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The Tibetan Plateau, known as the "Third Pole", is susceptible to ground-level ozone (O3) and fine particulate matter (PM2.5) pollution due to its unique high-altitude environment. This study constructed random forest regression models using multi-source data from ground measurements and meteorological satellites to predict variations in ground-level O3 and PM2.5 concentrations and their influencing factors across seven major cities in the Tibetan Plateau over two-year periods. The models successfully reproduced O3 and PM2.5 levels with satisfactory R-squared values of 0.71 and 0.73, respectively. Results reveal combustion-related carbon monoxide (CO) and nitrogen dioxide (NO2) as the most substantial influences on O3 and PM2.5 concentrations. Solar radiation, geographical factors, and meteorological variables also played crucial roles in driving pollutant variations. Conversely, transport-related and human activity factors exhibited relatively lower significance. High O3 and PM2.5 pollution occurred during pre-monsoon and post-monsoon/winter seasons, driven by solar radiation and emissions, respectively. While CO consistently contributed across cities and seasons, key influencing factors varied locally. This study unveils the key driving forces governing air pollutant variations across the Tibetan Plateau, shedding light on complex atmospheric processes in this unique high-altitude region.

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Ground-level ozone (O3) is the most phytotoxic secondary air pollutant in the atmosphere, severely affecting crop yields worldwide. The role of nanoparticles (NP) in the alleviation of ozone-induced yield losses in crops is not known. Therefore, in the present study, we investigated the effects of biogenicB-AgNPs on the mitigation of ozone-induced phytotoxicity in mung bean and compared its results with ethylenediurea (EDU) for the first time. Two mung bean cultivars (Vigna radiata L., Cv. SML-668 and PDM-139) were foliar sprayed with weekly applications of B-AgNPs (0 = control, 10 and 25 ppm) and EDU (0 = control, 200 and 300 ppm) until maturation phase. Morphological, physiological, enzymatic, and non-enzymatic antioxidant data were collected 30 and 60 days after germination (DAG). The mean O3 and AOT40 values (8 h day-1) during the cultivation period were approximately 52 ppb and 4.4 ppm.h, respectively. More biomass was accumulated at the vegetative phase due to the impact of B-AgNPs and EDU, and more photosynthates were transported to the reproductive phase, increasing yield. We observed that the 10 ppm B-AgNPs treatment had a more noticeable impact on yield parameters and lower Ag accumulation in seeds for both cultivars. Specifically, SML-668 cultivar treated with 10 ppm B-AgNPs (SN1) showed greater increases in seed weight plant-1 (124.97%), hundred seed weight (33.45%), and harvest index (37.53%) in comparison to control. Our findings suggest that B-AgNPs can enhance growth, biomass, yield, and seed quality, and can improve mung bean ozone tolerance. Therefore, B-AgNPs may be a promising protectant for mung bean.

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Monitoring ground-level ozone concentrations is a critical aspect of atmospheric environmental studies. Given the existing limitations of satellite data products, especially the lack of ground-level ozone characterization, and the discontinuity of ground observations, there is a pressing need for high-precision models to simulate ground-level ozone to assess surface ozone pollution. In this study, we have compared several widely utilized ensemble learning and deep learning methods for ground-level ozone simulation. Furthermore, we have thoroughly contrasted the temporal and spatial generalization performances of the ensemble learning and deep learning models. The 3-Dimensional Convolutional Neural Network (3-D CNN) model has emerged as the optimal choice for evaluating the daily maximum 8-h average ozone in Yunnan Province. The model has good performance: a spatial resolution of 0.05° × 0.05° and strong predictive power, as indicated by a Coefficient of Determination (R2) of 0.83 and a Root Mean Square Error (RMSE) of 12.54 µg/m³ in sample-based 5-fold cross-validation (CV). In the final stage of our study, we applied the 3-D CNN model to generate a comprehensive daily maximum 8-h average ozone dataset for Yunnan Province for the year 2021. This application has furnished us with a crucial high-resolution and highly accurate dataset for further in-depth studies on the issue of ozone pollution in Yunnan Province.

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Ground-level ozone (O3) pollution poses significant threats to both human health and air quality. This study uses ground observations and satellite retrievals to explore the spatiotemporal characteristics of ground-level O3 in Zhejiang Province, China. We created data-driven machine learning models that include meteorological, geographical and atmospheric parameters from multi-source remote sensing products, achieving good performance (Pearson's r of 0.81) in explaining regional O3 dynamics. Analyses revealed the crucial roles of temperature, relative humidity, total column O3, and the distributions and interactions of precursor (volatile organic compounds and nitrogen oxides) in driving the varied O3 patterns observed in Zhejiang. Furthermore, the interpretable modeling quantified multifactor interactions that sustain high O3 levels in spring and autumn, suppress O3 levels in summer, and inhibit O3 formation in winter. This work demonstrates the value of a combined approach using satellite and machine learning as an effective novel tool for regional air quality assessment and control.

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In January 2020, the novel coronavirus (COVID-19) outbreak emerged in China, prompting the enforcement of stringent lockdown measures nationwide to contain its spread. Multiple studies have demonstrated that these measures successfully reduced the levels of air pollutants except for ozone (O3). However, the potential risks of nationwide O3 changes during this period remain uncertain. To address this gap, we evaluated the ecological and health effects of O3 using hourly O3 data from 1 January to 17 June in both 2020 and 2019. Our results indicated that all health and ecological indicators, except SUM06 (sum of all hourly O3 over 60 ppb), during the COVID-19 pandemic in 2020 increased most obviously in Stages 2 and 3 with the strictest control measures, compared to the same period in 2019. The national premature deaths due to short-term O3 exposure during Stages 2-3 in 2020 totaled 146,558 (95% CI: 79,386-213,730) for all non-accidental causes and 82,408 (95% CI: 30,522-134,295) for cardiovascular diseases, increasing by 18.78% and 18.76% in 2019, respectively. The most significant increase in health risks occurred in Hubei, followed by Jiangxi, Zhejiang, Hunan, and Shaanxi. In addition, the estimated national winter wheat production losses (WWPL) attributable to O3 amounted to 50.6 and 51.1 million metric tons for 2019 and 2020, respectively. Among the major winter wheat-producing provinces, Anhui and Jiangsu experienced a larger increase in WWPL, while Shandong and Hebei suffered a greater decrease in 2020 compared to 2019, resulting in little overall change in WWPL between the two years. These findings provided direct evidence of the harmful effects of O3 during the COVID-19 pandemic and serve as a valuable reference for future air pollution control.

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Based on the air quality data and conventional meteorological data of the Nanjing Region from January 2015 to December 2016, to analyze the characteristics of O3 concentration changes in the Nanjing Region, a light gradient boosting machine (LightGBM) model was established to predict O3 concentration. The model was compared with three machine learning methods that are commonly used in air quality prediction, including support vector machine, recurrent neural network, and random forest methods, to verify its effectiveness and feasibility. Finally, the performance of the prediction model was analyzed under different meteorological conditions. The results showed that the variation in O3 concentration in Nanjing had significant seasonal differences and was affected by a combination of its pre-concentration, meteorological factors, and other air pollutant concentrations. The LightGBM model predicted the ground-level O3 concentration in the Nanjing area more precisely to a large extent (R2=0.92), and the model outperformed other models in prediction accuracy and computational efficiency. In particular, the model showed a significantly higher prediction accuracy and stability than that of other models under a high-temperature condition that was more likely prone to ozone pollution. The LightGBM model was characterized by its high prediction accuracy, good stability, satisfactory generalization ability, and short operation time, which broaden its application prospect in O3 concentration prediction.

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The representativeness of ambient air quality of an in situ measurement is key in the use and correct interpretation of the measured concentration values. Though the horizontal representativeness aspect is generally not neglected in air pollution studies, a detailed, high-resolution vertical distribution of ambient air pollutant concentrations is rarely addressed. The aim of this study is twofold: (i) to explore the vertical distribution of ground-level ozone (O3) concentrations measured at four heights above the ground-namely at 2, 8, 50, and 230 m-and (ii) to examine in detail the vertical O3 concentration gradient in air columns between 2 and 8, 8 and 50, and 50 and 230 m above the ground. We use the daily mean O3 concentrations measured continuously at the Kosetice station, representing the rural Central European background ambient air quality observed during 2015-2021. We use the semiparametric GAM (generalised additive model) approach (with complexity or roughness-penalised splines implementation) to analyse the data with sufficient flexibility. Our models for both O3 concentrations and O3 gradients use (additive) decomposition into annual trend and seasonality (plus an overall intercept). The seasonal and year-to-year patterns of the modelled O3 concentrations look very similar at first glance. Nevertheless, a more detailed look through O3 gradients shows that they differ substantially with respect to their seasonal and long-term dynamics. The vertical O3 concentration gradient in 2-230 m is not uniform but changes substantially with increasing height and shows by far the highest dynamics near the ground between 2 and 8 m, differing in both the seasonal and annual aspects for all the air columns inspected. We speculate that non-linear changes of both seasonal and annual components of vertical O3 gradients are due to atmospheric-terrestrial interactions and to meteorological factors, which we will explore in a future study.

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Mountain ecosystems are inhabited by species with specific characteristics enabling survival at high altitudes, which make them at risk from various pressures. In order to study these pressures, birds represent excellent model organisms due to their high diversity and position at the top of food chains. The pressures upon mountain bird populations include climate change, human disturbance, land abandonment, and air pollution, whose impacts are little understood. Ambient ozone (O3) is one of the most important air pollutants occurring in elevated concentrations in mountain conditions. Although laboratory experiments and indirect course-scale evidence suggest its negative effects on birds, population-level impacts remain unknown. To fill this knowledge gap, we analysed a unique 25-years long time series of annual monitoring of bird populations conducted at fixed sites under constant effort in a Central European mountain range, the Giant Mountains, Czechia. We related annual population growth rates of 51 bird species to O3 concentrations measured during the breeding season and hypothesized (i) an overall negative relationship across all species, and (ii) more negative O3 effects at higher altitudes due to increasing O3 concentration along altitudinal gradient. After controlling for the influence of weather conditions on bird population growth rates, we found an indication of the overall negative effect of O3 concentration, but it was insignificant. However, the effect became stronger and significant when we performed a separate analysis of upland species occupying the alpine zone above treeline. In these species, populations growth rates were lower after the years experiencing higher O3 concentration indicating an adverse impact of O3 on bird breeding. This impact corresponds well to O3 behaviour and mountain bird ecology. Our study thus represents the first step towards mechanistic understanding of O3 impacts on animal populations in nature linking the experimental results with indirect indications at the country-level.

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Numerous studies have reported adverse health effects of ambient air pollution on circulatory health outcomes mainly based on single-pollutant models. However, limited studies have focused on adjusted effect of multi-pollutant exposures on public health. This study aimed to examine short-term effects of three common air pollutants-ground-level ozone (ozone), nitrogen dioxide (NO2), and fine particulate matter (PM2.5)-through multi-pollutant models for mixed effect of adjustment. Daily data (circulatory hospitalization and mortality) and hourly data (air pollutants and temperature) were collected for 24 Canadian cities for 2001-2012. We applied generalized additive over-dispersion Poisson regression models with 1, 2, or 3 pollutants for city-specific risks, and Bayesian hierarchical models for national risks. This study found little mixed effect of adjustment through multi-pollutant models (ozone and/or NO2 and/or PM2.5) for circulatory hospitalization or mortality in Canada for 2001-2012, indicating that the 1-pollutant model did not result in considerable under- or over-estimates. It seemed weak-to-moderate correlations among air pollutants did not change the significant effect of one air pollutant after accounting for others. Inconsistent findings between other previous studies and this study indicate the need of comparable study design for multi-pollutant effect analysis.

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Studies examining long-term effects of ambient air pollution exposure, measured as annual averages, on pulmonary tuberculosis (TB) incidence are scarce, particularly in endemic, rural settings. We performed a small-area study in Ningxia Hui Autonomous Region (NHAR), a high TB-burden area in rural China, using township-level (n = 358 non-overlapping townships) annual TB notification data (2005-2017). We aimed to determine if annual average concentrations of ambient air pollution (particulate matter <2·5 µm [PM2·5], nitrogen dioxide [NO2] ozone [O3]) were associated with TB notification rates (as a proxy for incidence). Air pollution effects on TB notification rates at township-level were estimated as incidence rate ratios (IRR), fitted using a generalised estimating equation (GEE) adjusted for covariates (age, sex, occupation, education, ethnicity, remoteness [urban or rural], household crowding and solid fuel use). A total of 38,942 TB notifications were reported in NHAR between 2005 and 2017. The mean annual TB notification rate was 67 (standard deviation [SD]; 7) per 100,000 people. Median concentrations of PM2·5, NO2, and O3 were 42 µg/m3 (interquartile range [IQR]; 38-48 µg/m3), 15 ppb (IQR; 12-16 ppb), and 56 ppb (IQR; 56-57 ppb), respectively. In single pollutant models, adjusted for covariates, an interquartile range (IQR) increase (10 µg/m3) in PM2·5 was significantly associated with higher TB notification rates (IRR: 1∙35; 95% CI: 1·25-1·48). Comparable effects on notifications of TB were observed for increases in NO2 exposure (IRR: 1·20 per IQR (4 ppb) increase; 95% CI: 1·08-1·31). Ground-level ozone was not associated with TB notification rate in any models. The observed effects were consistent over time, in multi-pollutant models, and appeared robust to additional adjustment for indicators of household crowding, solid fuel use and remoteness. More rigorous study designs are needed to understand if improving air quality has population-level benefits on TB disease incidence in endemic settings.

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Ground-level ozone (O3) formation depends on meteorology, precursor emissions, and atmospheric chemistry. Understanding the key drivers behind the O3 formation and developing an accurate and efficient method for timely assessing the O3-VOCs-NOx relationships applicable in different O3 pollution events are essential. Here, we developed a novel machine learning ensemble model coupled with a Shapley additive explanation algorithm to predict the O3 formation regime and derive O3 formation sensitivity curves. The algorithm was tested for O3 events during the COVID-19 lockdown, a sandstorm event, and a heavy O3 pollution episode (maximum hourly O3 concentration >200 µg/m3) from 2019 to 2021. We show that increasing O3 concentrations during the COVID-19 lockdown and the heavy O3 pollution event were mainly caused by the photochemistry subject to local air quality and meteorological conditions. Influenced by the sandstorm weather, low O3 levels were mainly attributable to weak sunlight and low precursor levels. O3 formation sensitivity curves demonstrate that O3 formation in the study area was in a VOCs-sensitive regime. The VOCs-specific O3 sensitivity curves can also help make hybrid and timely strategies for O3 abatement. The results demonstrate that machine learning driven by observational data has the potential to be a very useful tool in predicting and interpreting O3 formation.

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In recent years, ozone pollution in China has been shown to increase in frequency and persistence despite the concentrations of fine particulate matter (PM2.5) decreasing steadily. Open crop straw burning (OCSB) activities are extensive in China and emit large amounts of trace gases during a short period that could lead to elevated ozone concentrations. This study addresses the impacts of OCSB emissions on ground-level ozone concentration and the associated health impact in China. Total VOCs and NOx emissions from OCSB in 2018 were 798.8 Gg and 80.6 Gg, respectively, with high emissions in Northeast China (31.7%) and North China (23.7%). Based on simulations conducted for 2018, OCSB emissions are estimated to contribute up to 0.95 µg/m3 increase in annual averaged maximum daily 8-hour (MDA8) ozone and up to 1.35 µg/m3 for the ozone season average. The significant impact of OCSB emissions on ozone is mainly characterized by localized and episodic (e.g., daily) changes in ozone concentration, up to 20 µg/m3 in North China and Yangtze River Delta region and even more in Northeast China during the burning season. With the implementation of straw burning bans, VOCs and NOx emissions from OCSB dropped substantially by 46.9%, particularly over YRD (76%) and North China (60%). Consequently, reduced OCSB emissions result in an overall decrease in annual averaged MDA8 ozone, and reductions in monthly MDA8 ozone could be over 10 µg/m3 in North China. The number of avoided premature death due to reduced OCSB emissions (considering both PM2.5 and ozone) is estimated to be 6120 (95% Confidence Interval: 5320-6800), with most health benefits gained over east and central China. Our results illustrate the effectiveness of straw burning bans in reducing ozone concentrations at annual and national scales and the substantial ozone impacts from OCSB events at localized and episodic scales.

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Ozone exposure is associated with various adverse health outcomes, but its impact on sleep quality is uncertain. Here we assessed the causal effect of long-term (yearly and monthly) exposure to ozone on nocturnal workday sleep time in a national representative sample from the China Family Panel Study, using a difference-in-differences approach. We further followed ninety healthy Chinese young adults four times in four seasons from September 2020 to June 2021, measured their daily sleep architecture using accelerometers, ascertained daily ozone exposure, recorded 5-min eye-closed resting-state electroencephalogram (EEG) signals at the last day of each one-week-long measurement session, and explored the effect of ozone exposure on objectively-measured sleep architecture. In the national sample, we found that every 1 interquartile range (IQR) µg/m3 increase in yearly and monthly ozone exposure was causally associated with 7.31 (p = 0.0039) and 4.19 (p = 0.040) minutes decline in nocturnal workday sleep time; the dose-response curve represented a quasi-linear pattern with no safety threshold, and plateaued at higher concentrations. In the small-scale study with objectively-measured sleep architecture, we found that every 1 IQR µg/m3 increase in the weekly ozone exposure was associated with 5.33 min decrease in night-time total sleep time (p = 0.031), 1.63 percentage points decrease in sleep efficiency (p < 0.001), 1.99 min increase in sleep latency (p = 0.0070), and 5.34 min increase in wake after sleep onset time (p = 0.0016) in a quasi-linear pattern. Notably, we found the accumulating trend of ozone exposure on sleep quality during both the short-term and long-term periods. We also found that short-term ozone exposure was associated with altered EEG patterns, mediated by sleep quality. This study indicates that long-term and short-term ozone exposures have negative and accumulating impacts on sleep quality and might impair brain functioning. More hidden health burdens of ozone are worth exploring.

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Surface ozone (O3), one of the harmful air pollutants, generated significantly negative effects on human health and plants. Existing O3 datasets with coarse spatiotemporal resolution and limited coverage, and the uncertainties of O3 influential factors seriously restrain related epidemiology and air pollution studies. To tackle above issues, we proposed a novel scheme to estimate daily O3 concentrations on a fine grid scale (1 km × 1 km) from 2018 to 2020 across China based on machine learning methods using hourly observed ground-level pollutant concentrations data, meteorological data, satellite data, and auxiliary data including digital elevation model (DEM), land use data (LUD), normalized difference vegetation index (NDVI), population (POP), and nighttime light images (NTL), and to identify the difference of influential factors of O3 on diverse urbanization and topography conditions. Some findings were achieved. The correlation coefficients (R2) between O3 concentrations and surface net solar radiation (SNSR), boundary layer height (BLH), 2 m temperature (T2M), 10 m v-component (MVW), and NDVI were 0.80, 0.40, 0.35, 0.30, and 0.20, respectively. The random forest (RF) demonstrated the highest validation R2 (0.86) and lowest validation RMSE (13.74 µg/m3) in estimating O3 concentrations, followed by support vector machine (SVM) (R2 = 0.75, RMSE = 18.39 µg/m3), backpropagation neural network (BP) (R2 = 0.74, RMSE = 19.26 µg/m3), and multiple linear regression (MLR) (R2 = 0.52, RMSE = 25.99 µg/m3). Our China High-Resolution O3 Dataset (CHROD) exhibited an acceptable accuracy at different spatial-temporal scales. Additionally, O3 concentrations showed decreasing trend and represented obviously spatiotemporal heterogeneity across China from 2018 to 2020. Overall, O3 was mainly affected by human activities in higher urbanization regions, while O3 was mainly controlled by meteorological factors, vegetation coverage, and elevation in lower urbanization regions. The scheme of this study is useful and valuable in understanding the mechanism of O3 formation and improving the quality of the O3 dataset.

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This paper analyses data by summarising the concentration values of ground-level ozone (GLO). The study area is situated in central Slovakia and is part of the Western Carpathians. These measurements were carried out between 2015 and 2020, implementing Werner's method working with passive samplers. The highest average and the highest absolute GLO deposition values were 30.93 ppb and 61.06 ppb, respectively, recorded in August 2015 in the forest in the Kremnické vrchy Mts. The lowest average GLO value in the whole measuring period was 17.72 ppb, measured in the town of Zvolen; the absolute minimum was 4.43 ppb, recorded in April 2016 on an open plot in the Kremnické vrchy Mts. The GLO formation over the study area has not yet reached a steady rate. Since 2007, the developmental trend has been increasing. Statistically significant differences in GLO concentrations were confirmed between the localities with different airborne pollutions. However, the analysis of the existing ozone concentration values showed considerable differences, especially related to the time pattern. The spatial variability was equalised. The extreme values, while remarkable, were dangerous, especially in the forest stands in the Kremnické vrchy Mts., where they were 14 times above the critical level of 32.5 ppb O3. The dominant factor influencing the GLO concentration was global radiation. The effects of average temperature and rainfall total were less important.

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Ground-level ozone (O3) has negative effects on agricultural crops. Maize is an important grain crop in China. The North China Plain (NCP) serves as the major crops' production area of China and experiences severe ozone pollution. Using the ground-level ozone simulated by an atmospheric chemistry transport model (WRF-Chem), we quantified the yield reduction and economic losses of maize during 2015-2018 over NCP based on exposure-response AOT40 (accumulation of hourly O3 concentration exceed 40 ppb) and flux-response POD6 (phytotoxic dose of ozone over 6 nmol m-2 s-1). Results showed that the ozone concentration, AOT40, and POD6 clearly increased from 2015 to 2018 in growing season of maize over NCP. The four-year annual mean ozone concentration, AOT40, and POD6 were 0.055 ppm, 18.02 ppm h, and 5.02 mmol m-2, respectively. At county level, the relative loss of maize yield (MRYL) based on AOT40 and POD6 had clearly spatio-temporal differences in NCP. The average MRYLs of AOT40 and of POD6 from 2015 to 2018 were 10.4% and 21.4%, respectively, and these reductions were associated with 2399 million and 5637 million US dollars, respectively. This study suggests that surface ozone increased the yield losses of maize, and indicates that further reductions in ozone concentrations can enhance the food security in China.

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Ground-level ozone (O3) has been an emerging air pollution in China and interacts with fine particulate matters (PM2.5). We synthesized observations of O3 and its precursors in two summer months of 2020 at 10 sites in the Zhejiang province, East China and simulated the in situ photochemistry. O3 pollution in the northeastern Zhejiang province was more serious than that in the southwest. The site-average daytime O3 increment correlated well (R2 = 0.73) with the total reactivity of volatile organic compounds (VOCs) and carbon monoxide toward the hydroxyl radical (OH) in urban areas. Model simulation revealed that the main function of nitrogen oxides (NOx) at the rural sites where isoprene accounted for >85% of OH reactivity of VOCs was to facilitate the radical cycling. With NOx reduction from 0 to 90%, the self-reactions between peroxy radicals (Self-Rxns), a proven pathway for secondary organic aerosol formation, were intensified by up to 23-fold in a NOx-rich environment. In contrast, reducing VOCs could weaken the Self-Rxns while reducing O3 production rate and atmospheric oxidation capacity. This study observes and simulates O3 chemistry based on extensive measurements in typical Chinese cities, highlighting the necessity of reducing VOCs for co-benefit of O3 and PM2.5.

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Improved air quality has been the silver lining of the pandemic since early 2020. The air quality in northern New Jersey (NJ) was continuously measured during the COVID-19 pandemic and through the three stages of recovery, i.e., the Stay-at-home stage, Reopening stage 1, and Reopening stage 2. A significant change in air quality was observed during the Stay-at-home stage (March 16 to May 16, 2020) as most people stayed home and industrial activity decreased 60%. Compared to 2019, carbon dioxide (CO2) decreased 17%, carbon monoxide (CO) decreased 7%, and nitrogen oxides (NOx) decreased 51% during the Stay-at-home stage in 2020. However, the ground-level ozone (O3) increased in 2020 because of the reduced NOx emission and the possibly increased levels of volatile organic compounds (VOCs) due to warmer weather. With the step-by-step reopening process, the difference in local CO2 levels between 2019 and 2020 was reduced, and the NOx concentration returned to its 2019 level. The CO2 concentrations were positively correlated with CO, and the NOx concentrations were negatively correlated with O3. Under the COVID-19 pandemic in 2020, NJ consumed 14% less natural gas and 21% less gasoline; therefore, the CO2, CO, and NOx emissions and concentration levels were reduced besides the effects of meteorology parameters on air quality in metropolitan New Jersey. Our findings support that replacing fossil fuels with electric or renewable energy in the transportation systems and industry could be beneficial for the concentration reduction of certain greenhouse gases. Supplementary Information: The online version contains supplementary material available at 10.1007/s11270-022-05764-w.

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Natural emissions play a key role in modulating the formation of ground-level ozone (O3), especially emissions of biogenic volatile organic compounds (BVOCs) and soil nitric oxide (SNO), and their individual effects on O3 formation have been previously quantified and evaluated. However, their synergistic effects remain unclear and have not yet been well assessed. By applying the Weather Research and Forecasting (WRF) model coupled with the Chemistry-Model of Emissions of Gases and Aerosols from Nature (WRF/Chem-MEGAN) model, this study reveals that in the presence of sufficient BVOC emissions, which act as a fuel, SNO emissions act as a fuel additive and promote the chemical reactions of BVOCs and the subsequent production of O3. Consequently, the synergistic effects of BVOC and SNO emissions on summertime O3 production surpassed the sum of their individual effects by as much as 10-20 µg m-3 in eastern China in 2014. In order to reduce O3 concentration to a level corresponding to no natural emissions of BVOC or SNO (i.e., the BASE scenario), the anthropogenic volatile organic compound (AVOC) emissions in the scenario considers BVOC and SNO emissions must be reduced by 1.76 times that of the BASE scenario. This study demonstrates that the synergistic effects of BVOC and SNO emissions can impede ground-level O3 regulation and can subsequently impose stricter requirements on anthropogenic precursor emission control in China. The results of this study can also inform efforts in other regions that are still combating ground-level O3 pollution.

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China experienced worsening ground-level ozone (O3) pollution from 2013 to 2019. In this study, meteorological parameters, including surface temperature (T 2 ), solar radiation (SW), and wind speed (WS), were classified into two aspects, (1) Photochemical Reaction Condition (PRC = T 2 × SW) and (2) Physical Dispersion Capacity (PDC = WS). In this way, a Meteorology Synthetic Index (MSI = PRC/PDC) was developed for the quantification of meteorology-induced ground-level O3 pollution. The positive linear relationship between the 90th percentile of MDA8 (maximum daily 8-h average) O3 concentration and MSI determined that the contribution of meteorological changes to ground-level O-3 varied on a latitudinal gradient, decreasing from ∼40% in southern China to 10%-20% in northern China. Favorable photochemical reaction conditions were more important for ground-level O3 pollution. This study proposes a universally applicable index for fast diagnosis of meteorological roles in ground-level O3 variability, which enables the assessment of the observed effects of precursor emissions reductions that can be used for designing future control policies. ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary material is available in the online version of this article at 10.1007/s00376-021-1257-x.