Multiscale Air Quality Modeling System Research Articles

Observation-constrained kinetic modeling of isoprene SOA formation in the atmosphere

Abstract. Isoprene has the largest global non-methane hydrocarbon emission, and the oxidation of isoprene plays a crucial role in the formation of secondary organic aerosol (SOA). Two primary processes are known to contribute to SOA formation from isoprene oxidation: (1) the reactive uptake of isoprene-derived epoxides on acidic or aqueous particle surfaces and (2) the absorptive gas–particle partitioning of low-volatility oxidation products. In this study, we developed a new multiphase condensed isoprene oxidation mechanism that includes these processes with key molecular intermediates and products. The new mechanism was applied to simulate isoprene gas-phase oxidation products and SOA formation from previously published chamber experiments under a variety of conditions and atmospheric observations during the Southern Oxidant and Aerosol Studies (SOAS) field campaign. Our results show that SOA formation from most of the chamber experiments is reasonably reproduced using our mechanism, except when the concentration ratios of initial nitric oxide to isoprene exceed ∼ 2, the formed SOA is significantly underpredicted. The SOAS simulations also reasonably agree with the measurements regarding the diurnal pattern and concentrations of different product categories, while the total isoprene SOA remains underestimated. The molecular compositions of the modeled SOA indicate that multifunctional low-volatility products contribute to isoprene SOA more significantly than previously thought, with a median mass contribution of ∼ 57 % to the total modeled isoprene SOA. However, this contribution is intricately intertwined with IEPOX-derived SOA (IEPOX: isoprene-derived epoxydiols), posing challenges for their differentiation using bulk aerosol composition analysis (e.g., the aerosol mass spectrometer with positive matrix factorization). Furthermore, the SOA from these pathways may vary greatly, mainly dependent on the volatility estimation and treatment of particle-phase processes (i.e., photolysis and hydrolysis). Our findings emphasize that the various pathways to produce these low-volatility species should be considered in models to more accurately predict isoprene SOA formation. The new condensed isoprene chemical mechanism can be further incorporated into regional-scale air quality models, such as the Community Multiscale Air Quality Modelling System (CMAQ), to assess isoprene SOA formation on a larger scale.

Brazilian Atmospheric Inventories – BRAIN: a comprehensive database of air quality in Brazil

Abstract. Developing air quality management systems to control the impacts of air pollution requires reliable data. However, current initiatives do not provide datasets with large spatial and temporal resolutions for developing air pollution policies in Brazil. Here, we introduce the Brazilian Atmospheric Inventories (BRAIN), the first comprehensive database of air quality and its drivers in Brazil. BRAIN encompasses hourly datasets of meteorology, emissions, and air quality. The emissions dataset includes vehicular emissions derived from the Brazilian Vehicular Emissions Inventory Software (BRAVES), industrial emissions produced with local data from the Brazilian environmental agencies, biomass burning emissions from FINN – Fire INventory from the National Center for Atmospheric Research (NCAR), and biogenic emissions from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) (https://doi.org/10.57760/sciencedb.09858, Hoinaski et al., 2023a; https://doi.org/10.57760/sciencedb.09886, Hoinaski et al., 2023b). The meteorology dataset has been derived from the Weather Research and Forecasting Model (WRF) (https://doi.org/10.57760/sciencedb.09857, Hoinaski and Will, 2023a; https://doi.org/10.57760/sciencedb.09885, Hoinaski and Will, 2023c). The air quality dataset contains the surface concentration of 216 air pollutants produced from coupling meteorological and emissions datasets with the Community Multiscale Air Quality Modeling System (CMAQ) (https://doi.org/10.57760/sciencedb.09859, Hoinaski and Will, 2023b; https://doi.org/10.57760/sciencedb.09884, Hoinaski and Will, 2023d). We provide gridded data in two domains, one covering the Brazilian territory with 20×20 km spatial resolution and another covering southern Brazil with 4×4 km spatial resolution. This paper describes how the datasets were produced, their limitations, and their spatiotemporal features. To evaluate the quality of the database, we compare the air quality dataset with 244 air quality monitoring stations, providing the model's performance for each pollutant measured by the monitoring stations. We present a sample of the spatial variability of emissions, meteorology, and air quality in Brazil from 2019, revealing the hotspots of emissions and air pollution issues. By making BRAIN publicly available, we aim to provide the required data for developing air quality policies on municipal and state scales, especially for under-developed and data-scarce municipalities. We also envision that BRAIN has the potential to create new insights into and opportunities for air pollution research in Brazil.​​​​​​​

Modeling polycyclic aromatic hydrocarbons (PAHs) concentrations from wildfires in California

In recent years, wildfires in California have increased in frequency and intensity due to climate change and prolonged drought. The air pollutants released by wildfires cause significant health consequences, among which polycyclic aromatic hydrocarbons (PAHs) are particularly toxic. Estimating PAH emissions from wildfires is challenging due to variability in vegetation types. In this study, we estimate PAH emission rates across California at a high resolution, based on laboratory-measured PAH emission rates from 22 different vegetation types and detailed vegetation mapping. By combining these estimates with biomass burning data from the NCAR Fire Inventory, the Community Multiscale Air Quality Modeling System simulates PAH concentrations for the 2017 fire season. The modeling results compare favorably to measurements from three PAH monitoring sites in California. The peak PAH emissions from wildfire events are up to be 80 times higher in the gas phase and 32 times higher in the particle phase compared to a case without fire emissions. The population-weighted PAH concentrations from the fire case (0.053 µg/m3) are 47 % higher compared to a non-fire case (0.036 µg/m3) in the particle phase and 11 % higher in the gas phase (9.82 ppt compared to 8.83 ppt) during the study period. While highly depended on the meteorological condition, the simulated spatial distribution indicates that gas-phase PAHs are less likely to travel long distances from the fire source and are prone to aging into the particle phase during transport. Consequently, populations are more likely to be exposed to particle-phase PAHs during wildfire events. This finding has important implications for understanding the health impacts of wildfire-induced PAH concentrations, as particle-phase PAHs may have different toxicological effects compared to gas-phase PAHs.

Impact of aerosol actinic radiative effect on ozone during haze pollution in the Pearl River Delta region

Large amounts of aerosol in the atmosphere can cause significant attenuation of photochemical radiation and have a substantial impact on ozone and other secondary pollutants involved in photochemical reactions. This study examines the impact of aerosol actinic radiative effect on ozone and secondary pollutants during typical haze processes in the Pearl River Delta (PRD) region. The investigation employed lidar data and Weather Research Forecast–Community Multiscale Air Quality (WRF-CMAQ) modeling system. The results revealed that the aerosol caused a significant decrease in ground-level photolysis rates of NO2 and ozone by 22% and 29%, respectively, when haze pollution was severe. The actinic radiative effect of aerosol caused an average reduction of more than 10 ppb in ground level ozone concentration in the PRD, with the core area experiencing a maximum reduction of 30 ppb. Additionally, the core area witnessed a maximum reduction of 2 ppb in peroxyacetyl nitrate (PAN) and 2 μg/m3 in PM2.5. Due to reduced photolysis rates, nitrogen oxides increased by more than 5 ppb on average, reaching up to 10 ppb, while volatile organic compounds (VOCs) increased by a maximum of 10 ppb in the core region. Temporally, the aerosol actinic radiative effect was most pronounced in the morning and less significant in the afternoon.

Designing regional joint prevention and control schemes of PM2.5 based on source apportionment of chemical transport model: A case study of a heavy pollution episode

Fine particular matter (PM2.5) pollution still occurs frequently in China. Despite recognizing the importance of regional joint prevention and control (RJPC) for mitigating haze pollution, there is still a limited understanding of scheme design and effectiveness. In this study, with the Fenhe Plain in China serving as the case, the Integrated Source Apportionment Method (ISAM), incorporated into the Community Multiscale Air Quality Modeling System (CMAQ), was used to quantitively identify PM2.5 sources during a typical heavy pollution episode from Jan. 20th to 25th, 2021. Informed by source apportionment, we designed and assessed multiple RJPC scenarios for eliminating heavy pollution, yielding recommended RJPC schemes. Source apportionment results revealed a substantial increase in the contribution percentage of regions within a 350-km radius during the pollution episode compared to the non-polluted period. Sulfate was predominantly contributed from long-distance transport, whereas nitrate was chiefly contributed from closer regions. Ammonium exhibited higher local contributions than nitrate from the Fenhe Plain. Moreover, Taiyuan and Linfen were identified as PM2.5 exporters, whereas Sanmenxia, Jinzhong, and Luliang served as PM2.5 importers, and Yuncheng acted as a comprehensive city. The proposed specific RJPC schemes aimed at eliminating heavy pollution for the Fenhe Plain emphasized the importance of controlling emissions of particulate matter, ammonia, nitrogen oxides, and volatile organic compounds in areas within an approximate 350-km radius outside the Fenhe Plain, in addition to strengthening the local emission control. Nevertheless, only through the comprehensive and in-depth implementation of emission control measures across a larger area can PM2.5 compliance be achieved.

Role of air stagnation in determining daily average PM2.5 concentrations in areas with significant impact of long-range transport

In areas where the regional transport of air pollutants exerts a significant impact, ascertaining whether short-term air stagnation affects PM2.5 concentrations is crucial for accurate air quality forecasting and effective management planning. However, this research area remains underexplored. In this study, we analyzed the relationship between stagnant atmospheric conditions and daily average PM2.5 concentrations in areas with a substantial long-range transport impact (LRT). Specifically, we focused on days with elevated PM2.5 concentrations (daily average ≥ 35 μg/m3) from January to March 2019 in South Korea. The analysis was performed using the Weather Research and Forecasting and the Community Multiscale Air Quality Modeling System. Stagnant conditions were quantified using ventilation index (VI), calculated as the product of planetary boundary layer height and 10-m wind speed. The correlation coefficient between daily average VI and PM2.5 concentration (r = −0.23) was lower than that between VI and NO2 concentration (r = −0.60). This can be attributed to the fact that LRT was 2.9-fold higher than local emission impact (LEI) during days of elevated PM2.5 concentrations. Notably, the correlation coefficient between LRT and VI (r = −0.03) was considerably lower than that of LEI (r = −0.70). Hence, predicting the daily average PM2.5 concentration solely based on VI proved challenging in an area characterized by substantial LRT. For future research, in areas where LRT plays a substantial role, accurate prediction of PM2.5 concentrations requires distinguishing the effects of LRT and LEI on PM2.5 concentrations.

Assessing the role of cold front passage and synoptic patterns on air pollution in the Korean Peninsula

Various numerical experiments using WRF (Weather Research & Forecasting Model) and CMAQ (Community Multiscale Air Quality Modeling System) were performed to analyze the phenomenon of rapidly high concentration PM2.5 after the passage of a cold front in an area with limited local emissions. The episode period was from January 14 to 23, 2018, and analysis was conducted by dividing it into two stages according to the characteristics of changes in PM2.5 concentrations during the period. Through the analysis of observational data during the episode period, we confirmed meteorological impacts (decrease in temperature, increase in wind speed and relative humidity) and an increase in air pollution (PM10 and PM2.5) attributed to the passage of a cold front. Using CMAQ's IPR (Integrated Process Rate) analysis, the contribution of the horizontal advection process was observed in transporting PM2.5 to Gangneung at higher altitudes, and the PM2.5 concentrations at the surface increased because the vertical advection process was influenced by the terrain. Notably, in Stage 2 (64 μg·m−3), a higher contribution of the vertical advection process compared to Stage 1 (35 μg·m−3) was observed, which is attributed to the differences in synoptic patterns following the passage of the cold front. During Stage 2, following the cold front, atmospheric stability (dominance of high-pressure system) led to air subsidence and the presence of a temperature inversion layer, creating favorable meteorological conditions for the accumulation of air pollutants. This study offers the mechanisms of air pollution over the Korean Peninsula under non-stationary meteorological conditions, particularly in relation to the passage of the cold front (low-pressure system). Notably, the influence of a cold front can vary according to the synoptic patterns that develop following its passage.

Risk of dementia due to Co-exposure to air pollution and neighborhood disadvantage

BackgroundCo-exposure to air pollution and neighborhood disadvantage may influence cognition decline. We tested these associations in the context of dementia risk. MethodsWe leveraged a cohort of adults ≥65 years (n = 5397) enrolled from 2011 to 2018 in the National Health and Aging Trends Study (NHATS). Particulate matter (PM) ≤ 10 μm in diameter, PM ≤ 2.5 μm in diameter, carbon monoxide, nitric oxide, and nitrogen dioxide - and neighborhood disadvantage were tested for joint associations with dementia risk. Pollutant concentrations at the 2010 census tract level were assigned using the US Environmental Protection Agency's Community Multiscale Air Quality Modeling System. Neighborhood disadvantage was defined using the tract Social Deprivation Index (SDI). Dementia was determined through self- or proxy-report or scores indicative of “probable dementia” according to NHATS screening tools. Joint effects of air pollutants and SDI were tested using quantile g-computation Cox proportional hazards models. We also stratified joint air pollution effects across SDI tertiles. Analyses adjusted for age at enrollment, sex, education, partner status, urbanicity, income, race and ethnicity, years at residence, census segregation, and census region. ResultsSDI score (aHR = 1.08; 95% CI 0.96, 1.22), joint air pollution (aHR = 1.03, 95% CI 0.92, 1.16) and joint SDI with air pollution (aHR = 1.04, 95% CI 0.89, 1.22) were not associated with dementia risk. After accounting for competing risk of death, joint SDI with air pollution was not associated with dementia risk (aHR = 1.06; 95% CI 0.87, 1.29). In stratified models, joint air pollution was associated with greater risk of dementia at high (aHR = 1.19; 95% CI 0.87, 1.63), but not at medium or low SDI. ConclusionAir pollution was associated with greater dementia risk in disadvantaged areas after accounting for competing risks. Air pollution associations with dementia incidence may be attenuated when other risk factors are more prominent in disadvantaged neighborhoods.

Climate change-induced impacts on PM2.5 in Taiwan under 2 and 4 °C global warming

Climate change can perturb meteorological parameters and affect air quality, human health, and the economy. This study examined the climate change-induced meteorological impacts on PM2.5 in Taiwan, an island with a dense population and complex topography. We conducted high-resolution simulations of the current and future climate using the National Center for Atmospheric Research's Weather Research and Forecasting model and the Community Multiscale Air Quality Modeling System. As verified by observations, the model could reproduce the spatial and seasonal characteristics of PM2.5 in Taiwan, including higher concentrations in cold seasons and lower concentrations in warm seasons. The number of polluted days in the current climate was more than 30 days per season in central Taiwan and more than 70 days in southern Taiwan in autumn and winter. Under the 2 °C and 4 °C global warming scenarios, climate change-induced impacts worsened the PM2.5 air quality in Taiwan in cold seasons. In winter, the number of polluted days increased by 3–6 days in the whole of Taiwan, while it decreased by 3–5 days in southern Taiwan in autumn. Analysis of climate change-induced impacts on northeasterly days, air stagnation, and ventilation indices revealed that the weaker low-level wind in cold seasons was the dominant factor of the worsened PM2.5 air quality under global warming in Taiwan. In addition, the climate change-induced worsening of PM2.5 air quality could be locally mitigated by northeasterly days and nighttime warming in urban areas in northern Taiwan and by precipitation in southern Taiwan. Overall, these findings indicated that climate change-induced impacts on air quality cover both urban and global scales. To better adapt to climate change, discussing interactions between the responses to global warming in urban areas using hierarchical models and high-resolution simulations is necessary.

Identifying sensitive precursors of ozone formation using photochemical indicators and transition values comprised by continuous automatic monitoring data

The O3 photochemical indicator is a tool used to identify the sensitive precursors of O3 formation. Establishing a reliable photochemical indicator and its transition value composed of continuous automatic monitoring of chemical components can not only analyze sensitive precursors of O3 formation in real-time but also analyze the time and space distribution characteristics of sensitive precursors of O3 formation at a relatively lower cost. Among the photochemical indicators that are often mentioned at present, the ratio of ozone to nitrogen oxides (O3/NOx, O3/NOZ, and O3/NOY) should have the greatest potential for continuous monitoring. This study used the Community Multiscale Air Quality Modeling System to evaluate whether these three indicators are applicable to environmental conditions in Taiwan. Research results show that the indicator O3/NOZ was less suitable for Taiwan, while the indicator O3/NOY (transition value = 5.3) and the indicator O3/NOx (transition value = 29.3) could more accurately evaluate the ozone sensitivity of Taiwan's overall grid, and had no obvious bias in judging the ozone sensitivity of rural and urban grids. This study also found that it is not appropriate to directly use the photochemical indicator transition values from the literature. Usually, because the value is too high or too low, there is an obvious bias in judging the ozone sensitivity of rural and urban grids. This result also demonstrates the need to consider local meteorological and emission conditions when estimating transition values of photochemical indicators. In addition, when using the photochemical indicator O3/NOY, attention should be paid to the calculation of NOY species composition. The more complete the NOY composition, the better its ability to correctly judge ozone sensitivity.

Association between short-term exposure to ambient air pollutants and biomarkers indicative of inflammation and oxidative stress: a cross-sectional study using KoGES-HEXA data.

Air pollution-induced systemic inflammation and oxidative stress are hypothesized to be the major biological mechanisms underlying pathological outcomes. We examined the association between short-term exposure to ambient air pollutants and biomarkers of inflammation and oxidative stress in 2199 general middle-aged Korean population residing in metropolitan areas. Serum levels of inflammatory cytokines (interleukin [IL]-1β, IL-6, IL-8, IL-10, and tumor necrosis factor [TNF]-α) and urinary levels of 8-hydroxy-2'-deoxyguanosine (8-OHdG) were measured. Daily concentrations of a series of air pollutants (particulate matter [PM]10, PM2.5, SO2, NO2, CO, and O3) were predicted using the Community Multiscale Air Quality modeling system, and participant-level pollutant exposure was determined using geocoded residential addresses. Short-term exposure was defined as the 1- to 7-day moving averages. The multivariable-adjusted linear models controlling for the sociodemographic, lifestyle, temporal, and meteorological factors identified positive associations of PM with IL-1β, IL-8, IL-10, TNF-α, and 8-OHdG levels; SO2 with IL-10 levels, CO with IL-1β, IL-10, and TNF-α levels; and O3 with IL-1β, IL-8, and 8-OHdG levels. O3 levels were inversely associated with IL-10 levels. For each pollutant, the strongest associations were observed for the 7-day average PM and CO with IL-1β (per 10-µg/m3 increase in PM10: 2.7%, 95% confidence interval [CI] = 0.6-4.8; per 10-µg/m3 increase in PM2.5: 6.4%, 95% CI = 2.4-10.5; per 0.1-ppm increase in CO: 3.3%, 95% CI = 0.3-6.5); the 2-day average SO2 with IL-10 levels (per 1-ppb increase in SO2: 1.1%, 95% CI = 0.1-2.1); and the 7-day average O3 with IL-8 levels (per 1-ppb increase in O3: 1.3%, 95% CI = 0.7-1.9). Short-term exposure to ambient air pollutants may induce oxidative damage and pro-inflammatory roles, together with counter-regulatory anti-inflammatory response.

Role of an Ultra-Large Coal-Fired Power Plant in PM2.5 Pollution in Taiwan

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.

On the sources of ambient SOA in PM2.5: An integrated analysis over Jinan city of China

Secondary organic aerosol (SOA) accounts for a significant fraction of fine particulate matter (PM2.5) and is a crucial component that affects climate and public health. However, the formation of SOA in urban areas remains unclear and sources contributed to SOA are poorly characterized. In this study, we combined ambient measurements and the Weather Research and Forecasting (WRF)-Community Multi-scale Air Quality (CMAQ) modeling system to investigate the evolution of SOA during winter over Jinan city in China. Relative contributions of different anthropogenic sources to ambient SOA were subsequently probed by the Integrated Source Apportionment Method (ISAM) to explore the impact of anthropogenic emissions. The mean concentration of SOA was 11.2 μg m−3, accounting for 29.4% in PM2.5, and SOA concentrations on clean days (4.9 μg m−3) increased by 4.8 times on polluted days (23.3 μg m−3), and strong correlation between the elevated PM2.5 episodes and increased SOA concentrations was found (R2 > 0.5). In addition, SOA concentrations and the proportion of SOA in PM2.5 were higher during the daytime (24.9 μg m−3, 32.4%) compared to nighttime (21.9 μg m−3, 25.1%) on polluted days, which is primarily attributed to the intense atmospheric photochemical reactions and anthropogenic activities. Mobile sources act as the dominant contributor to SOA (57.6%) and road diesel emissions (30.9%) made the largest contribution to SOA, followed by non-road mobile sources (17.4%) among subsectors in mobile sources. This study sheds new insights into ambient SOA formation and SOA source apportionment for typical megacities in China, guiding the design of source-oriented regulation efforts on SOA mitigation.

Influence of meteorological reanalysis field on air quality modeling in the Yangtze River Delta, China

The meteorological reanalysis field is one of the key inputs to drive mesoscale meteorology simulations. To investigate the impacts of different reanalysis data on predictions of the meteorology parameters, as well as predictions of air quality, this study utilized the Weather Research and Forecasting/Community Multiscale Air Quality modeling system (WRF/CMAQ) with two sets of meteorological reanalysis inputs, the NCEP Final Operational Global Analysis (FNL) and the ECMWF Reanalysis v5.0 (ERA5) to predict the concentrations of ozone (O3) and fine particulate matter (PM2.5) in the Yangtze River Delta (YRD) in 2018. The results showed that ERA5 outperformed FNL in simulating 2-m temperature and relative humidity, while FNL demonstrated better accuracy in predicting wind fields. Both FNL and ERA5 underestimated the PM2.5 in spring and summer while overestimating it in autumn and winter. Furthermore, the Normalized mean bias (NMB) value for ERA5 exceeded the standard in winter (0.34), and the Normalized mean error (NME) value exceeded the standard in autumn (0.54). Both FNL and ERA5 underestimated the PM2.5 concentration during the pollution episodes, and FNL's simulations of PM2.5 concentrations were closer to observations than ERA5's as pollution levels intensified. Regarding O3 concentrations, ERA5 exhibited better performance than FNL, with the NMB (0.30, 0.27) and NME (0.39, 0.37) of FNL and ERA5 both exceeding the standard only in wintertime.

The effect of emission source chemical profiles on simulated PM2.5 components: sensitivity analysis with the Community Multiscale Air Quality (CMAQ) modeling system version 5.0.2

Abstract. A chemical transport model (CTM) is an essential tool for air quality prediction and management, widely used in air pollution control and health risk assessment. However, the current models do not perform very well in reproducing the observations of some major chemical components, for example, sulfate, nitrate, ammonium and organic carbon. Studies have suggested that the uncertainties in the model chemical mechanism, source emission inventory and meteorological field can cause inaccurate simulation results. Still, the emission source profile (used to create speciated emission inventories for CTMs) of PM2.5 has not been fully taken into account in current numerical simulation. Based on the characteristics and variation rules of chemical components in typical PM2.5 sources, different simulation scenarios were designed and the sensitivity of simulated PM2.5 components to the source chemical profile was explored. Our findings showed that the influence of source profile changes on simulated PM2.5 components' concentrations cannot be ignored. Simulation results of some components were sensitive to the adopted source profile in CTMs. Moreover, there was a linkage effect: the variation in some components in the source profile would bring changes to the simulated results of other components. These influences are connected to chemical mechanisms of the model since the variation in species allocations in emission sources can affect the potential composition and phase state of aerosols, chemical reaction priority, and multicomponent chemical balance in thermodynamic equilibrium systems. We also found that the perturbation of the PM2.5 source profile caused variation in simulated gaseous pollutants, which indirectly indicated that the perturbation of source profile would affect the simulation of secondary PM2.5 components. Our paper highlights the necessity of paying enough attention to the representativeness and timeliness of the source profile when using CTMs for simulation.

Significant Impact of Reactive Chlorine on Complex Air Pollution Over the Yangtze River Delta Region, China

AbstractThe chlorine radical (Cl) plays a crucial role in the formation of secondary air pollutants by determining the total atmospheric oxidative capacity (AOC). However, there are still large discrepancies among studies on chlorine chemistry, mainly due to uncertainties from three aspects: (a) Anthropogenic emissions of reactive chlorine species from disinfectant usage are typically overlooked. (b) The heterogeneous reaction uptake coefficients used in air quality models resulted in certain differences. (c) The co‐effect of anthropogenic and natural emissions is rarely investigated. In this study, the Weather Research and Forecasting (WRF)‐Community Multiscale Air Quality (CMAQ) modeling system (updated with 21 new reactions and a comprehensive emissions inventory) was used to simulate the combined impact of chlorine emissions on the air quality of a coastal city cluster in the Yangtze River Delta (YRD) region. The results indicate that the new emissions of reactive chlorine and the updated gas‐phase and heterogeneous chlorine chemistry can significantly enhance the AOC by 21.3%, 8.7%, 43.3%, and 58.7% in spring, summer, autumn, and winter, respectively. This is more evident in inland areas with high Cl concentrations. Our updates to the chlorine chemistry also increases the monthly mean maximum daily 8‐hr average (MDA 8) O3 mixing ratio by 4.1–7.0 ppbv in different seasons. Additionally, chlorine chemistry promotes the formation of fine particulate matter (PM2.5), with maximum monthly average enhancements of 4.7–13.3 μg/m3 in different seasons. This study underlines the significance of adding full chlorine emissions and updating chlorine chemistry in air quality models, and demonstrates that chlorine chemistry may significantly impact air quality over coastal regions.

Enhancing Air Quality Forecasts Across the Contiguous United States (CONUS) During Wildfires Using Analog-Based Post-Processing Methods

With our growing understanding of the risks of air pollution to human health, air quality forecasting has become a very important tool to enable decision-makers to take preventive and corrective measures for current and future policies. In addition, accurate predictions of air quality can help predict and mitigate the impacts of wildfires on human health, which have an increased risk due to anthropogenic climate change. However, errors in air quality forecasts limit their value in decision-making processes. Thus, increasing the accuracy of air quality forecasts is of significant importance. In this study, we have utilized the Community Multiscale Air Quality (CMAQ) modeling system with a 12 km horizontal grid resolution to generate daily 48-h fine particulate matter (PM2.5) forecasts for the Contiguous United States (CONUS) domain for June 1st through September 29th (major wildfire season) during 2015–2021. We conduct CMAQ offline simulations using meteorological inputs generated by the NOAA’s Unified Forecast System (UFS) numerical weather prediction model. We analyze the performance of the CMAQ PM2.5 and UFS meteorological forecasts over seven years of simulations for Environmental Protection Agency (EPA)-defined ten regions using the Air Quality System (AQS) ambient air pollution data from over a thousand monitoring sites across the CONUS. We have found that on average, the CMAQ model performs better in the eastern CONUS with the lowest Root Mean Square Error (RMSE) (10–30 μg/m3) while in the west, where wildfires are prevalent, the model has the highest RMSE of up to 120 μg/m3. To address these substantial model errors, we advance the field by applying Analog Ensemble (AnEn), a state-of-the-art statistical-dynamical method, to enhance PM2.5 forecast accuracy, with a particular focus on wildfire events, when producing an accurate forecast is highly challenging. We further refine this approach by incorporating a Carbon Monoxide-FIRE (CO-FIRE) tracer to precisely monitor fire smoke. In parallel to our objective and for the first time, we introduce two novel predictors, i.e., a CO−FIRE tracer in the model, and the maximum observed PM2.5 concentrations from the previous day (PM2.5−preday−max), and evaluate their potential significance in improving forecast accuracy, which aligns with the primary goal of our study. Despite the challenges of using AnEn for wildfires, we demonstrate that it has the potential to improve the CMAQ model forecast over the CONUS. We find that AnEn decreases the model RMSE by up to 25%, including additional 7% and 15% reduction by CO−FIRE and PM2.5−preday−max predictors, respectively, at different forecast lead times. In addition, the correlation between AnEn forecasts and observations is 20%–40% higher than that between CMAQ and observations. The Mean Bias Error (MBE) for AnEn forecasts is consistent and approximately −0.5 μg/m3 whereas CMAQ MBE varies between −1 and +1 μg/m3 between 0–48 forecast hours. AnEn significantly improves the PM2.5 forecast results during its highest episodes. During the initial days of wildfires, AnEn performs similarly to CMAQ. However, it soon catches up and decreases the error significantly.

A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application

Abstract. Top-down atmospheric inversion infers surface–atmosphere fluxes from spatially distributed observations of atmospheric composition in order to quantify anthropogenic and natural emissions. In this study, we developed a Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) based on the Weather Research and Forecasting–Community Multiscale Air Quality (WRF–CMAQ) modeling system model, the three-dimensional variational (3D-Var) algorithm, and the ensemble square root filter (EnSRF) algorithm. This system can simultaneously assimilate hourly in situ CO, SO2, NO2, PM2.5, and PM10 observations to infer gridded emissions of CO, SO2, NOx, primary PM2.5 (PPM2.5), and coarse PM10 (PMC) on a regional scale. In each data assimilation window, we use a “two-step” scheme, in which the emissions are inferred first and then input into the CMAQ model to simulate initial conditions (ICs) of the next window. The posterior emissions are then transferred to the next window as prior emissions, and the original emission inventory is only used in the first window. Additionally, a “super-observation” approach is implemented to decrease the computational costs, observation error correlations, and influence of representative errors. Using this system, we estimated the emissions of CO, SO2, NOx, PPM2.5, and PMC in December and July 2016 over China using nationwide surface observations. The results show that compared to the prior emissions (2016 Multi-resolution Emission Inventory for China – MEIC 2016)), the posterior emissions of CO, SO2, NOx, PPM2.5, and PMC in December 2016 increased by 129 %, 20 %, 5 %, 95 %, and 1045 %, respectively, and the emission uncertainties decreased by 44 %, 45 %, 34 %, 52 %, and 56 %, respectively. With the inverted emissions, the RMSE of simulated concentrations decreased by 40 %–56 %. Sensitivity tests were conducted with different prior emissions, prior uncertainties, and observation errors. The results showed that the two-step scheme employed in RAPAS is robust in estimating emissions using nationwide surface observations over China. This study offers a useful tool for accurately quantifying multi-species anthropogenic emissions at large scales and in near-real time.

Assessment of tropospheric ozone simulations in a regional chemical transport model using GEOS-Chem outputs as chemical boundary conditions

Regional chemical transport models (e.g., Community Multiscale Air Quality (CMAQ) Modeling System) are widely used to simulate the physical and chemical process of regional ozone (O3) pollution and its variation trend in recent years. However, chemical boundary condition (CBC) is an important input of these models and contributes to the model bias against observations. In this study, we develop a tool named GC2CMAQ that provides the CMAQ model with the CBCs from the GEOS-Chem simulation. Two experiments using different CBCs were conducted to evaluate their effect on seasonal O3 simulation in China. The Default experiment utilized the model-default static condition (the relatively clean atmosphere in the eastern United States), and the GC experiment employed the GEOS-Chem simulation results. Compared with the observation, the GC experiment has a much better performance in reproducing elevated O3 levels in the higher troposphere and lower stratosphere during different seasons. Near the earth's surface, the simulated concentrations of pollutants O3 (and PM2.5) in the GC experiment were also closer to the observation in April and July. The accuracy of simulation results in provinces close to the boundary was improved by approximately 20 %–30 % relative to the Default experiment. The CBCs provided by GEOS-Chem enabled a better simulation of stratosphere-troposphere O3 exchange in late spring and early summer, which then affected the pollutant concentration near surfaces through vertical transport. This finding was confirmed by a case study in southwestern Tibet on April 28, 2017, in which we quantified the contributions of different physical and chemical processes to O3 variations at different altitudes using the process analysis method. This study highlights the importance of using a reliable CBC for the regional chemical transport model to derive a better performance of O3 simulation.

Atmospheric impact of isoprene-derived Criegee intermediates and isoprene hydroxy hydroperoxide on sulfate aerosol formation in the Asian region

This study revealed the importance of two isoprene-derived pathways in the formation of sulfate aerosol (SO42−(p)), which are related to the gas-phase reactions of Criegee intermediates (CIs) and liquid-phase reactions of isoprene hydroxy hydroperoxide (ISOPOOH), in the atmosphere in the Asian region, using the community multiscale air quality (CMAQ) modeling system. The results showed that the incorporation of both isoprene-derived CIs and ISOPOOH chemistry increased the SO42−(p) concentration in specific domains of Asia, particularly isoprene hotspots, with up to 1% of the total SO42−(p) concentration compared to that obtained by the basic chemical transport model. The impact of ISOPOOH to SO42−(p) formation exhibited a more localized pattern compared to that of CIs, attributed to the tendency of ISOPOOH to condense in low-NO regions. SO42−(p) formation from CIs affected the compositions of nitrate (NO3−), ammonium (NH4+), and secondary organic aerosols (SOA) in the particle phase, and in most of the analyzed sites, those compositions increased from 0.5% to 2.5%. In contrast, SO42−(p) formation from ISOPOOH exhibits a negative impact on SOA, resulting in a potential reduction of up to 5% due to the loss of SOA precursor by incorporating the ISOPOOH uptake reaction. The sensitivity analysis results showed that most of the SO42−(p) was generated by the gas-phase reaction of SO2 with OH; however, the CIs and ISOPOOH also affected the SO42−(p) formation more than the liquid-phase SO42−(p) formations by H2O2, O3, O2, etc.