Responses of PM2.5 and O3 concentrations to changes of meteorology and emissions in China

Meteorological effects on sources and future projection of nitrogen deposition to lakes in China

Lake ecosystems are extremely sensitive to nitrogen growth, which leads to water quality degradation and ecosystem health decline. Nitrogen depositions, as one of the main sources of nitrogen in water, are expected to change under future climate change scenarios. However, it remains not clear how nitrogen deposition to lakes respond to future meteorological conditions. In this study, a source-oriented version of Community Multiscale Air Quality (CMAQ) Model was used to estimate nitrogen deposition to 263 lakes in 2013 and under three RCP scenarios (4.5, 6.0 and 8.5) in 2046. Annual total deposition of 58.2 Gg nitrogen was predicted for all lakes, with 23.3 Gg N by wet deposition and 34.9 Gg N by dry deposition. Nitrate and ammonium in aerosol phase are the major forms of wet deposition, while NH3 and HNO3 in gas phase are the major forms of dry deposition. Agriculture emissions contribute to 57% of wet deposition and 44% of dry deposition. Under future meteorological conditions, wet deposition is predicted to increase by 5.5% to 16.4%, while dry deposition would decrease by 0.3% to 13.0%. Changes in wind speed, temperature, relative humidity (RH), and precipitation rates are correlated with dry and wet deposition changes. The predicted changes in deposition to lakes driven by meteorological changes can lead to significant changes in aquatic chemistry and ecosystem functions. Apart from future emission scenarios, different climate scenarios should be considered in future ecosystem health evaluation in response to nitrogen deposition.

MAX-DOAS observations of pollutant distribution and transboundary transport in typical regions of China

Studying the spatiotemporal distribution and transboundary transport of aerosols, NO2, SO2, and HCHO in typical regions is crucial for understanding regional pollution causes. In a 2-year study using multi-axis differential optical absorption spectroscopy in Qingdao, Shanghai, Xi'an, and Kunming, we investigated pollutant distribution and transport across Eastern China-Ocean, Tibetan Plateau-Central and Eastern China, and China-Southeast Asia interfaces. First, pollutant distribution was analyzed. Kunming, frequently clouded and misty, exhibited consistently high aerosol optical depth throughout the year. In Qingdao and Shanghai, NO2 and SO2, as well as SO2 in Xi'an, increased in winter. Elevated HCHO in summer in Shanghai and Xi'an, especially Xi'an, suggests potential ozone pollution issues. Subsequently, pollutant transportation across interfaces was studied. At the Eastern China-Ocean interface, the gas transport flux was the largest among other interfaces, with the outflux exceeding the influx, especially in winter and spring. The input of pollutants from the Tibetan Plateau to central-eastern China was larger than the output in winter and spring, with SO2 having the highest transport flux in winter. The pollution input from Southeast Asia to China significantly exceeded the output, with spring and winter inputs being 3.22 and 3.03 times the output, respectively. Lastly, the transportation characteristics of a pollution event at Kunming were studied. During this period, pollutants were transported from west to east, with the maximum SO2 transport flux at an altitude of 2.87 km equaling 27.74 µg/(m2·s). It is speculated that this pollution was caused by the transport from Southeast Asian countries to Kunming.

Chemical characteristics and sources apportionment of volatile organic compounds in the primary urban area of Shijiazhuang, North China Plain

VOCs (Volatile organic compounds) exert a vital role in ozone and secondary organic aerosol production, necessitating investigations into their concentration, chemical characteristics, and source apportionment for the effective implementation of measures aimed at preventing and controlling atmospheric pollution. From July to October 2020, online monitoring was conducted in the main urban area of Shijiazhuang to collect data on VOCs and analyze their concentrations and reactivity. Additionally, the PMF (positive matrix factorization) method was utilized to identify the VOCs sources. Results indicated that the TVOCs (total VOCs) concentration was (96.7 ± 63.4 µg/m3), with alkanes exhibiting the highest concentration of (36.1 ± 26.4 µg/m3), followed by OVOCs (16.4 ± 14.4 µg/m3). The key active components were alkenes and aromatics, among which xylene, propylene, toluene, propionaldehyde, acetaldehyde, ethylene, and styrene played crucial roles as reactive species. The sources derived from PMF analysis encompassed vehicle emissions, solvent and coating sources, combustion sources, industrial emissions sources, as well as plant sources, the contribution of which were 37.80%, 27.93%, 16.57%, 15.24%, and 2.46%, respectively. Hence, reducing vehicular exhaust emissions and encouraging neighboring industries to adopt low-volatile organic solvents and coatings should be prioritized to mitigate VOCs levels.

Multi-scale analysis of the chemical and physical pollution evolution process from pre-co-pollution day to PM2.5 and O3 co-pollution day

PM2.5 and O3 are two of the main air pollutants that have adverse impacts on climate and human health. The evolution process of PM2.5 and O3 co-pollution are of concern because of the increased frequency of PM2.5 and O3 co-pollution days. Here, we examined the chemical coupling and revealed the driving factors of the PM2.5 and O3 co-pollution evolution process from cleaning day, PM2.5 pollution day, or O3 pollution day, applied by theoretical analysis and model calculation methods. The results demonstrate that PM2.5 and O3 co-pollution day frequently occurred with high concentrations of gaseous precursors and higher sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR), which we attribute to the enhancement of atmospheric oxidation capacity (AOC). The AOC is positively correlated with O3 and weakly correlated with PM2.5. In addition, we found that the correlation coefficients of PM2.5-NO2 (0.62) were higher than that of PM2.5-SO2 (0.32), highlighting the priority of NOx controlling to mitigate PM2.5 pollution. Overall, our discovery can provide scientific evidence to design feasible solutions for the controlling PM2.5 and O3 co-pollution process.

Health-Oriented Emission Control Strategy of Energy Utilization and Its Co-CO2 Benefits: A Case Study of the Yangtze River Delta, China

Reducing air pollutants and CO2 emissions from energy utilization is crucial for achieving the dual objectives of clean air and carbon neutrality in China. Thus, an optimized health-oriented strategy is urgently needed. Herein, by coupling a CO2 and air pollutants emission inventory with response surface models for PM2.5-associated mortality, we shed light on the effectiveness of protecting human health and co-CO2 benefit from reducing fuel-related emissions and generate a health-oriented strategy for the Yangtze River Delta (YRD). Results reveal that oil consumption is the primary contributor to fuel-related PM2.5 pollution and premature deaths in the YRD. Significantly, curtailing fuel consumption in transportation is the most effective measure to alleviate the fuel-related PM2.5 health impact, which also has the greatest cobenefits for CO2 emission reduction on a regional scale. Reducing fuel consumption will achieve substantial health improvements especially in eastern YRD, with nonroad vehicle emission reductions being particularly impactful for health protection, while on-road vehicles present the greatest potential for CO2 reductions. Scenario analysis confirms the importance of mitigating oil consumption in the transportation sector in addressing PM2.5 pollution and climate change.

Characterization of winter PM2.5 source contributions and impacts of meteorological conditions and anthropogenic emission changes in the Sichuan Basin, 2002-2020

In this study, the Weather Research and Forecasting (WRF) model and Community Multiscale Air Quality-Integrated Source Apportionment Method (CMAQ-ISAM) were utilized, which were integrated with the Multiresolution Emission Inventory for China (MEIC) emission inventory, to simulate winter PM2.5 concentrations, regional transport, and changes in emission source contributions in the Sichuan basin (SCB) from 2002 to 2020, considering variations in meteorological conditions and anthropogenic emissions. The results indicated a gradual decrease in the basin's winter average PM2.5 concentration from 300 μg/m3 to 120 μg/m3, with the most significant decrease occurring after 2014, reflecting the actual impact of China's air pollution control measures. Spatially, the main pollution area shifted from Chongqing to Chengdu and the western basin. The sources of PM2.5 at the eastern and western margins of the basin have remained stable and have been dominated by local emissions for many years, while the sources of PM2.5 in the central part of the basin have evolved from a multiregional co-influenced source during the early period to a high proportion of local emissions; except for boundary condition sources, residential sources were the main PM2.5 sources in the basin (approximately 29.70 %), followed by industrial sources (approximately 14.11 %). Industrial sources exhibited higher contributions in Chengdu and Chongqing and gradually stabilized with residential sources over the years, while residential sources dominated in the eastern and western parts of the basin and exhibited a declining trend. Meteorological conditions exacerbated pollution in the whole basin from 2008 to 2014, especially in the west (21-40 μg/m3). The eastern basin and Chongqing exhibited more years with alleviated meteorological pollution, including a 40+ μg/m3 decrease in Chongqing from 2002 to 2005. Reduced anthropogenic emissions alleviated annual pollution levels, with a greater reduction (> -20 μg/m3) after 2011 due to pollution control measures.

Quantifying the contributions of meteorology, emissions, and transport to ground-level ozone in the Pearl River Delta, China

Ozone pollution presents a growing air quality threat in urban agglomerations in China. It remains challenge to distinguish the roles of emissions of precursors, chemical production and transportations in shaping the ground-level ozone trends, largely due to complicated interactions among these 3 major processes. This study elucidates the formation factors of ozone pollution and categorizes them into local emissions (anthropogenic and biogenic emissions), transport (precursor transport and direct transport from various regions), and meteorology. Particularly, we attribute meteorology, which affects biogenic emissions and chemical formation as well as transportation, to a perturbation term with fluctuating ranges. The Community Multiscale Air Quality (CMAQ) model was utilized to implement this framework, using the Pearl River Delta region as a case study, to simulate a severe ozone pollution episode in autumn 2019 that affected the entire country. Our findings demonstrate that the average impact of meteorological conditions changed consistently with the variation of ozone pollution levels, indicating that meteorological conditions can exert significant control over the degree of ozone pollution. As the maximum daily 8-hour average (MDA8) ozone concentrations increased from 20 % below to 30 % above the National Ambient Air Quality Standard II, contributions from emissions and precursor transport were enhanced. Concurrently, direct transport within Guangdong province rose from 13.8 % to 22.7 %, underscoring the importance of regional joint prevention and control measures under adverse weather conditions. Regarding biogenic emissions and precursor transport that cannot be directly controlled, we found that their contributions were generally greater in urban areas with high nitrogen oxides (NOx) levels, primarily due to the stronger atmospheric oxidation capacity facilitating ozone formation. Our results indicate that not only local anthropogenic emissions can be controlled in urban areas, but also the impacts of local biogenic emissions and precursor transport can be potentially regulated through reducing atmospheric oxidation capacity.

Exploring the spatial effects and influencing mechanism of ozone concentration in the Yangtze River Delta urban agglomerations of China

Ground-level ozone (O3) pollution has emerged as a significant concern impacting air quality in urban agglomerations, primarily driven by meteorological conditions and social-economic factors. However, previous studies have neglected to comprehensively reveal the spatial distribution and driving mechanism of O3 pollution. Based on the O3 monitoring data of 41 cities in the Yangtze River Delta (YRD) from 2014 to 2021, a comprehensive analysis framework of spatial analysis-spatial econometric regression was constructed to reveal the driving mechanism of O3 pollution. The results revealed the following: (1) O3 concentrations in the YRD exhibited a general increasing and then decreasing trend, indicating an improvement in pollution levels. The areas with higher O3 concentration are mainly the cities concentrated in central and southern Jiangsu, Shanghai, and northern Zhejiang. (2) The change of O3 concentration and distribution is the result of various factors. The effect of urbanization on O3 concentrations followed an inverted U-shaped curve, which implies that achieving higher quality urbanization is essential for effectively controlling urban O3 pollution. Traffic conditions and energy consumption have significant direct positive influences on O3 concentrations and spatial spillover effects. The indirect pollution contribution, considering economic weight, accounted for about 35%. Thus, addressing overall regional energy consumption and implementing traffic source regulations are crucial paths for O3 pollution control in the YRD. (3) Meteorological conditions play a certain role in regulating the O3 concentration. Higher wind speed will promote the diffusion of O3 and increase the O3 concentration in the surrounding city. These findings provide valuable insights for designing effective policies to improve air quality and mitigate ozone pollution in urban agglomeration area.

Spatially and Temporally Differentiated NOx and VOCs Emission Abatement Could Effectively Gain O3-Related Health Benefits

The increasing level of O3 pollution in China significantly exacerbates the long-term O3 health damage, and an optimized health-oriented strategy for NOx and VOCs emission abatement is needed. Here, we developed an integrated evaluation and optimization system for the O3 control strategy by merging a response surface model for the O3-related mortality and an optimization module. Applying this system to the Yangtze River Delta (YRD), we evaluated driving factors for mortality changes from 2013 to 2017, quantified spatial and temporal O3-related mortality responses to precursor emission abatement, and optimized a health-oriented control strategy. Results indicate that insufficient NOx emission abatement combined with deficient VOCs control from 2013 to 2017 aggravated O3-related mortality, particularly during spring and autumn. Northern YRD should promote VOCs control due to higher VOC-limited characteristics, whereas fastening NOx emission abatement is more favorable in southern YRD. Moreover, promotion of NOx mitigation in late spring and summer and facilitating VOCs control in spring and autumn could further reduce O3-related mortality by nearly 10% compared to the control strategy without seasonal differences. These findings highlight that a spatially and temporally differentiated NOx and VOCs emission control strategy could gain more O3-related health benefits, offering valuable insights to regions with severe ozone pollution all over the world.

Elucidating transport dynamics and regional division of PM2.5 and O3 in China using an advanced network model

Air pollution exhibits significant spatial spillover effects, complicating and challenging regional governance models. This study innovatively applied and optimized a statistics-based complex network method in atmospheric environmental field. The methodology was enhanced through improvements in edge weighting and threshold calculations, leading to the development of an advanced pollutant transport network model. This model integrates pollution, meteorological, and geographical data, thereby comprehensively revealing the dynamic characteristics of PM2.5 and O3 transport among various cities in China. Research findings indicated that, throughout the year, the O3 transport network surpassed the PM2.5 network in edge count, average degree, and average weighted degree, showcasing a higher network density, broader city connections, and greater transmission strength. Particularly during the warm period, these characteristics of the O3 network were more pronounced, showcasing significant transport potential. Furthermore, the model successfully identified key influential cities in different periods; it also provided detailed descriptions of the interprovincial spillover flux and pathways of PM2.5 and O3 across various time scales. It pinpointed major pollution spillover and receiving provinces, with primary spillover pathways concentrated in crucial areas such as the Beijing-Tianjin-Hebei (BTH) region and its surrounding areas, the Yangtze River Delta, and the Fen-Wei Plain. Building on this, the model divided the O3, PM2.5, and synergistic pollution transmission regions in China into 6, 7, and 8 zones, respectively, based on network weights and the Girvan Newman (GN) algorithm. Such division offers novel perspectives and strategies for regional joint prevention and control. The validity of the model was further corroborated by source analysis results from the WRF-CAMx model in the BTH area. Overall, this research provides valuable insights for local and regional atmospheric pollution control strategies. Additionally, it offers a robust analytical tool for research in the field of atmospheric pollution.

Co-occurrence of ozone and PM2.5 pollution in urban/non-urban areas in eastern China from 2013 to 2020: Roles of meteorology and anthropogenic emissions

We applied a three-dimensional (3-D) global chemical transport model (GEOS-Chem) to evaluate the influences of meteorology and anthropogenic emissions on the co-occurrence of ozone (O3) and fine particulate matter (PM2.5) pollution day (O3-PM2.5PD) in urban and non-urban areas of the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions during the warm season (April-October) from 2013 to 2020. The model captured the observed O3-PM2.5PD trends and spatial distributions well. From 2013 to 2020, with changes in both anthropogenic emissions and meteorology, the simulated values of O3-PM2.5PD in the urban (non-urban) areas of the BTH and YRD regions were 424.8 (330.1) and 309.3 (286.9) days, respectively, suggesting that pollution in non-urban areas also warrants attention. The trends in the simulated values of O3-PM2.5PD were -0.14 and -0.15 (+1.18 and +0.81) days yr-1 in the BTH (YRD) urban and non-urban areas, respectively. Sensitivity simulations revealed that changes in anthropogenic emissions decreased the occurrence of O3-PM2.5PD, with trends of -0.99 and -1.23 (-1.47 and -1.92) days yr-1 in the BTH (YRD) urban and non-urban areas, respectively. Conversely, meteorological conditions could exacerbate the frequency of O3-PM2.5PD, especially in the urban YRD areas, but less notably in the urban BTH areas, with trends of +2.11 and +0.30 days yr-1, respectively, owing to changes in meteorology only. The increases in T2m_max and T2m were the main meteorological factors affecting O3-PM2.5PD in most BTH and YRD areas. Furthermore, by conducting sensitivity experiments with different levels of pollutant precursor reductions in 2020, we found that volatile organic compound (VOC) reductions primarily benefited O3-PM2.5PD decreases in urban areas and that NOx reductions more notably influenced those in non-urban areas, especially in the YRD region. Simultaneously, reducing VOC and NOx emissions by 50 % resulted in considerable O3-PM2.5PD decreases (58.8-72.6 %) in the urban and non-urban areas of the BTH and YRD regions. The results of this study have important implications for the control of O3-PM2.5PD in the urban and non-urban areas of the BTH and YRD regions.

When and why PM2.5 is high in Seoul, South Korea: Interpreting long-term (2015-2021) ground observations using machine learning and a chemical transport model

Seoul has high PM2.5 concentrations and has not attained the national annual average standard so far. To understand the reasons, we analyzed long-term (2015-2021) hourly observations of aerosols (PM2.5, NO3-, NH4+, SO42-, OC, and EC) and gases (CO, NO2, and SO2) from Seoul and Baekryeong Island, a background site in the upwind region of Seoul. We applied the weather normalization method for meteorological conditions and a 3-dimensional chemical transport model, GEOS-Chem, to identify the effect of policy implementation and aerosol formation mechanisms. The monthly mean PM2.5 ranges between about 20 μg m-3 (warm season) and about 40 μg m-3 (cold season) at both sites, but the annual decreasing rates were larger at Seoul than at Baengnyeong (-0.7 μg m-3 a-1 vs. -1.8 μg m-3 a-1) demonstrating the effectiveness of the local air quality policies including the Special Act on Air Quality in the Seoul Metropolitan Area (SAAQ-SMA) and the seasonal control measures. The weather-normalized monthly mean data shows the highest PM2.5 concentration in March and the lowest concentration in August throughout the 7 years with NO3- accounting for about 40 % of the difference between the two months at both sites. Taking together with the GEOS-Chem model results, which reproduced the elevated NO3- in March, we concluded the elevated atmospheric oxidant level increases in HNO3 (which is not available from the observation) and the still low temperatures in March promote rapid production of NO3-. We used Ox (≡ O3 + NO2) from the observation and OH from the GEOS-Chem as a proxy for the atmospheric oxidant level which can be a source of uncertainty. Thus, direct observations of OH and HNO3 are needed to provide convincing evidence. This study shows that reducing HNO3 levels through atmospheric oxidant level control in the cold season can be effective in PM2.5 mitigation in Seoul.

Policy implications for synergistic management of PM2.5 and O3 pollution from a pattern-process-sustainability perspective in China

In recent years, the pattern of air pollution in China has changed profoundly, and PM2.5 and surface ozone (O3) have become the main air pollutants affecting the air quality of cities and regions in China. The synergistic control of the two has become the key to the sustainable improvement of air quality in China. In this study, we investigated and analyzed the spatial and temporal distribution patterns, exposure health risks, key drivers, and sustainable characteristics of PM2.5 and O3 concentrations in China from 2013 to 2022 at the national and city cluster scales by combining methodological models such as spatial statistics, trend analysis, exposure-response function, Hurst index, and multi-scale geographically weighted regression (MGWR) model. Ultimately, a synergistic management system for PM2.5 and O3 pollution was proposed. The results showed that: (1) The PM2.5 concentration decreased at a rate of 1.45 μg/m3 per year (p < 0.05), while the O3 concentration increased at a rate of 2.54 μg/m3 per year (p < 0.05). The trends of the two concentrations showed significant differences in spatial distribution. (2) Population exposure risks to pollutants showed an increasing trend, with PM2.5 and O3 increasing by 55.1 % and 42.7 %, respectively. The annual deaths associated with exposure to PM2.5 and O3 demonstrated a decreasing and inverted U-shaped trend, respectively, with annual average deaths of 1.312 million and 98,000. Significant regional disparities in health risks from these pollutants were influenced by socio-economic factors such as industrial activities and population density. In the future, it is expected that more than half of China's regions will be exposed to rising risks of PM2.5 and O3 population exposure. (3) Key drivers of regional exacerbation in PM2.5 and O3 levels include the number of industrial enterprises above designated size (NSIE) and population agglomeration (PA), while the disposable income of urban residents (URDI), technological innovation (TI), and government attention level (GAL) emerged as primary factors in controlling pollution hotspots, ranked in order of influence from greatest to least as TI > GAL > URDI. Overall, this study sheds light on the current status of air pollution and health risk sustainability in China and enhances the understanding of future air pollution dynamics in China. The results of the study may help to develop effective targeted control measures to synergize the management of PM2.5 and O3 in different regions.

Fast spreading of surface ozone in both temporal and spatial scale in Pearl River Delta

Surface ozone (O3) is a major air pollutant and draw increasing attention in the Pearl River Delta (PRD), China. Here, we characterize the spatial-temporal variability of ozone based on a dataset obtained from 57 national monitoring sites during 2013-2019. Our results show that: (1) the seasonal difference of ozone distribution in the inland and coastal areas was significant, which was largely affected by the wind pattern reversals related to the East Asian monsoon, and local ozone production and destruction; (2) the daily maximum 8hr average (MDA8 O3) showed an overall upward trend by 1.11 ppbv/year. While the trends in the nine cities varied differently by ranging from -0.12 to 2.51 ppbv/year. The hot spots of ozone were spreading to southwestern areas from the central areas since 2016. And ozone is becoming a year-round air pollution problem with the pollution season extending to winter and spring in PRD region. (3) at the central and southwestern PRD cities, the percentage of exceedance days from the continuous type (defined as ≥3 days) was increasing. Furthermore, the ozone concentration of continuous type was much higher than that of scattered exceedance type (<3 days). In addition, although the occurrence of continuous type starts to decline since 2017, the total number of exceedance days during the continuous type is increasing. These results indicate that it is more difficult to eliminate the continuous exceedance than the scatter pollution days and highlight the great challenge in mitigation of O3 pollution in these cities.

Impacts of anthropogenic emissions and meteorology on spring ozone differences in San Antonio, Texas between 2017 and 2021

San Antonio has been designated as ozone nonattainment under the current National Ambient Air Quality Standards (NAAQS). Ozone events in the city typically occur in two peaks, characterized by a pronounced spring peak followed by a late summer peak. Despite higher ozone levels, the spring peak has received less attention than the summer peak. To address this research gap, we used the Weather Research and Forecasting (WRF)-driven GEOS-Chem (WRF-GC) model to simulate San Antonio's ozone changes in the spring month of May from 2017 to 2021 and quantified the respective contributions from changes in anthropogenic emissions and meteorology. In addition to modeling, observations from the San Antonio Field Studies (SAFS), the Texas Commission on Environmental Quality (TCEQ) Continuous Ambient Monitoring Stations (CAMS), and the spaceborne TROPOspheric Monitoring Instrument (TROPOMI) are used to examine and validate changes in ozone and precursors. Results show that the simulated daytime mean surface ozone in May 2021 is 3.8 ± 0.6 ppbv lower than in May 2017, which is slightly less than the observed average differences of -5.3 ppbv at CAMS sites. The model predicted that the anthropogenic emission-induced changes contribute to a 1.4 ± 0.5 ppbv reduction in daytime ozone levels, while the meteorology-induced changes account for a 2.4 ± 0.6 ppbv reduction over 2017-2021. This suggests that meteorology plays a relatively more important role than anthropogenic emissions in explaining the spring ozone differences between the two years. We additionally identified (1) reduced NO2 and HCHO concentrations as chemical reasons, and (2) lower temperature, higher humidity, increased wind speed, and a stronger Bermuda High as meteorological reasons for lower ozone levels in 2021 compared to 2017. The quantification of the different roles of meteorology and ozone precursor concentrations helps understand the cause and variation of ozone changes in San Antonio over recent years.

Anthropogenically and meteorologically modulated summertime ozone trends and their health implications since China's clean air actions

Elevated ozone (O3) has emerged as the major air quality concern since China's clean air actions, offsetting the health benefits gained from improved air quality. Given the shifted ozone chemical regimes and recently boosted extreme weather in China, it's essential to rethink the O3 trends since 2013 for evaluations of air pollution mitigation policy. Here, we examine the anthropogenically and meteorologically modulated summertime O3 trends across China at different stages of the clean air actions using multi-source observations combined with multi-model calculations. Ozone increases steadily in China between 2013-2022, with a fast increase rate of 4.4 μg m-3 yr-1 in Phase I and a much smaller 0.6 μg m-3 yr-1 in Phase II of Action Plan. Results highlight that the deteriorative O3 pollution in Phase I and early Phase II is dominated by the nonlinear O3-emission response. Persistent decline in O3 precursors has shifted its chemical regime in urban areas and began to show a positive influence on ozone mitigation in recent years. Meteorological influence on O3 variations is minor until 2019 (∼10%), but it greatly accelerates or relieves the O3 pollution after then, showing comparable contribution to emissions. Epidemiological model predicts totally 0.8-3.0 thousand yr-1 more deaths across China with altered anthropogenic emissions since clean air actions, and additional health burdens by -1.5-0.3 thousand yr-1 from perturbated meteorology. This study calls for stringent emission control and climate adaptation strategies to attain the ozone pollution mitigation in China.

High-resolution estimation of near-surface ozone concentration and population exposure risk in China

Considering the spatial and temporal effects of atmospheric pollutants, using the geographically and temporally weighted regression and geo-intelligent random forest (GTWR-GeoiRF) model and Sentinel-5P satellite remote sensing data, combined with meteorological, emission inventory, site observation, population, elevation, and other data, the high-precision ozone concentration and its spatiotemporal distribution near the ground in China from March 2020 to February 2021 were estimated. On this basis, the pollution status, near-surface ozone concentration, and population exposure risk were analyzed. The findings demonstrate that the estimation outcomes of the GTWR-GeoiRF model have high precision, and the precision of the estimation results is higher compared with that of the non-hybrid model. The downscaling method enhances estimation results to some extent while addressing the issue of limited spatial resolution in some data. China's near-surface ozone concentration distribution in space shows obvious regional and seasonal characteristics. The eastern region has the highest ozone concentrations and the lowest in the northeastern region, and the wintertime low is higher than the summertime high. There are significant differences in ozone population exposure risks, with the highest exposure risks being found in China's eastern region, with population exposure risks mostly ranging from 0.8 to 5.

Exploring formation mechanism and source attribution of ozone during the 2019 Wuhan Military World Games: Implications for ozone control strategies

A series of emission reduction measures were conducted in Wuhan, Central China, to ensure good air quality during the 7th Military World Games (MWG) in October 2019. To better understand the implications for ozone (O3) pollution control strategies, we applied integrated analysis approaches based on the de-weathered statistical model, parameterization methods, chemical box model, and positive matrix factorization model. During the MWG, concentrations of O3, NOx, and volatile organic compound (VOCs), OFP (O3 formation potential), LOH (OH radical loss rate) were 83 µg/m3, 43 µg/m3, 26 ppbv, 188 µg/m3, and 3.9 s-1, respectively, which were 26%, 18%, 3%, 15%, and 13% lower than pre-MWG values and 6%, 39%, 30%, 33%, and 50% lower than post-MWG values, respectively. After removing meteorological influence, O3 and its precursors during the MWG decreased largely compared with post-MWG values, and only O3, NO2, and oxygenated VOCs (OVOCs) declined compared with pre-MWG values, which revealed the emission reduction measures during the MWG played an important role for O3 decline. For six VOCs sources, the mass contributions of biomass burning and solvents usage during the MWG decreased largely compared with pre-MWG values. O3 production was sensitive to VOCs and the key species were aromatics, OVOCs, and alkenes, which originated mainly from solvents usage, biomass burning, industrial-related combustion, and vehicle exhaust. Decreasing O3 concentration during the strict control was mainly caused by OVOCs reduction due to biomass burning control. Generally, the O3 abatement strategies of Wuhan should be focused on the mitigation of high-reactivity VOCs.

Spatiotemporal characteristics of air pollutants and their associated health risks in '2+26' cities in China during 2016-2020 heating seasons

To understand characteristics of air pollutants and their associated health risks in recent heating seasons in China, ambient monitoring data of six air pollutants in '2 + 26' cities in Beijing-Tianjin-Hebei and its surrounding areas (known as the BTH2+26 cities) during 2016-2020 heating seasons was analyzed. Results show that daily average concentrations of PM2.5, PM10, SO2, NO2, and CO dropped significantly in BTH2+26 cities from the 2016-2017 heating season to 2019-2020 heating season, while 8h O3 increased markedly. During 2016-2020 heating seasons, annual average values of total excess risks (ERtotal) were 2.3% mainly contributed by PM2.5 (54.4%) and PM10 (36.1%). With PM2.5 pollution worsening, PM10 and NO2 were the important contribution factors of the enhanced ERtotal. Higher health-risk based air quality index (HAQI) values were mainly concentrated in the western Hebei and northern Henan. HAQI showed spatial agglomeration effect in four heating seasons. Impact factors of HAQI varied in different heating seasons. These findings can provide useful insights for China to further propose effective control strategies to alleviate air pollution in the future.

Response of PM2.5 pollution to meteorological and anthropogenic emissions changes during COVID-19 lockdown in Hunan Province based on WRF-Chem model

In December 2019, the New Crown Pneumonia (the COVID-19) outbroke around the globe, and China imposed a nationwide lockdown starting as early as January 23, 2020. This decision has significantly impacted China's air quality, especially the sharp decrease in PM2.5 (aerodynamic equivalent diameter of particulate matter less than or equal to 2.5 μm) pollution. Hunan Province is located in the central and eastern part of China, with a "horseshoe basin" topography. The reduction rate of PM2.5 concentrations in Hunan province during the COVID-19 (24.8%) was significantly higher than the national average (20.3%). Through the analysis of the changing character and pollution sources of haze pollution events in Hunan Province, more scientific countermeasures can be provided for the government. We use the Weather Research and Forecasting with Chemistry (WRF-Chem, V4.0) model to predict and simulate the PM2.5 concentrations under seven scenarios before the lockdown (2020.1.1-2020.1.22) and during the lockdown (2020.1.23-2020.2.14). Then, the PM2.5 concentrations under different conditions is compared to differentiate the contribution of meteorological conditions and local human activities to PM2.5 pollution. The results indicate the most important cause of PM2.5 pollution reduction is anthropogenic emissions from the residential sector, followed by the industrial sector, while the influence of meteorological factors contribute only 0.5% to PM2.5. The explanation is that emission reductions from the residential sector contribute the most to the reduction of seven primary contaminants. Finally, we trace the source and transport path of the air mass in Hunan Province through the Concentration Weight Trajectory Analysis (CWT). We found that the external input of PM2.5 in Hunan Province is mainly from the air mass transported from the northeast, accounting for 28.6%-30.0%. To improve future air quality, there is an urgent need to burn clean energy, improve the industrial structure, rationalize energy use, and strengthen cross-regional air pollution synergy control.

A study on the analysis of dynamical transmission behavior and mining key monitoring stations in PM and O3 networks in the Beijing-Tianjin-Hebei region of China

To investigate the dynamical transmission behavior of pollutants and explore the roles played by monitoring stations in regional air pollutants transportation, we constructed a new model for the dynamical transmission index by adopting a statistics model that employs complex network analysis along with terrain data, meteorological variables, and air quality data. The study is conducted in Beijing-Tianjin-Hebei region with 70 stations in 13 cities. The findings indicated that the regional dynamical transmission networks were characterized by the participation of 67 out of 70 stations, as determined by node number. Among the model characteristics, the average path length and the average clustering coefficient, within the ranges of 2.08-2.32 and 0.26-0.51, respectively, maintained reasonable small-world characteristic. For the seasonal transmission features, the networks for PM2.5, PM10 in winter, and O3 in summer shared similar modeling characteristics with those of yearly networks. This suggested that the networks for these two seasons could represent the yearly transmission features. By employing the entropy weight method, the key monitoring stations numbered 1011 A, 1026 A, and 1010 A, which are located in Tianjin, Shijiazhuang, and Beijing, exerted significant impacts on air pollution transmission path in cities. The novel model has demonstrated its soundness and effectiveness in terms of capturing the behavior of transmission as well as the distinguishing roles of these crucial monitoring stations. This methodology could be employed for the construction of additional monitoring stations, identification of possible pollution sources, and prioritization of key pollution areas, thus providing valuable insights for environmental protection and management.

A machine learning-based study on the impact of COVID-19 on three kinds of pollution in Beijing-Tianjin-Hebei region

Large-scale restrictions on anthropogenic activities in China in 2020 due to the Corona Virus Disease 2019 (COVID-19) indirectly led to improvements in air quality. Previous studies have paid little attention to the changes in nitrogen dioxide (NO2), fine particulate matter (PM2.5) and ozone (O3) concentrations at different levels of anthropogenic activity limitation and their interactions. In this study, machine learning models were used to simulate the concentrations of three pollutants during periods of different levels of lockdown, and compare them with observations during the same period. The results show that the difference between the simulated and observed values of NO2 concentrations varies at different stages of the lockdown. Variation between simulated and observed O3 and PM2.5 concentrations were less distinct at different stages of lockdowns. During the most severe period of the lockdowns, NO2 concentrations decreased significantly with a maximum decrease of 65.28 %, and O3 concentrations increased with a maximum increase of 75.69 %. During the first two weeks of the lockdown, the titration reaction in the atmosphere was disrupted due to the rapid decrease in NO2 concentrations, leading to the redistribution of Ox (NO2 + O3) in the atmosphere and eventually to the production of O3 and secondary PM2.5. The effect of traffic restrictions on the reduction of NO2 concentrations is significant. However, it is also important to consider the increase in O3 due to the constant volatile organic compounds (VOCs) and the decrease in NOx (NO+NO2). Traffic restrictions had a limited effect on improving PM2.5 pollution, so other beneficial measures were needed to sustainably reduce particulate matter pollution. Research on COVID-19 could provide new insights into future clean air action.

Regional joint PM2.5-O3 control policy benefits further air quality improvement and human health protection in Beijing-Tianjin-Hebei and its surrounding areas

Beijing-Tianjin-Hebei and its surrounding areas (hereinafter referred to as "2+26" cities) are one of the most severe air pollution areas in China. The fine particulate matter (PM_2.5) and surface ozone (O₃) pollution have aroused a significant concern on the national scale. In this study, we analyzed the pollution characteristics of PM_2.5 and O₃ in "2+26" cities, and then estimated the health burden and economic loss before and after the implementation of the joint PM_2.5-O₃ control policy. During 2017-2019, PM_2.5 concentration reduced by 19% while the maximum daily 8 hr average (MDA8) O₃ stayed stable in "2+26" cities. Spatially, PM_2.5 pollution in the south-central area and O₃ pollution in the central region were more severe than anywhere else. With the reduction in PM_2.5 concentration, premature deaths from PM_2.5 decreased by 18% from 2017 to 2019. In contrast, premature deaths from O₃ increased by 5%. Noticeably, the huge potential health benefits can be gained after the implementation of a joint PM_2.5-O₃ control policy. The premature deaths attributed to PM_2.5 and O₃ would be reduced by 91.6% and 89.1%, and the avoidable economic loss would be 60.8 billion Chinese Yuan (CNY), and 68.4 billion CNY in 2035 compared with that in 2019, respectively. Therefore, it is of significance to implement the joint PM_2.5-O₃ control policy for improving public health and economic development.

Machine learning based quantification of VOC contribution in surface ozone prediction

The prediction of surface ozone is essential attributing to its impact on human and environmental health. Volatile organic compounds (VOCs) are crucial in driving ozone concentration; particularly in urban areas where VOC limited regimes are prominent. The limited measurements of VOCs, however, hinder assessing the VOC-ozone relationship. This work applies machine learning (ML) algorithms for temporal forecasting of surface ozone over a metropolitan city in India. The availability of continuous VOCs measurement data along with meteorology and other pollutants during 2014-2016 makes it possible to deduce the influence of various input parameters on surface ozone prediction. After evaluating the best ML model for ozone prediction, simulations were carried out using varied input combinations. The combination with isoprene, meteorology, NO_x, and CO (Isop + MNC) was the best with RMSE 4.41 ppbv and MAPE 6.77%. A season-wise comparison of simulations having all data, only meteorological data and Isop + MNC as input showed that Isop + MNC simulation gives the best results during the summer season (RMSE: 5.86 ppbv, MAPE: 7.05%). This shows the increased ability of the model to capture ozone peaks (high ozone during summer) relatively better when isoprene data is used. The overall results highlight that using all available data doesn't necessarily give best prediction results; also critical thinking is essential when evaluating the model results.

Meteorological influences on co-occurrence of O3 and PM2.5 pollution and implication for emission reductions in Beijing-Tianjin-Hebei

Co-occurrence of surface ozone (O3) and fine particulate matter (PM2.5) pollution (CP) was frequently observed in Beijing-Tianjin-Hebei (BTH). More than 50% of CP days occurred during April-May in BTH, and the CP days reached up to 11 in two months of 2018. The PM2.5 or O3 concentration associated with CP was lower than but close to that in O3 and PM2.5 pollution, indicating compound harms during CP days with double-high concentrations of PM2.5 and O3. CP days were significantly facilitated by joint effects of the Rossby wave train that consisted of two centers associated with the Scandinavia pattern and one center over North China as well as a hot, wet, and stagnant environmental condition in BTH. After 2018, the number of CP days decreased sharply while the meteorological conditions did not change significantly. Therefore, changes in meteorological conditions did not really contribute to the decline of CP days in 2019 and 2020. This implies that the reduction of PM2.5 emission has resulted in a reduction of CP days (about 11 days in 2019 and 2020). The differences in atmospheric conditions revealed here were helpful to forecast the types of air pollution on a daily to weekly time scale. The reduction in PM2.5 emission was the main driving factor behind the absence of CP days in 2020, but the control of surface O3 must be stricter and deeper.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at 10.1007/s11430-022-1070-y.

Exploring drivers of the aggravated surface O3 over North China Plain in summer of 2015-2019: Aerosols, precursors, and meteorology

Continuous aggravated surface O₃ over North China Plain (NCP) has attracted widely public concern. Herein, we evaluated the effects of changes in aerosols, precursor emissions, and meteorology on O₃ in summer (June) of 2015-2019 over NCP via 8 scenarios with WRF-Chem model. The simulated mean MDA8 O₃ in urban areas of 13 major cities in NCP increased by 17.1%∼34.8%, which matched well with the observations (10.8%∼33.1%). Meanwhile, the model could faithfully reproduce the changes in aerosol loads, precursors, and meteorological conditions. A relatively-even O₃ increase (+1.2%∼+3.9% for 24-h O₃ and +1.0%∼+3.8% for MDA8 O₃) was induced by PM_2.5 dropping, which was consistent with the geographic distribution of regional PM_2.5 reduction. Meanwhile, the NO₂ reduction coupled with a near-constant VOCs led to the elevated VOCs/NO_x ratios, and then caused O₃ rising in the areas under VOCs-limited regimes. Therein, the pronounced increases occurred in Handan, Xingtai, Shijiazhuang, Tangshan, and Langfang (+10.7%∼+13.6% for 24-h O₃ and +10.2%∼+12.2% for MDA8 O₃); while the increases in other cities were 5.7%∼10.5% for 24-h O₃ and 4.9%∼9.2% for MDA8 O₃. Besides, the meteorological fluctuations brought about the more noticeable O₃ increases in northern parts (+12.5%∼+13.5% for 24-h O₃ and +11.2%∼+12.4% for MDA8 O₃) than those in southern and central parts (+3.2%∼+9.3% for 24-h O₃ and +3.7%∼+8.8% for MDA8 O₃). The sum of the impacts of the three drivers reached 16.7%∼21.9%, which were comparable to the changes of the observed O₃. Therefore, exploring reasonable emissions-reduction strategies is essential for the ozone pollution mitigation over this region.

Assessment of NO2 concentrations over industrial state Jharkhand, at the time frame of pre, concurrent, and post-COVID-19 lockdown along with the meteorological behaviour: an overview from satellite and ground approaches

Burning of fossil fuels in the form of coal or gasoline in thermal power plants, industries, and automobiles is a prime source of nitrogen dioxide (NO₂), a major air pollutant causing health problems. In this paper, spatio-temporal unevenness of NO₂ concentrations via both spaceborne Sentinel-5P and ground-based in situ data have been studied for the period of 2017-2021. Annual and seasonal distribution of TROPOMI-NO₂ depict consistency over the Jharkhand region, highlighting six hotspot regions. As compared to 2019, a notable dip of 11% in the spatial annual average TROPOMI-NO₂ was achieved in 2020, which were elevated again by 22% in 2021 as the lockdown gradually goes out of the picture. Among eight ground-monitoring stations, Tata and Golmuri stations always displayed a higher level of TROPOMI-NO₂ ranges up to 15.2 ×10¹⁵molecules.cm^-2 and 16.9 ×10¹⁵molecules.cm^-2 respectively, as being located in the highly industrialised district of Jamshedpur. A big percentage reduction of up to 30% in TROPOMI-NO₂ has been reported in Jharia and Bastacola stations in Dhanbad in the lockdown phase of 2020 compared to 2019. Good agreement between TROPOMI-NO₂ and surface-NO₂ has been achieved with R = 0.8 and R = 0.71 during winter and post-monsoon respectively. Among four meteorological parameters, TROPOMI-NO₂ was majorly found to be influenced by precipitation, having R = 0.6-0.8 for almost all stations. More advanced satellite algorithms and ground-based data may be used to estimate NO₂ in places where monitoring facilities are limited and thus can help in air pollution control policy.

Investigating the nonlinear and non-stationary relationship between PM2.5 and air pollutants by wavelet signal analysis in central Taiwan

In recent years, PM_2.5 has become a critical factor as an environmental indicator, causing severe air pollution that has negatively impacted nature and human health. This study used hourly data gathered in central Taiwan from 2015 to 2019 and applied spatiotemporal data analysis and wavelet analysis methods to investigate the cross-correlation between PM_2.5 and other air pollutants. Furthermore, it explored the correlation differences between adjacent stations after excluding major environmental factors such as climate and terrain. Wavelet coherence shows that PM_2.5 and air pollutants mostly have a significant correlation at the half-day and one-day frequencies, while the differences between PM_2.5 and PM₁₀ are only particle size; hence, not only is the correlation the most consistent among all air pollutants but also the lag time is the most negligible. Carbon monoxide (CO) is the primary source pollutant of PM_2.5 as it is also significantly correlated with PM_2.5 at most timescales. Sulfur dioxide (SO₂) and nitrogen oxide (NO_x) are related to the generation of secondary aerosols, which are important components of PM_2.5; therefore, the consistency of significant correlations improves as the timescale increases and the lag time becomes amplified. The pollution source mechanism of ozone (O₃) and PM_2.5 is not identical, so the correlation is lower than for other air pollutants; the lag time is also obviously influenced by the season changes that have significant fluctuations. At stations near the ocean such as Xianxi station and Shulu station, PM_2.5 and PM₁₀ have a higher correlation in the 24-h frequency, while the SO₂ and PM_2.5 at Sanyi station and Fengyuan station, which are close to industrial areas, have significant correlations in the 24-h frequency. This study hopes to help better understand the impact mechanisms behind different pollutants, and thus construct a better reference for establishing a complete air pollution prediction model in the future.

Improved estimation of particulate matter in China based on multisource data fusion

Particulate matter (PM) is a global health concern and causes millions of premature deaths worldwide annually. High-resolution and full-coverage PM datasets are essential to support the accurate assessment of PM exposure. Here, a three-stage model framework is developed based on the Community Multiscale Air Quality (CMAQ) simulations (12 km) and multisource data fusion to estimate 1 km daily PM concentrations across China in 2015, including PM_2.5 (<2.5 μm) and PM₁₀ (<10 μm). The three-stage model performs well with cross-validation coefficient of determination (R²) of 0.91 and 0.87, and root mean square error (RMSE) of 17.3 μg/m³ and 27.2 μg/m³ for PM_2.5 and PM₁₀, respectively. After data fusion from multiple sources, the concentrations of PM_2.5 and PM₁₀ are in better agreement with ground observations compared to the CMAQ simulation with RMSE reduced by 72 % and 67 %. High PM_2.5 events mainly occur in the North China Plain, Yangtze River Delta, and Sichuan Basin, and PM₁₀ show similar spatial patterns to PM_2.5 in eastern China. These full-coverage PM datasets enable in-depth analysis of PM pollution over small areas and support future epidemiological studies and health assessments.

Understanding the inter-city causality and regional transport of atmospheric PM2.5 pollution in winter in the Harbin-Changchun megalopolis in China: A perspective from local and regional

Harbin-Changchun megalopolis (HCM) is the typical cold urban agglomeration in China, where PM_2.5 pollution is still serious in winter against the backdrop of continuous improvement in annual air quality in China. To further understand interactions of atmospheric pollution among HCM cities, inter-city causality and regional transport of PM_2.5 in the winter in the HCM were comprehensively investigated by using convergent cross mapping (CCM) and CMAQ-BFM methods. CCM analysis results suggest strong bidirectional causal relationships between cities in the HCM, and the causality during polluted episodes were significantly larger than that during clean period. In addition, the influence on local PM_2.5 from the HCM western cities were larger than that from cities in the southeast. Inter-city and regional transport contributions results demonstrated that although local emission were the largest contributors among 14 sub-regions for most HCM cities, interactions among cities were strong. Regional transport (42.8%-77.4%) largely contributes to HCM cities' PM_2.5 concentrations. Among three regions outside the HCM, NMG (including part of inner Mongolia and Baicheng city in Jilin, 9.1%) was the largest contributor to the PM_2.5 concentration in the whole HCM, followed by JLS (including Liaoning Province, Tonghua and Baishan cities in Jilin province, 5.1%) and HLJ (including cities of Heihe, Yichun, Jiamusi, Hegang, Shuangyashan, Jixi, Qitaihe in the Heilongjiang province, 3.8%). Regional transport contribution to the most HCM cities increased significantly from excellent to heavily polluted days. Furthermore, close relationships between transport paths/intensity and wind direction/speed in studied region suggests that we can quantitatively guide the regional joint emergency prevention and control before and during heavily polluted events based on regional weather forecasts in the future.

Extreme Emission Reduction Requirements for China to Achieve World Health Organization Global Air Quality Guidelines

A big gap exists between current air quality in China and the World Health Organization (WHO) global air quality guidelines (AQG) released in 2021. Previous studies on air pollution control have focused on emission reduction demand in China but ignored the influence of transboundary pollution, which has been proven to have a significant impact on air quality in China. Here, we develop an emission-concentration response surface model coupled with transboundary pollution to quantify the emission reduction demand for China to achieve WHO AQG. China cannot achieve WHO AQG by its own emission reduction for high transboundary pollution of both PM_2.5 and O₃. Reducing transboundary pollution will loosen the reduction demand for NH₃ and VOCs emissions in China. However, to meet 10 μg·m^-3 for PM_2.5 and 60 μg·m^-3 for peak season O₃, China still needs to reduce its emissions of SO₂, NO_x, NH₃, VOCs, and primary PM_2.5 by more than 95, 95, 76, 62, and 96% respectively, on the basis of 2015. We highlight that both extreme emission reduction in China and great efforts in addressing transboundary air pollution are crucial to reach WHO AQG.

Improving the accuracy of O3 prediction from a chemical transport model with a random forest model in the Yangtze River Delta region, China

Due to inherent errors in the chemical transport models, inaccuracies in the input data, and simplified chemical mechanisms, ozone (O₃) predictions are often biased from observations. Accurate O₃ predictions can better help assess its impacts on public health and facilitate the development of effective prevention and control measures. In this study, we used a random forest (RF) model to construct a bias-correction model to correct the bias in the predictions of hourly O₃ (O₃-1h), daily maximum 8-h O₃ (O₃-Max8h), and daily maximum 1-h O₃ (O₃-Max1h) concentrations from the Community Multi-Scale Air Quality (CMAQ) model in the Yangtze River Delta region. The results show that the RF model successfully captures the nonlinear response relationship between O₃ and its influence factors, and has an outstanding performance in correcting the bias of O₃ predictions. The normalized mean biases (NMBs) of O₃-1h, O₃-Max8h, and O₃-Max1h decrease from 15.8%, 20.0%, and 17.0.% to 0.5%, -0.8%, and 0.1%, respectively; correlation coefficients increase from 0.78, 0.90, and 0.89 to 0.94, 0.95, and 0.94, respectively. For O₃-1h and O₃-Max8h, the original CMAQ model shows an obvious bias in the central and southern Zhejiang region, while the RF model decreases the NMB values from 54% to -1% and 34% to -4%, respectively. The O₃-1h bias is mainly caused by the bias of nitrogen dioxide (NO₂). Relative humidity and temperature are also important factors that lead to the bias of O₃. For high O₃ concentrations, the temperature bias and O₃ observations are the major reasons for the discrepancy between the model and the observations.

Causes of the unexpected slowness in reducing winter PM2.5 for 2014-2018 in Henan Province

Toughest-ever clean air actions in China have been implemented nationwide to improve air quality. However, it was unexpected that from 2014 to 2018, the observed wintertime PM_2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) concentrations showed an insignificant decrease in Henan Province (HNP), a region in the west of the North China Plain. Emission controls seem to have failed to improve winter air quality in HNP, which has caused great confusion in formulating the next air improvement strategy. We employed a deweathering technique to decouple the impact of meteorological conditions. The results showed that the deweathered PM_2.5 trend was -3.3%/yr in winter from 2014 to 2018, which had a larger decrease than the observed concentrations (-0.9%/yr), demonstrating that emission reduction was effective at improving air quality. However, compared with the other two megacity clusters, Beijing-Tianjin-Hebei (BTH) (-8.4%/yr) and Yangtze River Delta (YRD) (-7.4%/yr), the deweathered decreasing trend of PM_2.5 for HNP remained slow. The underlying mechanism driving the changes in PM_2.5 and its chemical components was further explored, using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Model simulations indicated that nitrate dominated the increase of PM_2.5 components in HNP and the proportions of nitrate to total PM_2.5 increased from 22.4% in January 2015 to 39.7% in January 2019. There are two primary reasons for this phenomenon. One is the limited control of nitrogen oxide emissions, which facilitates the conversion of nitric acid to particulate nitrate by ammonia. The other is unfavourable meteorological conditions, particularly increasing humidity, further enhancing nitrate formation through multiphase reactions. This study highly emphasizes the importance of reducing nitrogen oxide emissions owing to their impact on the formation of particulate nitrate in China, especially in the HNP region.

Lockdown effects of the COVID-19 on the spatio-temporal distribution of air pollution in Beijing, China

To prevent the spread of COVID-19, China enacted a series of strict policies, which reduced anthropogenic activities to a near standstill. This provided a precious window to explore its effects on the spatio-temporal distribution of air pollution in Beijing, China. In this study, continuous wavelet transforms and spatial interpolation methods were used to explore the spatiotemporal variations in air pollutants and their lockdown effects. The results indicate that except O₃, the annual average concentration of NO₂, PM_2.5 and SO₂ showed a decreasing trend during 2016 and 2019; NO₂, PM_2.5 and SO₂ show a trend of "low in summer and high in winter"; the diurnal variation of NO₂ concentration was mainly related to the rush hours of traffic volume, with the first peak at the morning peak (7:00), and then accumulating gradually to second peak (22:00). The continuous wavelet analysis shows that PM2.5, SO₂ and NO₂ had four primary periods, while O₃ only had two primary periods. The high NO₂ concentration areas were mainly in Dongcheng, Xicheng, Chaoyang and Fengtai, while the low concentration areas were located in the northern areas, such as Miyun and Huairou; the PM_2.5 concentration decreased from south to north; this characteristic presented more obviously in winter. Compared to the pre-lockdown, NO₂ and SO₂ decreased considerably during lockdown, whereas PM_2.5 and O₃ increased dramatically. The contribution rates of transportation activities to the NO₂, O₃, PM_2.5 and SO₂ were estimated be 9.4 % ∼ 17.2 %, -76.4 % ∼ -42.9 %, -39.5 % ∼ -22.8 % and 5.7 % ∼ 43.7 %, respectively; the contribution rates of industrial activities were 19.9 % ∼ 26.7 %, 7.8 % ∼ 30.9 %, 1.6 % ∼ 36.2 % and -10.5 % ∼ 15.9 %, respectively. Considering meteorological factors, we inferred that pauses in anthropogenic activities indeed help improving air pollution, but it is difficult to offset the impact of extreme weather. These findings can enhance our understanding on the sources of air pollution, and can therefore provide insights on urban air pollution mitigation.

Multi-scale analysis of the impacts of meteorology and emissions on PM2.5 and O3 trends at various regions in China from 2013 to 2020 3. Mechanism assessment of O3 trends by a model

A multiscale analysis of meteorological trends was carried out to investigate the impacts of the large-scale circulation types as well as the local-scale key weather elements on the complex air pollutants, i.e., PM_2.5 and O₃ in China. Following accompanying papers on synoptic circulation impact and key weather elements and emission contributions (Gong et al., 2022a; Gong et al., 2022b), an emission-driven Observation-based Box Model (e-OBM) was developed to study the impact mechanisms on O₃ trend and quantitatively assess the effects of variation in the emissions control over 2013-2020 for Beijing, Chengdu, Guangzhou and Shanghai. Compared with the original OBM, the e-OBM not only improves the performance to simulate the hourly O₃ peak concentration in daytime, but also reasonably reproduces the maximum daily 8-hour average (MDA8) O₃ concentrations in the four cities. Based upon the sensitivity experiments, it is found that the meteorology is the dominant driver for the MDA8 O₃ trend, contributing from about 32 % to 139 % to the variations. From the mechanistic point of view, the variations of meteorology lead to the enhancement of atmospheric oxidation capacity and the acceleration of O₃ production. Further evaluation to the emission changes in four cities shows that the O₃-precursors relationships of the four cities have been changed from the VOC-limited regime in 2013 to the transition regime or near-transition regime in 2020. Though the NO_x/VOCs ratios have been obviously decreased, the emission reductions up to 2020 were still not enough to mitigate O₃ pollution in these cities. It is emphasized in this study that the strengthened control measures with maintaining a certain ratio of NO_x and VOCs should be implemented to further curb the increasing trend of O₃ in urban areas.

Efficacy of Low-Cost Sensor Networks at Detecting Fine-Scale Variations in Particulate Matter in Urban Environments

The negative health impacts of air pollution are well documented. Not as well-documented, however, is how particulate matter varies at the hyper-local scale, and the role that proximal sources play in influencing neighborhood-scale patterns. We examined PM2.5 variations in one airshed within Indianapolis (Indianapolis, IN, USA) by utilizing data from 25 active PurpleAir (PA) sensors involving citizen scientists who hosted all but one unit (the control), as well as one EPA monitor. PA sensors report live measurements of PM2.5 on a crowd sourced map. After calibrating the data utilizing relative humidity and testing it against a mobile air-quality unit and an EPA monitor, we analyzed PM2.5 with meteorological data, tree canopy coverage, land use, and various census variables. Greater proximal tree canopy coverage was related to lower PM2.5 concentrations, which translates to greater health benefits. A 1% increase in tree canopy at the census tract level, a boundary delineated by the US Census Bureau, results in a ~0.12 µg/m3 decrease in PM2.5, and a 1% increase in "heavy industry" results in a 0.07 µg/m3 increase in PM2.5 concentrations. Although the overall results from these 25 sites are within the annual ranges established by the EPA, they reveal substantial variations that reinforce the value of hyper-local sensing technologies as a powerful surveillance tool.

Quantifying the drivers of PM2.5 variation in Shenyang, China: A factor decomposition model

Taking variations in PM_2.5 as indicators for assessing the performance of authority in air quality management will probably lead to misjudgment, as PM_2.5 concentration is affected not only by anthropogenic emissions but also by uncontrollable circumstances. To solve this problem, we proposed a decomposition method to attribute the variations in PM_2.5 to the contributions of meteorological conditions, cross-regional transports of pollutants, secondary aerosols, and local emissions. This method estimated the relationship between PM_2.5 concentration and the various influencing factors using a semi-parametric generalized additive model. A case study was conducted in Shenyang, a heavily polluted city in northeast China, based on up to 595,000 hourly data samples from 2014 to 2017. The decomposition results indicated that the average PM_2.5 in 2017 decreased by 39.80% compared with 2014, far exceeding the government's target of 15%, but only 11.79% of the decrease was benefited from the control of local emissions. The severe pollution event that occurred in November 2015 was induced by the combination of massive emissions from heating and meteorological conditions conducive to pollutant accumulation. Furthermore, the approach we proposed can be extended to any location that has monitoring data on air pollutant concentrations and meteorological conditions.

Modelling driving factors of PM2.5 concentrations in port cities of the Yangtze River Delta

PM_2.5 is one of the major air pollutants in port cities of the Yangtze River Delta (YRD) of China. Understanding the driving factors of PM_2.5 is essential to guide air pollution prevention and control. We selected 17 major port cities in YRD to study the driving factors of PM_2.5 in 2019 and 2020. Generalized Additive Models were built to model the non-linear effects of single, multiple and interactions of driving factors on the variations of PM_2.5. NO₂, SO₂ and the day of year are most strongly associated with the variation of PM_2.5 concentration when used alone. Anthropogenic emissions play complicated roles in regulating PM_2.5 concentration. Although the effect of cargo throughput (CT) on PM_2.5 concentration is non-monotonic, higher PM_2.5 levels are found to be associated with higher levels of SO₂ and CT. This work can potentially provide a scientific basis for formulating PM_2.5 prevention and control policies in the region.

Evaluating the effects of air pollution control policies in China using a difference-in-differences approach

Air pollution has caused wide concern in China, and many governance policies and plans have been implemented in recent years. Based on counterfactual quasi-natural experiments, we analyzed the implementation effects of autumn and winter air pollution control policies in the Jing-Jin-Ji region and surrounding areas using a difference-in-differences (DID) model. The control group was selected based on geographical and meteorological factors, and we analyzed the impact of the policies on six pollutants. The results show that the policies reduced air pollution overall, but not every pollutant. Due to the policy contribution, the concentrations of PM_2.5 and PM₁₀ in autumn and winter from 2017 to 2018 decreased by 6.9 % and 8.5 %, respectively. The numerical value of PM_2.5, PM₁₀, CO, and AQI in 2018-2019 decreased by 18.2 %, 7.2 %, 13.9 %, and 8.8 %, respectively. The role in the reduction of O₃, SO₂, and NO₂ was not obvious. This work provides a research paradigm for evaluating the effects of atmospheric environment policy which can be applied to other studies and provide references for formulating additional policies.

Statistical modeling approach for PM10 prediction before and during confinement by COVID-19 in South Lima, Perú

A total of 188,859 meteorological-PM$$_{10}$$ 10 data validated before (2019) and during the COVID-19 pandemic (2020) were used. In order to predict PM$$_{10}$$ 10 in two districts of South Lima in Peru, hourly, daily, monthly and seasonal variations of the data were analyzed. Principal Component Analysis (PCA) and linear/nonlinear modeling were applied. The results showed the highest annual average PM$$_{10}$$ 10 for San Juan de Miraflores (SJM) (PM$$_{10}$$ 10 -SJM: 78.7 $$\upmu$$ μ g/m$$^{3}$$ 3 ) and the lowest in Santiago de Surco (SS) (PM$$_{10}$$ 10 -SS: 40.2 $$\upmu$$ μ g/m$$^{3}$$ 3 ). The PCA showed the influence of relative humidity (RH)-atmospheric pressure (AP)-temperature (T)/dew point (DP)-wind speed (WS)-wind direction (WD) combinations. Cool months with higher humidity and atmospheric instability decreased PM$$_{10}$$ 10 values in SJM and warm months increased it, favored by thermal inversion (TI). Dust resuspension, vehicular transport and stationary sources contributed more PM$$_{10}$$ 10 at peak times in the morning and evening. The Multiple linear regression (MLR) showed the best correlation (r = 0.6166), followed by the three-dimensional model LogAP-LogWD-LogPM$$_{10}$$ 10 (r = 0.5753); the RMSE-MLR (12.92) exceeded that found in the 3D models (RMSE $$<0.3$$ < 0.3 ) and the NSE-MLR criterion (0.3804) was acceptable. PM$$_{10}$$ 10 prediction was modeled using the algorithmic approach in any scenario to optimize urban management decisions in times of pandemic.

A numerical study of reducing the concentration of O3 and PM2.5 simultaneously in Taiwan

Since the 24-hr PM_2.5 (particle aerodynamic diameter less than 2.5 μm) concentration standard was regulated in Taiwan in 2012, the PM_2.5 concentration has been decreasing year by year, but the ozone (O₃) concentration remains almost the same. In particular, the daily maximum 8-hr average O₃ (MDA8 O₃) concentration frequently exceeds the standard. The goal of this study is to find a solution for reducing PM_2.5 and O₃ simultaneously by numerical modeling. After the Volatile Organic Compounds (VOC_S)-limited and nitrogen oxides (NO_X)-limited areas were defined in Taiwan, then, in total, 50 scenarios are simulated in this study. In terms of the average in Taiwan, the effect of VOC_S emission reduction is better than that of NO_X on the decrease in PM_2.5 concentration, when the same reduction proportion (20%, 40%) is implemented. While the effect of further NO_X emission reduction (60%) will exceed that of VOC_S. The decrease in PM_2.5 is proportional to the reduction in precursor emissions such as NO_X, VOC_S, sulfur dioxides (SO₂), and ammonia (NH₃). The lower reduction of NO_X emission for whole Taiwan caused O₃ increases on average but higher reduction can ease the increase, which suggests the implement of NO_X emission reductions must be cautious. When comparing administrative jurisdictions in terms of grids, districts/towns, and cities/counties, it was found that controlling NO_X and VOC_S at a finer spatial resolution of control units did not benefit the decrease in PM_2.5 but did benefit the decrease in O₃. The enhanced O₃ control strategies obviously cause a higher decrease of O₃ throughout Taiwan due to NO_X and VOC_S emission changes when they are implemented in the right places. Finally, three sets of short-term and long-term goals of controlling PM_2.5 and O₃ simultaneously are drawn from the comprehensive rankings for all simulated scenarios, depending on whether PM_2.5 or O₃ control is more urgent. In principle, the short-term scenarios could be ordinary or enhanced version of O₃ decrease with lower NO_X/VOC_S emissions, while the long-term scenario is enhanced version of O₃ decrease plus high emission reductions for all precursors.

MERRA-2 PM2.5 mass concentration reconstruction in China mainland based on LightGBM machine learning

MERRA-2 developed by the National Aeronautics and Space Administration (NASA) provides the long-term record of surface PM_2.5 mass concentration since 1980s, but needs great improvement over mainland China according to recent studies. In this study, a newly developed light gradient boosting machine (LGBM) model is introduced to correct the MERRA-2 PM_2.5 record over mainland China by incorporating the meteorological reanalysis and satellite AOD retrievals. A 40-year surface PM_2.5 record covering mainland China is reconstructed from 1980 to 2019, providing a new dataset for exploring the interactions between climate variability and air pollution. The new record exhibits not only much better magnitude but also more excellent variabilities of surface PM_2.5 loading compared to original MERRA-2 products. The correlation coefficient, the root-mean-square error and the mean error between the observed and reconstructed records are 0.8, less than 28.5 μg·m^-3, and 0.33 μg·m^-3, respectively, which are much better than those of 0.27, 45.8 μg·m^-3, and 1.64 μg·m^-3 between the observed and MERRA-2 PM_2.5 records. The PM_2.5 record with longer term and higher accuracy developed in this study provides a better base for the research on the climate change variability and air pollution in mainland China. However, limitations of the reconstructed record still exist, especially in the Tibetan Plateau and marine regions with very sparse surface measurements, which need further correction in the future studies.

Short-term air quality forecasting model based on hybrid RF-IACA-BPNN algorithm

Despite the apparent improvement in air quality in recent years through a series of effective measures, the concentration of PM_2.5 and O₃ in Chengdu city remains high. And both the two pollutants can cause serious damage to human health and property; consequently, it is imperative to accurately forecast hourly concentration of PM_2.5 and O₃ in advance. In this study, an air quality forecasting method based on random forest (RF) method and improved ant colony algorithm coupled with back-propagation neural network (IACA-BPNN) are proposed. RF method was used to screen out highly correlated input variables, and the improved ant colony algorithm (IACA) was adopted to combine with BPNN to improve the convergence performance. Two datasets based on two different kinds of monitoring stations along with meteorological data were applied to verify the performance of this proposed model and compared with another five plain models. The results showed that the RF-IACA-BPNN model has the minimum statistical error of the mean absolute error, root mean square error, and mean absolute percentage error, and the values of R² consistently outperform other models. Thus, it is concluded that the proposed model is suitable for air quality prediction. It was also detected that the performance of the models for the forecasting of the hourly concentrations of PM_2.5 were more acceptable at suburban station than downtown station, while the case is just the opposite for O₃, on account of the low variability dataset at suburban station.

Rural vehicle emission as an important driver for the variations of summertime tropospheric ozone in the Beijing-Tianjin-Hebei region during 2014-2019

Tropospheric ozone (O₃) pollution is increasing in the Beijing-Tianjin-Hebei (BTH) region despite a significant decline in atmospheric fine aerosol particles (PM_2.5) in recent years. However, the intrinsic reason for the elevation of the regional O₃ is still unclear. In this study, we analyzed the spatio-temporal variations of tropospheric O₃ and relevant pollutants (PM_2.5, NO₂, and CO) in the BTH region based on monitoring data from the China Ministry of Ecology and Environment during the period of 2014-2019. The results showed that summertime O₃ concentrations were constant in Beijing (BJ, 0.06 µg/(m³•year)) but increased significantly in Tianjin (TJ, 9.09 µg/(m³•year)) and Hebei (HB, 6.06 µg/(m³•year)). Distinct O₃ trends between Beijing and other cities in BTH could not be attributed to the significant decrease in PM_2.5 (from -5.08 to -6.32 µg/(m³•year)) and CO (from -0.053 to -0.090 mg/(m³•year)) because their decreasing rates were approximately the same in all the cities. The relatively stable O₃ concentrations during the investigating period in BJ may be attributed to a faster decreasing rate of NO₂ (BJ: -2.55 µg/(m³•year); TJ: -1.16 µg/(m³•year); HB: -1.34 µg/(m³•year)), indicating that the continued reduction of NO_x will be an effective mitigation strategy for reducing regional O₃ pollution. Significant positive correlations were found between daily maximum 8 hr average (MDA8) O₃ concentrations and vehicle population and highway freight transportation in HB. Therefore, we speculate that the increase in rural NO_x emissions due to the increase in vehicle emissions in the vast rural areas around HB greatly accelerates regional O₃ formation, accounting for the significant increasing trends of O₃ in HB.

Implications of Mitigating Ozone and Fine Particulate Matter Pollution in the Guangdong-Hong Kong-Macau Greater Bay Area of China Using a Regional-To-Local Coupling Model

Ultrahigh-resolution air quality models that resolve sharp gradients of pollutant concentrations benefit the assessment of human health impacts. Mitigating fine particulate matter (PM2.5) concentrations over the past decade has triggered ozone (O3) deterioration in China. Effective control of both pollutants remains poorly understood from an ultrahigh-resolution perspective. We propose a regional-to-local model suitable for quantitatively mitigating pollution pathways at various resolutions. Sensitivity scenarios for controlling nitrogen oxide (NOx) and volatile organic compound (VOC) emissions are explored, focusing on traffic and industrial sectors. The results show that concurrent controls on both sectors lead to reductions of 17%, 5%, and 47% in NOx, PM2.5, and VOC emissions, respectively. The reduced traffic scenario leads to reduced NO2 and PM2.5 but increased O3 concentrations in urban areas. Guangzhou is located in a VOC-limited O3 formation regime, and traffic is a key factor in controlling NOx and O3. The reduced industrial VOC scenario leads to reduced O3 concentrations throughout the mitigation domain. The maximum decrease in median hourly NO2 is >11 μg/m³, and the maximum increase in the median daily maximum 8-hr rolling O3 is >10 μg/m³ for the reduced traffic scenario. When controls on both sectors are applied, the O3 increase reduces to <7 μg/m³. The daily averaged PM2.5 decreases by <2 μg/m³ for the reduced traffic scenario and varies little for the reduced industrial VOC scenario. An O3 episode analysis of the dual-control scenario leads to O3 decreases of up to 15 μg/m³ (8-hr metric) and 25 μg/m³ (1-hr metric) in rural areas.

Responses of surface O3 and PM2.5 trends to changes of anthropogenic emissions in summer over Beijing during 2014-2019: A study based on multiple linear regression and WRF-Chem

Owing to the implementation of air pollution control actions, anthropogenic emissions in Beijing have changed in recent years. Understanding the impact of changes in anthropogenic emissions on O₃ and PM_2.5 trends is helpful for developing air quality management strategies. Herein, we investigated the variations of air pollutants in summer over Beijing using long-term data sets from 2014 to 2019, and explored the responses of O₃ and PM_2.5 trends to changes in anthropogenic emissions based on multiple linear regression (MLR) analysis and WRF-Chem model. The results indicated a significant decrease in PM_2.5, but a near constant level of O₃ during 2014-2019. The decrease rate of PM_2.5, which was lower than that of SO₂, might be due to the effect of NO₂ on atmospheric PM_2.5. Both the slightly increasing correlations between PM_2.5 and NO₂ and the WRF-Chem model simulations implied that atmospheric PM_2.5 in Beijing is trending to be more sensitive to NO_x than SO₂. The emissions of NO_x and VOCs from industry and transportation were found to make great contribution to O₃ production in Beijing. Due to the titration of NO_x in VOC-limited regime, the relatively low emission ratios of NO_x and VOCs from industry and transportation in Beijing provided convincing evidence for the persistently high O₃ concentrations during 2014-2019. However, the noticeable increase of the O₃ trends in other areas (e.g., Hebei, Tianjin) could be explained by the significant decline in the emission ratios of NO_x and VOCs from anthropogenic emissions especially industry during 2014-2019. Controlling the emission of NO_x can substantially reduce PM_2.5 pollution, but may aggravate O₃ pollution, and thus effective VOC emission control strategies need to be considered for simultaneously controlling O₃ and PM_2.5 pollution in Beijing and other regions of China.

Spatiotemporal variations of air pollution and population exposure in Shandong Province, eastern China, 2014-2018

To clarify the characteristics and interannual variation of air pollution since the implementation of China's clean air actions, hourly in situ measurements of six gaseous and particulate criteria pollutants at 100 sites in Shandong Province were studied during 2014-2018. General decreasing trends in the concentrations of PM_2.5, PM₁₀, NO₂, SO₂, and CO were observed, while O₃ increased continuously. In 2018, the annual average PM_2.5, PM₁₀, NO₂, SO₂, and CO concentration in Shandong was 50, 100, 35, 16 μg m^-3, and 1.5 mg m^-3, representing decreases of 39%, 30%, 24%, 73%, and 35% from 2014, respectively. These decreases occurred throughout the province. Seven "2 + 26" cities (in Beijing-Tianjin-Hebei and its surrounds) in western Shandong had higher average concentrations and greater reductions than other areas. In contrast, O₃ concentration rose, with occurrences of the 90^th percentile of all daily maximum 8-h averages increasing by 12% from 159 to 181 μg m^-3, during 2014-2018. From May to September, O₃ pollution dominated as the sole primary pollutant on non-attainment days, and PM_2.5 contributed to more than 90% of polluted days in wintertime months. Population exposures were investigated based on high-resolution monitoring data and population distribution, and high exposure to pollution was displayed. The population-weighted exposure to PM_2.5 in Shandong was 50 μg m^-3, a decrease of 33%. Eighty-nine percentage of the provincial population was exposed to PM_2.5 > 35 μg m^-3, while for 99.2% of population in the seven "2 + 26" cities, PM_2.5 exposure exceeded 50 μg m^-3.

Modelling and Forecasting Temporal PM2.5 Concentration Using Ensemble Machine Learning Methods

Exposure of humans to high concentrations of PM2.5 has adverse effects on their health. Researchers estimate that exposure to particulate matter from fossil fuel emissions accounted for 18% of deaths in 2018—a challenge policymakers argue is being exacerbated by the increase in the number of extreme weather events and rapid urbanization as they tinker with strategies for reducing air pollutants. Drawing on a number of ensemble machine learning methods that have emerged as a result of advancements in data science, this study examines the effectiveness of using ensemble models for forecasting the concentrations of air pollutants, using PM2.5 as a representative case. A comprehensive evaluation of the ensemble methods was carried out by comparing their predictive performance with that of other standalone algorithms. The findings suggest that hybrid models provide useful tools for PM2.5 concentration forecasting. The developed models show that machine learning models are efficient in predicting air particulate concentrations, and can be used for air pollution forecasting. This study also provides insights into how climatic factors influence the concentrations of pollutants found in the air.

Mimicking atmospheric photochemical modelling with a deep neural network

Fast and accurate prediction of ambient ozone (O₃) formed from atmospheric photochemical processes is crucial for designing effective O₃ pollution control strategies in the context of climate change. The chemical transport model (CTM) is the fundamental tool for O₃ prediction and policy design, however, existing CTM-based approaches are computationally expensive, and resource burdens limit their usage and effectiveness in air quality management. Here we proposed a novel method (noted as DeepCTM) that using deep learning to mimic CTM simulations to improve the computational efficiency of photochemical modeling. The well-trained DeepCTM successfully reproduces CTM-simulated O₃ concentration using input features of precursor emissions, meteorological factors, and initial conditions. The advantage of the DeepCTM is its high efficiency in identifying the dominant contributors to O₃ formation and quantifying the O₃ response to variations in emissions and meteorology. The emission-meteorology-concentration linkages implied by the DeepCTM are consistent with known mechanisms of atmospheric chemistry, indicating that the DeepCTM is also scientifically reasonable. The DeepCTM application in China suggests that O₃ concentrations are strongly influenced by the initialized O₃ concentration, as well as emission and meteorological factors during daytime when O₃ is formed photochemically. The variation of meteorological factors such as short-wave radiation can also significantly modulate the O₃ chemistry. The DeepCTM developed in this study exhibits great potential for efficiently representing the complex atmospheric system and can provide policymakers with urgently needed information for designing effective control strategies to mitigate O₃ pollution.

Ground-level ozone simulation using ensemble WRF/Chem predictions over the Southeast United States

Being detrimental to human health and vegetation growth, ground-level ozone (O₃) is becoming a huge concern as an air pollutant. The processes of formation, diffusion, transformation, and transport of O₃ in the atmosphere are highly affected by meteorological conditions such as solar radiation, temperature, precipitation, and wind. Chemical transport models (CTMs) are widely used in simulating O₃ pollution with two main inputs of the meteorological condition and emission inventory. Meteorological inputs play a crucial role in the model simulation accuracy especially in areas where emission has been well constrained such as the United States (U.S.). However, most O₃ simulations today still use only one set of meteorological input, which leaves room for model performance improvement by using ensemble meteorological conditions. In this study, O₃ over the Southeast U.S. was simulated for one week in the summer of each year from 2016 to 2018 by using ensemble meteorological inputs offered by Short Range Ensemble Forecast products. The predictions were conducted through the Weather Research and Forecasting model coupled with Chemistry. The calculated ensemble prediction results got at least 66.7% improvement in agreement with O₃ observations compared with single runs in the three selected cities (Miami, Atlanta, and Baton Rouge) from 2016 to 2018. This study emphasized the accuracy and provided a new idea of using ensemble meteorological inputs to improve O₃ prediction than using traditional single meteorology by CTMs.