Multiscale Air Quality Modeling System Research Articles

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.

Sensitivity of northeastern US surface ozone predictions to the representation of atmospheric chemistry in the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMMv1.0).

Chemical mechanisms describe how emissions of gases and particles evolve in the atmosphere and are used within chemical transport models to evaluate past, current, and future air quality. Thus, a chemical mechanism must provide robust and accurate predictions of air pollutants if it is to be considered for use by regulatory bodies. In this work, we provide an initial evaluation of the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMMv1.0) by assessing CRACMMv1.0 predictions of surface ozone (O3) across the northeastern US during the summer of 2018 within the Community Multiscale Air Quality (CMAQ) modeling system. CRACMMv1.0 O3 predictions of hourly and maximum daily 8 h average (MDA8) ozone were lower than those estimated by the Regional Atmospheric Chemistry Mechanism with aerosol module 6 (RACM2_ae6), which better matched surface network observations in the northeastern US (RACM2_ae6 mean bias of +4.2 ppb for all hours and +4.3 ppb for MDA8; CRACMMv1.0 mean bias of +2.1 ppb for all hours and +2.7 ppb for MDA8). Box model calculations combined with results from CMAQ emission reduction simulations indicated a high sensitivity of O3 to compounds with biogenic sources. In addition, these calculations indicated the differences between CRACMMv1.0 and RACM2_ae6 O3 predictions were largely explained by updates to the inorganic rate constants (reflecting the latest assessment values) and by updates to the representation of monoterpene chemistry. Updates to other reactive organic carbon systems between RACM2_ae6 and CRACMMv1.0 also affected ozone predictions and their sensitivity to emissions. Specifically, CRACMMv1.0 benzene, toluene, and xylene chemistry led to efficient NO x cycling such that CRACMMv1.0 predicted controlling aromatics reduces ozone without rural O3 disbenefits. In contrast, semivolatile and intermediate-volatility alkanes introduced in CRACMMv1.0 acted to suppress O3 formation across the regional background through the sequestration of nitrogen oxides (NO x ) in organic nitrates. Overall, these analyses showed that the CRACMMv1.0 mechanism within the CMAQ model was able to reasonably simulate ozone concentrations in the northeastern US during the summer of 2018 with similar magnitude and diurnal variation as the current operational Carbon Bond (CB6r3_ae7) mechanism and good model performance compared to recent modeling studies in the literature.

Evaluating Data Product Exposure Metrics for Use in Epidemiologic Studies of Dust Storms.

Dust storms are increasing in frequency and correlate with adverse health outcomes but remain understudied in the United States (U.S.), partially due to the limited spatio-temporal coverage, resolution, and accuracy of current data sets. In this work, dust-related metrics from four public areal data products were compared to a monitor-based "gold standard" dust data set. The data products included the National Weather Service (NWS) storm event database, the Modern-Era Retrospective analysis for Research and Applications-Version 2, the EPA's Air QUAlity TimE Series (EQUATES) Project using the Community Multiscale Air Quality Modeling System (CMAQ), and the Copernicus Atmosphere Monitoring Service global reanalysis product. California, Nevada, Utah, and Arizona, which account for most dust storms reported in the U.S., were examined. Dichotomous and continuous metrics based on reported dust storms, particulate matter concentrations (PM10 and PM2.5), and aerosol-type variables were extracted or derived from the data products. Associations between these metrics and a validated dust storm detection method utilizing Interagency Monitoring of Protected Visual Environments monitors were estimated via quasi-binomial regression. In general, metrics from CAMS yielded the strongest associations with the "gold standard," followed by the NWS storm database metric. Dust aerosol (0.9-20μm) mixing ratio, vertically integrated mass of dust aerosol (9-20μm), and dust aerosol optical depth at 550nm from CAMS generated the highest standardized odds ratios among all metrics. Future work will apply machine-learning methods to the best-performing metrics to create a public dust storm database suitable for long-term epidemiologic studies.

Benefits of Short-term Premature Mortality Reduction Attributed to PM2.5 Pollution: A Case Study in Long an Province, Vietnam.

PM2.5 pollution exposure is the leading cause of disease burden globally, especially in low- and middle-income countries, including Vietnam. Therefore, economic damage in this context must be quantified. Long An province in the Southern Key Economic (SKE) region was selected as a research area. This study aimed to evaluate PM2.5-related human health effects causing early deaths attributable to respiratory, cardiovascular, and circulatory diseases in all ages and genders. Health end-points and health impact estimation, economic loss model, groups of PM2.5 concentration data, data of exposed population, data of baseline premature mortality rate, and data of health impact functions were used. Hourly PM2.5 concentration data sets were generated specifically using the coupled Weather Research and Forecasting Model (WRF)/Community Multiscale Air Quality Modelling System (CMAQ) models. Daily PM2.5 pollution levels considered mainly in the dry season (from January to April 2018) resulted in 12.9 (95% CI -0.6; 18.7) all-cause premature deaths per 100,000 population, of which 7.8 (95% CI 1.1; 7.1), 1.5 (95% CI -0.2; 3.1), and 3.6 (95% CI -1.5; 8.5) were due to respiratory diseases (RDs; 60.54%), cardiovascular diseases (CVDs; 11.81%), and circulatory system diseases (CSDs; 27.65%) per 100,000 population, respectively. The total economic losses due to acute PM2.5 exposure-related premature mortality cases reached 62.0 (95% CI -2.7; 89.6) billion VND, equivalent to 8.3 (95% CI -0.4; 12.0) million USD. The study outcomes contributed remarkably to the generation and development of data sources for effectively managing ambient air quality in Long An.

Linking gas, particulate, and toxic endpoints to air emissions in the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM)

Abstract. Chemical mechanisms describe the atmospheric transformations of organic and inorganic species and connect air emissions to secondary species such as ozone, fine particles, and hazardous air pollutants (HAPs) like formaldehyde. Recent advances in our understanding of several chemical systems and shifts in the drivers of atmospheric chemistry warrant updates to mechanisms used in chemical transport models such as the Community Multiscale Air Quality (CMAQ) modeling system. This work builds on the Regional Atmospheric Chemistry Mechanism version 2 (RACM2) and develops the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM) version 1.0, which demonstrates a fully coupled representation of chemistry leading to ozone and secondary organic aerosol (SOA) with consideration of HAPs. CRACMMv1.0 includes 178 gas-phase species, 51 particulate species, and 508 reactions spanning gas-phase and heterogeneous pathways. To support estimation of health risks associated with HAPs, nine species in CRACMM cover 50 % of the total cancer and 60 % of the total non-cancer emission-weighted toxicity estimated for primary HAPs from anthropogenic and biomass burning sources in the US, with the coverage of toxicity higher (> 80 %) when secondary formaldehyde and acrolein are considered. In addition, new mechanism species were added based on the importance of their emissions for the ozone, organic aerosol, or atmospheric burden of total reactive organic carbon (ROC): sesquiterpenes, furans, propylene glycol, alkane-like low- to intermediate-volatility organic compounds (9 species), low- to intermediate-volatility oxygenated species (16 species), intermediate-volatility aromatic hydrocarbons (2 species), and slowly reacting organic carbon. Intermediate- and lower-volatility organic compounds were estimated to increase the coverage of anthropogenic and biomass burning ROC emissions by 40 % compared to current operational mechanisms. Autoxidation, a gas-phase reaction particularly effective in producing SOA, was added for C10 and larger alkanes, aromatic hydrocarbons, sesquiterpenes, and monoterpene systems including second-generation aldehydes. Integrating the radical and SOA chemistry put additional constraints on both systems and enabled the implementation of previously unconsidered SOA pathways from phenolic and furanone compounds, which were predicted to account for ∼ 30 % of total aromatic hydrocarbon SOA under typical atmospheric conditions. CRACMM organic aerosol species were found to span the atmospherically relevant range of species carbon number, number of oxygens per carbon, and oxidation state with a slight high bias in the number of hydrogens per carbon. In total, 11 new emitted species were implemented as precursors to SOA compared to current CMAQv5.3.3 representations, resulting in a bottom-up prediction of SOA, which is required for accurate source attribution and the design of control strategies. CRACMMv1.0 is available in CMAQv5.4.

Impacts of land cover changes on biogenic emission and its contribution to ozone and secondary organic aerosol in China

Abstract. The greening impacts on China from 2000 to 2017 led to an increase in vegetated areas and thus enhanced biogenic volatile organic compound (BVOC) emissions. BVOCs are regarded as important precursors for ozone (O3) and secondary organic aerosol (SOA). As a result, accurate estimation of BVOC emissions is critical to understand their impacts on air quality. In this study, the Model of Emissions of Gases and Aerosols from Nature (MEGAN) v2.1 was used to investigate the impact of different leaf area index (LAI) and land cover (LC) datasets on BVOC emissions in China in 2016, and the effects on O3 and SOA were evaluated based on the Community Multiscale Air Quality (CMAQ) modeling system. Three LAI satellite datasets of the Global LAnd Surface Satellite (GLASS), the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2H version 6 (MOD15), and the Copernicus Global Land Service (CGLS), as well as three LC satellite datasets of the MODIS MCD12Q1 LC products, the Copernicus Climate Change Service (C3S) LC products, and the CGLS LC products, were used in five parallel experiments (cases: C1–C5). Results show that changing LAI and LC datasets of the model input has an impact on BVOC estimations. BVOC emissions in China ranged from 25.42 to 37.39 Tg in 2016 and were mainly concentrated in central and southeastern China. Changing the LC inputs for the MEGAN model has a more significant difference in BVOC estimates than using different LAI datasets. The combination of C3S LC and GLASS LAI performs better in the CMAQ model, indicating that it is the better choice for BVOC estimations in China. The highest contribution of BVOCs to O3 and SOA can reach 12 ppb and 9.8 µg m−3, respectively. Changing the MEGAN inputs further impacts the concentrations of O3 and SOA, especially changing LC datasets. The relative difference between MCD12Q1 LC and C3S LC is over 52 % and 140 % in O3 and biogenic SOA (BSOA) in central and eastern China. The BSOA difference is mainly attributed to the isoprene SOA (ISOA), a major contributor to BSOA. The relative differences in ISOA between different cases are up to 160 % in eastern China. Therefore, our results suggest that the uncertainties in MEGAN inputs should be fully considered in future O3 and SOA simulations.

Estimates of Dust Emissions and Organic Carbon Losses Induced by Wind Erosion in Farmland Worldwide from 2017 to 2021

Wind erosion can cause high dust emissions from agricultural land and can lead to a significant loss of carbon and nutrients from the soil. The carbon balance of farmland soil is an integral part of the carbon cycle, especially under the current drive to develop carbon-neutral practices. However, the amount of global carbon lost due to the wind erosion of farmland is unknown. In this study, global farmland dust emissions were estimated from a dust emission inventory (0.1° × 0.1°, daily) built using the improved Community Multiscale Air Quality Modeling System–FENGSHA (CMAQ-FENGSHA), and global farmland organic carbon losses were estimated by combining this with global soil organic carbon concentration data. The average global annual dust emissions from agricultural land from 2017 to 2021 were 1.75 × 109 g/s. Global dust emissions from agricultural land are concentrated in the UK, Ukraine, and Russia in Europe; in southern Canada and the central US in North America; in the area around Buenos Aires, the capital of Argentina, in South America; and in northeast China in Asia. The global average annual organic carbon loss from agricultural land was 2970 Gg for 2017–2021. The spatial distribution of emissions is roughly consistent with that of dust emissions, which are mainly concentrated in the world’s four major black soil regions. These estimates of dust and organic carbon losses from agricultural land are essential references that can inform the global responses to the carbon cycle, dust emissions, and black soil conservation.

Evaluation of air quality simulation with a coupled atmosphere-ocean model: A case study on natural marine and biogenic emissions

In this study, a chemical transport model (i.e., Community Multi-scale Air Quality (CMAQ) modeling system with brute-force method (BFM)) was used in combination with atmosphere-ocean coupling to evaluate the impact of natural emissions (e.g., marine dimethyl sulfide (DMS), sea salt aerosol (SSA), and biogenic compounds) on the air quality of South Korea in the spring of 2019 (May 1–31). Overall, the coupled simulation results exhibited good agreement with the observations for meteorological fields and air quality (fine particulate matter (PM2.5) and ozone (O3)) compared to those obtained using the non-coupled simulation. The coupling effect in the study area tended to be strong in the presence of relatively strong winds (≥4 m s−1). The mean contributions of natural marine (DMS and SSA) and biogenic emissions to total PM2.5 mass reached ~8.2 % over the marine area and ~ 9.1 % over the land area, respectively. On average, biogenic emissions contributed 8.6 %, 29.3 % (and 27.3 %) to the concentrations of O3, secondary organic aerosol (SOA) (and organic carbon (OC)), respectively, over the land area. Isoprene and monoterpene contributed 40 % and 20 %, respectively, to biogenic SOA production over the land area and biogenic SOA accounted for 1.7 % and 7.8 % of the total O3 and PM2.5, respectively. Secondary aerosol formation was enhanced by gas-to-particle conversion processes due to the coupling effect. Therefore, this modeling study confirmed the non-negligible impact of natural emissions on the air quality in the study area. In addition, the study area is likely to be associated with VOC-limited conditions because of significantly enhanced photochemical O3 production owing to biogenic emissions.

A Data Assimilation Method Combined with Machine Learning and Its Application to Anthropogenic Emission Adjustment in CMAQ

Anthropogenic emissions play an important role in air quality forecasting. To improve the forecasting accuracy, the use of nudging as the data assimilation method, combined with extremely randomized trees (ExRT) as the machine learning method, was developed and applied to adjust the anthropogenic emissions in the Community Multiscale Air Quality modeling system (CMAQ). This nudging–ExRT method can iterate with the forecast and is suitable for linear and nonlinear emissions. For example, an episode between 15 and 30 January 2019 was simulated for China’s Beijing–Tianjin–Hebei (BTH) region. For PM2.5, the correlation coefficient of the site averaged concentration (Ra) increased from 0.85 to 0.94, and the root mean square error (RMSEa) decreased from 24.41 to 9.97 µg/m3. For O3, the Ra increased from 0.75 to 0.81, and the RMSEa decreased from 13.91 to 12.07 µg/m3. These results showed that nudging–ExRT can significantly improve forecasting skills and can be applied to routine air quality forecasting in the future.

A three-dimensional simulation and process analysis of tropospheric ozone depletion events (ODEs) during the springtime in the Arctic using CMAQ (Community Multiscale Air Quality Modeling System)

Abstract. The tropospheric ozone depletion event (ODE), first observed at Barrow (now known as Utqiaġvik), Alaska, is a phenomenon that frequently occurs during the springtime in the Arctic. In this study, we performed a three-dimensional model study on ODEs occurring at Barrow and its surrounding areas between 28 March and 6 April 2019 using a 3-D multi-scale air quality model, CMAQ (Community Multiscale Air Quality Modeling System). Several ODEs observed at Barrow were captured, and two of them were thoroughly analyzed using the process analysis method to estimate contributions of horizontal transport, vertical transport, dry deposition, and the overall chemical process to the variations in ozone and bromine species during ODEs. We found that the ODE occurring between 30 and 31 March 2019 (referred to as ODE1) was primarily caused by the horizontal transport of low-ozone air from the Beaufort Sea to Barrow. The formation of this low-ozone air over the sea was largely attributed to a release of sea-salt aerosols from the Bering Strait under strong wind conditions, stemming from a cyclone generated on the Chukotka Peninsula. It was also discovered that the surface ozone dropped to less than 5 ppb over the Beaufort Sea, and the overall chemical process contributed up to 10 ppb to the ozone loss. Moreover, BrO over the sea reached a maximum of approximately 80 ppt. This low-ozone air over the sea was then horizontally transported to Barrow, leading to the occurrence of ODE1. Regarding another ODE on 2 April (ODE2), we found that its occurrence was also dominated by the horizontal transport from the sea, but under the control of an anticyclone. The termination of this ODE was mainly attributed to the replenishment of ozone-rich air from the free troposphere by a strong vertical transport.

Differences between VOCs and NOx transport contributions, their impacts on O3, and implications for O3 pollution mitigation based on CMAQ simulation over the Yangtze River Delta, China

The relationship between O3 and its precursors during urban polluted episodes remains unclear. In this study, the simultaneous source apportionment of VOCs, NOx, and O3 over the Yangtze River Delta (YRD) region during the O3 polluted episode on July 24–30, 2018, was performed based on the Integrated Source Apportionment Method (ISAM) embedded in the Community Multiscale Air Quality Modeling System (CMAQ). The results of the ISAM were compared with those of the Brute Force Method (BFM) and Positive Matrix Factorization (PMF). Furthermore, the differences between the transport contributions of VOCs and NOx, and their impacts on O3 were analyzed. The results indicate that observations of VOCs species can be well captured by simulated VOCs, and the ISAM has a significant advantage in the source apportionment of VOCs, especially for sources emitting highly reactive species. In the clean and polluted periods, the local contribution percentages of VOCs in urban sites ranged from 60 % to 77 %, much higher than those of NOx (31 %–43 %) and O3 (16 %–33 %). NOx and O3 have strong transport abilities with high and close contribution percentages, which are highly correlated, mainly because oxygen atoms produced by the photolysis of NO2 in the aged air mass combined rapidly with O2 to form O3 during transport. The VOCs chemical loss caused by the oxidation of OH radicals during transport makes the ability of VOCs for long-distance transport much weaker than that of NOx. Furthermore, owing to the sufficient aging of VOCs, those contributed by long-distance transport have little effect on O3. To a certain extent, controlling one's NOx emissions can help other cities more, while controlling one's VOCs emissions can help itself more. Therefore, it is recommended to attach enough importance to joint prevention and control of NOx among cities and even long-distance areas to alleviate regional O3 pollution.

Atmospheric dry deposition fluxes of trace metals over the Eastern China Marginal Seas: Impact of emission controls

Atmospheric deposition is an important exogenous input of trace metals to Eastern China Marginal Seas (ECMS), which is strongly affected by human activities. With emission control practices implemented in China, it still remains unknown what changes have taken place in the atmospheric dry depositions of the trace metals over ECMS. This study aimed to estimate the atmospheric dry depositions of Zn, Pb, Cu, and Cd over ECMS via Weather Research and Forecasting Model-Community Multiscale Air Quality Modeling System (WRF-CMAQ) in the two winter periods of January 2012 and January 2019 as well as to explore the impacts of emission control on the depositions. The anthropogenic metal emissions from China, the Korean Peninsula, Japan, and marine ships were investigated in this study. In 2012, the dry deposition fluxes of Zn, Pb, Cu, and Cd over ECMS were in the ranges of 0.50–3.4 μg m−2 d−1, 0.22–1.9 μg m−2 d−1, 0.14–0.90 μg m−2 d−1, and 12–88 ng m−2 d−1, respectively. The deposition fluxes of the four metals over Bohai Sea (BS) and Yellow Sea (YS) were 2–3 times those over East China Sea (ECS). Outflow of polluted air masses from East Asia increased the metal depositions by 3– 5-fold relative to clear days. Compared with 2012, a 5–85 % reduction in the metal depositions over ECMS were estimated in 2019, largest reductions were found over YS and BS. Meteorological variation was able to decrease or increase the metal depositions. However, the emission control only caused a reduction in the entire study region. The metal inputs to the sea were significantly lower from the ship emissions than from the continental anthropogenic emissions, although the proportion of the ship emissions in the total metal depositions rose slightly from 2012 to 2019.

Role of vertical advection and diffusion in long-range PM2.5 transport in Northeast Asia

This study quantitatively analyzed the role of vertical mixing in long-range transport (LRT) of PM2.5 during its high concentration episode in Northeast Asia toward the end of February 2014. The PM2.5 transport process from an upwind to downwind area was examined using the Community Multi-scale Air Quality (CMAQ) modeling system with its instrumented tool and certain code modifications. We identified serial distinctive roles of vertical advection (ZADV) and diffusion (VDIF) processes. The surface PM2.5 in an upwind area became aloft by VDIF— during daytime—to the planetary boundary layer (PBL) altitude of 1 km or lower. In contrast, ZADV updraft effectively transported PM2.5 vertically to an altitude of 2–3 km above the PBL. Furthermore, we found that the VDIF and ZADV in the upwind area synergistically promoted the vertical mixing of air pollutants up to an altitude of 1 km and higher. The aloft PM2.5 in the upwind area was then transported to the downwind area by horizontal advection (HADV), which was faster than HADV at the surface layer. Additionally, VDIF and ZADV over the downwind area mixed down the aloft PM2.5 on the surface. During this period, the VDIF and ZADV increased the PM2.5 concentrations in the downwind area by up to 15 μg·m−3 (15%) and 101 μg·m−3 (60%), respectively. This study highlights the importance of vertical mixing on long-range PM2.5 transport and warrants more in-depth model analysis with three-dimensional observations to enhance its comprehensive understanding.

A SPATIAL CAUSAL ANALYSIS OF WILDLAND FIRE-CONTRIBUTED PM2.5 USING NUMERICAL MODEL OUTPUT.

Wildland fire smoke contains hazardous levels of fine particulate matter (PM2.5), a pollutant shown to adversely effect health. Estimating fire attributable PM2.5 concentrations is key to quantifying the impact on air quality and subsequent health burden. This is a challenging problem since only total PM2.5 is measured at monitoring stations and both fire-attributable PM2.5 and PM2.5 from all other sources are correlated in space and time. We propose a framework for estimating fire-contributed PM2.5 and PM2.5 from all other sources using a novel causal inference framework and bias-adjusted chemical model representations of PM2.5 under counterfactual scenarios. The chemical model representation of PM2.5 for this analysis is simulated using Community Multiscale Air Quality Modeling System (CMAQ), run with and without fire emissions across the contiguous U.S. for the 2008-2012 wildfire seasons. The CMAQ output is calibrated with observations from monitoring sites for the same spatial domain and time period. We use a Bayesian model that accounts for spatial variation to estimate the effect of wildland fires on PM2.5 and state assumptions under which the estimate has a valid causal interpretation. Our results include estimates of the contributions of wildfire smoke to PM2.5 for the contiguous U.S. Additionally, we compute the health burden associated with the PM2.5 attributable to wildfire smoke.

Characterizing changes in extreme ozone levels under 2050s climate conditions: An extreme-value analysis in California

Ground-level ozone has a well established climate penalty associated with changing meteorological conditions under future climate change, but most existing research focuses exclusively on ozone-temperature relationships defined at specific locations. Using an extreme-value theory approach, we model relationships between temperature, humidity, and vapor pressure deficit and maximum daily 8 h ozone concentrations (MDA8) across California using a combination of ozone station network data and Community Multiscale Air Quality Modeling System (CMAQ) model output from 2008 to 2017. We use a spatial regression with time varying bias coefficients to fuse station observations and modeled output into a spatially explicit gridded ozone dataset at 12 km resolution, then fit independent Point Process models to every grid cell to estimate 5, 20, and 50 year ozone return-levels across California. We evaluate the impact of climate change on ozone return levels and episode days under RCP4.5 conditions using an ensemble of 18 downscaled CMIP5 climate models, finding changes of -6t to 8 ppb in effective return levels and −8 to 16 episode days, Our results further strengthen the evidence for anozone climate penalty in California and show the advantages of considering multiple climate variables when modeling ground-level ozone. • Extreme ozone levels are affected by multiple meteorological variables, including temperature, relative humidity, and vapor pressure deficit. • Climate change increases extreme ozone levels across most of California. • Extreme value theory model fits are improved with meteorological covariates.