Surface Ozone Pollution Research Articles

Estimation of full-coverage surface ozone using sentinel-5P data and a GAN-LGBM model in the YRDUA, China

ABSTRACT Recent years have seen increasing academic attention to surface ozone pollution due to its significant impacts on air quality and human health. To overcome the spatial coverage limitation of surface ozone ground monitoring, we proposed a novel approach that integrated the Generative Adversarial Network (GAN) with the Light Gradient Boosting Machine (LGBM) for full-coverage surface ozone estimation in the Yangtze River Delta Urban Agglomeration (YRDUA), using ground monitoring data and Sentinel-5P satellite data. We assessed the performance of the GAN-LGBM model against other decision-tree-based models (XGBoost, LGBM) using three cross-validation (CV) methods: sample-based, space-based, and time-based. The results demonstrated that the GAN-LGBM model consistently outperformed other models across all evaluation metrics and validation scenarios, achieving the highest correlation coefficient (R 2) of 0.94 in sample-based CV. Spatiotemporal evaluations further showed the robustness of the GAN-LGBM model and its ability to capture complex surface ozone concentration patterns. This study introduces a promising method for full-coverage surface ozone estimation and explores the potential of incorporating unsupervised learning methods into regression models to address complex correlations in environmental datasets.

An Innovative Collaboration-Based Ozone and Aerosol Observation Network in Northeast Asia

Abstract Situated in close proximity to the Korea Peninsula and the Japan Islands, Shandong Province in China (SDP) has emerged as a focal point for global attention due to its significant surface ozone (O3) and aerosol pollution. Despite this attention, there are notable gaps in knowledge across various interconnected research domains, encompassing climate change, atmospheric circulation, anthropogenic emissions, and the chemistry of O3 and aerosols. The impact of frequent heat waves on regional O3 and aerosol pollution remains unclear, while our comprehension of atmospheric circulation dynamics and the chemistry involved with O3 and aerosols is still hampered by substantial limitations. The unique topography and geographical setting of SDP, with its complex interplay of factors like sea–land breezes, mountain–valley winds, and urban heat islands, make it an ideal location for investigating the dynamics of O3 and aerosol chemistry. Moreover, it is essential to explore the effects of transboundary transport on O3 and aerosol pollution and delve into the underlying mechanisms contributing to their combined pollution effects. To bridge these knowledge gaps, the Innovative Collaboration-Based Ozone and Aerosol Observation Network in Northeast Asia has been established through collaborative efforts involving the Bureaus of Ecology and Environment, Meteorology in SDP, and the Chinese Academy of Sciences. This network encompasses various components, including air quality and weather stations, a network of low-cost air quality sensors, monitoring stations focused on atmospheric photochemical smog and aerosol chemical speciation, and vertical measurement systems targeting O3 and its precursor gases (utilizing light detection and ranging, balloon sounding, and drone-based measurements), as well as measurements conducted via vehicles and ships. Additionally, preliminary findings from this comprehensive observational campaign will be shared.

Exogenous protectants alleviate ozone stress in Trifolium repens: Impacts on plant growth and endophytic fungi

Industrialization-driven surface ozone (O3) pollution significantly impairs plant growth. This study evaluates the effectiveness of exogenous protectants [3 mg L⁻1 abscisic acid (ABA), 400 mg L⁻1 ethylenediurea (EDU), and 80 mg L⁻1 spermidine (Spd)] on Trifolium repens subjected to O3 stress in open-top chambers, focusing on plant growth and dynamics of culturable endophytic fungal communities. Results indicate that O3 exposure adversely affects photosynthesis, reducing root biomass and altering root structure, which further impacts the ability of plant to absorb essential nutrients such as potassium (K), magnesium (Mg), and zinc (Zn). Conversely, the application of ABA, EDU, and Spd significantly enhanced total biomass and chlorophyll content in T. repens. Specifically, ABA and Spd significantly improved root length, root surface area, and root volume, while EDU effectively reduced leaves' malondialdehyde levels, indicating decreased oxidative stress. Moreover, ABA and Spd treatments significantly increased leaf endophytic fungal diversity, while root fungal abundance declined. The relative abundance of Alternaria in leaves was substantially reduced by these treatments, which correlated with enhanced chlorophyll content and photosynthesis. Concurrently, EDU and Spd treatments increased the abundance of Plectosphaerella, enhance the absorption of K, Ca, and Mg. In roots, ABA treatment increased the abundance of Paecilomyces, while Spd treatment enhanced the presence of Stemphylium, linked to improved nitrogen (N), phosphorus (P), and K uptake. These findings suggest that specific symbiotic fungi mitigate O3-induced stress by enhancing nutrient absorption, promoting growth. This study highlights the potential of exogenous protectants to enhance plant resilience against O3 pollution through modulating interactions with endophytic fungal communities.

Effects of heat waves on ozone pollution in a coastal industrial city: Meteorological impacts and photochemical mechanisms

Surface ozone (O3) pollution triggered by extreme weather conditions is attracting increasing attention. Heat waves in summer of 2022 were chosen to explore the photochemical and meteorological impacts on O3 formation in the southeastern coastal industrial city of Quanzhou in China. The machine-learning (ML)-based weather normalization showed that the de-weathered O3 concentrations (increased by 6.69 μg m−3) in 2022 were higher than those from 2015 to 2021. Temperature is the most important variable (33.5%), followed by solar radiation (23.5%) and RH (10.1%), differing from the O3 pollution in inland cities. The Observation-Based Model (OBM) analysis results showed that hydroxyl radical (OH) was the predominant oxidant for daytime atmospheric oxidation capacity (AOC). And oxygenated volatile organic compounds (OVOCs), NO2, and CO were the dominant contributors to OH reactivity, accelerating the recycling of ROx radicals and O3 formation. The daytime reaction rate of HO2+NO during the O3 pollution episodes was 20.0 ppb h−1, accounting for 65% of the total O3 production. The contribution of RO2+NO to the O3 production enhanced the possibility of the MDA8h O3 exceeding the Air Quality Standard of China. This study improves the understanding of O3 formation mechanisms in a coastal industrial city during heat waves, and the elevated contributions of meteorological conditions to O3 pollution become more challenge for the reduction of anthropogenic emissions controll.

The Relationship of Surface Ozone Pollution with Meteorological Conditions in Determining Episode Periods

Jakarta, the capital city of Indonesia, is located in a tropical region with abundant sunlight and high temperatures year-round. Ozone and particulate matter (PM) are critical parameters causing unhealthy air pollution. Meteorological data were obtained from the NASA Power website. This study aims to explore the relationship between ozone formation and meteorological factors in Jakarta. Ozone air quality data were measured using the Backman model 950A ozone Analyzer, which detects concentrations as low as 0.05 ppm, with measurements taken every 40 seconds. From January to October 2023, ozone concentrations increased during the dry months of May to October, with the highest hourly value recorded at 263 μg/m³. During this period, average temperatures ranged from 27-29°C, rainfall was 0.3-5.6 mm, wind speeds were 3.14-4.64 m/s, wind direction was 92-171 degrees, and air humidity was 74-82%. Significant episodes were identified on (i) May 5-9, (ii) July 12-15, (iii) September 6-7, (iv) September 13-14, (v) September 21-22, and (vi) October 29-30, 2023. Daily, monthly, and seasonal ozone variations aligned with meteorological conditions, showing higher concentrations during the dry months. Further studies, including photochemical modeling, are required to identify dominant factors causing high ozone concentrations during these episodes. Understanding NOx or VOC emission sensitivities is crucial for effective ozone abatement strategies in Jakarta.

Kolmogorov-Zurbenko filter coupled with machine learning to reveal multiple drivers of surface ozone pollution in China from 2015 to 2022

Near-surface ozone pollution is a significant concern in China. Its meteorological drivers are uncontrolled, stressing an urgent need to quantify the anthropogenic-driven components. This study employs explainable machine learning (ML) algorithms to predict ozone concentrations, emphasizing the anthropogenic-driven trends after accounting for meteorological effects. Results present that radiation is the most important meteorological factor affecting ozone pollution in the Yangtze River Delta (YRD), Pearl River Delta (PRD), and Sichuan Basin (SCB), while temperature is dominant for the North China Plain (NCP). Even at lower temperatures, stronger solar radiation can still lead to the accumulation of higher ozone concentrations. The anthropogenic-driven ozone concentration showed an upward trend in China, with an interannual growth rate of 2.61 μg/m3 a−1 from 2015 to 2022. Nonetheless, its rising trend experienced a post-2019 downturn, due to the COVID-19 lockdown and emission reduction strategies. It started to rise in 2022. Regionally, NCP has the highest ozone concentration, the SCB has the most pronounced increase in 2022, but the PRD has no noticeable variation and no significant seasonal change after 2019. As for precursor emissions, the urban areas in China are mostly located in the VOC-limited (volatile organic component) and transitional regimes, highlighting that VOC control is more cost-effective.

Quantitative impacts of dominant large-scale circulation systems on surface ozone pollution in China

Tropospheric ozone pollution has been worsened over most regions of China, adversely affecting human health and ecosystems. The long-term ozone concentration depends largely upon atmospheric circulations. Here, we conducted meteorological adjustment to quantitatively assess the influences of meteorological factors on the ozone evolution in China's seven city clusters during the warm season (April to October) from 2013 to 2020. Our analysis indicated that northern and eastern regions experienced ozone increases driven by emission changes. Southern regions, particularly the Pearl River Delta (PRD), exhibited ozone rise primarily due to meteorological conditions despite emission changes. In the Sichuan Basin (SCB) and Central Yangtze River Plain (CYP), where ozone levels decreased, meteorological conditions played a significant role in suppressing the ascent of ozone. Empirical orthogonal functions (EOF) analyses suggested that the spatiotemporal trend of meteorology-associated ozone was strongly correlated with the variation of East Asian Trough (EAT), South Asian High (SAH) and the western Pacific subtropical high (WPSH). The top three EOF patterns explained 33.4 %, 21.8 %, and 16.0 % of the total variance in meteorology-associated ozone. Absolute principal component scores-multiple linear regression (APCS-MLR) analyse quantitatively identified that enhanced EAT and SAH with a northward location of WPSH were favourable to surface ozone formation in central and eastern regions, but unfavourable to ozone formation in edge regions such as SCB.

Characteristics of surface ozone pollution and its correlations with meteorological factors: the case of Xiangtan.

Based on ozone (O3) monitoring data for Xiangtan and meteorological observation data for 2020-2022, we examined ozone pollution characteristics and the effects of meteorological factors on daily maximum 8-h average ozone (O3-8h) concentrations in Xiangtan. Thus, we observed significant increases as well as notable seasonal variations in O3-8h concentrations in Xiangtan during the period considered. The ozone and temperature change response slope (KO3-T) indicated that local emissions had no significant effect on O3-8h generation. Further, average O3-8h concentration and maximum temperature (Tmax) values showed a polynomial distribution. Specifically, at Tmax < 27 °C, it increased almost linearly with increasing temperature, and at Tmax between 27 and 37 °C, it showed an upward curvilinear trend as temperature increased, but at a much lower rate. Then, at Tmax > 37 °C, it decreased with increasing temperature. With respect to relative humidity (RH), the average O3-8h concentration primarily exceeded the standard value when RH varied in the range of 45-65%, which is the key humidity range for O3 pollution, and the inflection point for the correlation curve between O3-8h concentration and RH appeared at ~55%. Furthermore, at wind speeds (WSs) below 1.5 m∙s-1, O3-8h concentration increased rapidly, and at WSs in the 1.5-2 m∙s-1 range, it increased at a much faster rate. However, at WSs > 2 m∙s-1, it decreased slowly with increasing WS. O3-8h concentration also showed the tendency to exceed the standard value when the dominant wind directions in Xiangtan were easterly or southeasterly.

Stratospheric influence on surface ozone pollution in China

Events of stratospheric intrusions to the surface (SITS) can lead to severe ozone (O3) pollution. Still, to what extent SITS events impact surface O3 on a national scale over years remains a long-lasting question, mainly due to difficulty of resolving three key SITS metrics: frequency, duration and intensity. Here, we identify 27,616 SITS events over China during 2015-2022 based on spatiotemporally dense surface measurements of O3 and carbon monoxide, two effective indicators of SITS. An overview of the three metrics is presented, illustrating large influences of SITS on surface O3 in China. We find that SITS events occur preferentially in high-elevation regions, while those in plain regions are more intense. SITS enhances surface O3 by 20 ppbv on average, contributing to 30-45% of O3 during SITS periods. Nationally, SITS-induced O3 peaks in spring and autumn, while over 70% of SITS events during the warm months exacerbate O3 pollution. Over 2015-2022, SITS-induced O3 shows a declining trend. Our observation-based results can have implications for O3 mitigation policies in short and long terms.

Identifying the geospatial relationship of surface ozone pollution in China: Implications for key pollution control regions

Surface ozone pollution, as a pressing environmental concern, has garnered widespread attention across China. Due to air mass transport, effective control of ozone pollution is highly dependent on collaborative efforts across neighboring regions. However, specific regions with strong internal interactions of ozone pollution are not yet well identified. Here, we introduced the Geospatial SHapley Additive exPlanation (GeoSHAP) approach, which primarily involves machine learning and geostatistical algorithms. Based on extensive atmospheric environmental monitoring data from 2017 to 2021, machine learning models were employed to train and predict ozone concentrations at the target location. The R2 values on the test sets of different scale regions all reached 0.98 in the overall condition, indicating that the core model has good accuracy and generalization ability. The results highlight key regions with high ozone geospatial relationship (OGR) index, predominantly located in the Northern District (ND), spanning the Fen-Wei Plain, the Loess Plateau, and the North China Plain, as well as within portions of the Yangtze River Delta (YRD) and the Pearl River Delta (PRD). Further investigation indicated that high geospatial relationships stem from a synergy between anthropogenic and natural factors, with anthropogenic factors serving as a pivotal element. This study revealed key regions with the most urgent need for joint control of anthropogenic sources to mitigate ozone pollution.

Urban ozone variability using automated machine learning: inference from different feature importance schemes.

Tropospheric ozone is an air pollutant at the ground level and a greenhouse gas which significantly contributes to the global warming. Strong anthropogenic emissions in and around urban environments enhance surface ozone pollution impacting the human health and vegetation adversely. However, observations are often scarce and the factors driving ozone variability remain uncertain in the developing regions of the world. In this regard, here, we conducted machine learning (ML) simulations of ozone variability and comprehensively examined the governing factors over a major urban environment (Ahmedabad) in western India. Ozone precursors (NO2, NO, CO, C5H8 and CH2O) from the CAMS (Copernicus Atmosphere Monitoring Service) reanalysis and meteorological parameters from the ERA5 (European Centre for Medium-Range Weather Forecast's (ECMWF) fifth-generation reanalysis) were included as features in the ML models. Automated ML (AutoML) fitted the deep learning model optimally and simulated the daily ozone with root mean square error (RMSE) of ~2 ppbv reproducing 84-88% of variability. The model performance achieved here is comparable to widely used ML models (RF-Random Forest and XGBoost-eXtreme Gradient Boosting). Explainability of the models is discussed through different schemes of feature importance, including SAGE (Shapley Additive Global importancE) and permutation importance. The leading features are found to be different from different feature importance schemes. We show that urban ozone could be simulated well (RMSE = 2.5 ppbv and R2 = 0.78) by considering first four leading features, from different schemes, which are consistent with ozone photochemistry. Our study underscores the need to conduct science-informed analysis of feature importance from multiple schemes to infer the roles of input variables in ozone variability. AutoML-based studies, exploiting potentials of long-term observations, can strongly complement the conventional chemistry-transport modelling and can also help in accurate simulation and forecast of urban ozone.

Interactions between aerosols and surface ozone in arid and semi-arid regions of China.

Atmospheric aerosols affect surface ozone concentrations by influencing radiation, but the mechanism and dominant factors are unclear. Therefore, this paper analyses the changes in aerosol-radiative-surface ozone in China's arid and semi-arid regions with the help of the Atmospheric Radiative Transfer (SBDART) model. The results suggest that Aerosol Optical Depth (AOD) and coarse Particulate Matter (PM10) have the same trend, with high values in spring and winter and low values in summer and autumn. Surface ozone is high in spring and summer and low in autumn and winter. Surface ozone is higher in spring and summer and lower in autumn and winter. In winter, mainly secondary pollutants are dominated by high pollution levels. In the rest of the seasons, a mixture of dust, motor vehicle exhaust, and soot is dominated by low pollution levels. Surface ozone is positively correlated with fine particles and negatively correlated with coarse particles. Temperature is positively correlated with surface ozone in all seasons and negatively correlated with PM10 in summer, autumn, and winter. Precipitation negatively correlates with PM10 each season and surface ozone in winter and spring. Analysis of surface ozone and PM10 sources in the more polluted city of Hohhot based on the back-line trajectory model showed that airflow trajectories mainly transported surface ozone and PM10 pollution from northwestern Inner Mongolia and western Mongolia. During dusty solid weather, the decrease in radiation reaching the Earth's surface and the cooling effect of aerosols lead to lower temperatures, which slows down the rate of chemical reactions of precursors of surface ozone, resulting in lower ozone concentrations at the surface. This study can provide a theoretical reference for aerosol and surface ozone control in arid and semi-arid areas of China.

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

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

High-spatial resolution ground-level ozone in Yunnan, China: A spatiotemporal estimation based on comparative analyses of machine learning models

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.

Can ethylenediurea (EDU) alter the effects of ozone on the source-sink regulation of nitrogen uptake and remobilization during grain filling period in rice?

Increased surface ozone (O3) pollution seriously threatens crop production, and ethylenediurea (EDU) can alleviate crop yield reduction caused by O3. However, the reason for the decrease in grain nitrogen (N) accumulation caused by O3 and whether EDU serves as N fertilizer remain unclear. An experiment was conducted to investigate the impacts of factorial combinations of O3 enrichment (ambient air plus 60 ppb) and EDU (foliage spray with 450 ppm solutions) on N concentration, accumulation and remobilization in hybrid rice seedlings. Compared to ambient condition, elevated O3 significantly inhibited the N accumulation in vegetative organs during anthesis and grain N accumulation during the maturity stage. Elevated O3 significantly decreased the total N accumulation during anthesis and maturity stages, with a greater impact at the latter stage. The decrease in grain N accumulation caused by O3 was attributed to a decrease in N remobilization of vegetative organs during the grain filling period as well as to a decrease in post-anthesis N uptake. However, there was no significant change in the proportion of N remobilization and N uptake in grain N accumulation. The inhibitory effect of O3 on N remobilization in the upper canopy leaves was greater than that in the lower canopy leaves. In addition, elevated O3 increased the N accumulation of panicles at the anthesis stage, mainly by resulting in earlier heading of rice. EDU only increased N accumulation at the maturity stage, which was mainly attributed to an increase in rice biomass by EDU. EDU had no significant effect on N concentration, N remobilization process, and N harvest index. The findings are helpful to better understand the utilization of N fertilizer by rice under O3 pollution, and can also provide a theoretical basis for sustainable nutrient management to alleviate the negative impact of O3 on crop yield and quality.

Seasonal predictability of the dominant surface ozone pattern over China linked to sea surface temperature

Mitigation surface ozone pollution becomes increasingly pivotal in improving China’s air quality. However, the impact of global sea surface temperature anomalies (SSTA) on the long-term predictability of China’s surface ozone remains challenging. In this study, we employ eigen techniques to effectively characterize dominant surface ozone patterns over China, and establish cross-correlations between the dominant patterns and global SSTA time series. Our findings reveal that China’s summer ozone pollution is strongly associated with crucial SSTA clusters linked to atmospheric circulations, i.e., the West Pacific Subtropical High and the Pacific-North American teleconnection pattern. For winter, ozone pollution is attributed to SSTA clusters related to the Southern Oscillation, the Madden-Julian Oscillation and others. We propose a multivariate regression model capable of predicting surface ozone patterns with a lead time of at least 3 months. Evaluation of our model using a testing dataset yields an R-value of around 0.5 between predicted and observed data, surpassing statistical significance threshold. This suggests the viability and potential applicability of our predictive model in surface ozone forecasting and mitigation strategies in China.

Ambient atmospheric application and influencing factors of ozone catalytic decomposition materials in a channel test

Surface ozone pollution is a significant environmental problem. In addition to the control of ozone precursors, direct catalytic decomposition of ozone is an effective control method with broad prospects. At present, the evaluation of the ozone decomposition performance of catalysts is typically performed in the laboratory, and the lack of scientific evaluation means to apply them to complex ambient atmosphere limits the practical application of ozone removal catalysts. Here, we devised a new evaluation method for ozone catalytic decomposition materials in the ambient atmosphere that can comprehensively evaluate the influence of various factors in the ambient atmosphere on performance. The results showed that ozone catalytic decomposition materials can maintain a decomposition efficiency of more than 20% in ambient atmosphere. The decomposition efficiency is mainly affected by distance, the closer the distance; the higher the decomposition efficiency. In addition, the concentration of ozone in the environment also affects the decomposition efficiency and shows a positive correlation. As the ozone concentration increased, the decomposition efficiency increased. An increase in relative humidity can cause a decline in decomposition efficiency, but this effect is eliminated with a decrease in humidity. In addition, the ozone catalytic decomposition materials can reduce the ozone concentration in the environment from 160 μg/m3to less than 100 μg/m3, which can play a great role in environmental ozone pollution control.

A comprehensive assessment of yield loss in rice due to surface ozone pollution in India during 2005–2020: A great concern for food security

CONTEXTAbout 60% of the world population relies primarily on rice as their staple food, and India ranks second in terms of global rice production. Studies have shown the adverse impact of surface ozone pollution on agriculture, particularly the yield loss (YL) of major staple crops. OBJECTIVE(i) To assess the bias associated with ozone data used for YL estimation, (ii) to find the uncertainties in ozone exposure/crop-response methods applied for computing YL and (iii) to analyse the spatio-temporal variability of YL in rice due to surface ozone in India for the period 2005–2020 to assess food security of the country. METHODSWe use the Tropospheric Emission Spectrometer chemical reanalysis (TCR-2) surface ozone data and the ozone exposure/crop-response functions to compute YL in rice. RESULTS AND CONCLUSIONSBy using the AOT40 crop-response method, we find a crop production loss (CPL) of about 7.39 million tonnes (Mt) of rice in 2005, which increased to 11.46 Mt. in 2020. The estimated average CPL for the study period is sufficient to feed about 233 million people per year. It also has incurred an economic loss of about $2.92 billion in 2020. SIGNIFICANCEAtmospheric pollution must be reduced to protect crop health and ensure food security, as evidenced by the two-fold rise of YL in rice due to ozone pollution during the past decade in India. This is also applicable to all agrarian economies of the world with high atmospheric pollution; reiterating the global significance of this study.

Meteorological impacts on the unexpected ozone pollution in coastal cities of China during the unprecedented hot summer of 2022

Surface ozone pollution under climate warming has become a serious environmental issue. In the summer of 2022, abnormal warming spread over most of the Northern Hemisphere and resulted in the abnormal increase in O3 concentrations. In this study, we focused on the coastal cities in China and investigated the O3 trends in July during 2015 to 2022. Four regions with different locations and emission levels were selected for comparison. A significant increase of O3 concentration in July 2022 were observed in the southern coastal cities (16.7–22.8 μg m−3) while the opposite characteristics were found in the northern coastal cities (decrease of 0.26–2.18 μg m−3). The results indicated various distribution patterns of the O3 concentrations responded to heat wave across China. The weakening of East Asian summer monsoon, extension of the western Pacific subtropical high, significant warming, stronger solar radiation, lower relative humidity, less rainfall and sinking motion of atmosphere in 2022 were beneficial for O3 generation and accumulation in the southern coastal areas. Meteorological changes in July 2022 could lead to an increase of 15.6 % in O3 concentrations in southern coastal cities compared to that in 2015–2021, based on the analysis of machine learning. Air temperature was the main contributor to high O3 concentrations in the coast of Fujian province, while other coastal cities depended on relative humidity. This study indicated the challenge of O3 pollution control in coastal areas under global warming, especially in extreme heat wave events.

Anomalous high ozone in the Pearl River Delta, China in 2019: A cause attribution analysis

Surface ozone (O3) pollution in the Pearl River Delta (PRD), especially persistent O3 pollution episodes lasting three days or longer (OPE3), showed an anomalous peak during the autumn of 2019 (bulge-2019). Our research reveals a robust connection between the occurrence of OPE3 events and days with low cloud cover. Specifically, low-cloud days comprised 55% of the total number of OPE3 events in the PRD during the autumn season spanning from 2015 to 2021. Notably, as much as 74% of the bulge-2019 is related to low-cloud days. However, establishing a direct causal link remains challenging. In autumn 2019, an anomalous cyclonic center developed over the Philippine Sea, with anomalous northerly winds on its northwestern flank, leading to anomalous downdraft over South China. This pattern was characterized by stagnant, low relative humidity, intensified solar radiation, and low cloud cover conditions that were conducive to the photochemical formation and accumulation of O3. Further analysis of the relationship between low-cloud days and El Niño–Southern Oscillation shows that eastern Pacific El Niño events are associated with fewer low-cloud days in the PRD in autumn, whereas central Pacific (CP) El Niño events result in more low-cloud days. In addition, the anomalous cyclonic center over the Philippine Sea serves as the crucial atmospheric circulation system connecting CP El Niño and high O3 level over southern China. Therefore, we propose that the increased downdraft of PRD brought by northward tropical cyclones (TCs) during more low-cloud days in 2019 is highly likely the primary cause of the occurrence of the bulge-2019.