PCR), PCA-FFANN, and PCA-RBFANN. The performances of the models were evaluated using four performance measures, i.e., Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Index of Agreement (IA), and Coefficient of Determination (R2). Surface O3 level was best described using linear regression model (MLR) with the smallest calculated error (MAE = 6.06; RMSE = 7.77) and the highest value of IA and R2 (0.85 and 0.91 respectively). The non-linear models (FFANN and RBFANN) fitted the observed O3 level well, but were slightly less accurate compared to MLR. Nonetheless, all the unmodified models (MLR, ANN, and RBF) outperformed the modified-version models (PCR, PCA-FFANN, and PCA-RBFANN). Verification of the best model (MLR) was done using air pollutant data in 2018. The MLR model fitted the dataset of 2018 very well in predicting the daily O3 level in the specified selected areas with the range of R2 values of 0.85 to 0.95. These indicate that MLR can be used as one of the reliable methods to predict daytime O3 level in Malaysia. Thus, it can be used as a predictive tool by the authority to forecast high ozone concentration in providing early warning to the population. [ABSTRACT FROM AUTHOR]

The introduction of fuel cell electric vehicles (FCEV) operating on hydrogen is a key strategy to mitigate pollutant emissions from the light duty vehicle (LDV) transportation sector in pursuit of air quality (AQ) improvements. Further, concomitant increases in renewable power generation could assist in achieving benefits via electrolysis-provided hydrogen as a vehicle fuel. However, it is unclear (1) reductions in emissions translate to changes in primary and secondary pollutant concentrations and (2) how effects compare to those from emissions in other transport sectors including heavy duty vehicles (HDV). This work assesses how the adoption of FCEVs in counties expected to support alternative LDV technologies affect atmospheric concentrations of ozone and fine particulate matter (PM2.5) throughout California (CA) in the year 2055 relative to a gasoline vehicle baseline. Further, impacts of reducing HDV emissions are explored to facilitate comparison among technology classes. A base year emissions inventory is grown to 2055 representing a business-as-usual progression of economic sectors, including primarily petroleum fuel consumption by LDV and HDVs. Emissions are spatially and temporally resolved and used in simulations of atmospheric chemistry and transport to evaluate distributions of primary and secondary pollutants respective to baseline. Results indicate that light-duty FCEV Cases achieve significant reductions in ozone and PM2.5 when LDV market shares reach 50–100% in early adoption counties, including areas distant from deployment sites. Reflecting a cleaner LDV baseline fleet in 2055, emissions from HDVs impact ozone and PM2.5 at comparable or greater levels than light duty FCEVs. Additionally, the importance of emissions from petroleum fuel infrastructure (PFI) activity is demonstrated in impacts on ozone and PM2.5 burdens, with large refinery complexes representing a key source of air pollution in 2055. Results presented provide insight into light duty FCEV deployment strategies that can achieve maximum reductions in ozone and PM2.5 and will assist decision makers in developing effective transportation sector AQ mitigation strategies.

The introduction of fuel cell electric vehicles (FCEV) operating on hydrogen is a key strategy to mitigate pollutant emissions from the light duty vehicle (LDV) transportation sector in pursuit of air quality (AQ) improvements. Further, concomitant increases in renewable power generation could assist in achieving benefits via electrolysis-provided hydrogen as a vehicle fuel. However, it is unclear (1) reductions in emissions translate to changes in primary and secondary pollutant concentrations and (2) how effects compare to those from emissions in other transport sectors including heavy duty vehicles (HDV). This work assesses how the adoption of FCEVs in counties expected to support alternative LDV technologies affect atmospheric concentrations of ozone and fine particulate matter (PM2.5) throughout California (CA) in the year 2055 relative to a gasoline vehicle baseline. Further, impacts of reducing HDV emissions are explored to facilitate comparison among technology classes. A base year emissions inventory is grown to 2055 representing a business-as-usual progression of economic sectors, including primarily petroleum fuel consumption by LDV and HDVs. Emissions are spatially and temporally resolved and used in simulations of atmospheric chemistry and transport to evaluate distributions of primary and secondary pollutants respective to baseline. Results indicate that light-duty FCEV Cases achieve significant reductions in ozone and PM2.5 when LDV market shares reach 50–100% in early adoption counties, including areas distant from deployment sites. Reflecting a cleaner LDV baseline fleet in 2055, emissions from HDVs impact ozone and PM2.5 at comparable or greater levels than light duty FCEVs. Additionally, the importance of emissions from petroleum fuel infrastructure (PFI) activity is demonstrated in impacts on ozone and PM2.5 burdens, with large refinery complexes representing a key source of air pollution in 2055. Results presented provide insight into light duty FCEV deployment strategies that can achieve maximum reductions in ozone and PM2.5 and will assist decision makers in developing effective transportation sector AQ mitigation strategies.