Meteorological influences on PM2.5 and O3 trends and associated health burden since China's clean air actions

Machine-learning-based corrections of CMIP6 historical surface ozone in China during 1950-2014

Due to a lack of long-term observations in China, reports on historical ozone concentration are severely limited. In this study, by combining observation, reanalysis and model simulation data, XGBoost machine learning algorithm is used to correct the surface ozone concentration from CMIP6 climate model, and the long-term and large-scale surface ozone concentration of China during 1950-2014 is obtained. The long-term evolutions and trends of ozone and meteorological effects on interannual ozone variations are further analyzed. The results reveal that CMIP6 historical simulations have a large underestimation in ozone concentrations and their trends. The XGB-derived ozone are closer to observations, with R2 value of 0.66 and 0.74 for daily and monthly retrievals, respectively. Both the concentrations and exceedances of ozone in most parts of China have shown increasing trends from 1950 to 2014. The daily mean ozone concentration without climate change effects is estimated to be 117 ppb in the year 1950 averaged over China. It indicates that the increase in anthropogenic emissions of China has a significant contribution to ozone enhancement between 1950 and 2014. The higher ozone growth rates of XGB retrievals than those from the model indicate a regional surface ozone penalty due to the warming climate. The relatively significant increment in ozone are estimated in the Central and Western China. Seasonally, the ozone enhancement is largest in spring, indicating a shift in seasonal variation of ozone. Given the uncertainty in simulating historical ozone by climate model, we show that machine learning approaches can provide improved assessment of evolution in surface ozone, along with valuable information to guide future model development and formulate future ozone pollution prevention and control policies.

Identifying Key Sources for Air Pollution and CO2 Emission Co-control in China

China is confronting the dual challenges of air pollution and climate change, mandating the co-control of air pollutants and CO2 emissions from their shared sources. Here we identify key sources for co-control that prioritize the mitigation of PM2.5-related health burdens, given the homogeneous impacts of CO2 emissions from various sources. By applying an integrated analysis framework that consists of a detailed emission inventory, a chemical transport model, a multisource fused dataset, and epidemiological concentration-response functions, we systematically evaluate the contribution of emissions from 390 sources (30 provinces and 13 socioeconomic sectors) to PM2.5-related health impacts and CO2 emissions, as well as the marginal health benefits of CO2 abatement across China. The estimated source-specific contributions exhibit substantial disparities, with the marginal benefits varying by 3 orders of magnitude. The rural residential, transportation, metal, and power and heating sectors emerge as pivotal sources for co-control, with regard to their relatively large marginal benefits or the sectoral total benefits. In addition, populous and heavily industrialized provinces such as Shandong and Henan are identified as the key regions for co-control. Our study highlights the significance of incorporating health benefits into formulating air pollution and carbon co-control strategies for improving the overall social welfare.

Atmospheric Oxidation Capacity Elevated during 2020 Spring Lockdown in Chengdu, China: Lessons for Future Secondary Pollution Control

This study presents the measurement of photochemical precursors during the lockdown period from January 23, 2020, to March 14, 2020, in Chengdu in response to the coronavirus (COVID-19) pandemic. To derive the lockdown impact on air quality, the observations are compared to the equivalent periods in the last 2 years. An observation-based model is used to investigate the atmospheric oxidation capacity change during lockdown. OH, HO2, and RO2 concentrations are simulated, which are elevated by 42, 220, and 277%, respectively, during the lockdown period, mainly due to the reduction in nitrogen oxides (NOx). However, the radical turnover rates, i.e., OH oxidation rate L(OH) and local ozone production rate P(O3), which determine the secondary intermediates formation and O3 formation, only increase by 24 and 48%, respectively. Therefore, the oxidation capacity increases slightly during lockdown, which is partly attributed to unchanged alkene concentrations. During the lockdown, alkene ozonolysis seems to be a significant radical primary source due to the elevated O3 concentrations. This unique data set during the lockdown period highlights the importance of controlling alkene emission to mitigate secondary pollution formation in Chengdu and may also be applicable in other regions of China given an expected NOx reduction due to the rapid transformation to electrified fleets in the future.

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.

Comprehensive analysis of long-term trends, meteorological influences, and ozone formation sensitivity in the Jakarta Greater Area

Jakarta Greater Area (JGA) has encountered recurrent challenges of air pollution, notably, high ozone levels. We investigate the trends of surface ozone (O3) changes from the air quality monitoring stations and resolve the contribution of meteorological drivers in urban Jakarta (2010-2019) and rural Bogor sites (2017-2019) using stepwise Multi Linear Regression. During 10 years of measurement, 41% of 1-h O3 concentrations exceeded Indonesia' s national threshold in Jakarta. In Bogor, 0.1% surpassed the threshold during 3 years of available data records. The monthly average of maximum daily 8-h average (MDA8) O3 anomalies exhibited a downward trend at Jakarta sites while increasing at the rural site of Bogor. Meteorological and anthropogenic drivers contribute 30% and 70%, respectively, to the interannual O3 anomalies in Jakarta. Ozone formation sensitivity with satellite demonstrates that a slight decrease in NO2 and an increase in HCHO contributed to declining O3 in Jakarta with 10 years average of HCHO to NO2 ratio (FNR) of 3.7. Conversely, O3 increases in rural areas with a higher FNR of 4.4, likely due to the contribution from the natural emission of O3 precursors and the influence of meteorological factors that magnify the concentration.

Explainable ensemble machine learning revealing the effect of meteorology and sources on ozone formation in megacity Hangzhou, China

Megacity Hangzhou, located in eastern China, has experienced severe O3 pollution in recent years, thereby clarifying the key drivers of the formation is essential to suppress O3 deterioration. In this study, the ensemble machine learning model (EML) coupled with Shapley additive explanations (SHAP), and positive matrix factorization were used to explore the impact of various factors (including meteorology, chemical components, sources) on O3 formation during the whole period, pollution days, and typical persistent pollution events from April to October in 2021-2022. The EML model achieved better performance than the single model, with R2 values of 0.91. SHAP analysis revealed that meteorological conditions had the greatest effects on O3 variability with the contribution of 57 %-60 % for different pollution levels, and the main drivers were relative humidity and radiation. The effects of chemical factors on O3 formation presented a positive response to volatile organic compounds (VOCs) and fine particulate matter (PM2.5), and a negative response to nitrogen oxides (NOx). Oxygenated compounds (OVOCs), alkenes, and aromatic of VOCs subgroups had higher contribution; additionally, the effects of PM2.5 and NOx were also important and increased with the O3 deterioration. The impact of seven emission sources on O3 formation in Hangzhou indicated that vehicle exhaust (35 %), biomass combustion (16 %), and biogenic emissions (12 %) were the dominant drivers. However, for the O3 pollution days, the effects of biomass combustion and biogenic emissions increased. Especially in persistent pollution events with highest O3 concentrations, the magnitude of biogenic emission effect elevated significantly by 156 % compared to the whole situations. Our finding revealed that the combination of the EML model and SHAP analysis could provide a reliable method for rapid diagnosis of the cause of O3 pollution at different event scales, supporting the formulation of control measures.

First temporal distribution model of ambient air pollutants (PM2.5, PM10, and O3) in Yangon City, Myanmar during 2019-2021

Air pollution has emerged as a significant global concern, particularly in urban centers. This study aims to investigate the temporal distribution of air pollutants, including PM2.5, PM10, and O3, utilizing multiple linear regression modeling. Additionally, the research incorporates the calculation of the Air Quality Index (AQI) and Autoregressive Integrated Moving Average (ARIMA) time series modeling to predict the AQI for PM2.5 and PM10. The concentrations and AQI values for PM2.5 ranged from 0 to 93.6 μg/m3 and 0 to 171, respectively, surpassing the Word Health Organization's (WHO) acceptable threshold levels. Similarly, concentrations and AQI values for PM10 ranged from 0.1 to 149.27 μg/m3 and 2-98 μg/m3, respectively, also exceeding WHO standards. Particulate matter pollution exhibited notable peaks during summer and winter. Key meteorological factors, including dew point temperature, relative humidity, and rainfall, showed a significant negative association with all pollutants, while ambient temperature exhibited a significant positive correlation with particulate matter. Multiple linear regression models of particulate matter for winter season demonstrated the highest model performance, explaining most of the variation in particulate matter concentrations. The annual multiple linear regression model for PM2.5 exhibited the most robust performance, explaining 60% of the variation, while the models for PM10 and O3 explained 45% of the variation in their concentrations. Time series modeling projected an increasing trend in the AQI for particulate matter in 2022. The precise and accurate results of this study serve as a valuable reference for developing effective air pollution control strategies and raising awareness of AQI in Myanmar.

Multi-timescale variation characteristics of PM2.5 in different regions of China during 2014-2022

Over the past decade, China has achieved a significant reduction in PM2.5 concentrations. Due to the diversity of natural and artificial factors, regional differences are remarkable in the variation characteristics and have not been well addressed in previous studies. Based on hourly observed PM2.5 concentrations from 2014 to 2022, this study conducted a comprehensive analysis of variation characteristics on annual, seasonal, and diurnal scales, with a special focus on differences across major regions. Driving factors of the variations, the effectiveness of air pollution control efforts as well as future priorities were discussed. The annual PM2.5 concentrations in all regions showed an overall downward trend from 2014 to 2022, but the decline rates differed notably across the regions, with the maximum value nearly two times higher than the minimum value. The seasonal decline rates also differ from region to region, which could be partially attributed to the burning of crop residues and dust events. Northeast China was significantly affected by the burning of crop residues and experienced a big drop in the number of fire points in autumn, but a remarkable increase in spring. The spring dust events may greatly contribute to PM2.5 concentrations in northern and western China. For diurnal variation, nighttime concentrations were generally greater than daytime concentrations, and the nighttime concentrations were likely to increase in eastern regions and decrease in western regions. Furthermore, the daytime and nighttime ratios (calculated by daytime/nighttime concentration divided by the daily-mean concentration) exhibited different interannual trends, with the daytime ratios decreasing and nighttime ratios increasing, especially in the northeastern and western regions. The findings indicate that the air pollution control efforts have been generally successful, but with large regional disparities, and highlight the importance of controlling crop residue burning, dust events, and nighttime emissions for specific seasons and regions.

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.

Achievements and challenges in improving air quality in China: Analysis of the long-term trends from 2014 to 2022

Due to the implementation of air pollution control measures in China, air quality has significantly improved, although there are still additional issues to be addressed. This study used the long-term trends of air pollutants to discuss the achievements and challenges in further improving air quality in China. The Kolmogorov-Zurbenko (KZ) filter and multiple-linear regression (MLR) were used to quantify the meteorology-related and emission-related trends of air pollutants from 2014 to 2022 in China. The KZ filter analysis showed that PM2.5 decreased by 7.36 ± 2.92% yr-1, while daily maximum 8-h ozone (MDA8 O3) showed an increasing trend with 3.71 ± 2.89% yr-1 in China. The decrease in PM2.5 and increase in MDA8 O3 were primarily attributed to changes in emission, with the relative contribution of 85.8% and 86.0%, respectively. Meteorology variations, including increased ambient temperature, boundary layer height, and reduced relative humidity, also contributed to the reduction of PM2.5 and the enhancement of MDA8 O3. The emission-related trends of PM2.5 and MDA8 O3 exhibited continuous decrease and increase, respectively, from 2014 to 2022, while the variation rates slowed during 2018-2020 compared to that during 2014-2017, highlighting the challenges in further improving air quality, particularly in simultaneously reducing PM2.5 and O3. This study recommends reducing NH3 emissions from the agriculture sector in rural areas and transport emissions in urban areas to further decrease PM2.5 levels. Addressing O3 pollution requires the reduction of O3 precursor gases based on site-specific atmospheric chemistry considerations.

Impacts of changes in climate, land use, and emissions on global ozone air quality by mid-21st century following selected Shared Socioeconomic Pathways

Surface ozone (O3) is a major air pollutant and greenhouse gas with significant risks to human health, vegetation, and climate. Uncertainties around the impacts of various critical factors on O3 is crucial to understand. We used the Community Earth System Model to investigate the impacts of land use and land cover change (LULCC), climate, and emissions on global O3 air quality under selected Shared Socioeconomic Pathways (SSPs). Our findings show that increasing forest cover by 20 % under SSP1 in East China, Europe, and the eastern US leads to higher isoprene emissions leading 2-5 ppb increase in summer O3 levels. Climate-induced meteorological changes, like rising temperatures, further enhance BVOC emissions and increase O3 levels by 10-20 ppb in urban areas with high NOx levels. However, higher BVOC emissions can reduce O3 levels by 5-10 ppb in remote environments. Future NOx emissions control reduces O3 levels by 5-20 ppb in the US and Europe in all SSPs, but reductions in NOx and changes in oxidant titration increase O3 in southeast China in SSP5. Increased NOx emissions in southern Africa and India significantly elevate O3 levels up to 15 ppb under different SSPs. Climate change is equally important as emissions changes, sometimes countering the benefits of emissions control. The combined effects of emissions, climate, and land cover result in worse O3 air quality in northern India (+40 %) and East China (+20 %) under SSP3 due to anthropogenic NOx and climate-induced BVOC emissions. Over the northern hemisphere, surface O3 decreases due to reduced NOx emissions, although climate and land use changes can increase O3 levels regionally. By 2050, O3 levels in most Asian regions exceed the World Health Organization safety limit for over 150 days per year. Our study emphasizes the need to consider complex interactions for effective air pollution control and management in the future.

The impacts of ship emissions on ozone in eastern China

Tropospheric ozone (O3), which is one of the main pollutants impeding air quality compliance, has received considerable attention in China. As maritime transportation continues to expand, the effect of ship emissions on air quality is becoming increasingly important. In this study, the Weather Research and Forecast model (WRF), the Community Multiscale Air Quality model (CMAQ), and the integrated process rate (IPR) module provided in the CMAQ are applied to evaluate the impacts of ship emissions on O3 concentration at a national scale in China, including the spatiotemporal characteristics and influencing pathways. Ship emissions can increase or decrease O3 concentrations, with varying effects in different seasons and regions. In the winter, spring, and fall, ship emissions were predicted to decrease O3 concentrations in most areas, whereas in the summer, they increase the O3 concentration, even in regions far away from the coastline, thus adversely affecting the Yangtze River Delta (YRD) and Pearl River Delta (YRD). Additionally, owing to differences in the emissions of volatile organic compounds and nitrogen oxides, the northern and southern regions of the YRD respond differently to ship emissions. Additionally, the influence of ship emissions on the diurnal variation of O3 in the summer was investigated, where significant differences were indicated between cities. The IPR was used to investigate the individual processes contributing to changes in the O3 concentration caused by ship emissions. The transport process appears to be the primary contributor to O3 production, whereas chemistry and dry deposition played key roles in O3 loss. This study provides an in-depth insight into the impacts of ship emissions on O3 in China, which can facilitate the formulation of corresponding environmental policies.

Contrasting effects of clean air actions on surface ozone concentrations in different regions over Beijing from May to September 2013-2020

Due to the nonlinear impacts of meteorology and precursors, the response of ozone (O3) trends to emission changes is very complex over different regions in megacity Beijing. Based on long-term in-situ observations at 35 air quality sites (four categories, i.e., urban, traffic, northern suburban and southern suburban sites) and satellite data, spatiotemporal variability of O3, gaseous precursors, and O3-VOCs-NOx sensitivity were explored through multiple metrics during the warm season from 2013 to 2020. Additionally, the contribution of meteorology and emissions to O3 was separated by a machine-learning-based de-weathered method. The annual averaged MDA8 O3 and O3 increased by 3.7 and 2.9 μg/m3/yr, respectively, with the highest at traffic sites and the lowest in northern suburb, and the rate of Ox (O3 + NO2) was 0.2 μg/m3/yr with the highest in southern suburb, although NO2 declined strongly and HCHO decreased slightly. However, the increment of O3 and Ox in the daytime exhibited decreasing trends to some extent. Additionally, NOx abatements weakened O3 loss through less NO titration, which drove narrowing differences in urban-suburban O3 and Ox. Due to larger decrease of NO2 in urban region and HCHO in northern suburb, the extent of VOCs-limited regime fluctuated over Beijing and northern suburb gradually shifted to transition or NOx-limited regime. Compared with the directly observed trends, the increasing rate of de-weathered O3 was lower, which was attributed to favorable meteorological conditions for O3 generation after 2017, especially in June (the most polluted month); whereas the de-weathered Ox declined except in southern suburb. Overall, clean air actions were effective in reducing the atmospheric oxidation capacity in urban and northern suburban regions, weakening local photochemical production over Beijing and suppressing O3 deterioration in northern suburb. Strengthening VOCs control and keeping NOx abatement, especially in June, will be vital to reverse O3 increase trend in Beijing.

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

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

Chronic and acute health effects of PM2.5 exposure and the basis of pollution control targets

Ho Chi Minh City (HCMC) is changing and expanding quickly, leading to environmental consequences that seriously threaten human health. PM2.5 pollution is one of the main causes of premature death. In this context, studies have evaluated strategies to control and reduce air pollution; such pollution-control measures need to be economically justified. The objective of this study was to assess the socio-economic damage caused by exposure to the current pollution scenario, taking 2019 as the base year. A methodology for calculating and evaluating the economic and environmental benefits of air pollution reduction was implemented. This study aimed to simultaneously evaluate the impacts of both short-term (acute) and long-term (chronic) PM2.5 pollution exposure on human health, providing a comprehensive overview of economic losses attributable to such pollution. Spatial partitioning (inner-city and suburban) on health risks of PM2.5 and detailed construction of health impact maps by age group and sex on a spatial resolution grid (3.0 km × 3.0 km) was performed. The calculation results show that the economic loss from premature deaths due to short-term exposure (approximately 38.86 trillion VND) is higher than that from long-term exposure (approximately 14.89 trillion VND). As the government of HCMC has been developing control and mitigation solutions for the Air Quality Action Plan towards short- and medium-term goals in 2030, focusing mainly on PM2.5, the results of this study will help policymakers develop a roadmap to reduce the impact of PM2.5 during 2025-2030.

Assessing the health impacts of PM2.5 and ozone pollution and their comprehensive correlation in Chinese cities based on extended correlation coefficient

The coordinated control of PM_2.5 and ozone pollution is becoming more and more important in the current and next stage of Chinese environmental pollution control. Existing studies are unable to provide sufficient quantitative assessments of the correlation of PM_2.5 and ozone pollution to support the coordinated control of the two air pollutants. This study develops a systematic method to comprehensively assess the correlation between PM_2.5 and ozone pollution, including the evaluation of the impact of two air pollutants on human health and the extended correlation coefficient (ECC) for assessing the bivariate correlation index of PM_2.5-ozone pollution in Chinese cities. According to the latest studies on epidemiology conducted in China, we take cardiovascular and cerebrovascular diseases and respiratory diseases as the ozone pollution's health burden when evaluating the health impact of ozone pollution. The results show that the health impact of PM_2.5 in China decreases by 25.9 % from 2015 to 2021, while the health impact of ozone increases by 11.8 %. The ECC of 335 cities in China shows an increasing-decreasing trend but has generally increased from 2015 to 2021. The study provides important support for an in-depth understanding of the correlation and development trend of Chinese PM_2.5 and ozone pollution by classifying the comprehensive PM_2.5-ozone correlation performances of Chinese cities into four types. China or other countries will get better environmental benefits by implementing different coordinated management approaches for different correlative types of regions based on the assessment method in this study.

Substantial short- and long-term health effect due to PM2.5 and the constituents even under future emission reductions in China

Heavy pollution events of fine particulate matter (PM_2.5) frequently occur in China, seriously affecting the human health. However, how meteorological factors and anthropogenic emissions affect PM_2.5 and the major constituents, as well as the subsequent health effect, remains unclear. Here, based on regional climate and air quality models Weather Research and Forecasting (WRF) and Community Multiscale Air Quality (CMAQ), the PM_2.5 and major constituents in China at present and mid-century under the carbon neutral scenario Shared Socioeconomic Pathways (SSP)1-2.6 are simulated. Due to anthropogenic emission reduction, concentrations of PM_2.5 and the constituents decrease substantially in SSP1-2.6. The long-term exposure premature deaths at present are 2.23 million per year in mainland China, which is projected to increase by 76 % under SSP1-2.6 despite emission reduction, primarily attributable to aging which strikingly offsets the effect of air quality improvement. The number of annual premature deaths resulting from short-term exposure is 228,104 in mainland China at present, which is projected to decrease in the future. Using North China Plain as an example, we identify that among the major constituents of PM_2.5, organic carbon leads to the most short-term exposure deaths considering the largest exposure-response coefficient. Regarding the abnormally meteorological conditions, we find, relative to low relative humidity (RH) and non-stagnation, the compound events, defined as concurrence of high RH and atmospheric stagnation, exhibit an amplified role inducing larger premature deaths compared to the additive effect of the individual event of high RH and atmospheric stagnation. This nonlinear effect occurs at both present and future, but diminished in future due to emission reductions. Our study highlights the importance of considering both the long- and short-term premature deaths associated with PM_2.5 and the constituents, as well as the critical effect of extreme weather events.

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.

Increases in ozone-related mortality in China over 2013-2030 attributed to historical ozone deterioration and future population aging

We systematically examine historical and future changes in premature respiratory mortalities attributable to ozone (O₃) exposure (O₃-mortality) in China and identify the leading cause of respective change for the first time. The historical assessment for 2013-2019 is based on gridded O₃ concentrations generated by a multi-source-data-fusion algorithm; the future prediction for 2019-2030 uses gridded O₃ concentrations projected by four Coupled Model Intercomparison Project Phase 6 (CMIP6) models under three Shared Socioeconomic Pathways (SSP) scenarios. During 2013-2019, national annual O₃-mortality is 176.3 thousand (95%CI: 123.5-224.0 thousand) averaged over 2013-2019 with an increasing trend of 14.1 thousand yr^-1 (95%CI: 10.2-17.4 thousand yr^-1); sensitivity experiments show that the O₃-mortality varies at a rate of +12.7 (95%CI: 9.2-15.6), +5.8 (95%CI: 4.0-7.4), +1.0 (95%CI: 0.7-1.2), -5.4 (95%CI: -6.9 to -3.7) thousand yr^-1, owing to changes in O₃ concentration, population age structure, population size, mortality rate for respiratory disease, respectively. The deterioration of O₃ air quality, shown as significant increase in O₃ concentration, is identified as the primary factor which contributes 90.1 % of 2013-2019 O₃-mortality rise. Compared with O₃-mortality estimated in this study, the widely-used O₃-mortality assessment method based on urban-site-dominant O₃ measurements generates close national O₃-mortality but overestimates (underestimates) provincial O₃-mortality in coastal (central) provinces. From 2019 to 2030, national O₃-mortality is projected to increase by 50.4-103.7 thousand under different SSP scenarios. The change in age structure (i.e. population aging) alone will result in significant O₃-mortality rises of 137.9-160.5 thousand. Compared with 2013-2019 rapid O₃ increase (+2.5 μg m^-3 yr^-1 at national level), O₃ concentrations are projected to increase at a lower rate (+0.4 μg m^-3 yr^-1 in SSP5-8.5) or even decrease (-0.7 μg m^-3 yr^-1 in SSP1-2.6) from 2019 to 2030. Therefore, population aging, in place of O₃ air quality deterioration, will become the leading cause of future O₃-mortality rises during the coming decade.

Modeling study on the roles of the deposition and transport of PM2.5 in air quality changes over central-eastern China

The role of PM_2.5 (particles with aerodynamic diameters ≤ 2.5 µm) deposition in air quality changes over China remains unclear. By using the three-year (2013, 2015, and 2017) simulation results of the WRF/CUACE v1.0 model from a previous work (Zhang et al., 2021), a non-linear relationship between the deposition of PM_2.5 and anthropogenic emissions over central-eastern China in cold seasons as well as in different life stages of haze events was unraveled. PM_2.5 deposition is spatially distributed differently from PM_2.5 concentrations and anthropogenic emissions over China. The North China Plain (NCP) is typically characterized by higher anthropogenic emissions compared to southern China, such as the middle-low reaches of Yangtze River (MLYR), which includes parts of the Yangtze River Delta and the Midwest. However, PM_2.5 deposition in the NCP is significantly lower than that in the MLYR region, suggesting that in addition to meteorology and emissions, lower deposition is another important factor in the increase in haze levels. Regional transport of pollution in central-eastern China acts as a moderator of pollution levels in different regions, for example by bringing pollution from the NCP to the MLYR region in cold seasons. It was found that in typical haze events the deposition flux of PM_2.5 during the removal stages is substantially higher than that in accumulation stages, with most of the PM_2.5 being transported southward and deposited to the MLYR and Sichuan Basin region, corresponding to a latitude range of about 24°N-31°N.

Secondary PM2.5 dominates aerosol pollution in the Yangtze River Delta region: Environmental and health effects of the Clean air Plan

The Clean Air Plan has been active in China since 2013 to mitigate severe PM_2.5 pollution. In this study, we applied the air quality model WRF-Chem to simulate PM_2.5 in the Yangtze River Delta (YRD) region of China in 2017, with the aim of assessing the air quality improvement and its associated health burden in the final year of the Clean Air Plan. To better describe the fate of various PM_2.5 compositions, we updated the chemical mechanisms in the model beforehand, including heterogeneous sulfate reactions, aqueous secondary organic aerosol (SOA) uptake, and volatility basis set (VBS) based SOA production. Both the observation and simulation results agreed that the stringent clear air action effectively reduced the PM_2.5 pollution levels by ∼ 30 %. The primary PM_2.5 (-6 ∼  - 16 % yr^-1) showed a more significant decreasing trend than the secondary PM_2.5 (-2 ∼  - 8 % yr^-1), which was mainly caused by the directivity of the clear air actions and the worsening ozone pollution in the recent years. The inconsistent decreasing trends of PM_2.5 components subsequently led to an increasing proportion of secondary PM_2.5. Nitrate particles, higher in the central and western YRD region, have replaced sulfate and have become the largest component of secondary inorganic aerosols year-round, except in summer, when strong ammonium nitrate evaporation occurs. In addition, SOA remains an important component (21 ∼ 22 %) especially in summer, most of which is produced from the oxidation and ageing of semi/intermediate volatile organic compounds (S/IVOC). Furthermore, we quantified the associated health impacts and found that the Clean Air Plan has largely reduced premature mortality due to PM_2.5 exposure in the YRD region from 399.1 thousand to 295.7 thousand. Our study highlights the benefits of the Clean Air Plan and suggests that subsequent PM_2.5 improvement should be geared more towards controlling secondary pollutants.

Multiple Impacts of Aerosols on O3 Production Are Largely Compensated: A Case Study Shenzhen, China

Tropospheric ozone (O₃) is a harmful gas compound to humans and vegetation, and it also serves as a climate change forcer. O₃ is formed in the reactions of nitrogen oxides and volatile organic compounds (VOCs) with light. In this study, an O₃ pollution episode encountered in Shenzhen, South China in 2018 was investigated to illustrate the influence of aerosols on local O₃ production. We used a box model with comprehensive heterogeneous mechanisms and empirical prediction of photolysis rates to reproduce the O₃ episode. Results demonstrate that the aerosol light extinction and NO₂ heterogeneous reactions showed comparable influence but opposite signs on the O₃ production. Hence, the influence of aerosols from different processes is largely counteracted. Sensitivity tests suggest that O₃ production increases with further reduction in aerosols in this study, while the continued NO_x reduction finally shifts O₃ production to an NO_x-limited regime with respect to traditional O₃-NO_x-VOC sensitivity. Our results shed light on the role of NO_x reduction on O₃ production and highlight further mitigation in NO_x not only limiting the production of O₃ but also helping to ease particulate nitrate, as a path for cocontrol of O₃ and fine particle pollution.

Health Risk Assessment of Inhalation Exposure to Airborne Particle-Bound Nitrated Polycyclic Aromatic Hydrocarbons in Urban and Suburban Areas of South China

Airborne particulates (PM_2.5 and TSP) were collected from outdoor and indoor areas at urban (Haizhu District) and suburban (Huadu District) sites from 2019 to 2020 in Guangzhou. Three nitro-polycyclic aromatic hydrocarbons (nitro-PAHs) in the airborne particulates were identified by a gas chromatograph equipped with a triple-quadrupole mass spectrometer. In the Haizhu District and Huadu District, the nitro-PAH concentrations in PM_2.5 and TSP did not show a significant decrease from winter to summer. From 2019 to 2020, the difference in the average concentration of nitro-PAHs in PM_2.5 and TSP in Guangzhou was relatively low and had no statistical significance. The diagnostic ratios of 2-nitrofluorene (2-NF)/1-nitropyrene (1-NP) in TSP are less than five, while for 2-NF/1-NP in outdoor PM_2.5 in the summer of 2019 and 2020 are more than five, which indicates that nitro-PAHs in the atmospheric PM_2.5 in Guangzhou during summer mainly originated from the secondary formation of atmospheric photochemical reactions between parent PAHs and oxidants (·OH, NO₃, and O₃). 9-Nitroanthracene (9-NT) made the most significant contribution to the total nitro-PAH concentration. The incremental lifetime cancer risks (ILCRs) of nitro-PAHs in PM_2.5 and TSP by inhalation exposure indicated low potential health risks in the urban-suburban of Guangzhou.

Multi-Scale Effects of Meteorological Conditions and Anthropogenic Emissions on PM2.5 Concentrations over Major Cities of the Yellow River Basin

The mechanism behind PM2.5 pollution is complex, and its performance at multi-scales is still unclear. Based on PM2.5 monitoring data collected from 2015 to 2021, we used the GeoDetector model to assess the multi-scale effects of meteorological conditions and anthropogenic emissions, as well as their interactions with PM2.5 concentrations in major cities in the Yellow River Basin (YRB). Our study confirms that PM2.5 concentrations in the YRB from 2015 to 2021 show an inter-annual and inter-season decreasing trend and that PM2.5 concentrations varied more significantly in winter. The inter-month variation of PM2.5 concentrations shows a sinusoidal pattern from 2015 to 2021, with the highest concentrations in January and December and the lowest from June to August. The PM2.5 concentrations for major cities in the middle and downstream regions of the YRB are higher than in the upper areas, with high spatial distribution in the east and low spatial distribution in the west. Anthropogenic emissions and meteorological conditions have similar inter-annual effects, while air pressure and temperature are the two main drivers across the whole basin. At the sub-basin scale, meteorological conditions have stronger inter-annual effects on PM2.5 concentrations, of which temperature is the dominant impact factor. Wind speed has a significant effect on PM2.5 concentrations across the four seasons in the downstream region and has the strongest effect in winter. Primary PM2.5 and ammonia are the two main emission factors. Interactions between the factors significantly enhanced the PM2.5 concentrations. The interaction between ammonia and other emissions plays a dominant role at the whole and sub-basin scales in summer, while the interaction between meteorological factors plays a dominant role at the whole-basin scale in winter. Our study not only provides cases and references for the development of PM2.5 pollution prevention and control policies in YRB but can also shed light on similar regions in China as well as in other regions of the world.

A systematic assessment of city-level climate change mitigation and air quality improvement in China

China is facing dual challenges of air pollution and climate change. By using city-level data, we comprehensively assessed air quality and CO₂ emission changes from 2015 to 2019 for 335 Chinese cities. We selected important regions for air pollution control and categorized all cities into different classes according to their development levels. Our novel approach revealed new insights on different patterns of changes of PM_2.5, O₃, and CO₂ by region and city class. We found that PM_2.5 concentrations decreased remarkably due to mandatory city-level reduction targets, especially in the Beijing-Tianjin-Hebei (-27%) region. Nonetheless, O₃ concentrations and CO₂ emissions increased in 91% and 69% of Chinese cities, respectively. Observed CO₂ emission reductions in more developed cities were mainly due to prominent energy intensity reduction and energy structure improvement. Our study indicates a lack of synergy in air pollution control and CO₂ mitigation under current policies in China. To address both challenges holistically, we suggest setting mandatory city-level CO₂ emission reduction targets and reinforcing clean energy and energy efficiency measures.

Analysis of the meteorological factors affecting the short-term increase in O3 concentrations in nine global cities during COVID-19

Surface ozone (O₃) is a major air pollutant around the world. This study investigated O₃ concentrations in nine cities during the Coronavirus disease 2019 (COVID-19) lockdown phases. A statistical model, named Generalized Additive Model (GAM), was also developed to assess different meteorological factors, estimate daily O₃ release during COVID-19 lockdown and determine the relationship between the two. We found that: (1) Daily O₃ significantly increased in all selected cities during the COVID-19 lockdown, presenting relative increases from -5.7% (in São Paulo) to 58.9% (in Guangzhou), with respect to the average value for the same period in the previous five years. (2) In the GAM model, the adjusted coefficient of determination (R²) ranged from 0.48 (Sao Paulo) to 0.84 (Rome), and it captured 51-85% of daily O₃ variations. (3) Analyzing the expected O₃ concentrations during the lockdown, using GAM fed by meteorological data, showed that O₃ anomalies were dominantly controlled by meteorology. (4) The relevance of different meteorological variables depended on the cities. The positive O₃ anomalies in Beijing, Wuhan, Guangzhou, and Delhi were mostly associated with low relative humidity and elevated maximum temperature. Low wind speed, elevated maximum temperature, and low relative humidity were the leading meteorological factors for O₃ anomalies in London, Paris, and Rome. The two other cities had different leading factor combinations.

Meteorological influences on daily variation and trend of summertime surface ozone over years of 2015-2020: Quantification for cities in the Yangtze River Delta

We quantify the meteorological influences on daily variations and trends of maximum daily 8-h average ozone (MDA8 O₃) concentrations by using multiple linear regression (MLR) and Lindeman, Merenda, and Gold (LMG) approaches. Different from previous region-based studies, we pay special attention to meteorological influences at city scale. Over 2015-2019, daily changes in key meteorological parameters could explain 47%-74% of the observed daily variations in summertime MDA8 O₃ concentrations in Yangtze River Delta (YRD) and four cities (Shanghai, Nanjing, Hangzhou, and Hefei), with RH being the top driver. Over years of 2015-2020, daily concentrations of MDA8 O₃ obtained from MLR equations (MDA8O₃_MLR) of the local cities always had better performance than those of YRD. Compared with the observed daily MDA8 O₃ in June-July-August (JJA) over the studied period, daily MDA8O₃_MLR of the local cities (of YRD) had correlation coefficients of 0.73 (0.63), 0.75 (0.74), 0.79 (0.78), and 0.76 (0.73) in Shanghai, Nanjing, Hangzhou, and Hefei, respectively, and the MDA8O₃_MLR of the local cities (of YRD) captured 54% (17%), 63% (51%), 52% (27%) of the observed O₃-polluted days (days with MDA8 O₃ concentration exceeding 160 μg m^-3) in Shanghai, Nanjing, and Hangzhou, respectively. The meteorologically driven trends (Trend_Met) in MDA8 O₃ were calculated using the established MLR equations. Over 2015-2019, the observed trends (Trend_Obs) and Trend_Met in MDA8 O₃ were mostly positive in YRD, Nanjing, Hangzhou, and Hefei. In Shanghai, Trend_Obs, Trend_Met, and anthropogenically driven trend (estimated as Trend_Obs minus Trend_Met) of MDA8 O₃ in JJA over 2015-2019 were -1.3, +1.0, and -2.3 μg m^-3 y^-1, respectively, indicating that the emission control measures alleviated O₃ pollution in this city. Our results suggest that it is necessary to establish MLR equations at city scale to account for the role of meteorology in the actions of O₃ pollution control.

Evaluating the real changes of air quality due to clean air actions using a machine learning technique: Results from 12 Chinese mega-cities during 2013-2020

China has implemented two national clean air actions in 2013-2017 and 2018-2020, respectively, with the aim of reducing primary emissions and hence improving air quality at a national level. It is important to examine the effectiveness of such emission reductions and assess the resulting changes in air quality. However, such evaluation is difficult as meteorological factors can amplify, or obscure the changes of air pollutants, in addition to the emission reduction. In this study, we applied the random forest machine learning technique to decouple meteorological influences from emissions changes, and examined the deweathered trends of air pollutants in 12 Chinese mega-cities during 2013-2020. The observed concentrations of all criteria pollutants except O₃ showed significant declines from 2013 to 2020, with PM_2.5 annual decline rates of 6-9% in most cities. In contrast, O₃ concentrations increased with annual growth rates of 1-9%. Compared with the observed results, all the pollutants showed smoothed but similar variation in trend and annual rate-of-change after weather normalization. The response of O₃ to NO₂ concentrations indicated significant regional differences in photochemical regimes, and the differences between observed and deweathered results provided implications for volatile organic compound emission reductions in O₃ pollution mitigation. We further evaluated the effectiveness of first and second clean air actions by removing the meteorological influence. We found that the meteorology can make negative or positive contribution in reducing pollutant concentrations from emission reduction, depending on type of pollutants, locations, and time period. Among the 12 mega-cities, only Beijing showed a positive meteorological contribution in amplifying reductions in main pollutants except O₃ during both clean air action periods. Considering the large and variable impact of meteorological effects in changing air quality, we suggest that similar deweathered analysis is needed as a routine policy evaluation tool on a regional basis.

Trends and characteristics of ozone and nitrogen dioxide related health impacts in Chinese cities

Ambient ozone pollution has been becoming severe and attributed to considerable health impacts in China. Nitrogen dioxide (NO₂) is involved in atmospheric ozone production while also affecting public health directly. Joint control ozone and NO₂ pollution would be of significance. This study quantitatively assessed the health impact attributed to ambient ozone and NO₂ pollution in 338 Chinese cities from 2015 to 2020. The results reveal the generally opposite trends of ozone- and NO₂-related health impacts in China. From 2015-2020, respiratory and chronic obstructive pulmonary disease (COPD) health impacts attributed to ozone in 338 cities increased by 65.30% and 63.98%. The NO₂-attributed health impacts decreased by 24.80% and 24.62%. In 2020, the ozone- and NO₂-related respiratory health impacts were 3.96 million DALYs (disability-adjusted life years) and 1.47 million DALYs. High health impacts are concentrated in big cities and city clusters. In 2020, the sum of ozone- and NO₂-related respiratory health impacts in the top 20 cities was 0.98 million DALYs and 0.44 million DALYs, accounting for 24.70% and 30.24% of the 338 cities. The population attribution fraction analysis identified the increasing distributional consistency of ozone and NO₂-related health impacts, emphasizing the necessity and possible efficiency of ozone-NO₂ joint control. Emission source analysis based on gridded data provided a reference for understanding health impacts and developing targeted strategies.

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 2. Key weather elements and emissions

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 an accompanying paper on synoptic circulation impact (Gong et al., 2022), using a multi-linear regression model, the trends of key meteorological elements at local scale, i.e., temperature, relative humidity, solar radiation, PBL height, precipitation and wind speed, are analyzed and correlated with the trends of PM_2.5 and O₃ levels to identify significantly influencing factors in seven Chinese cities. Furthermore, with additional emission surrogates introduced in the regression model, the impacts on the trends by meteorology and emission were separated and quantified. Results show that the increasing trends of O₃ at most Chinese cities were largely attributed to the trends of meteorological elements of temperature and solar radiation, while the trends of PM_2.5 are mostly contributed by the emission reduction measures of PM_2.5 and its precursors. The meteorology alone can explain approximately 57-80% of the O₃ variations and only 20-33% of the PM_2.5 variations. With the addition of emission surrogates, this explanation percentage is increased to about 57-82% for O₃ but significantly enhanced to 71-83% for PM_2.5.

Tracking PM2.5 and O3 Pollution and the Related Health Burden in China 2013-2020

Based on the exposure data sets from the Tracking Air Pollution in China (TAP, http://tapdata.org.cn/), we characterized the spatiotemporal variations in PM_2.5 and O₃ exposures and quantified the long- and short-term exposure related premature deaths during 2013-2020 with respect to the two-stage clean air actions (2013-2017 and 2018-2020). We find a 48% decrease in national PM_2.5 exposure during 2013-2020, although the decrease rate has slowed after 2017. At the same time, O₃ pollution worsened, with the average April-September O₃ exposure increased by 17%. The improved air quality led to 308 thousand and 16 thousand avoided long- and short-term exposure related deaths, respectively, in 2020 compared to the 2013 level, which was majorly attributed to the reduction in ambient PM_2.5 concentration. It is also noticed that with smaller PM_2.5 reduction, the avoided long-term exposure associated deaths in 2017-2020 (13%) was greater than that in 2013-2017 (9%), because the exposure-response curve is nonlinear. As a result of the efforts in reducing PM_2.5-polluted days with the daily average PM_2.5 higher than 75 μg/m³ and the considerable increase in O₃-polluted days with the daily maximum 8 h average O₃ higher than 160 μg/m³, deaths attributable to the short-term O₃ exposure were greater than those due to PM_2.5 exposure since 2018. Future air quality improvement strategies for the coordinated control of PM_2.5 and O₃ are urgently needed.

A potential controlling approach on surface ozone pollution based upon power big data

Surface ozone pollution has attracted extensive attention with the decreasing of haze pollution, especially in China. However, it is still difficult to efficiently control the pollution in time despite numbers of reports on mechanism of ozone pollution. Here we report a method for implementing effective control of ozone pollution through power big data. Combining the observation of surface ozone, NO2, meteorological parameters together with hourly electricity consumption data from volatile organic compounds (VOCs) emitting companies, a generalized additive model (GAM) is established for quantifying the influencing factors on the temporal and spatial distribution of surface ozone pollution from 2020 to 2021 in Anhui province, central China. The average R2 value for the modelling results of 16 cities is 0.82, indicating that the GAM model effectively captures the characteristics of ozone. The model quantifies the contribution of input variables to ozone, with both NO2 and industrial VOCs being the main contributors to ozone, contributing 33.72% and 21.12% to ozone formation respectively. Further analysis suggested the negative correlation between ozone and NO2, revealing VOCs primarily control the increase in ozone. Under scenarios controlling for a 10% and 20% reduction in electricity use in VOC-electricity sensitive industries that can be identified by power big data, ozone concentrations decreased by 9.7% and 19.1% during the pollution period. This study suggests a huge potential for controlling ozone pollution through power big data and offers specific control pathways.

Spatiotemporal characteristics of PM2.5 and ozone concentrations in Chinese urban clusters

Despite China's public commitment to emphasise air pollution investigation and control, trends in PM_2.5 and ozone concentrations in Chinese urban clusters remain unclear. This study quantifies the spatiotemporal variations in PM_2.5 and surface ozone at the scale of Chinese urban clusters by using a long-term integrated dataset from 2015 to 2020. Nonlinear Granger causality testing was used to explore the spatial association patterns of PM_2.5 and ozone pollution in five megacity cluster regions. The results show a significant downward trend in annual mean PM_2.5 concentrations from 2015 to 2020, with a decline rate of 2.8 μg m^-3 yr^-1. By contrast, surface ozone concentrations increased at a rate of 2.1 μg m^-3 yr^-1 over the 6 years. The annual mean PM_2.5 concentrations in urban clusters show significant spatial clustering characteristics, mainly in Beijing-Tianjin-Hebei (BTH), Fenwei Plain (FWP), Northern slope of Tianshan Mountains urban cluster (NSTM), Sichuan Basin urban cluster (SCB), and Yangtze River Delta (YRD). Surface ozone shows severe summertime pollution and distributional variability, with increased ozone pollution in major urban clusters. The highest increases were observed in BTH, Yangtze River midstream urban cluster (YRMR), YRD, and Pearl River Delta (PRD). Nonlinear Granger causality tests showed that PM_2.5 was a nonlinear Granger cause of ozone, further supporting the literature's findings that PM_2.5 reduction promoted photochemical reaction rates and stimulated ozone production. The nonlinear test statistic passed the significance test in magnitude and statistical significance. FWP was an exception, with no significant long-term nonlinear causal link between PM_2.5 and ozone. This study highlights the challenges of compounded air pollution caused primarily by ozone and secondary PM_2.5. These results have implications for the design of synergistic pollution abatement policies for coupled urban clusters.

Decomposing PM2.5 air pollution rebounds in Northern China before COVID-19

China's efforts to curb air pollution have drastically reduced its concentrations of fine particulate matter (PM_2.5) from 2013 to 2018 nationwide. However, few studies examined the most recent changes in PM_2.5 concentrations and questioned if the previous PM_2.5 declining trend was sustained or not. This study took a deep dive into the PM_2.5 trend for 136 northern cities of China from 2015 to early 2020 before the coronavirus disease 2019 (the COVID-19 hereafter) crisis, using ground-based PM_2.5 data notably adjusted for a key measurement method change. We find that mean PM_2.5 concentrations in northern China increased by 5.16 µg/m³ in 2019, offsetting 80% of the large reduction achieved in 2018. The rebound was more significant during the heating seasons (HS; Nov to next Mar) over the 2 years: 10.49 µg/m³ from the 2017 HS to the 2019 HS. A multiple linear regression analysis further revealed that anthropogenic factors contributed to around 50% of the PM_2.5 rebound in northern cities of China. Such a significant role of anthropogenic factors in driving the rebound was tightly linked to deep cuts in PM_2.5 concentrations in the previous year, systemic adjustment of policy targets and mitigation measures by the government, and the rising marginal cost of these measures. These findings suggest the need to chart a more sustainable path for future PM_2.5 emission reductions, with an emphasis on key regions during key pollution periods.

An Improved Hybrid Transfer Learning-Based Deep Learning Model for PM2.5 Concentration Prediction

With the improvement of the living standards of the residents, it is a very important and challenging task to continuously improve the accuracy of PM2.5 (particulate matter less than 2.5 μm in diameter) prediction. Deep learning-based networks, such as LSTM and CNN, have achieved good performance in recent years. However, these methods require sufficient data to train the model. The performance of these methods is limited for the sites where the data is lacking, such as the newly constructed monitoring sites. To deal with this problem, an improved deep learning model based on the hybrid transfer learning strategy is proposed for predicting PM2.5 concentration in this paper. In the proposed model, the maximum mean discrepancy (MMD) is used to select which station in the source domain is most suitable for migration to the target domain. An improved dual-stage two-phase (DSTP) model is used to extract the spatial–temporal features of the source domain and the target domain. Then the domain adversarial neural network (DANN) is used to find the domain invariant features between the source and target domains by domain adaptation. Thus, the model trained by source domain site data can be used to assist the prediction of the target site without degradation of the prediction performance due to domain drift. At last, some experiments are conducted. The experimental results show that the proposed model can effectively improve the accuracy of the PM2.5 prediction at the sites lacking data, and the proposed model outperforms most of the latest models.

Exploring the causes for co-pollution of O3 and PM2.5 in summer over North China

Co-pollution of surface O₃ and PM_2.5 has become the most predominant type of air pollutions in Beijing-Tianjin-Hebei region in the hot season since 2017, particularly in May-July. Analysis based on observational data showed that co-pollution was always accompanied by high temperature, moderate relative humidity, extremely high SO₂, and higher NO₂. We also found that the meteorology and precursor dependence of O₃ was similar between co-pollution and O₃- single pollution. While PM_2.5 in co-pollution was more related to temperature, relative humidity, and precursors, that in PM_2.5-singe pollution were more related to small winds. These results indicate that co-pollution seemed to be more affected by atmospheric chemistry. According to the PM_2.5 components, secondary inorganic aerosols (SIA) composed 44.3-48.7% of PM_2.5 in co-pollution, while those accounting for 42.1-46.5% and 41.2-44.3%, respectively, in O₃- and PM_2.5-single pollution, which further confirmed the relatively stronger atmospheric chemistry processes in co-pollution. And the high proportion of SIA in co-pollution was mainly attributed to SO₄^2-, which was observed to rapidly boom in non-refractory submicron aerosol (NR-PM₁) on the condition of high level of O₃ at daytime. Additionally, we further explored the interactions of O₃ and PM_2.5 in co-pollution. It was found that most (~61.9%) co-pollution episodes were initiated by high O₃ at daytime; while for other episodes, high PM_2.5 firstly occurred under the more stable meteorological conditions, and then accumulation of precursors further induced high O₃. A higher SIA concentration was observed in O₃-initiated co-pollution, indicating that the atmospheric oxidation in co-pollution caused by chemical processes was stronger than that by physical processes, which was further approved by the higher values of SOR and NOR in O₃-initiated co-pollution. This observational study revealed that controlling O₃ and precursor SO₂ is the key to abating co-pollution in the hot season.

The dynamic multi-box algorithm of atmospheric environmental capacity

It is very important for air pollution prevention and control to accurately quantify atmospheric environment capacity (AEC) in the planetary boundary layer (PBL). This study developed a high temporal-resolution dynamic multi-box algorithm to estimate PM_2.5 AEC with a PBL ceilometer and Doppler wind profile lidar in Beijing City. Compared with the traditional A-value method, two primary improvements are introducing the time coefficient and vertical multi-box assumption into the original box model. The algorithm can accurately calculate the PM_2.5 AEC under different circulation patterns and predict the short-time dynamic change of AEC. The results show that the time coefficient effectively reduced the estimation errors when the initial PM_2.5 concentration, horizontal wind speed and PBL heights change greatly with time, such situation is consistent with most circulation patterns. And the improvement of multi-box model is much more remarkable when the PM_2.5 concentration and horizontal wind change greatly in the vertical direction, such as A, NE and W type circulations. The ideal AEC under polluted circulation patterns won't increase infinitely with wind speed and PBL height, generally less than 30 t/h. The horizontal advection has a much greater effect on expanding the capacity of PM_2.5 than the vertical diffusion under clean circulation patterns, and the maximum value of ideal AEC can reach 50 t/h. The positive residual AEC under clean circulations indicates surplus capacity for PM_2.5 because of vigorous turbulences, while weak diffusion and ventilation conditions under polluted circulations lead to negative residual AEC and insufficient capacity of atmosphere.

Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions

Evaluating the influence of anthropogenic-emission changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emission changes. However, the ability of these widely used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using simulations from a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic-emission changes in the US (2011 to 2017) and China (2013 to 2017) on PM_2.5 and O₃, we show that widely used regression methods do not perform well in correcting for meteorological variability and identifying long-term trends in ambient pollution related to changes in emissions. The estimation errors, characterized as the differences between meteorology-corrected trends and emission-driven trends under constant meteorology scenarios, can be reduced by 30%-42% using a random forest model that incorporates both local- and regional-scale meteorological features. We further design a correction method based on GEOS-Chem simulations with constant-emission input and quantify the degree to which anthropogenic emissions and meteorological influences are inseparable, due to their process-based interactions. We conclude by providing recommendations for evaluating the impacts of anthropogenic-emission changes on air quality using statistical approaches.

Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions

Evaluating the influence of anthropogenic-emission changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emission changes. However, the ability of these widely used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using simulations from a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic-emission changes in the US (2011 to 2017) and China (2013 to 2017) on PM_2.5 and O₃, we show that widely used regression methods do not perform well in correcting for meteorological variability and identifying long-term trends in ambient pollution related to changes in emissions. The estimation errors, characterized as the differences between meteorology-corrected trends and emission-driven trends under constant meteorology scenarios, can be reduced by 30%-42% using a random forest model that incorporates both local- and regional-scale meteorological features. We further design a correction method based on GEOS-Chem simulations with constant-emission input and quantify the degree to which anthropogenic emissions and meteorological influences are inseparable, due to their process-based interactions. We conclude by providing recommendations for evaluating the impacts of anthropogenic-emission changes on air quality using statistical approaches.

Long-term variation of boundary layer height and possible contribution factors: A global analysis

Boundary layer height (BLH) plays an important role in regulating global weather/climate, as well as the dispersion and transportation of pollutants. Until now, however, the attribution and contributions of different controlling factors to BLH long-term variability and trends have not been quantified on a global scale. The long-term radiosonde dataset was used in this study to retrieve global BLH climatology; seasonal, diurnal, long-term variation and trends were analyzed over a 39-year period (1980-2018). Statistical results show that the global distribution of the BLH and its trend have apparent day-night differences. BLH during daytime is deeper during clear-sky conditions compared to cloudy sky conditions, indicating a significant effect of clouds; BLH during nighttime is deeper under cloudy conditions. BLH was also found to vary over different land types; dry and hot soil exhibits a deeper BLH than those of wet and cool soil. The long-term variation and trend of BLH are highly influenced by near-surface meteorological parameters. In particular, based on multiple linear stepwise regression models and the contribution calculation method, this investigation initiatively quantifies the influences of meteorological parameters on global BLH long-term variation and trend. Our results emphasized that a 10 m wind speed (WS) and low tropospheric stability (LTS) have significant contributions to long-term BLH variation; WS and LTS anomalies alternately dominated the contribution of the diurnal cycle of the BLH anomaly. Annual BLH recorded an average increasing trend (38.9-42.1 m/decade), and LTS is more dominant than WS from a contribution perspective, especially for increased BLH anomaly. Contributions from near-surface temperature (T) and relative humidity (RH) also play important roles. However, a decreasing WS trend dominated the decreased trends of BLH anomaly, accounting for nearly 40% of the total contribution.

Removing the effects of meteorological factors on changes in nitrogen dioxide and ozone concentrations in China from 2013 to 2020

Previous studies on long-term ozone (O₃) variations in China have reported inconsistent conclusions on the role of meteorological factors in controlling said variations. In this study, we used an observation-based decomposition model to conduct an up-to-date investigation of the effects of meteorological factors on the variations in nitrogen dioxide (NO₂) and O₃ concentrations in China in the summer from 2013 to 2020. The variations in NO₂ and O₃ concentrations after removing the major meteorological effects were then analyzed to improve our understanding of O₃ formation regimes. Ground measurements show that both NO₂ and O₃ concentrations decreased in eastern, central, and southeastern China (e.g., NO₂ and O₃ concentrations in Wuhan reduced by 4.3 and 6.2 ppb, respectively), which was not anticipated. Analyses of meteorological effects showed that reduced wind strength, decreased temperature, and increased relative humidity significantly reduced O₃ concentrations in eastern and central China (e.g., by 10.5 ppb in Wuhan). After removing the major meteorological effects, the O₃ trends were reversed in eastern and central China (e.g., increased by 4.9 ppb in Wuhan). The contrasting trends in NO₂ and O₃ concentrations suggest that their O₃ formations were sensitive to volatile organic compounds (VOC-limited regime). In southeastern China, both NO₂ and O₃ concentrations decreased, implying that the O₃ formation regimes changed to mixed sensitive or nitrogen oxide-limited (NO_x-limited) regimes. The meteorological effects varied by region and may play a dominant role in controlling the long-term O₃ variation. Our results indicate that the attribution of O₃ variation to emission control without accounting for meteorological effects can be misleading.

Assessing the health impacts attributable to PM2.5 and ozone pollution in 338 Chinese cities from 2015 to 2020

China has effectively reduced the fine particulate (PM_2.5) pollution from 2015 to 2020. Ozone pollution and related health impacts have become severe contemporaneously. The coordinated control of PM_2.5 and ozone is becoming a new issue for China's air pollution control. This study quantitatively assessed the health impacts attributed to PM_2.5 and ozone pollution in 338 Chinese cities from 2015 to 2020 and estimated the possible health benefits from achieving dual concentration targets during 2021-2025. Results show PM_2.5 caused a total health impact of 2.45 × 10⁷ disability-adjusted life years (DALYs) in 2020. All-cause and respiratory ozone-related health impact in 2020 was 1.04 × 10⁷ DALYs and 1.56 × 10⁶ DALYs. Between 2015 and 2020, the PM_2.5-related health impacts decreased by 14.97%, while those ozone-related increased by 94.61% and 96.54% for all-cause and respiratory. Cities in the North China Plain have suffered higher health impacts attributable to PM_2.5 and ozone pollution, indicating that the two-pollutant coordinated control is primarily needed. By achieving aggressive concentration target (decreasing 10%) between 2020 and 2025, China will reduce the PM_2.5-related health impacts in 338 cities by 1.56 × 10⁶ DALYs (improving 6.37%). By achieving general target (decreasing 10% or within the Interim target-1 of World Health Organization), the PM_2.5-related health benefit will be 7.98 × 10⁵ DALYs (improving 3.25%). The deteriorating ozone health risks will also be improved. Controlling air pollution in large cities and regional center cities can achieve remarkable health benefits. Due to the inter-region, inter-province, and inter-city difference of health impacts, targeted and differentiated pollution prevention and control need to be implemented.

Effects of using different exposure data to estimate changes in premature mortality attributable to PM2.5 and O3 in China

The assessment of premature mortality associated with the dramatic changes in fine particulate matter (PM_2.5) and ozone (O₃) has important scientific significance and provides valuable information for future emission control strategies. Exposure data are particularly vital but may cause great uncertainty in health burden assessments. This study, for the first time, used six methods to generate the concentration data of PM_2.5 and O₃ in China between 2014 and 2018, and then quantified the changes in premature mortality due to PM_2.5 and O₃ using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) model. The results show that PM_2.5-related premature mortality in China decreases by 263 (95% confidence interval (CI95): 142-159) to 308 (CI95: 213-241) thousands from 2014 to 2018 by using different concentration data, while O₃-related premature mortality increases by 67 (CI95: 26-104) to 103 (CI95: 40-163) thousands. The estimated mean changes are up to 40% different for the PM_2.5-related mortality, and up to 30% for the O₃-related mortality if different exposure data are chosen. The most significant difference due to the exposure data is found in the areas with a population density of around 10³ people/km², mostly located in Central China, for both PM_2.5 and O₃. Our results demonstrate that the exposure data source significantly affects mortality estimations and should thus be carefully considered in health burden assessments.

Influencing factors of PM2.5 and O3 from 2016 to 2020 based on DLNM and WRF-CMAQ

In this study, distributed lag nonlinear models (DLNM) were built to characterize the non-linear exposure-lag-response relationship between the concentration of PM_2.5 and O₃ and multiple influencing factors, including basic meteorological elements and precursors. Then, a stratified analysis of different years, seasons, pollution levels, and wind direction was conducted. DLNMs and coupled Weather Research and Forecasting Model-Community Multi-scale Air Quality Model (WRF-CMAQ) were used to evaluate PM_2.5 and O₃ changes attributed to meteorological conditions and anthropogenic emissions comparing 2020 with 2016. As DLNMs showed, PM_2.5 pollution was promoted by low wind speed, high temperature, low humidity, and high concentrations of SO₂, NO₂, and O₃, among which NO₂ tended to be the dominant influencing factor. O₃ pollution was promoted by low wind speed, high temperature, low humidity, high concentration of PM_2.5 and low concentration of NO₂, among which temperature tended to be the dominant influencing factor. Moreover, north-south and easterly winds showed the greatest contribution to PM_2.5 and O₃, respectively. Both DLNMs and CMAQ showed that anthropogenic factors alleviated PM_2.5 pollution but aggravated O₃ pollution in 2020 in comparison with 2016, so did meteorological factors, but with smaller impacts. And anthropogenic influences were more evident in heavily polluted seasons for both PM_2.5 and O₃. This research may help understand the influencing factors of PM_2.5 and O₃ and provide scientific guide for abatement policies. Moreover, the good consistency in the results obtained from DLNMs and CMAQ indicated the reliability of the two models.

Health and economic impacts from PM2.5 pollution transfer attributed to domestic trade in China: a provincial-level analysis

In recent years, severe air pollution has had a serious impact on the health and economy of residents and has attracted great attention. Due to the spatial separation between consumption and production, the transfer of PM_2.5 pollution and its health and economic effects caused by interprovincial trade have not been taken seriously. In this study, economic, atmospheric, and epidemiological models were combined to assess air pollution transfer and its health and economic impacts that are attributed to provincial trade in China. The analyses were performed under the PM_2.5 transfer scenario in which economically developed areas in eastern China transferred many health and economic impacts to inland areas through interprovincial trade in 2012. As a result of interprovincial trade, 1980 (95% CI 0, 4114) extra deaths and 208,000 (95% CI 74.5, 395.6) additional illnesses accrued, but 0.184 (95% CI 0.017, 0.372) billion USD of residents' economic loss was avoided in China. The results illustrate the serious impact of domestic trade on regional health and economics. It is necessary to comprehensively consider supply chains in designing policies to mitigate the negative health and economic impacts of air pollution across China.

Persistent high PM2.5 pollution driven by unfavorable meteorological conditions during the COVID-19 lockdown period in the Beijing-Tianjin-Hebei region, China

Lockdown measures to curtail the COVID-19 pandemic in China halted most non-essential activities on January 23, 2020. Despite significant reductions in anthropogenic emissions, the Beijing-Tianjin-Hebei (BTH) region still experienced high air pollution concentrations. Employing two emissions reduction scenarios, the Community Multiscale Air Quality (CMAQ) model was used to investigate the PM2.5 concentrations change in this region. The model using the scenario (C3) with greater traffic reductions performed better compared to the observed PM2.5. Compared with the no reductions base-case (scenario C1), PM2.5 reductions with scenario C3 were 2.70, 2.53, 2.90, 2.98, 3.30, 2.81, 2.82, 2.98, 2.68, and 2.83 μg/m3 in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Hengshui, Tangshan, and Xingtai, respectively. During high-pollution days in scenario C3, the percentage reductions in PM2.5 concentrations in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Hengshui, Tangshan, and Xingtai were 3.76, 3.54, 3.28, 3.22, 3.57, 3.56, 3.47, 6.10, 3.61, and 3.67%, respectively. However, significant increases caused by unfavorable meteorological conditions counteracted the emissions reduction effects resulting in high air pollution in BTH region during the lockdown period. This study shows that effective air pollution control strategies incorporating these results are urgently required in BTH to avoid severe pollution.

Satellite Retrieval of Air Pollution Changes in Central and Eastern China during COVID-19 Lockdown Based on a Machine Learning Model

With the implementation of the 2018–2020 Clean Air Action Plan (CAAP) the and impact from COVID-19 lockdowns in 2020, air pollution emissions in central and eastern China have decreased markedly. Here, by combining satellite remote sensing, re-analysis, and ground-based observational data, we established a machine learning (ML) model to analyze annual and seasonal changes in primary air pollutants in 2020 compared to 2018 and 2019 over central and eastern China. The root mean squared errors (RMSE) for the PM2.5, PM10, O3, and CO validation dataset were 9.027 μg/m3, 20.312 μg/m3, 10.436 μg/m3, and 0.097 mg/m3, respectively. The geographical random forest (RF) model demonstrated good performance for four main air pollutants. Notably, PM2.5, PM10, and CO decreased by 44.1%, 43.2%, and 35.9% in February 2020, which was likely influenced by the COVID-19 lockdown and primarily lasted until May 2020. Furthermore, PM2.5, PM10, O3, and CO decreased by 16.4%, 24.2%, 2.7%, and 19.8% in 2020 relative to the average values in 2018 and 2019. Moreover, the reduction in O3 emissions was not universal, with a significant increase (~20–40%) observed in uncontaminated areas.

Ozone pollution mitigation in guangxi (south China) driven by meteorology and anthropogenic emissions during the COVID-19 lockdown

With the implementation of COVID-19 restrictions and consequent improvement in air quality due to the nationwide lockdown, ozone (O₃) pollution was generally amplified in China. However, the O₃ levels throughout the Guangxi region of South China showed a clear downward trend during the lockdown. To better understand this unusual phenomenon, we investigated the characteristics of conventional pollutants, the influence of meteorological and anthropogenic factors quantified by a multiple linear regression (MLR) model, and the impact of local sources and long-range transport based on a continuous emission monitoring system (CEMS) and the HYSPLIT model. Results show that in Guangxi, the conventional pollutants generally declined during the COVID-19 lockdown period (January 24 to February 9, 2020) compared with their concentrations during 2016-2019, while O₃ gradually increased during the resumption (10 February to April 2020) and full operation periods (May and June 2020). Focusing on Beihai, a typical Guangxi region city, the correlations between the daily O₃ concentrations and six meteorological parameters (wind speed, visibility, temperature, humidity, precipitation, and atmospheric pressure) and their corresponding regression coefficients indicate that meteorological conditions were generally conducive to O₃ pollution mitigation during the lockdown. A 7.84 μg/m³ drop in O₃ concentration was driven by meteorology, with other decreases (4.11 μg/m³) explained by reduced anthropogenic emissions of O₃ precursors. Taken together, the lower NO₂/SO₂ ratios (1.25-2.33) and consistencies between real-time monitored primary emissions and ambient concentrations suggest that, with the closure of small-scale industries, residual industrial emissions have become dominant contributors to local primary pollutants. Backward trajectory cluster analyses show that the slump of O₃ concentrations in Southern Guangxi could be partly attributed to clean air mass transfer (24-58%) from the South China Sea. Overall, the synergistic effects of the COVID-19 lockdown and meteorological factors intensified O₃ reduction in the Guangxi region of South China.

Enhanced PM2.5 Decreases and O3 Increases in China During COVID-19 Lockdown by Aerosol-Radiation Feedback

We apply an online-coupled meteorology-chemistry model (WRF-Chem) embedded with an improved process analysis to examine aerosol-radiation feedback (ARF) impacts on effectiveness of emission control due to Coronavirus Disease 2019 (COVID-19) lockdown over North China Plain. Emission reduction alone induces PM2.5 decrease by 16.3 μg m-3 and O3 increase by 10.2 ppbv during COVID-19 lockdown. The ARF enhances PM2.5 decrease by 2.7 μg m-3 (16.6%) and O3 increase by 0.8 ppbv (7.8%). The ARF-induced enhancement of PM2.5 decline is mostly attributed to aerosol chemistry process, while enhancement of O3 rise is ascribed to physical advection and vertical mixing processes. A set of sensitivity experiments with emission reductions in different degrees indicate that the ARF-induced enhancements of PM2.5 declines (O3 rises) follow a robust linear relationship with the emission-reduction-induced PM2.5 decreases. The fitted relationship has an important implication for assessing the effectiveness of emission abatement at any extent.

Air quality change during the COVID-19 pandemic lockdown over the Auvergne-Rhône-Alpes region, France

Under the rapid spread of coronavirus diseases (COVID-19) worldwide, a complete lockdown was imposed in France from March 17th to May 11th, 2020 to limit the virus spread. This lockdown affected significantly the atmospheric pollution levels due to the restrictions of human activities. In the present study, we investigate the evolution of air quality in the Auvergne-Rhône-Alpes region, focusing on nine atmospheric pollutants (NO₂, NO, PM₁₀, PM_2.5, O₃, VOC, CO, SO₂, and isoprene). In Lyon, center of the region, the results indicated that NO₂, NO, and CO levels were reduced by 67%, 78%, and 62%, respectively, resulting in a decrease in road traffic by 80%. However, O₃, PM₁₀, and PM_2.5 were increased by 105%, 23%, and 53%, respectively, during the lockdown. The increase in ozone is explained by the dropping in NO and other gases linked to human activity, which consume ozone. Thus, the increase of solar radiation, sunshine, temperature, and humidity promoted the O₃ formation during the lockdown. Besides, rising temperature enhances the BVOC emissions such as isoprene. In addition, volatile organic component (VOC) and SO₂ remain almost stable and oxidation of these species leads to the formation of ozone and organic aerosol, which also explains the increase in PM during the lockdown. This study shows the contribution of atmospheric photochemistry to air pollution.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11869-020-00965-w.