Regional Air Quality Model Research Articles

Development of a city-level surface ozone forecasting system using deep learning techniques and air quality model: Application in eastern China

Utilizing regional air quality models to accurately forecast surface ozone (O3) concentrations, particularly high concentrations, is essential for protecting public health. However, forecasts of air quality model often deviate from site observations due to the limitation of grid resolution and uncertainties from emission sources, meteorological conditions, and chemical reaction mechanisms. Especially, the underestimation is significant under condition of high O3 concentrations. Moreover, such deviations tend to accumulate as forecast lead time increases, compounding the challenges associated with reliable air quality forecast. In this study, we employed AlexNet architecture, a classical convolutional neural network, combined with multiple variables related to meteorology, chemistry, emission and geography to establish a non-linear relationship between grid-scale input variables and site-scale hourly O3 forecast biases in Eastern China, aiming to realize accurate city-level ozone forecast based on a regional air quality prediction model (i.e., Nested Air Quality Prediction Model System, NAQPMS). By assigning weights to high-bias samples and high-concentration samples within the loss function, the proposed Weighted AlexNet model (W_AlexNet) effectively reduced forecast biases and enhanced its capability to predict O3 pollution levels. Compared to NAQPMS, W_AlexNet model demonstrated a 25.71% improvement in RMSE and a 7.17% increase in IOA averagely for hourly O3 (O3-1h) forecasts across four different lead times (24-h, 48-h, 72-h, and 96-h). Notably, W_AlexNet model alleviated the tendency of NAQPMS to underestimate high concentrations and showed a superior performance in improving O3-1h pollution level forecasts, particularly for the 72-h and 96-h lead times. W_AlexNet model can effectively mitigate the bias accumulation effect over increasing lead times, thereby enhancing the reliability of longer-term forecasts. Thus, the W_AlexNet model serves as a post-processing model that can calibrate forecast biases in air quality prediction models, significantly improving the accuracy of O3 high concentration forecasts and providing more precise early warnings of O3 pollution. This underscores its utility in air quality management.

Dominant influence of biomass combustion and cross-border transport on nitrogen-containing organic compound levels in the southeastern Tibetan Plateau

Abstract. The Tibetan Plateau (TP) is highly susceptible to climate change, and nitrogen-containing organic compounds (NOCs) in fine particulate matter (PM2.5) represent one of the largest uncertainties with respect to their impact on the climate in high-altitude areas. Previous studies have shown that NOCs play a vital role in the nitrogen budget of PM2.5. However, our understanding of the composition and sources of NOCs in PM2.5, particularly in the TP, is limited. Here, we aim to enhance our understanding of NOCs in the TP region by examining their identification, concentration levels, sources, and origins. We conducted field sampling at a regional background sampling site in Gaomeigu, in the southeastern margin of the TP from 11 March to 13 May 2017, followed by laboratory analysis of the NOCs collected on the filters. The daily mass concentrations of NOCs ranged from 714.4 to 3887.1 ng m−3, with an average of 2119.4 ± 875.0 ng m−3 during the campaign. This average concentration was approximately 40 % higher than that reported at a typical regional site in the North China Plain (NCP), highlighting a more significant presence of NOCs in the Tibetan area. Biomass burning and secondary sources were identified as the major contributors to total NOCs. This was further substantiated by a regional air quality model, which indicated that over 80 % of the aerosol in the southeast of the TP originated from neighboring countries. This study improves our understanding of NOCs' contribution to PM2.5 in the TP and their potential impacts on climate stability in high-altitude areas.

The source, transport, deposition and direct radiative effect of mineral dust over western China: A modeling study of July 2022 with focus on the Tibetan Plateau

A Regional Air Quality Model System (RAQMS) driven by WRF was applied to explore the emission, transport, deposition, and direct radiative effect of mineral dust over western China in July 2022, with focus on the Tibetan Plateau (TP). Model validation against ground and satellite observations demonstrated the model reproduced meteorological variables, PM10 concentration, aerosol optical depth (AOD), and extinction coefficients in the vertical reasonably well. There was a dust event during 3–7 July 2022, which was originated from the Taklimakan Desert (TKD) and affected eastern and central TP under anticyclonic flows, resulting in the maximum hourly PM10 concentration exceeding 50 μg m−3 in Lhasa. Shortwave radiation was reduced considerably by dust aerosols over eastern TP, with the maximum decrease in daytime mean shortwave radiation reaching 30 W m−2 around Nyingchi on 5 July. Anthropogenic aerosols dominated PM10 mass in the capital cities of western China (54–67 %), while dust aerosols were dominant in the cities near the deserts. During this dust event, dust aerosols from TKD and Qaidam Desert (QDD) significantly influenced eastern TP, with dust contributions to PM10 mass concentration of 52 %, 76 % and 69 %, respectively, in Chamdo, Lhasa and Nyingzhi, respectively. The total dust emission in western China was about 10.6 Tg in July 2022, with the largest contribution from TKD (63.5 %), followed by Gobi Desert (GB) (26 %). The total deposition of dust was estimated to be 6.2 Tg, in which TKD and GB contributed 66 % and 22 %, respectively. During the study period, about 418 Gg dust aerosols were deposited on TP, 49 % of which was from TKD and 25 % from QDD. Foreign dust sources contributed approximately 7 % and 9 % to dust concentration and total deposition over TP, respectively. Over southern TP, the source contribution to dust deposition was estimated to be 42 %, 24 % and 21 % from TKD, foreign sources and QDD, respectively, suggesting potentially important impact of long-range transboundary dust transport on deposition, surface albedo and climate over TP.

The co-benefits of a low-carbon future for PM2.5 and O3 air pollution in Europe

Abstract. There is considerable academic interest in the potential for air quality improvement as a co-benefit of climate change mitigation. Few studies use regional air quality models for simulating future co-benefits, but many use global chemistry–climate model output. Using regional atmospheric chemistry could provide a better representation of air quality changes than global chemistry–climate models, especially by improving the representation of elevated urban concentrations. We use a detailed regional atmospheric-chemistry model (WRF-Chem v4.2) to model European air quality in 2050 compared to 2014 following three climate change mitigation scenarios. We represent different climate futures by using air pollutant emissions and chemical boundary conditions (from CESM2-WACCM output) for three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5 and SSP3-7.0: high-, medium- and low-mitigation pathways respectively). We find that in 2050, following SSP1-2.6, mean population-weighted PM2.5 concentrations across European countries are reduced by 52 % compared to 2014. Under SSP2-4.5, this average reduction is 34%. The smallest average reduction is 18 %, achieved following SSP3-7.0. Maximum 6-monthly-mean daily-maximum 8 h (6mDM8h) ozone (O3) is reduced across Europe by 15 % following SSP1-2.6 and by 3 % following SSP2-4.5, but it increases by 13 % following SSP3-7.0. This demonstrates clear co-benefits of climate mitigation. The additional resolution allows us to analyse regional differences and identify key sectors. We find that the mitigation of agricultural emissions will be key for attaining meaningful co-benefits of mitigation policies, as evidenced by the importance of changes in NO3 aerosol mass to future PM2.5 air quality and changes in CH4 emissions to future O3 air quality.

Decreasing trends in PM2.5 and BC concentrations observed on central and southwestern Japanese Islands

The interannual trends in fine particulate matter (PM2.5) and black carbon (BC) concentrations at remote stations in central (Noto) and southwestern (Fukue) Japan were investigated using statistical trend analysis and the chemical transport model, Regional Air Quality Model 2 (RAQM2ver3). The concentrations of PM2.5 and BC in Noto and Fukue exhibited seasonal variations and decreased from 2013 to 2020. Higher concentrations of PM2.5 and BC were observed during the spring season (April) in both locations. The PM2.5 concentrations peaked in Fukue approximately one month earlier than in Noto, while the BC concentration peak in Noto occurred one month earlier than that of PM2.5. The total reductions in PM2.5 concentrations in Noto and Fukue were 7.5 ± 5.0 and 3.8 ± 0.95 μg m−3, corresponding to reduction rates 1.1 ± 0.7 and 0.5 ± 0.95 μg m−3year−1, respectively, compared to the concentrations in 2013. The total reduction in BC concentrations in Noto from 2013 to 2019 was estimated to be 0.13 ± 0.1 μg m−3, amounting to 0.018 ± 0.014 μg m−3year−1 reduction rate. The results of the source–receptor relationships analysis suggest that the decreases in PM2.5 and BC concentrations in Fukue and Noto were significantly influenced by reductions in amounts transported from central China (CCHN, 30–40°N), northern China (NCHN, >40°N) and Japan (JPN) after 2015/2016. The decrease in emissions from these three regions accounted for the observed reductions in particulate concentrations.

Significant impact of urban tree biogenic emissions on air quality estimated by a bottom-up inventory and chemistry transport modeling

Abstract. Biogenic volatile organic compounds (BVOCs) are emitted by vegetation and react with other compounds to form ozone and secondary organic matter (OM). In regional air quality models, biogenic emissions are often calculated using a plant functional type approach, which depends on the land use category. However, over cities, the land use is urban, so trees and their emissions are not represented. Here, we develop a bottom-up inventory of urban tree biogenic emissions in which the location of trees and their characteristics are derived from the tree database of the Paris city combined with allometric equations. Biogenic emissions are then computed for each tree based on their leaf dry biomass, tree-species-dependent emission factors, and activity factors representing the effects of light and temperature. Emissions are integrated in WRF-CHIMERE air quality simulations performed over June–July 2022. Over Paris city, the urban tree emissions have a significant impact on OM, inducing an average increase in the OM of about 5 %, reaching 14 % locally during the heatwaves. Ozone concentrations increase by 1.0 % on average and by 2.4 % during heatwaves, with a local increase of up to 6 %. The concentration increase remains spatially localized over Paris, extending to the Paris suburbs in the case of ozone during heatwaves. The inclusion of urban tree emissions improves the estimation of OM concentrations compared to in situ measurements, but they are still underestimated as trees are still missing from the inventory. OM concentrations are sensitive to terpene emissions, highlighting the importance of favoring urban tree species with low-terpene emissions.

Application of regional meteorology and air quality models based on the microprocessor without interlocked piped stages (MIPS) and LoongArch CPU platforms

Abstract. The microprocessor without interlocked piped stages (MIPS) and LoongArch are reduced instruction set computing (RISC) processor architectures, which have advantages in terms of energy consumption and efficiency. There are few studies on the application of MIPS and LoongArch central processing units (CPUs) in geoscientific numerical models. In this study, the Loongson 3A4000 CPU platform with the MIPS64 architecture and the Loongson 3A6000 CPU platform with the LoongArch architecture were used to establish the runtime environment for the air quality modelling system Weather Research and Forecasting–Comprehensive Air Quality Model with extensions (WRF-CAMx) in the Beijing–Tianjin–Hebei region. The results show that the relative errors for the major species (NO2, SO2, O3, CO, PNO3, and PSO4) between the MIPS and X86 benchmark platforms are within ±0.1 %. The maximum mean absolute error (MAE) of major species ranged up to 10−2 ppbV or µg m−3, the maximum root mean square error (RMSE) ranged up to 10−1 ppbV or µg m−3, and the mean absolute percentage error (MAPE) remained within 0.5 %. The CAMx takes about 195 min on the Loongson 3A4000 CPU, 71 min on the Loongson 3A6000 CPU, and 66 min on the Intel Xeon E5-2697 v4 CPU, when simulating a 24 h case with four parallel processes using MPICH. As a result, the single-core computing capability of the Loongson 3A4000 CPU for the WRF-CAMx modelling system is about one-third of the Intel Xeon E5-2697 v4 CPU, and the one of Loongson 3A6000 CPU is slightly lower than that of Intel Xeon E5-2697 v4 CPU; but, the thermal design power (TDP) of Loongson 3A4000 is 40 W, while the TDP of Loongson 3A6000 is 38 W, only about one-fourth of that of Intel Xeon E5-2697 v4, whose TDP is 145 W. The results also verify the feasibility of cross-platform porting and the scientific usability of the ported model. This study provides a technical foundation for the porting and optimization of numerical models based on MIPS, LoongArch, or other RISC platforms.

Diurnal emission variation of ozone precursors: Impacts on ozone formation during Sep. 2019

With the issue of ozone (O3) pollution having increasingly gained visibility and prominence in China, the Chinese government explored various policies to mitigate O3 pollution. In some provinces and cities, diurnal regulations of O3 precursor were implemented, such as shifting O3 precursor emission processes to nighttime and offering preferential refueling at night. However, the effectiveness of these policies remains unverified, and their impact on the O3 generation process requires further elucidation. In this study, we utilized a regional climate and air quality model (WRF-Chem, v4.5) to test three scenarios aimed at exploring the impact of diurnal industry emission variation of O3 precursors on O3 formation. Significant O3 variations were observed mainly in urban areas. Shifting volatile organic compounds (VOCs) to nighttime have slight decreased daytime O3 levels while moving nitrogen oxides (NOx) to nighttime elevates O3 levels. Simultaneously moving both to nighttime showed combined effects. Process analysis indicates that the diurnal variation in O3 was mainly attributed to chemical process and vertical mixing in urban areas, while advection becomes more important in non-urban areas, contributing to the changes in O3 and O3 precursors levels through regional transportation. Further photochemical analysis reveals that the O3 photochemical production in urban areas was affected by reduced daytime O3 precursors emissions. Specifically, decreasing VOCs lowered the daytime O3 production by reducing the ROx radicals (ROx = HO + HO˙2 + RO˙2), whereas decreasing NOx promoted the daytime O3 production by weakening ROx radical loss. Our results demonstrate that diurnal regulation of O3 precursors will disrupt the ROx radical and O3 formation in local areas, resulting in a change in O3 concentration and atmospheric oxidation capacity, which should be considered in formulating new relevant policies.

Impacts of open biomass burning in Southeast Asia on atmospheric PM2.5 concentrations over south China from 2009 to 2018

Open biomass burning (BB) frequently occurs in spring over Southeast Asia (SEA). The concentration of fine particulate (PM2.5) produced by BB and its impacts on air quality in downwind aeras exhibit significant interannual variations, which are controlled by emission intensity and meteorological circulation. In this study, a regional air quality model system (RAQMS) was applied to investigate the contributions of BB emissions in SEA to atmospheric PM2.5 concentrations over South China in March (a spring month) during 2009–2018 and their interannual variations. Higher near-surface BB-PM2.5 (refers to the PM2.5 produced by BB in SEA) concentrations and contributions were mainly distributed in the source region and its vicinity, while the higher tropospheric BB-PM2.5 concentrations and contributions were characterized by a long belt extending from BB source regions to Yangtze River Delta (YRD) in South China. Combined with the vertical distribution of BB-PM2.5 on the transported path profile, it indicated that higher BB-PM2.5 concentrations were mainly concentrated in 0∼1500 m above the ground over source regions, while the maximum BB-PM2.5 concentrations in the downwind area were distributed in 1500∼3500 m above the ground. The monthly mean tropospheric BB-PM2.5 concentrations were higher in March 2009, 2010, 2012, and 2013, with evident correlation with interannual variation of BB emissions. Sensitivity experiments suggested that the near-surface and tropospheric BB-PM2.5 concentrations over South China had changed by-48.1%∼190.5% and −48.0%∼213.8%, respectively in March 2009–2018 by fixing BB emissions unchanged at the mean of the 10 years, being greater than the changes caused by meteorological conditions and other affecting factors (∼20%). In addition, the relative contributions of BB emission from various regions in SEA on the mean near-surface and tropospheric BB-PM2.5 concentrations over South China were further identified.

Comparative analysis of CAMS aerosol optical depth data and AERONET observations in the Eastern Mediterranean over 19 years

Aerosol optical depth (AOD) is an essential metric for evaluating the atmospheric aerosol load and its impacts on climate, air quality, and public health. In this study, the AOD data from the Copernicus Atmosphere Monitoring Service (CAMS) were validated against ground-based measurements from the Aerosol Robotic Network (AERONET) throughout the Eastern Mediterranean, a region characterized by diverse aerosol types and sources. A comparative analysis was performed on 3-hourly CAMS AOD values at 550 nm against observations from 20 AERONET stations across Cyprus, Greece, Israel, Egypt, and Turkey from 2003 to 2021. The CAMS AOD data exhibited a good overall agreement with AERONET AOD data, demonstrated by a Pearson correlation coefficient of 0.77, a mean absolute error (MAE) of 0.08, and a root mean square error (RMSE) of 0.11. Nonetheless, spatial and temporal variations were observed in the CAMS AOD data performance, with site-specific correlation coefficients ranging from 0.57 to 0.85, the lowest correlations occurring in Egypt and the highest in Greece. An underestimation of CAMS AOD was noted at inland sites with high AOD levels, while a better agreement was observed at coastal sites with lower AOD levels. The diurnal variation analysis indicated improved CAMS reanalysis performance during the afternoon and evening hours. Seasonally, CAMS reanalysis showed better agreement with AERONET AODs in spring and autumn, with lower correlation coefficients noted in summer and winter. This study marks the first comprehensive validation of CAMS AOD performance in the Eastern Mediterranean, offering significant enhancements for regional air quality and climate modeling, and underscores the essential role of consistent validation in refining aerosol estimations within this complex and dynamic geographic setting.

Temperature-Dependent Evaporative Anthropogenic VOC Emissions Significantly Exacerbate Regional Ozone Pollution.

The evaporative emissions of anthropogenic volatile organic compounds (AVOCs) are sensitive to ambient temperature. This sensitivity forms an air pollution-meteorology connection that has not been assessed on a regional scale. We parametrized the temperature dependence of evaporative AVOC fluxes in a regional air quality model and evaluated the impacts on surface ozone in the Beijing-Tianjin-Hebei (BTH) area of China during the summer of 2017. The temperature dependency of AVOC emissions drove an enhanced simulated ozone-temperature sensitivity of 1.0 to 1.8 μg m-3 K-1, comparable to the simulated ozone-temperature sensitivity driven by the temperature dependency of biogenic VOC emissions (1.7 to 2.4 μg m-3 K-1). Ozone enhancements driven by temperature-induced AVOC increases were localized to their point of emission and were relatively more important in urban areas than in rural regions. The inclusion of the temperature-dependent AVOC emissions in our model improved the simulated ozone-temperature sensitivities on days of ozone exceedance. Our results demonstrated the importance of temperature-dependent AVOC emissions on surface ozone pollution and its heretofore unrepresented role in air pollution-meteorology interactions.

Coordinated Control of Carbon Emission Reduction and Air Quality Improvement in the Industrial Sector in Hunan Province

Climate warming and air pollution are the main environmental problems in China. This study used China's Carbon Accounting Database, energy economic model, and air quality model to analyze the potential carbon emission peaking path and synergistic air quality improvement gain in the industrial sector in Hunan Province. Based on China's Carbon Accounting Database and the local industry/energy statistical yearbooks in Hunan, the total CO2 emissions in Hunan Province in 2019 were 310.6 Mt, of which the industrial sector accounted for over 70% of the emissions, mainly from the production and supply of electricity, steam, and heat; the production of non-metallic minerals; and the smelting and pressing of ferrous metals. Three potential industrial carbon emission peaking scenarios were analyzed using the LEAP energy economic model, including the business-as-usual scenario (peaking by 2030), moderate emission reduction scenario (peaking by 2028), and aggressive emission reduction scenario (peaking by 2025), by employing different economic growth rates, energy technology progress, and energy structures of the industrial sector. Furthermore, by combining the anthropogenic air pollutant emission inventory and the regional air quality model WRF-Chem, we analyzed the air quality improvement associated with various carbon emission peak paths. The results showed that the annual mean concentrations of major air pollutants had decreased in the three scenarios, especially in the Chang-Zhu-Tan Region. The aggressive emission reduction scenario was the most effective scenario, followed by the moderate emission reduction scenario and the business-as-usual scenario. Manufacturing was the sector with the most significant synergistic effect of pollution and carbon reduction. When carbon emission peaks were achieved, the annual average concentrations of PM2.5 and PM10 in Hunan Province could be synergistically reduced by 0.6-1.8 μg·m-3 and 1.8-8.9 μg·m-3, respectively. Our findings offer important insights into carbon emission peaking and can provide useful information for potential mitigation actions.

Frequent haze events associated with transport and stagnation over the corridor between the North China Plain and Yangtze River Delta

Abstract. PM2.5 pollution is a major air quality issue that deteriorates human health, and numerous studies have focused on PM2.5 pollution in major regions such as the North China Plain (NCP) and Yangtze River Delta (YRD). However, the characteristics of PM2.5 concentrations and the associated formation mechanism in the transport corridor (referred to as SWLY) between the NCP and YRD are largely ignored. Based on observational data, we find that the number of PM2.5 pollution events in SWLY is comparable to that in the NCP, far exceeding that in the YRD, which is indicative of the severity of air pollution in this area. Utilizing a regional climate and air quality model, we isolate the effect of seesaw transport events, e.g., transport between the NCP and YRD, and atmospheric stagnation on the accumulation of PM2.5 over SWLY. Specifically, seesaw events and stagnation, comparable to each other, collectively account for an average of 67 % of pollution days, with PM2.5 exceeding 75 µg m−3, and this fraction (85 %) is even larger for severe haze events, with PM2.5 exceeding 150 µg m−3. Furthermore, the connection between seesaw transport and large-scale circulation is examined. The transregional transport of pollutants from the NCP to the YRD (YRD to NCP) is likely stimulated by positive (negative) to negative (positive) geopotential height anomalies at 500 hPa located in northern China. The health effect due to short-term PM2.5 exposure induced by the transregional transport and stagnation is investigated, yielding a total of 8634 (95 % CI: 6023–11 223) and 9496 (95 % CI: 6552–12 413) premature deaths, respectively, in SWLY during winter 2014–2019, which is as high as 9 % of the total premature deaths in China, even though SWLY takes up less than 1 % of China's area. While atmospheric stagnation is in general projected to occur more frequently under a warming climate, this study indicates the importance of regional emission control to alleviate PM2.5 pollution from seesaw transport and stagnation.

Comprehensive study on air-quality impacts from chemical plant flare emissions under planned or emergency shutdown operations†

Chemical plant shutdown (CPS) operations will employ a flare system to combust waste, unwanted, and off-spec gases dumped from process units to protect the plant safety and the local community. However, the incomplete combustion of flare source under CPS operations including plant planned shutdown (PPS) and plant emergency shutdown (PES) will cause considerable air pollution events. Hitherto, quantitative studies on both primary (CO and NO2) and secondary (ozone) pollutant impacts induced by CPS flaring are still lacking. In this paper, the dynamic effect of flare emissions from two types of CPS (PPS and PES) on ambient air-quality impacts will be systematically and quantitatively studied. The study integrates the dynamic process simulation via Aspen Plus Dynamics with the regional air-quality modelling via Comprehensive Air-quality Model with extensions (CAMx). Case studies indicated that the primary pollutants from flare emissions will be significantly diluted due to the atmospheric transportation, and the maximum concentrations of CO and NO2 can respectively reach 119.0 and 11.0 ppb under PPS, and 47.5 and 4.5 ppb under PES. Meanwhile, the increasing height of the flaring stack will decrease CO and NO2 concentrations but increase the ozone concentration under CPS. Furthermore, CPS in daytime periods will greatly promote ozone generations due to the enhanced photochemical reactions under solar radiation. The maximum 1-hr and 8-hr ozone increment can be 16.6 and 7.1 ppb under PPS while 10.7 and 3.3 ppb under PES, respectively. Besides, the decreasing of flare destruction and removal efficiency will increase ozone and CO under PES.

Intercomparison of Air Quality Models in a Megacity: Toward an Operational Ensemble Forecasting System for São Paulo

AbstractAn intercomparison of four regional air quality models is performed in the tropical megacity of São Paulo with the perspective of developing a forecasting system based on a model ensemble. Modeled concentrations of the main regulated pollutants are compared with combined observations in the megacity center, after analyzing the spatial scale of representativeness of air monitoring stations. During three contrasting periods characterized by different types of pollution events, the hourly concentrations of carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO2), and particulate matter (PM2.5 and PM10) modeled by the ensemble are in moderate agreement with observations. The median of the ensemble provides the best performance (R ≈ 0.7 for CO, 0.7 for NOx, 0.5 for SO2, 0.5 for PM2.5, and 0.4 for PM10) because each model has periods and pollutants for which it has the best agreement. NOx concentration is modeled with a large inter‐model variability, highlighting potential for improvement of anthropogenic emissions. Pollutants transported by biomass burning events strongly affect the air quality in São Paulo and are associated with significant inter‐model variability. Modeled hourly concentration of ozone (O3) is overestimated during the day (≈20 ppb) and underestimated at night (≈10 ppb), while nitrogen dioxide (NO2) is overestimated at night (≈20 ppb). The observed O3 concentration is best reproduced by the median of the ensemble (R ≈ 0.8), taking advantage of the variable performance of the models. Therefore, an operational air quality forecast system based on a regional model ensemble is promising for São Paulo.

Impacts of Biomass Burning in Southeast Asia on Tropospheric CO2 Concentration Over South China

AbstractThe field observations imply that the long‐range transport of carbon dioxide (CO2) emitted by biomass burning (BB) in Southeast Asia might impact the atmospheric CO2 in South China, while the transboundary path and the contributions of long‐range transport on atmospheric CO2 concentrations in South China remain poorly understood. In this study, a regional air quality model system (RAQMS) was developed by incorporating the atmospheric process of CO2 to investigate the impacts of BB in Southeast Asia on tropospheric CO2 concentration over South China in spring during 2009–2018. CO2 emissions from BB in Southeast Asia in spring varied greatly from 2009 to 2018, and high monthly mean emissions were concentrated in the west of Myanmar, the border between Myanmar, Thailand and Laos, the north of Laos, and Cambodia, with the maximum reaching 1.0 g m−2 hr−1. Higher monthly mean concentrations of near surface CO2 and tropospheric CO2 produced by BB were mainly distributed in the source regions of Southeast Asia and South China. The monthly mean tropospheric CO2 concentrations produced by BB in Southeast Asia were higher in March 2010, 2014, and 2015, which were obviously related to the interannual variation of BB emission, and affected by the atmospheric circulation as well. The contribution of BB emission in Southeast Asia to the net change of tropospheric CO2 concentration in South China presented a belt distribution from the southwest to the northeast, with a domain average of 5.7%–38.6% over South China in March 2009–2018.

Ozone responses to reduced precursor emissions: A modeling analysis on how attainable goals can improve air quality in the Mexico City Metropolitan Area

High tropospheric ozone (O3) concentrations prevent the improvement of the air quality in the Mexico City Metropolitan Area (MCMA). Although the problem has improved considerably since the 1990s, a rebound in O3 levels in recent years has raised concerns about the deteriorating air quality. The nonlinear relationship between O3 formation and the emissions of its main precursors, i.e., volatile organic compounds (VOCs) and nitrogen oxides (NOx), is a challenge when measures are enacted for effective mitigation of the O3 problem. This study evaluated the reduction in precursors, VOCs and NOx, using an up-to-date regional air quality model (HERMES-Mex-WRF-CMAQ). For evaluating realizable scenarios, the decline in VOC achieved in Japan after policy implementation was the targeted VOC reduction (40 % from area sources), and the NOx reduction observed in the MCMA during the COVID-19 pandemic was the targeted NOx reduction (40 % from mobile sources). The analysis evaluated the O3 responses to changes in a single precursor and a combination of both during a period of high O3 concentrations (April 2019). The results showed that 40 % reduction in VOC emissions would decrease the O3 8-h maximum concentrations by 16 %. However, 40 % reduction in NOx emissions would increase O3 by >15 %. The simultaneous reduction of both precursors did not significantly affect O3 levels. The diagnosis of ozone sensitivity using the H2O2/HNO3 ratios reinforced the simulation findings, indicating that VOC emissions limited ozone formation in most MCMA areas. As the simulated scenarios were based on factual case studies, our research offers insights into the realistic aims of MCMA policies to reduce O3 levels.

Numerical analysis of CH4 concentration distributions over East Asia with a regional chemical transport model

Atmospheric CH4 concentrations have increased rapidly and exhibited large spatiotemporal variations. But their values were generally fixed in regional air quality models, without considering the processes of emissions, transport, and diffusion. In this study, a regional air quality modeling system RAMS-CMAQ was developed by incorporating the emissions, transport, and diffusion processes of CH4 to produce CH4 concentration distributions. The developed model was applied to simulate CH4 concentrations over East Asia in 2020. As a result, the comparisons between the simulated CH4 vertical column densities (VCDS) and the observations derived from GOSAT showed that the developed model can reasonably reflect the crucial features in CH4 concentration variations with the correlation coefficients larger than 0.94. The temporal-spatial patterns of surface CH4 concentrations showed that concentrations were high (≥2.3 ppm) over Sichuan Basin, central-eastern China and South China as well as India, where the highest was in summer because of the largest agricultural emissions, and the lowest was in spring due to less emissions and stronger OH sinks compared to winter. The low surface CH4 concentrations (≤1.9 ppm) appeared over Northwest China with small seasonal variations (high in summer and low in winter and autumn) and less local emissions. The temporal-spatial patterns of CH4 VCDS showed that VCDS were high (3.6–4.5 × 1019 molec/cm2) over eastern China with significant seasonal variations (high in summer and autumn), and low (≤3.0 × 1019 molec/cm2) over Tibet Plateau and western Mongolia with slight variations.

Using machine learning to improve the estimate of U.S. background ozone

U.S. background ozone is defined as ground-level ozone in the absence of any domestic anthropogenic emissions. It plays an important role in the U.S. regulatory framework, as it corresponds to the theoretical minimum ozone that can be achieved at a location. Although some studies estimated background ozone from observations, it cannot be measured directly, and therefore air quality models have been used to estimate its levels across different parts of the country. These estimates are uncertain given the inherent uncertainty in air quality modeling. Recently data fusion techniques have been used to improve those estimates using ground-level ozone observations and satellite data. Here, we used two adjustment methods to improve on air quality model-derived estimates of background ozone: a Multi-Variate Linear Regression (MVLR) model and a machine learning (ML) algorithm, called Random Forest (RF), with a focus on thirteen urban areas in the U.S. These adjustments regress observed ozone on the simulated background and anthropogenic ozone (by a regional air quality model) and their combinations with spatial and temporal variables. The RF-ML algorithm showed the biggest improvement in model performance, compared to MVLR and the base air quality model predictions. Applying the MVLR adjustment, the background ozone estimates increased in most locations. Furthermore, applying the RF ML model adjustment increased background ozone estimates even more, highlighting the enhanced contribution of background ozone in recent years that constitutes a larger portion of total ozone and exceeds NAAQS in some regions. Our results are consistent with other observational and modeling studies in that the highest background ozone levels were estimated in the western U.S. and over higher elevations.

Nighttime ozone in the lower boundary layer: insights from 3-year tower-based measurements in South China and regional air quality modeling

Abstract. Nighttime ozone in the lower boundary layer regulates atmospheric chemistry and surface ozone air quality, but our understanding of its vertical structure and impact is largely limited by the extreme sparsity of direct measurements. Here we present 3-year (2017–2019) measurements of ozone in the lower boundary layer (up to 500 m) from the Canton Tower in Guangzhou, the core megacity in South China, and interpret the measurements with a 1-month high-resolution chemical simulation from the Community Multiscale Air Quality (CMAQ) model. Measurements are available at 10, 118, 168, and 488 m, with the highest (488 m) measurement platform higher than the typical height of the nighttime stable boundary layer that allows direct measurements of ozone in the nighttime residual layer (RL). We find that ozone increases with altitude in the lower boundary layer throughout the day, with a vertical ozone gradient between the 10 and 488 m heights (ΔO3/ΔH10–488 m) of 3.6–6.4 ppbv hm−1 in nighttime and 4.4–5.8 ppbv hm−1 in daytime. We identify a high ozone residual ratio, defined as the ratio of ozone concentration averaged over nighttime to that in the afternoon (14:00–17:00 LT), of 69 %–90 % in January, April, and October, remarkably higher than that in the other three layers (29 %–51 %). Ozone in the afternoon convective mixing layer provides the source of ozone in the RL, and strong temperature inversion facilitates the ability of RL to store ozone from the daytime convective mixing layer. The tower-based measurement also indicates that the nighttime surface Ox (Ox= O3+NO2) level can be an effective indicator of RL ozone if direct measurement is not available. We further find significant influences of nocturnal RL ozone on both the nighttime and the following day's daytime surface ozone air quality. During the surface nighttime ozone enhancement (NOE) event, we observe a significant decrease in ozone and an increase in NO2 and CO at the 488 m height, in contrast to their changes at the surface, a typical feature of enhanced vertical mixing. The enhanced vertical mixing leads to an NOE event by introducing ozone-rich and NOx-poor air into the RL to enter the nighttime stable boundary layer. The CMAQ model simulations also demonstrate an enhanced positive contribution of vertical diffusion (ΔVDIF) to ozone at the 10 and 118 m heights and a negative contribution at the 168 and 488 m heights during the NOE event. We also observe a strong correlation between nighttime RL ozone and the following day's surface maximum daily 8 h average (MDA8) ozone. This is tied to enhanced vertical mixing with the collapse of nighttime RL and the development of a convective mixing layer, which is supported by the CMAQ diagnosis of the ozone budget, suggesting that the mixing of ozone-rich air from nighttime RL downward to the surface via the entrainment is an important mechanism for aggravating ozone pollution the following day. We find that the bias in CMAQ-simulated surface MDA8 ozone the following day shows a strong correlation coefficient (r= 0.74) with the bias in nighttime ozone in the RL, highlighting the necessity to correct air quality model bias in the nighttime RL ozone for accurate prediction of daytime ozone. Our study thus highlights the value of long-term tower-based measurements for understanding the coupling between nighttime ozone in the RL, surface ozone air quality, and boundary layer dynamics.