Community Multiscale Air Quality Research Articles

Expanding the simulation of East Asian super dust storms: physical transport mechanisms impacting the western Pacific

Abstract. Dust models are widely applied over the East Asian region for the simulation of dust emission, transport, and deposition. However, due to the uncertainties in estimates of dust transport, these methods still lack the necessary precision to capture the complexity of transboundary dust events. This study demonstrates an improvement in the Community Multiscale Air Quality (CMAQ) model dust treatment during long-range transport of dust from northwestern China to the South China Sea (SCS). To accomplish this, we considered a super dust storm (SDS) event in March 2010 and evaluated the dust scheme by including adjustments to the recent calibration (Dust_Refined_1) and bulk density (Dust_Refined_2) refinements individually and in combination (Dust_Refined_3). The Dust_Refined_3 normalized mean bias of PM10 was −30.65 % for the 2010 SDS event, which was lower in magnitude compared to Dust_Refined_1 (−41.18 %) and Dust_Refined_2 (−49.88 %). Indeed, Dust_Refined_3 improved the simulated aerosol optical depth (AOD) value during significant dust cases, e.g., in March 2005, March 2006, and April 2009. Dust_Refined_3 also showed more clearly that, in March 2010, a “double plume” (i.e., one plume originating from the Taiwan Strait and the other from the western Pacific) separated by the Central Mountain Range (CMR) of Taiwan affected dust transport on the island of Dongsha in the SCS. On 15–21 April 2021, both CMAQ simulations and satellite data highlighted the influence of Typhoon Surigae on dust transport to downwind Taiwan and the western Pacific Ocean (WPO). The CMAQ Dust_Refined_3 simulations further revealed that many dust aerosols were removed over the WPO due to Typhoon Surigae. Hence, the model indicated a near-zero dust particle concentration over the WPO, which was significantly different from previous dust transport episodes over the Taiwan region. Therefore, our study suggested an effective method to improve dust management of CMAQ under unique topographical and meteorological conditions.

The first application of a numerically exact, higher-order sensitivity analysis approach for atmospheric modelling: implementation of the hyperdual-step method in the Community Multiscale Air Quality Model (CMAQ) version 5.3.2

Abstract. Sensitivity analysis in chemical transport models quantifies the response of output variables to changes in input parameters. This information is valuable for researchers engaged in data assimilation and model development. Additionally, environmental decision-makers depend upon these expected responses of concentrations to emissions when designing and justifying air pollution control strategies. Existing sensitivity analysis methods include the finite-difference method, the direct decoupled method (DDM), the complex variable method, and the adjoint method. These methods are either prone to significant numerical errors when applied to nonlinear models with complex components (e.g. finite difference and complex step methods) or difficult to maintain when the original model is updated (e.g. direct decoupled and adjoint methods). Here, we present the implementation of the hyperdual-step method in the Community Multiscale Air Quality Model (CMAQ) version 5.3.2 as CMAQ-hyd. CMAQ-hyd can be applied to compute numerically exact first- and second-order sensitivities of species concentrations with respect to emissions or concentrations. Compared to CMAQ-DDM and CMAQ-adjoint, CMAQ-hyd is more straightforward to update and maintain, while it remains free of subtractive cancellation and truncation errors, just as those augmented models do. To evaluate the accuracy of the implementation, the sensitivities computed by CMAQ-hyd are compared with those calculated with other traditional methods or a hybrid of the traditional and advanced methods. We demonstrate the capability of CMAQ-hyd with the newly implemented gas-phase chemistry and biogenic aerosol formation mechanism in CMAQ. We also explore the cross-sensitivity of monoterpene nitrate aerosol formation to its anthropogenic and biogenic precursors to show the additional sensitivity information computed by CMAQ-hyd. Compared with the traditional finite difference method, CMAQ-hyd consumes fewer computational resources when the same sensitivity coefficients are calculated. This novel method implemented in CMAQ is also computationally competitive with other existing methods and could be further optimized to reduce memory and computational time overheads.

Air quality and health impacts of the 2020 wildfires in California

Abstract Background Wildfires in 2020 ravaged California to set the annual record of area burned to date. Clusters of wildfires in Northern California surrounded the Bay Area covering the skies with smoke and raising the air pollutant concentrations to hazardous levels. This study uses the Fire Inventory from the National Center for Atmospheric Research database and the Community Multiscale Air Quality model to estimate the effects of wildfire emissions on air quality during the period from August 16 to October 28 of 2020. In addition, low-cost sensor data for fine particulate matter (PM2.5) from the PurpleAir network is used to enhance modeled PM2.5 concentrations. The resulting impacts on ozone and PM2.5 are used to quantify the health impacts caused by wildfires using the Benefits Mapping and Analysis Program – Community Edition. Results Wildfire activity significantly increased direct PM2.5 emissions and emissions of PM2.5 and ozone precursors. Direct PM2.5 emissions surged up to 38 times compared to an average day. Modeling results indicated that wildfires alone led to a rise in ozone daily maximum 8-h average by up to 10 ppb and exceeded PM2.5 air quality standards in numerous locations by up to 10 times. While modeled PM2.5 concentrations were lower than measurements, correcting these with PurpleAir data improved the accuracy. The correction using PurpleAir data increased estimates of wildfire-induced mortality due to PM2.5 exposure by up to 16%. Conclusions The increased hospital admissions and premature mortality attributed to wildfires were found to be comparable to the health impacts avoided by strategies aimed at meeting ozone and PM2.5 air quality standards. This suggests that widespread wildfire emissions can negate years of efforts dedicated to controlling air pollution. The integration of low-cost sensor data proved invaluable in refining the estimates of health impacts from PM2.5 resulting from wildfires.

Current status of model predictions of volatile organic compounds and impacts on surface ozone predictions during summer in China

Abstract. Volatile organic compounds (VOCs) play a crucial role in the formation of tropospheric ozone (O3) and secondary organic aerosols. VOC emissions are generally considered to have larger uncertainties compared to other pollutants, such as sulfur dioxide and fine particulate matter (PM2.5). Although predictions of O3 and PM2.5 have been extensively evaluated in air quality modeling studies, there has been limited reporting on the evaluation of VOCs, mainly due to a lack of routine VOC measurements at multiple sites. In this study, we utilized VOC measurements from the “Towards an Air Toxic Management System in China” (ATMSYC) project at 28 sites across China and assessed the predicted VOC concentrations using the Community Multiscale Air Quality (CMAQ) model with the widely used Multi-resolution Emission Inventory for China (MEIC). The ratio of predicted to observed total VOCs was found to be 0.74 ± 0.40, with underpredictions ranging from 2.05 to 50.61 ppbv (5.77 % to 85.40 %) at 24 sites. A greater bias in VOC predictions was observed in industrial cities in the north and southwest, such as Jinan, Shijiazhuang, Lanzhou, Chengdu, and Guiyang. In terms of different VOC components, alkanes, alkenes, non-naphthalene aromatics (ARO2MN), alkynes, and formaldehyde (HCHO) had prediction-to-observation ratios of 0.53 ± 0.38, 0.51 ± 0.48, 0.31 ± 0.38, 0.41 ± 0.47, and 1.21 ± 1.61, respectively. Sensitivity experiments were conducted to assess the impact of the VOC prediction bias on O3 predictions. While emission adjustments improved the model performance for VOCs, resulting in a change in the ratio of total VOCs to 0.86 ± 0.47, they also exacerbated O3 overprediction relative to the base case by 0.62 % to 6.27 % across the sites. This study demonstrates that current modeling setups and emission inventories are likely to underpredict VOC concentrations, and this underprediction of VOCs contributes to lower O3 predictions in China.

Assessing the impact of shipping emissions on ozone concentrations in East Asia: Insights from KORUS-AQ and SIJAQ 2021 campaign periods

This study analyzed the impact of shipping emissions on ozone (O3) concentrations in East Asia during the Korea-United States Air Quality (KORUS-AQ) and the Satellite Integrated Joint monitoring for Air Quality (SIJAQ) 2021 campaign period. Numerical simulations were performed during the KORUS-AQ (May 2016, the KORUS case) and the SIJAQ 2021 (October–November 2021, the SIJAQ case) using Community Multi-scale Air Quality (CMAQ), the three-dimensional chemical transport model. We additionally simulated an experiment without shipping emissions to compare the impact with and without shipping emissions. Using the validated simulation results, the average O3 concentrations in inland regions were found to be 35.63 ppb and 20.62 ppb in the KORUS and SIJAQ cases, respectively. The average O3 concentrations were higher in the KORUS case owing to the difference in weather conditions between the two cases in various seasons. The results of examining the impact of shipping emissions on O3 concentrations in the ocean area indicate that both the KORUS and SIJAQ cases resulted in a decrease in O3 concentrations in the Yellow Sea region, while the O3 concentrations increased in the East Sea and North Pacific regions. In particular, in the Yellow Sea and East China Sea, where the O3 concentrations decreased in the KORUS case, O3 sensitivity results showed volatile organic compounds (VOC)-sensitive regime when the shipping emissions were removed but changed to nitrogen oxide (NOx)-sensitive regime when the shipping emissions were added. Meanwhile, in the inland regions, the SIJAQ case decreased O3 concentrations in most areas, whereas the KORUS case showed an increase in O3 concentrations in the Korean Peninsula and Japan. Analysis of O3 sensitivity revealed that the regions of the Korean Peninsula and Japan, where O3 concentrations increased, exhibited a NOx-sensitive regime. Particularly, in the case of the Korean Peninsula, the initial transport of shipping emissions in the western part led to a decrease in O3 concentrations. However, as shipping emissions were further transported from west to east, they became more diluted, and additional NOx generated by O3 titration reactions was also transported, gradually increasing O3 concentrations. Furthermore, we found that during seasons with abundant BVOC emissions, the transport of shipping emissions could contribute to additional O3 production. These results confirmed that shipping emissions in East Asia primarily reduce O3 concentrations, however, differences in seasonal and regional O3 sensitivity and transportation patterns can contribute to O3 production.

Dominant contribution of combustion-related ammonium during haze pollution in Beijing

Aerosol ammonium (NH4+), mainly produced from the reactions of ammonia (NH3) with acids in the atmosphere, has significant impacts on air pollution, radiative forcing, and human health. Understanding the source and formation mechanism of NH4+ can provide scientific insights into air quality improvements. However, the sources of NH3 in urban areas are not well understood, and few studies focus on NH3/NH4+ at different heights within the atmospheric boundary layer, which hinders a comprehensive understanding of aerosol NH4+. In this study, we perform both field observation and modeling studies (the Community Multiscale Air Quality, CMAQ) to investigate regional NH3 emission sources and vertically resolved NH4+ formation mechanisms during the winter in Beijing. Both stable nitrogen isotope analyses and CMAQ model suggest that combustion-related NH3 emissions, including fossil fuel sources, NH3 slip, and biomass burning, are important sources of aerosol NH4+ with more than 60% contribution occurring on heavily polluted days. In contrast, volatilization-related NH3 sources (livestock breeding, N-fertilizer application, and human waste) are dominant on clean days. Combustion-related NH3 is mostly local from Beijing, and biomass burning is likely an important NH3 source (∼15%–20%) that was previously overlooked. More effective control strategies such as the two-product (e.g., reducing both SO2 and NH3) control policy should be considered to improve air quality.

Improving Machine Learning Based PM2.5 Prediction by Segregating Biomass Emission Factor from Chemical Transport Model

Located in the heart of Mainland Southeast Asia, Thailand is associated with high biomass burning (BB) activities from local and neighbouring countries. The seasonal pattern of BB manifests itself as a potential predictor for PM2.5 concentration. Consequently, we enhanced machine learning based PM2.5 prediction by segregating BB factor from the Community Multiscale Air Quality (CMAQ). Two Light Gradient Boosting Machine (LightGBM) models with different CMAQ predictors were developed: the BB-integrated model, which incorporated CMAQ-simulated PM2.5 from all emission sources and the BB-segregated model, which incorporated CMAQ-simulated PM2.5 from sources other than BB (CMAQ_PM25_Othr) and CMAQ-simulated PM2.5 from BB emissions (CMAQ_PM25_BB). The two models had shared control predictors, which included simulated meteorological variables from WRF model, population, elevation, and land-use variables, and they were evaluated using a crossvalidation (CV). The BB-segregated model outperformed the BB-integrated model, achieving overall-CV R2 values of 0.86 and 0.82, respectively. The analysis of feature importance for the BB-segregated model indicates that CMAQ_PM25_Othr and CMAQ_PM25_BB are the two most significant predictors. These findings emphasize the importance of considering BB emissions when predicting PM2.5 concentrations, particularly in regions with high BB activities.

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.

Improved estimation of CO2 emissions from thermal power plants based on OCO-2 XCO2 retrieval using inline plume simulation

CO2 emissions from power plants are the dominant source of global CO2 emissions, thus in the context of global warming, accurate estimation of CO2 emissions from power plants is essential for the effective control of carbon emissions. Based on the XCO2 retrievals from the Orbiting Carbon Observatory 2 (OCO-2) and the Gaussian Plume Model (GPM), a series of studies have been carried out to estimate CO2 emission from power plants. However, the GPM is an ideal model, and there are a number of assumptions that need to be made when using this model, resulting in large uncertainties in the inverted emissions. Here, based on 6 cases of power plant plumes observed by the OCO-2 satellite over the Yangtze River Delta, China, we use an inline plume rise module coupled in the Community Multi-scale Air Quality model (CMAQ) to simulate the plumes and invert the emissions, and compare the simulated plumes and inverted emissions using the GPM model. We found that CO2 emissions can be significantly overestimated or underestimated based on the GPM simulations, and that the CMAQ inline plume simulation could significantly improve the estimates. However, the simulation bias in wind speed can significantly affect the inversion results. These results indicate that accurate meteorological field and plume simulations are critical for future inversion of point source emissions.

Unveiling the importance of VOCs from pesticides applicated in main crops for elevating ozone concentrations in China

Volatile organic compounds (VOCs) are considered as important precursors of ozone in the air, while the contribution of VOCs from pesticide application (PVOCs) to ozone production is unknown. Utilizing data from the Ministry of Agriculture and Rural Affairs of the People's Republic of China and ChinaCropPhen1km, this paper developed PVOC emission inventories with a resolution of 1 km for the main crops (rice, maize, and wheat) from 2012 to 2019 in China. The results revealed that pesticide application is an important VOC emission source in China. Specially, the PVOC emissions from the major grain-producing regions in June accounted for approximately 30% of the annual total PVOC emissions in the local regions. The simulation with the Weather Research and Forecasting Community Multiscale Air Quality model (WRF-CMAQ) indicated that the PVOC emissions increased the mean maximum daily 8-hour average (MDA8) ozone concentration across China by 2.5 ppb in June 2019. During the same period, PVOCs in the parts of North China Plain contributed 10% of the ozone formation. Under the comprehensive emission reduction scenario, it is anticipated that by 2025, the joint implementation of measures including reducing pesticide application, improving pesticide utilization efficiency and promoting solvent substitution will decrease PVOC emissions by 60% compared with 2019, thereby mitigating ozone pollution.

Updating Biogenic Volatile Organic Compound (BVOC) Emissions With Locally Measured Emission Factors in South China and the Effect on Modeled Ozone and Secondary Organic Aerosol Production

AbstractBiogenic volatile organic compounds (BVOCs) emitted from terrestrial plants contribute substantially to ozone (O3) and secondary organic aerosol (SOA) formation in the troposphere. Accurate estimation of BVOC emissions is highly challengeable with a variety of uncertainties, one of which is the use of default emission factors (EFs) particularly for underrepresented regions without local data. In this study, locally measured BVOC‐EFs in south China, a subtropical region with abundant vegetation, were used to update regional BVOC emissions as estimated by the Model of Emissions of Gases and Aerosols from Nature (MEGAN). These EFs were recently determined in situ with characterized dynamic chambers for the emissions of isoprene, monoterpenes, and sesquiterpenes from tree species. The Community Multiscale Air Quality (CMAQ) model was then employed to see how much the regional O3 and SOA production is altered with the updated BVOC emissions. Results revealed lower BVOC emission estimates in south China when using the localized EFs than the MEGAN default ones, particularly for sesquiterpenes with a notable average reduction rate of approximately 40%. Using the updated BVOC emissions improved model O3 predictions in all seasons when compared to surface O3 monitoring, yet the lower BVOC emissions resulted in modeled O3 and SOA concentrations decreased by up to −6 ppb and −1.5 μg m−3, respectively, throughout south China. This study highlights the significance of localized EFs in refining emission estimates and air quality predictions in regions with a wealth of vegetation.

Coordination Relationship of Carbon Emissions and Air Pollutants under Governance Measures in a Typical Industrial City in China

Coordinating and controlling carbon and atmospheric pollutant emissions in industrial cities poses challenges, making it difficult to formulate effective environmental governance strategies in China. This study used the Community Multiscale Air Quality (CMAQ) and Long-range Energy Alternatives Planning (LEAP) models, with a typical industrial city in the Sichuan Basin as the case study. Five emission reduction scenarios, one integration scenario, and one baseline scenario were set to quantitatively analyze the synergistic effect between carbon emissions and atmospheric pollutant emissions. The results indicate a high synergy between sulfur dioxide and greenhouse gases. For every one-point decrease in the Air Quality Composite Index (AQCI), the Industrial Restructuring Scenario (IR), Other Source Management Scenario (OSM), Transportation Energy Efficiency Improvement Scenario (TEEI), Industrial Energy Efficiency Improvement Scenario (IEEI), and Transportation Restructuring (TR) scenarios would require a reduction in carbon emissions by 56,492.79 kilotons, 39,850.45 kilotons, 34,027.5 kilotons, 22,356.58 kilotons, and 3243.33 kilotons, respectively. The results indicate that governance measures, such as improving transportation structure and upgrading industrial technologies, provide stronger support for simultaneous carbon emissions reductions and air quality improvement.

An intercomparison of weather normalization of PM2.5 concentration using traditional statistical methods, machine learning, and chemistry transport models

Traditional statistical methods (TSM) and machine learning (ML) methods have been widely used to separate the effects of emissions and meteorology on air pollutant concentrations, while their performance compared to the chemistry transport model has been less fully investigated. Using the Community Multiscale Air Quality Model (CMAQ) as a reference, a series of experiments was conducted to comprehensively investigate the performance of TSM (e.g., multiple linear regression and Kolmogorov–Zurbenko filter) and ML (e.g., random forest and extreme gradient boosting) approaches in quantifying the effects of emissions and meteorology on the trends of fine particulate matter (PM2.5) during 2013−2017. Model performance evaluation metrics suggested that the TSM and ML methods can explain the variations of PM2.5 with the highest performance from ML. The trends of PM2.5 showed insignificant differences (p > 0.05) for both the emission-related (PM2.5EMI\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\rm{PM}}}_{2.5}^{{\\rm{EMI}}}$$\\end{document}) and meteorology-related components between TSM, ML, and CMAQ modeling results. PM2.5EMI\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\rm{PM}}}_{2.5}^{{\\rm{EMI}}}$$\\end{document} estimated from ML showed the least difference to that from CMAQ. Considering the medium computing resources and low model biases, the ML method is recommended for weather normalization of PM2.5. Sensitivity analysis further suggested that the ML model with optimized hyperparameters and the exclusion of temporal variables in weather normalization can further produce reasonable results in emission-related trends of PM2.5.

Evolution of Ozone Formation Sensitivity during a Persistent Regional Ozone Episode in Northeastern China and Its Implication for a Control Strategy.

In recent years, the magnitude and frequency of regional ozone (O3) episodes have increased in China. We combined ground-based measurements, observation-based model (OBM), and the Weather Research and Forecasting and Community Multiscale Air Quality (WRF-CMAQ) model to analyze a typical persistent O3 episode that occurred across 88 cities in northeastern China during June 19-30, 2021. The meteorological conditions, particularly the wind convergence centers, played crucial roles in the evolution of O3 pollution. Daily analysis of the O3 formation sensitivity showed that O3 formation was in the volatile organic compound (VOC)-limited or transitional regime at the onset of the pollution episode in 92% of the cities. Conversely, it tended to be or eventually became a NOx-limited regime as the episode progressed in the most polluted cities. Based on the emission-reduction scenario simulations, mitigation of the regional O3 pollution was found to be most effective through a phased control strategy, namely, reduction of a high ratio of VOCs to NOx at the onset of the pollution and lower ratio during evolution of the O3 episode. This study presents a new possibility for regional O3 pollution abatement in China based on a reasonable combination of OBM and the WRF-CMAQ model.

Quantifying anthropogenic emission of iron in marine aerosol in the Northwest Pacific with shipborne online measurements

Anthropogenic emissions are recognized as significant contributors to atmospheric soluble iron (Fe) in recent years, which may affect marine primary productivity, especially in Fe-limited areas. However, the contribution of different emission sources to Fe in marine aerosol has been primarily estimated by modeling approaches. Quantifying anthropogenic Fe based on field measurements remains a great challenge. In this study, online multi-element measurements and Positive Matrix Factorization (PMF) were combined for the first time to quantify sources of atmospheric Fe and soluble Fe in the Northwest Pacific during a cruise in spring 2015. Fe concentration in 624 atmospheric PM2.5 samples measured online was 74.58 ± 90.87 ng/m3. The PMF results showed anthropogenic activities, including industrial coal combustion, biomass burning, and maritime transport, were important in this region, contributing 31.4 % of atmospheric Fe on average. In addition, anthropogenic Fe concentration resolved by PMF was comparable to the simulation results of the CMAQ (Community Multiscale Air Quality) and GEOS-Chem (Goddard Earth Observing System-Chemical transport) models, with better correlation to CMAQ (r = 0.76) than GEOS-Chem (r = 0.26). This study developed a new method to estimate atmospheric soluble Fe, which integrates Fe source apportionment results and Fe solubility from different sources. Soluble Fe concentration was estimated as 3.93 ± 5.14 ng/m3, of which 87.0 % was attributed to anthropogenic emissions. Notably, ship emission alone contributed 27.5 % of soluble Fe, though its contribution to total Fe was only 2.2 %. Finally, the total deposition fluxes of atmospheric Fe (37.11 ± 38.43 μg/m2/day) and soluble Fe (1.85 ± 2.13 μg/m2/day) were estimated. This study developed a new methodology for quantifying contribution of anthropogenic emissions to Fe in marine aerosol, which could greatly help the assessment of impacts of human activities on marine environment.

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.

Improved O3 predictions in China by combining chemical transport model and multi-source data with machining learning techniques

Accurate ozone (O3) predictions is crucial for assessing its impact on public health and developing effective prevention and control measures. While ground-based observations are considered highly accurate, the limited and uneven spatial coverage poses challenges. Air quality models can provide complete spatiotemporal coverage but are subject to biases due to simplifications in physicochemical mechanisms and uncertainties associated with emission and meteorology inputs. In this study, we aimed to improve the accuracy of the predicted O3 concentrations from the Community Multiscale Air Quality (CMAQ) model by using the machine learning techniques. First, we compared three machine learning algorithms, namely Light Gradient Boosting Machine (LightGBM), Random Forest, and eXtreme Gradient Boosting. Results showed that LightGBM exhibited the highest correlation coefficient (R) of 0.84, making it the preferred algorithm for further analysis. Subsequently, two multi-source data prediction models based on LightGBM were constructed to improve the accuracy of predicted daily maximum 8-h O3 (O3-Max8h). The first model, referred to as LGBR, utilized the predicted air pollutant concentrations and meteorological fields as input variables, while the second model, named LGBR-CHAP, incorporated ChinaHighAirPollutants (CHAP) O3-Max8h as an additional input variable. Validation results demonstrated significant improvements in the LGBR and LGBR-CHAP models compared to the original CMAQ model. On the daily scale, the root mean square error and mean bias of the predicted O3-Max8h decreased by 3.15 and 2.07 μg/m3, respectively, for the LGBR model, while they decreased by 5.61 and 4.18 μg/m3, respectively, for the LGBR-CHAP model. On the monthly scale, the R of the original CMAQ model varied from 0.2 in the March to 0.91 in June, and LGBR improved all months (0.4–0.92) as did LGBR-CHAP (0.5–0.94). Spatially, the O3-Max8h simulated by the original CMAQ model is better in eastern China but less skillful in western China. After optimization by the LGBR and LGBR-CHAP models, the national average R improved from 0.77 to 0.83 and 0.88, respectively. The LGBR-CHAP model exhibited better predictive capacity than the LGBR model and was subsequently employed to generate high-resolution (10 km × 10 km) and full-coverage (100%) O3-Max8h data. This dataset will prove valuable for future air pollution and health studies.

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.