Agriculture generates ~83% of total US ammonia (NH) emissions, potentially adversely impacting sensitive ecosystems through wet and dry deposition. Regions with intense livestock production, such as the dairy region of south-central Idaho, generate hotspots of NH emissions. Our objective was to measure the spatial and temporal variability of NH across this region and estimate its dry deposition. Ambient NH was measured using diffusive passive samplers at 8 sites in two transects across the region from 2018-2020. NH fluxes were estimated using the Surface Tiled Aerosol and Gaseous Exchange (STAGE) model. Peak NH concentrations were 4-5 times greater at a high-density dairy site compared to mixed agriculture/dairy or agricultural sites, and 26 times greater than non-agricultural sites with prominent seasonal trends driven by temperature. Annual estimated dry deposition rates in areas of intensive dairy production can approach 45 kg N ha y, compared to <1 kg N ha y in natural landscapes. Our results suggest that the natural sagebrush steppe landscapes interspersed within and surrounding agricultural areas in southern Idaho receive NH dry deposition rates within and above the range of nitrogen critical loads for North American deserts. Finally, our results highlight a need for improved understanding of the role of soil processes in NH dry deposition to arid and sparsely vegetated natural ecosystems across the western US.

We examine the impact of dimethylsulfide (DMS) emissions on sulfate concentrations over the continental U.S. by using the Community Multiscale Air Quality (CMAQ) model version 5.4 and performing annual simulations without and with DMS emissions for 2018. DMS emissions enhance sulfate not only over seawater but also over land, although to a lesser extent. On an annual basis, the inclusion of DMS emissions increase sulfate concentrations by 36% over seawater and 9% over land. The largest impacts over land occur in California, Oregon, Washington, and Florida, where the annual mean sulfate concentrations increase by ~25%. The increase in sulfate causes a decrease in nitrate concentration due to limited ammonia concentration especially over seawater and an increase in ammonium concentration with a net effect of increased inorganic particles. The largest sulfate enhancement occurs near the surface (over seawater) and the enhancement decreases with altitude, diminishing to 10-20% at an altitude of ~5 km. Seasonally, the largest enhancement of sulfate over seawater occurs in summer, and the lowest in winter. In contrast, the largest enhancements over land occur in spring and fall due to higher wind speeds that can transport more sulfate from seawater into land.

Air pollution is a major public health threat that is associated with asthma, cardiovascular disease, respiratory disease and all-cause mortality. Among the most important acute air pollution events occurring each year are celebrations involving fireworks, such as the 4th of July holiday in the United States. In this community-engaged study, academic partners and residents collaborated to collect indoor and outdoor PM concentration measurements in the disadvantaged city of Santa Ana, California, using low-cost AtmoTube sensor devices before, during and after the July 4th firework celebration, while also examining July 4th data extracted from the PurpleAir sensor network across over a hundred other cities in southern California. Average outdoor PM concentrations on July 4th were found to be three-to-five times higher than baseline, with hourly concentrations exceeding 160 μg/m. Outdoor averages were roughly 30% to 100% higher than indoor levels. The most polluted cities exhibited 15-times higher PM levels compared with the least contaminated cities and were often those where household-level fireworks were legal for sale and use. Race/ethnicity was found to be the leading predictor of July 4th-related air pollution across three counties in southern California, with greater PM being associated with higher proportions of Hispanic residents and lower proportions of White residents. The findings from this study underscore the importance of environmental justice as it relates to firework-related air pollution exposure, and the critical role city- and county-level firework policies play in determining exposure.

Anthropogenic climate change is an existential threat whose influences continue to increase in severity. It is pivotal to understand the implications of climate change and their effects on mental health. This integrative review aims to summarize the relevant evidence examining the harm climate change may have on mental health, suggest potential mechanisms and discuss implications. Empirical evidence has begun to indicate that negative mental health outcomes are a relevant and notable consequence of climate change. Specifically, these negative outcomes range from increased rates of psychiatric diagnoses such as depression, anxiety and post-traumatic stress disorder to higher measures of suicide, aggression and crime. Potential mechanisms are thought to include neuroinflammatory responses to stress, maladaptive serotonergic receptors and detrimental effects on one's own physical health, as well as the community wellbeing. While climate change and mental health are salient areas of research, the evidence examining an association is limited. Therefore, further work should be conducted to delineate exact pathways of action to explain the mediators and mechanisms of the interaction between climate change and mental health.

The Healthy Air, Healthy Schools Study was established to better understand the impact of ultrafine particles (UFPs) on indoor air quality in communities surrounding Seattle-Tacoma (Sea-Tac) International Airport. The study team took multipollutant measurements of indoor and outdoor air pollution at five participating school locations to estimate infiltration indoors. The schools participating in this project were located within a 7-mile radius of Sea-Tac International Airport and within 0.5 mile of an active flight path. Based on experimental measures in an unoccupied classroom, infiltration rates of (a) UFPs of aircraft origin, (b) UFPs of traffic origin, and (c) wildfire smoke or other outdoor pollutants were characterized before and after the introduction of a portable high-efficiency particulate air (HEPA) filter intervention. The portable HEPA cleaners were an effective short-term intervention to improve the air quality in classroom environments, reducing the UFP count concentration from one-half to approximately one-tenth of that measured outside. This study is unique in focusing on UFPs in schools and demonstrating that UFPs measured in classroom spaces are primarily of outdoor origin. Although existing research suggests that reducing particulate matter in homes can significantly improve asthma outcomes, further investigation is necessary to establish the benefits to student health and academic performance of reducing UFP exposures in schools.

Cairpol and Aeroqual air quality sensors measuring CO, CO, NO, and other species were tested in fresh biomass burning plumes in field and laboratory environments. We evaluated sensors by comparing 1-minute sensor measurements to collocated reference instrument measurements. Sensors were evaluated based on the coefficient of determination ( ) between the sensor and reference measurements, by the accuracy, collocated precision, root mean square error (RMSE), and other metrics. In general, CO and CO sensors performed well (in terms of accuracy and values) compared to NO sensors. Cairpol CO and NO sensors had better sensor-versus-sensor agreement (e.g., collocated precision) than Aeroqual CO and NO sensors of the same species. Tests of other sensors (e.g., NH, HS, VOC, NMHC) provided more inconsistent results and need further study. Aeroqual NO sensors had an apparent O interference that was not observed in the Cairpol NO sensors. Although the sensor accuracy lags that of reference-level monitors, with location-specific calibrations they have the potential to provide useful data about community air quality and personal exposure to smoke impacts.

High-spatial-resolution air quality (AQ) mapping is important for identifying pollution sources to facilitate local action. Some of the most populated cities in the world are not equipped with the infrastructure required to monitor AQ levels on the ground and must rely on other sources, like satellite derived estimates, to monitor AQ. Current satellite-data-based models provide AQ mapping on a kilometer scale at best. In this study we focus on producing hundred-meter-scale AQ maps for urban environments in developed cities. We examined the feasibility of an image-based object-detection analysis approach using very high-spatial-resolution (2.5 m) commercial satellite imagery. We fed the satellite imagery to a deep neural network (DNN) to learn the association between visual urban features and air pollutants. The developed model, which solely uses satellite imagery, was tested and evaluated using both ground monitoring observations and land-use regression modeled PM and NO concentrations over London, Vancouver (BC), Los Angeles, and New York City. The results demonstrate a low error with a total RMSE < 2 µg/m and highlight the contribution of specific urban features, such as green areas and roads, to continuous hundred-meter-scale AQ estimation. This approach offers promise for scaling to global applications in developed and developing urban environments. Further analysis on domain transferability will enable application of a parsimonious model based merely on satellite images to create hundred-meter-scale AQ maps in developing cities, where current and historical ground data is limited.

Optimal use of Hierarchical Bayesian Model (HBM)-assembled aerosol optical depth (AOD)-PM fused surfaces in epidemiologic studies requires homogeneous temporal and spatial fused surfaces. No analytical method is available to evaluate spatial heterogeneity. The temporal case-crossover design was modified to assess the spatial association between four experimental AOD-PM fused surfaces and four respiratory-cardiovascular hospital events in 12 km grids. The maximum number of adjacent lag grids with significant odds ratios (ORs) identified homogeneous spatial areas (HOSAs). The largest HOSA included five grids (lag grids 04; 720 km) and the smallest HOSA contained two grids (lag grids 01; 288 km). Emergency department asthma and inpatient asthma, myocardial infarction, and heart failure ORs were significantly higher in rural grids without air monitors than in urban grids with air monitors at lag grids 0, 1, and 01. Rural grids had higher AOD-PM concentration levels, population density, and poverty percentages than urban grids. Warm season ORs were significantly higher than cold season ORs for all health outcomes at lag grids 0, 1, 01, and 04. The possibility of elevated fine and ultrafine PM and other demographic and environmental risk factors synergistically contributing to elevated respiratory-cardiovascular chronic diseases in persons residing in rural areas was discussed.

PM is an air pollutant that is widely associated with adverse health effects, and which tends to be disproportionately located near low-income communities and communities of color. We applied a community-engaged research approach to assess the distribution of PM concentrations in the context of community concerns and urban features within and around the city of Santa Ana, CA. Approximately 183 h of one-minute average PM measurements, along with high-resolution geographic coordinate measurements, were collected by volunteer community participants using roughly two dozen low-cost AtmoTube Pro air pollution sensors paired with real-time GPS tracking devices. PM varied by region, time of day, and month. In general, concentrations were higher near the city's industrial corridor, which is an area of concern to local community members. While the freeway systems were shown to correlate with some degree of elevated air pollution, two of four sampling days demonstrated little to no visible association with freeway traffic. Concentrations tended to be higher within socioeconomically disadvantaged communities compared to other areas. This pilot study demonstrates the utility of using low-cost air pollution sensors for the application of community-engaged study designs that leverage community knowledge, enable high-density air monitoring, and facilitate greater health-related awareness, education, and empowerment among communities. The mobile air-monitoring approach used in this study, and its application to characterize the ambient air quality within a defined geographic region, is in contrast to other community-engaged studies, which employ fixed-site monitoring and/or focus on personal exposure. The findings from this study underscore the existence of environmental health inequities that persist in urban areas today, which can help to inform policy decisions related to health equity, future urban planning, and community access to resources.

Reducing PM and ozone concentrations is important to protect human health and the environment. Chemical transport models, such as the Community Multiscale Air Quality (CMAQ) model, are valuable tools for exploring policy options for improving air quality but are computationally expensive. Here, we statistically fit an efficient polynomial function in a response surface model (pf-RSM) to CMAQ simulations over the eastern U.S. for January and July 2016. The pf-RSM predictions were evaluated using out-of-sample CMAQ simulations and used to examine the nonlinear response of air quality to emission changes. Predictions of the pf-RSM are in good agreement with the out-of-sample CMAQ simulations, with some exceptions for cases with anthropogenic emission reductions approaching 100%. NO emission reductions were more effective for reducing PM and ozone concentrations than SO, NH, or traditional VOC emission reductions. NH emission reductions effectively reduced nitrate concentrations in January but increased secondary organic aerosol (SOA) concentrations in July. More work is needed on SOA formation under conditions of low NH emissions to verify the responses of SOA to NH emission changes predicted here. Overall, the pf-RSM performs well in the eastern U.S., but next-generation RSMs based on deep learning may be needed to meet the computational requirements of typical regulatory applications.

Particulate air pollution (PM) is a mixture of heterogenous components from natural and anthropogenic sources and contributes to a variety of serious illnesses, including neurological and behavioral effects, as well as millions of premature deaths. Ultrafine (PM) and fine-size ambient particles (PM) can enter the circulatory system and cross the blood-brain barrier or enter through the optic nerve, and then upregulate inflammatory markers and increase reactive oxygen species (ROS) in the brain. Toxic and neurotoxic metals such as manganese (Mn), zinc (Zn), lead (Pb), copper (Cu), nickel (Ni), and barium (Ba) can adsorb to the PM surface and potentially contribute to the neurotoxic effects associated with PM exposure. Epidemiological studies have shown a negative relationship between exposure to PM-associated Mn and neurodevelopment amongst children, as well as impaired dexterity in the elderly. Inhaled PM-associated Cu has also been shown to impair motor performance and alter basal ganglia in schoolchildren. This paper provides a brief review of the epidemiological and toxicological studies published over the last five years concerning inhaled PM, PM-relevant metals, neurobiology, and mental health outcomes. Given the growing interest in mental health and the fact that 91% of the world's population is considered to be exposed to unhealthy air, more research on PM and PM-associated metals and neurological health is needed for future policy decisions and strategic interventions to prevent public harm.

This study focuses on the long-term aerosol and precipitation chemistry measurements from colocated monitoring sites in Southern Florida between 2013 and 2018. A positive matrix factorization (PMF) model identified six potential emission sources impacting the study area. The PMF model solution yielded the following source concentration profiles: (i) combustion; (ii) fresh sea salt; (iii) aged sea salt; (iv) secondary sulfate; (v) shipping emissions; and (vi) dust. Based on these results, concentration-weighted trajectory maps were developed to identify sources contributing to the PMF factors. Monthly mean precipitation pH values ranged from 4.98 to 5.58, being positively related to crustal species and negatively related to SO . Sea salt dominated wet deposition volume-weighted concentrations year-round without much variability in its mass fraction in contrast to stronger seasonal changes in PM composition where fresh sea salt was far less influential. The highest mean annual deposition fluxes were attributed to Cl, NO , SO , and Na between April and October. Nitrate is strongly correlated with dust constituents (unlike sea salt) in precipitation samples, indicative of efficient partitioning to dust. Interrelationships between precipitation chemistry and aerosol species based on long-term surface data provide insight into aerosol-cloud-precipitation interactions.

Bayesian additive regression tree (BART) is a recent statistical method that combines ensemble learning and nonparametric regression. BART is constructed under a probabilistic framework that also allows for model-based prediction uncertainty quantification. We evaluated the application of BART in predicting daily concentrations of four fine particulate matter (PM) components (elemental carbon, organic carbon, nitrate, and sulfate) in California during the period 2005 to 2014. We demonstrate in this paper how BART can be tuned to optimize prediction performance and how to evaluate variable importance. Our BART models included, as predictors, a large suite of land-use variables, meteorological conditions, satellite-derived aerosol optical depth parameters, and simulations from a chemical transport model. In cross-validation experiments, BART demonstrated good out-of-sample prediction performance at monitoring locations ( from 0.62 to 0.73). More importantly, prediction intervals associated with concentration estimates from BART showed good coverage probability at locations with and without monitoring data. In our case study, major PM components could be estimated with good accuracy, especially when collocated PM total mass observations were available. In conclusion, BART is an attractive approach for modeling ambient air pollution levels, especially for its ability to provide uncertainty in estimates that may be useful for subsequent health impact and health effect analyses.

This study uses Las Vegas near-road measurements of carbon monoxide (CO) and nitrogen oxides (NO) to test the consistency of onroad emission constraint methodologies. We derive commonly used CO to NO ratios (ΔCO:ΔNO) from cross-road gradients and from linear regression using ordinary least squares (OLS) regression and orthogonal regression. The CO to NO ratios are used to infer NO emission adjustments for a priori emissions estimates from EPA's MOtor Vehicle Emissions Simulator (MOVES) model assuming unbiased CO. The assumption of unbiased CO emissions may not be appropriate in many circumstances but was implemented in this analysis to illustrate the range of NOx scaling factors that can be inferred based on choice of methods and monitor distance alone. For the nearest road estimates (25m), the cross-road gradient and ordinary least squares (OLS) agree with each other and are not statistically different from the MOVES-based emission estimate while ΔCO:ΔNO from orthogonal regression is significantly higher than the emitted ratio from MOVES. Using further downwind measurements (i.e., 115m and 300m) increases OLS and orthogonal regression estimates of ΔCO:ΔNO but not cross-road gradient ΔCO:ΔNO. The inferred NO emissions depend on the observation-based method, as well as the distance of the measurements from the roadway and can suggest either that MOVES NO emissions are unbiased or that they should be adjusted downward by between 10% and 47%. The sensitivity of observation-based ΔCO:ΔNO estimates to the selected monitor location and to the calculation method characterize the inherent uncertainty of these methods that cannot be derived from traditional standard-error based uncertainty metrics.

Marine biogenic dimethyl sulfide (DMS) is an important natural source of sulfur in the atmosphere, which may play an important role in air quality. In this study, the WRF-CMAQ model is employed to assess the impact of DMS on the atmospheric environment at the regional scale of eastern coastal China and urban scale of Shanghai in 2017. A national scale database of DMS concentration in seawater is established based on the historical DMS measurements in the Yellow Sea, the Bohai Sea and the East China Sea in different seasons during 2009~2017. Results indicate that the sea-to-air emission flux of DMS varies greatly in different seasons, with the highest in summer, followed by spring and autumn, and the lowest in winter. The annual DMS emissions from the Yellow Sea, the Bohai Sea and the East China Sea are 0.008, 0.059, and 0.15 Tg S a, respectively. At the regional scale, DMS emissions increase atmospheric sulfur dioxide (SO) and sulfate concentrations over the East China seas by a maximum of 8% in summer and a minimum of 2% in winter, respectively. At the urban scale, the addition of DMS emissions increase the SO and levels by 2% and 5%, respectively, and reduce ozone (O) in the air of Shanghai by 1.5%~2.5%. DMS emissions increase fine-mode ammonium particle concentration distribution by 4% and 5%, and fine-mode nss- concentration distributions by 4% and 9% in the urban and marine air, respectively. Our results indicate that although anthropogenic sources are still the dominant contributor of atmospheric sulfur burden in China, biogenic DMS emissions source cannot be ignored.

Brazil, one of the world's fastest-growing economies, is the fifth most populous country and is experiencing accelerated urbanization. This combination of factors causes an increase in urban population that is exposed to poor air quality, leading to public health burdens. In this work, the Weather Research and Forecasting Model with Chemistry is applied to simulate air quality over Brazil for a short time period under three future emission scenarios, including current legislation (CLE), mitigation scenario (MIT), and maximum feasible reduction (MFR) under the Representative Concentration Pathway 4.5 (RCP4.5), which is a climate change scenario under which radiative forcing of greenhouse gases (GHGs) reach 4.5 W m by 2100. The main objective of this study is to determine the sensitivity of the concentrations of ozone (O) and particulate matter with aerodynamic diameter 2.5 µm or less (PM) to changes in emissions under these emission scenarios and to determine the signal and spatial patterns of these changes for Brazil. The model is evaluated with observations and shows reasonably good agreement. The MFR scenario leads to a reduction of 3% and 75% for O and PM respectively, considering the average of grid cells within Brazil, whereas the CLE scenario leads to an increase of 1% and 11% for O and PM respectively, concentrated near urban centers. These results indicate that of the three emission control scenarios, the CLE leads to poor air quality, while the MFR scenario leads to the maximum improvement in air quality. To the best of our knowledge, this work is the first to investigate the responses of air quality to changes in emissions under these emission scenarios for Brazil. The results shed light on the linkage between changes of emissions and air quality.

The gas-phase reaction between -2-methyl-2-butenal and chlorine (Cl) atoms has been studied in a simulation chamber at 298 ± 2 K and 760 ± 5 Torr of air under free-NO conditions. The rate coefficient of this reaction was determined as = (2.45 ± 0.32) × 10- cm molecule s by using a relative method and Fourier transform infrared spectroscopy. In addition to this technique, gas chromatography coupled to mass spectrometry and proton transfer time-of-flight mass spectrometry were used to detect and monitor the time evolution of the gas-phase reaction products. The major primary reaction product from the addition of Cl to the C-3 of -2-methyl-2-butenal was 3-chloro-2-butanone, with a molar yield (Y) of (52.5 ± 7.3)%. Acetaldehyde (Y = (40.8 ± 0.6)%) and HCl were also identified, indicating that the H-abstraction by Cl from the aldehyde group is a reaction pathway as well. Secondary organic aerosol (SOA) formation was investigated by using a fast mobility particle sizer spectrometer. The SOA yield in the Cl + -2-methyl-2-butenal reaction is reported to be lower than 2.4%, thus its impact can be considered negligible. The atmospheric importance of the titled reaction is similar to the corresponding OH reaction in areas with high Cl concentration.

Scientific evidence spanning experimental and epidemiologic studies has shown that air pollution exposures can lead to a range of health effects. Quantitative approaches that allow for the estimation of the adverse health impacts attributed to air pollution enable researchers and policy analysts to convey the public health impact of poor air quality. Multiple tools are currently available to conduct such analyses, which includes software packages designed by the World Health Organization (WHO): AirQ+, and the U.S. Environmental Protection Agency (U.S. EPA): Environmental Benefits Mapping and Analysis Program - Community Edition (BenMAP - CE), to quantify the number and economic value of air pollution-attributable premature deaths and illnesses. WHO's AirQ+ and U.S. EPA's BenMAP - CE are among the most popular tools to quantify these effects as reflected by the hundreds of peer-reviewed publications and technical reports over the past two decades that have employed these tools spanning many countries and multiple continents. Within this paper we conduct an analysis using common input parameters to compare AirQ+ and BenMAP - CE and show that the two software packages well align in the calculation of health impacts. Additionally, we detail the research questions best addressed by each tool.

In order to further evaluate the behavior of ionospheric variations at Mars, we investigate the Martian ionosphere-thermosphere (IT) perturbations associated with non-migrating thermal tides using over four years of Mars Atmosphere and Volatile Evolution (MAVEN) in situ measurements of the IT electron and neutral densities. The results are consistent with those of previous studies, namely strong correlation between the tidal perturbations in electron and neutral densities on the dayside at altitudes ~150-185 km, as expected from photochemical theory. In addition, there are intervals during which this correlation extends to higher altitudes, up to ~270 km, where diffusive transport of plasma plays a dominant role over photochemical processes. This is significant because at these altitudes the thermosphere and ionosphere are only weakly coupled through collisions. The identified non-migrating tidal wave variations in the neutral thermosphere are predominantly wave-1, wave-2, and wave-3. Wave-1 is often the dominant wavenumber for electron density tidal variations, particularly at high altitudes over crustal fields. The Mars Climate Database (MCD) neutral densities (below 300 km along the MAVEN orbit) shows clear tidal variations which are predominantly wave-2 and wave-3, and have similar wave amplitudes to those observed.

The Asia Pacific Mercury Monitoring Network (APMMN) cooperatively measures mercury in precipitation in a network of sites operating in Asia and the Western Pacific region. The network addresses significant data gaps in a region where mercury emission estimates are the highest globally, and available measurement data are limited. The reduction of mercury emissions under the Minamata Convention on Mercury also justifies the need for continent-wide and consistent observations that can help determine the magnitude of the problem and assess the efficacy of reductions over time. The APMMN's primary objectives are to monitor wet deposition and atmospheric concentrations of mercury and assist partners in developing their own monitoring capabilities. Network planning began in 2012 with wet deposition sampling starting in 2014. Currently, eight network sites measure mercury in precipitation following standardized procedures adapted from the National Atmospheric Deposition Program. The network also has a common regional analytical laboratory (Taiwan), and quality assurance and data flagging procedures, which ensure the network makes scientifically valid and consistent measurements. Results from our ongoing analytical and field quality assurance measurements show minimal contamination in the network and accurate analytical analyses. We are continuing to monitor a potential concentration and precipitation volume bias under certain conditions. The average mercury concentration in precipitation was 11.3 (+9.6) ng L for 139 network samples in 2018. Concentrations for individual sites vary widely. Low averages compare to the low concentrations observed on the U.S. West Coast; while other sites have average concentrations similar to the high values reported from many urban areas in China. Future APMMN goals are to (1) foster new network partnerships, (2) continue to collect, quality assure, and distribute results on the APMMN website, (3) provide training and share best monitoring practices, and (4) establish a gaseous concentration network for estimating dry deposition.

Data assimilation for multiple air pollutant concentrations has become an important need for modeling air quality attainment, human exposure and related health impacts, especially in China that experiences both PM and O pollution. Traditional data assimilation or fusion methods are mainly focused on individual pollutants, and thus cannot support simultaneous assimilation for both PM and O. To fill the gap, this study proposed a novel multipollutant assimilation method by using an emission-concentration response model (noted as RSM-assimilation). The new method was successfully applied to assimilate precursors for PM and O in the 28 cities of the North China Plain (NCP). By adjusting emissions of five pollutants (i.e., NO, SO, NH, VOC and primary PM) in the 28 cities through RSM-assimilation, the RMSEs (root mean square errors) of O and PM were reduced by about 35% and 58% from the original simulations. The RSM-assimilation results small sensitivity to the number of observation sites due to the use of prior knowledge of the spatial distribution of emissions; however, the ability to assimilate concentrations at the edge of the control region is limited. The emission ratios of five pollutants were simultaneously adjusted during the RSM-assimilation, indicating that the emission inventory may underestimate NO in January, April and October, and SO in April, but overestimate NH in April and VOC in January and October. Primary PM emissions are also significantly underestimated, particularly in April (dust season in NCP). Future work should focus on expanding the control area and including NH observations to improve the RSM-assimilation performance and emission inventories.