Ground-level O3 Concentrations Research Articles

First estimation of hourly full-coverage ground-level ozone from Fengyun-4A satellite using machine learning

Ground-level ozone (O3), renowned for its adverse impacts on human health and crop production, has garnered significant attention from governmental and public sectors. To address the limitations posed by sparse and uneven ground-level O3 observations, this study proposes an innovative method for hourly full-coverage ground-level O3 estimation using machine learning. Meteorological data from National Centers for Environmental Prediction global forecasting system, satellite data from Fengyun-4 A(FY-4 A) and Ozone Monitoring Instrument, emission inventory from Multi-resolution Emission Inventory for China, and other auxiliary data are utilized as input variables, while ground-based O3 observations serve as the response variable. The method is applied on a monthly basis across China for the year 2022, resulting in the generation of an hourly full-coverage high-resolution (4 km) ground-level O3 estimation, termed ML-derived-O3. Cross-validation results demonstrate the robustness of ML-derived-O3 yielding a coefficient of determination (R 2) of 0.96 (0.91) for sample-based (site-based) evaluations and a root-mean-square error (RMSE) of 9.22 (13.65) µg m−3. However, the date-based evaluation is less satisfactory due to the imbalanced training data, resulting from the pronounced daily variations in ground-level O3 concentrations. Nevertheless, the seasonal and hourly ML-derived-O3 exhibits high prediction accuracy, with R 2 values surpassing 0.95 and RMSE remaining below 7.5 µg m−3. This study marks a significant milestone as the first successful attempt to obtain hourly full-coverage ground-level O3 data across China. The diurnal variation of ML-derived-O3 demonstrates high consistency with ground-based observations, irrespective of clear or cloudy days, effectively capturing ground-level O3 pollution exposure events. This novel estimation method will be employed to establish a long-term high spatial-temporal resolution ground-level O3 dataset, which holds valuable applications for air pollution monitoring and environmental health research in future endeavors.

High-Spatiotemporal-Resolution Estimation of Ground-Level Ozone in China Based on Machine Learning

High concentrations of ground-level ozone (O3) pose a significant threat to human health. Obtaining high-spatiotemporal-resolution information about ground-level O3 is of paramount importance for O3 pollution control. However, the current monitoring methods have a lot of limitations. Ground-based monitoring falls short in providing extensive coverage, and remote sensing based on satellites is constrained by specific spectral bands, lacking sensitivity to ground-level O3. To address this issue, we combined brightness temperature data from the Himawari-8 satellite with meteorological data and ground-based station data to train four machine learning models to obtain high-spatiotemporal-resolution information about ground-level O3, including Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and Random Forest (RF). Among these, the CatBoost model exhibited superior performance, achieving a ten-fold cross-validation R2 of 0.8534, an RMSE of 17.735 μg/m3, and an MAE of 12.6594 μg/m3. Furthermore, all the selected feature variables in our study positively influenced the model. Subsequently, we employed the CatBoost model to estimate averaged hourly ground-level O3 concentrations at a 2 km resolution. The estimation results indicate a close relationship between ground-level O3 concentrations and human activities and solar radiation.

Insighting Drivers of Population Exposure to Ambient Ozone (O3) Concentrations across China Using a Spatiotemporal Causal Inference Method

Ground-level ozone (O3) is a well-known atmospheric pollutant aside from particulate matter. China as a global populous country is facing serious surface O3 pollution. To detect the complex spatiotemporal transformation of the population exposure to ambient O3 pollution in China from 2005 to 2019, the Bayesian multi-stage spatiotemporal evolution hierarchy model was employed. To insight the drivers of the population exposure to ambient O3 pollution in China, a Bayesian spatiotemporal LASSO regression model (BST-LASSO-RM) and a spatiotemporal propensity score matching (STPSM) were firstly applied; then, a spatiotemporal causal inference method integrating the BST-LASSO-RM and STPSM was presented. The results show that the spatial pattern of the annual population-weighted ground-level O3 (PWGLO3) concentrations, representing population exposure to ambient O3, in China has transformed since 2014. Most regions (72.2%) experienced a decreasing trend in PWGLO3 pollution in the early stage, but in the late stage, most areas (79.3%) underwent an increasing trend. Some drivers on PWGLO3 concentrations have partial spatial spillover effects. The PWGLO3 concentrations in a region can be driven by this region’s surrounding areas’ economic factors, wind speed, and PWGLO3 concentrations. The major drivers with six local factors in 2005–2014 changed to five local factors and one spatial adjacent factor in 2015–2019. The driving of the traffic and green factors have no spatial spillover effects. Three traffic factors showed a negative driving effect in the early stage, but only one, bus ridership per capita (BRPC), retains the negative driving effect in the late stage. The factor with the maximum driving contribution is BRPC in the early stage, but PM2.5 pollution in the late stage, and the corresponding driving contribution is 17.57%. Green area per capita and urban green coverage rates have positive driving effects. The driving effects of the climate factors intensified from the early to the later stage.

Characteristics and mechanism of a persistent ozone pollution event in Pearl River Delta induced by typhoon and subtropical high

Ozone (O3) is a significant air pollutant in the Pearl River Delta (PRD) region, with severe pollution events primarily associated with the Western Pacific Subtropical High (WPSH) and typhoon periphery synoptic weather patterns. To investigate the continuity and differences in O3 pollution processes influenced by these patterns, we analyze a severe O3 pollution event in Zhuhai from September 10 to 13, 2022. The study collects vertical profiles of O3 from the differential absorption O3 lidar and wind fields observed by the Doppler wind profile lidar. We utilize ground-level monitoring data from national ambient air quality monitoring network and meteorological stations to analyze diurnal variations of air pollutant concentrations and ground-level meteorological conditions. The study reveals that both the WPSH and the typhoon periphery create favorable meteorological conditions for severe O3 pollution events. The highest temperatures during the WPSH and typhoon periphery stages are 32.1 °C and 37.5 °C, respectively. During the WPSH control stage, the combined effects of the inversion layer and light static wind hinder O3 dispersion. The ground-level O3 concentration reaches a maximum of 106 ppb. Then the typhoon periphery control stage, the ground-level O3 peak increases to 170 ppb, showing a 60% increase compared to the WPSH stage. O3 horizontal and vertical transport processes play a crucial role in the O3 episode during this stage. The northerly winds predominantly transport pollutants from upstream urban clusters to the local area, and intense downdrafts facilitate the downward transport of O3 from the residual layer. The study investigates the mechanisms of three-dimensional O3 transport within the boundary layer influenced by the WPSH and typhoon periphery in the PRD region. The results contribute valuable insights to pollution events in the PRD region under typical synoptic weather patterns.

Relationships between ozone and particles during air pollution episodes in arid continental climate

For human health, tropospheric ozone (O3), particles (PM2.5 and PM10, particles with aerodynamic diameter <2.5 and 10 μm), and nitrogen dioxide (NO2) are the most harmful air pollutants. We have investigated the simultaneity of PM2.5, PM10, NO2, and O3 occurrence by using data from 21 ground-based monitoring stations in a megacity of the Middle East, Tehran (Iran), between 2011 and 2022. In Tehran, the daily PM2.5 (21–45 μg m−3), PM10 (52–108 μg m−3) and NO2 (75–146 μg m−3) mean concentrations have largely exceeded the 24-h limit value established by the 2021 World Health Organization for the protection of human health, i.e., 15, 45 and 25 μg m−3, respectively, in particular for NO2. The ground-level daily O3 concentrations ranged from 26 to 47 μg m−3. Changes in aerosols burden (e.g., PM2.5 and PM10) affect surface O3 through changes in aerosol chemistry and photolysis rates. Following the dust events and for the days with PM2.5 and PM10 exceeding 50 and 100 μg m−3, respectively, we observed a reduction of surface O3 levels (- 30.0% and - 13.5%), concurrently to surface NO2 (+27.9% and +16.6%) levels increases compared to the non-dusty clear-sky days over Tehran city. Surface O3 formation is suppressed under high particulate matter (PM) levels in the atmosphere, in particular PM2.5, likely due to weakened photochemical reactions (lower solar radiation and air temperature), dilution effect, and heterogeneous chemical processes occurring onto the PM2.5 surface (reactive uptake of nitrogen oxides, NOx, and hydrogen oxide radicals, HOx). To achieve O3-PM co-improvement in cities, we recommend reductions in NOx emissions concurrently with significant reductions in PM and Volatile Organic Compounds emissions, for instance by suitable greening and re-naturing programs.

Atmospheric Ozone Concentration Prediction in Nanjing Based on LightGBM

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.

Assessment of rice yield and economic losses caused by ground-level O3 exposure in the Mekong delta region, Vietnam

The Lower Mekong Delta region (LMD) accounts for 90% of Vietnam's rice exports; however, the air quality in the LMD is remarkably reduced by ground-level ozone (O3) pollution. This study aimed to quantify the relative yield and economic value losses in rice-growing crop seasons affected by ground-level O3 concentrations across the LMD. The results of this study can serve as a basis for extensive assessments for the following years and support environmental managers to propose control measures of O3 precursor emissions (NOx and VOCs) from man-made sectors, as well as build protective solutions for rice farming in LMD. Two ground-level O3 exposure metrics of M7 and AOT40 reflecting ground-level O3 pollution impacts, combined with the model of exposure-relative yield relationship (or surface O3-crop models), were used to assess losses of crop production (CPL) and economic cost losses (ECL) caused by rice crop yield reductions. For the M7 metric of ground-level O3 exposure, the average value was 14.746 ppbV, with levels ranging from 13.959 ppbV to 15.502 ppbV, and the affected area was spread across 1309.39 thousand hectares. The AOT40 exposure metric reached an average value of 11.490 ppbV, with a range of 0.000–31.665 ppbV. The highest exposure level was 17.503–31.653 ppbV, covering an area of 747.01 thousand hectares. The total CPL of the three rice crops over the LMD was 9593.52 tonnes (accounting for 0.039% of the total value of rice production in the region), with a total corresponding EPL of 62.405 billion VND (equivalent to 2761.01 thousand USD). The results are considered a baseline study to serve as a basis for extensive assessments for the following years and support for the environmental managers to propose control measures of O3 precursor emissions (NOx and VOCs) from man-made sectors as well as build protective solutions in rice farming in LMD shortly.

Estimation of ground-level O3 concentration in the Yangtze River Delta region based on a high-performance spatiotemporal model MixNet

In recent years, the escalating ozone (O3) concentration has significantly damaged human health. The machine learning models are widely used to estimate ground-level O3 concentrations, but the spatial and temporal features in the data are less considered. To address the issue, this study proposed a novel framework named MixNet to estimate daily O3 concentration from 2020 to 2021 over the Yangtze River Delta. The MixNet utilized image convolution to extract the potential spatial information related to O3 fully. The temporal features were extracted by a Long Short-Term Memory (LSTM). A U-Net, a new jump connection method with an attention mechanism and residual blocks, facilitated a more comprehensive extraction of spatial features in the data. The extracted temporal and spatial features were fused to estimate ground-level O3. Meanwhile, a novel training method was proposed to enhance the accuracy of MixNet. The daily mean O3 maps have high validation results in comparison with ground-level O3 measurement, with R2 (RMSE) of 0.903 (14.511 μg/m3) for sample-based validation, 0.831 (19.036 μg/m3) for site-based validation, and 0.712 (25.108 μg/m3) for time-based validation. The season-average maps indicate that O3 concentration is summer > autumn > spring > winter. The highest value was 137.41 μg/m3 in the summer of 2021 over the Yangtze River Delta urban agglomeration, and the lowest value was 52.73 μg/m3 in winter 2020. The MixNet showed better performance compared with other models, and thus the “point-plane image thinking” will contribute to future studies in developing better methods to estimate atmospheric pollutants.

Effect of Future Climate Change on Stratosphere-to-Troposphere-Exchange Driven Ozone in the Northern Hemisphere.

Future estimates of atmospheric pollutant concentrations serve as critical information for policy makers to formulate current policy indicators to achieve future targets. Tropospheric burden of O3 is modulated not only by anthropogenic and natural precursor emissions, but also by the downward transport of O3 associated with stratosphere to troposphere exchange (STE). Hence changes in the estimates of STE and its contributions are key to understand the nature and intensity of future ground level O3 concentrations. The difference in simulated O3 mixing ratios with and without the O3-Potential Vorticity (PV) parameterization scheme is used to represent the model estimated influence of STE on tropospheric O3 distributions. Though STE contributions remain constant in Northern hemisphere as a whole, regional differences exist with Europe (EUR) registering increased STE contribution in both spring and winter while Eastern China (ECH) reporting increased contribution in spring in 2050 (RCP8.5) as compared to 2015. Importance of climate change can be deduced from the fact that ECH and EUR recorded increased STE contribution to O3 in RCP8.5 compared to RCP4.5. Comparison of STE and non-STE meteorological process contributions to O3 due to climate change revealed that contributions of non-STE processes were highest in summer while STE contributions were highest in winter. EUR reported highest STE contribution while ECH reported highest non-STE contribution. None of the 3 regions show consistent low STE contribution due to future climate change (< 50%) in all seasons indicating the significance of STE to ground level O3.

Impacts of COVID-19 Lockdown on Traffic Flow, Active Travel and Gaseous Pollutant Concentrations; Implications for Future Emissions Control Measures in Oxford, UK

The COVID-19 lockdown provided a unique opportunity to test the impacts of changes in travel patterns on air quality and the environment. Therefore, this study provides insights into the impacts of COVID-19 emergency public health “lockdown” measures upon traffic flow, active travel and gaseous pollutant concentrations (NO, NO2 and O3) in Oxford city centre during 2020 using time-series analysis and linear regression methods. Comparisons of traffic counts indicated pronounced changes in traffic volume associated with national lockdown periods. Car volume reduced by 77.5% (statistically significant) during the first national lockdown, with lesser changes in goods vehicles and public transport (bus) activity during the second lockdown. Cycle flow reduced substantively during the first lockdown only. These changes resulted in a reduction in nitric oxide (NO) and nitrogen dioxide (NO2) concentrations of 75.1% and 47.4%, respectively, at roadside, and 71.8% and 34.1% at urban background during the first lockdown period. In contrast ozone (O3) concentrations increased at the urban background site by 22.3% during the first lockdown period, with no significant changes in gaseous concentrations during the second lockdown at either roadside or urban background location. The diurnal pattern of peak mean NO and NO2 concentrations reduced in magnitude and was shifted approximately 2 h earlier in the morning and 2 h later in the evening (roadside) and 3 h earlier in the morning and 3 h later in the evening (urban background). Our findings provide an example of how gaseous air quality in urban environments could respond to future urban traffic restrictions, suggesting benefits from reductions in peak and daily NO2 exposures may be offset by health harms arising from increases in ground level O3 concentrations in the summer months.

Does Light Pollution Affect Nighttime Ground-Level Ozone Concentrations?

Ground-level ozone (O3) is mainly produced during daytime in the presence of ultraviolet (UV) light and later destroyed by nitrogen oxides during nighttime. However, light pollution caused by the excessive use of artificial lights may disrupt the chemistry of night-time ground-level O3 by providing enough energy to initiate nighttime ground-level O3 production. In this study, nighttime (7 p.m. to 7 a.m.) ground-level O3, nitrogen oxide (NO), and nitrogen dioxides (NO2) concentrations were observed for three years (2013, 2014, and 2015). The existence of O3 was found during nighttime, especially in urban areas with a concentration range of 8–20 ppb. The results suggested that nighttime variations of ground-level O3 concentrations were higher in urban areas than in suburban areas. The mean nighttime O3 concentration at urban sites varied, possibly because the distribution of anthropogenic lights around the urban sites is brighter than in suburban locations, as indicated by the data from the light-pollution map. This anthropogenic light has not caused the suspected nighttime photolysis processes, which directly slowed nighttime oxidation. The photochemistry rate of JNO2/k3 was supposed to be near zero because of the absence of photochemical reactions at night. However, the minimum concentration in all urban and suburban sites ranged from 2–3 ppb, indicating that O3 might also form at night, albeit not due to light pollution.

Ground-level ozone in the Mekong Delta region: precursors, meteorological factors, and regional transport.

The Mekong Delta region (MDR), also known as Vietnam's rice bowl, produced a bountiful harvest of about 23.8 million tons in 2020, accounting for 55.7% of the country's total production, providing food security for 20% of the world population. With the rapid pace of industrialisation and urbanisation, the concentration of ozone in the lower atmosphere has risen to a level that reduces crop yields, especially rice, and is therefore the subject of research. This study aims to simulate the spatiotemporal distribution of ground-level ozone in the area and evaluate the impact of precursor emissions and meteorological factors on the spatiotemporal distributions of ozone concentrations. The study area was divided into seven zones, including six agro-ecological zones (AEZs) and one low-mountainous area, mainly to clarify the role of emissions in each AEZ. The simulation results showed that ground-level O3 in the MDR ranged from 40.39 to 52.13µg/m3. In six agro-ecological zones, the average annual ground-level O3 concentration was relatively high and was the highest in zone 6 (CPZ) and zone 3 (LXZ) with values of 96.18µg/m3 (exceeding 1.60 times the WHO Guidelines 2021) and 94.86µg/m3 (exceeding 1.58 times the WHO Guidelines 2021), respectively. In each zone, the annual average O3 concentration tended to gradually increase from the inner delta to coastal areas. Two types of precursors, NOx and NMVOCs, are the main contributors to O3 pollution, with the largest contribution coming from zone 1 (FAZ) with 91.5 thousand tons of NOx/year and 455.2 thousand tons of NMVOCs/year. Among the meteorological factors considered, temperature (T), relative humidity (RH), and surface pressure (P) were the three main factors that contributed to the increase in ground-level ozone. The spatio-temporal distribution of ground-level O3 in the MDR was influenced by emission precursors from different zones as well as meteorological factors. The present results can help policy-makers formulate plans for agro-industrial development in the entire region.

Impact of a subtropical high and a typhoon on a severe ozone pollution episode in the Pearl River Delta, China

Abstract. A record-breaking severe ozone (O3) pollution episode occurred in the Pearl River Delta (PRD) in early autumn 2019 when the PRD was under the influence of a Pacific subtropical high followed by Typhoon Mitag. In this study, we analyzed the effects of meteorological and photochemical processes on the O3 concentration in the PRD during this episode by carrying out the Weather Research Forecast–Community Multiscale Air Quality (WRF-CMAQ) model simulations. Results showed that low relative humidity, high boundary layer height, weak northerly surface wind, and strong downdrafts were the main meteorological factors contributing to O3 pollution. Moreover, delayed sea breezes that lasted into the night would transport O3 from the sea back to the land and resulted in secondary O3 maxima at night. In addition, O3 and its precursors stored in the residual layer above the surface layer at night can be mixed down to the surface in the next morning, further enhancing the daytime ground-level O3 concentration on the following day. Photochemical production of O3, with a daytime average production rate of about 7.2 ppb h−1 (parts per billion), is found to be the predominate positive contributor to the O3 budget of the boundary layer (0–1260 m) during the entire O3 episode, while the horizontal and vertical transport fluxes are the dominant negative contributors. This O3 episode accounted for 10 out of the yearly total of 51 d when the maximum daily 8 h average (MDA8) O3 concentration exceeded the national standard of 75 ppb in the PRD in 2019. Based on these results, we propose that the enhanced photochemical production of O3 during the episode is a major cause of the most severe O3 pollution year since the official O3 observation started in the PRD in 2006. Moreover, since this O3 episode is a synoptic-scale phenomenon covering the entire eastern China, we also suggest that the enhanced photochemical production of O3 in this O3 episode is a major cause of the extraordinarily high O3 concentrations observed in eastern China in 2019.

Weather Extremes Led to Large Variability in O3 Pollution and Associated Premature Deaths in East of China

As global warming intensifies, hot extremes and heavy precipitation frequently happen in East of China. Meanwhile, severe surface ozone (O3) pollution resulting from the interactions of anthropogenic emissions and meteorological conditions also occur more frequently. In this study, we quantified the impact of weather extremes on ground-level O3 concentration during the summers of 2015–2021 and associated premature deaths in East of China. The O3 pollution influenced by hot extremes [maximum 8-h average O3 concentration (MDA8 O3) = 152.7 μg m−3] was 64.2% more severe than that associated with heavy rain (MDA8 O3 = 93 μg m−3) on the daily time scale. The compound hot and dry air extremes had a larger impact, and the associated MDA8 O3 could be up to 165.5 μg m−3. Thus, weather extremes could drastically perturb the O3 level in the air to exhibit large variability. Based on GEOS-Chem simulations with fixed anthropogenic emissions, forcing of weather extremes could successfully reproduce the large daily variability of O3 concentration because the weather extremes significantly influenced the physicochemical processes in the atmosphere. Furthermore, hot extremes magnified the single-day O3-related premature death to 153% of that under other-condition events, while heavy rain events decreased it to 70% in East of China. The findings of the present study have the potential to promote daily to weekly O3 forecasts and further improve our comprehensive understanding of the health effects of weather extremes and air pollution.

Separately resolving NOx and VOC contributions to ozone formation

Ozone (O3) is widely recognized as a significant air pollutant that affects public health across the globe. O3 is formed from precursor emissions of oxides of nitrogen (NOx) and volatile organic compounds (VOCs) that react in the atmosphere, making it complex to identify the major source contributions to O3 concentrations. O3 source apportionment calculations within chemical transport models (CTMs) provide a specialized approach to resolve source contributions. Traditional O3 apportionment techniques track source contributions based on the chemical formation regime, but they do not separately distinguish between NOx and VOC source contributions. In this study, a new O3 source apportionment technique was developed to explicitly resolve the contributions from both NOx and VOC sources in order to provide a more detailed view on O3 source origins so that policy makers can design more effective emission control strategies. The new technique is flexible and can be configured to identify the original source of precursors that contribute to O3 formation or the most recent source depending on the choice of the tagging method. The detailed features of the new technique are demonstrated during a peak O3 event in September 2010 in Los Angeles, while trends in O3 source contributions over time are evaluated during two additional simulations in July 2005 and August 2015. Quality control checks show that the new source apportionment methodology does not alter predicted total O3 concentrations, and the detailed source apportionment information can be averaged to yield results that are consistent with traditional O3 source apportionment calculations. The detailed O3 source apportionment results during Sept 2010 show that, among NOx sources, on-road gasoline, on-road diesel, off-road diesel, and soil NOx account for over 60% of the ground level O3 concentrations. Among VOC sources, upwind boundary conditions and biogenic sources account for approximately 90% of the ground-level O3 formed. The formaldehyde to NO2 ratio suggests that the chemical regime in the year 2015 was VOC-limited, but given the uncontrollable nature of the VOC emissions, the results suggest that NOx emission controls would have been the preferred emission control strategy to reduce O3 concentration in Los Angeles at that time, with the understanding that some period of O3 disbenefits would need to be tolerated until the emissions control program shifts the atmospheric chemistry back into the NOx-limited regime. The NOx source apportionment results for O3 identify the largest NOx sources that could be reduced in an effort to reach NOx-limited conditions. The chemical regime in the Los Angeles atmosphere is continuously evolving, and so these calculations would need to be repeated under current conditions to determine if we have arrived at this NOx limited regime. Future studies will undertake this analysis.

Satellite-Based Long-Term Spatiotemporal Patterns of Surface Ozone Concentrations in China: 2005-2019.

Background:Although short-term ozone () exposure has been associated with a series of adverse health outcomes, research on the health effects of chronic exposure is still limited, especially in developing countries because of the lack of long-term exposure estimates.Objectives:The present study aimed to estimate the spatiotemporal distribution of monthly mean daily maximum 8-h average concentrations in China from 2005 to 2019 at a 0.05° spatial resolution.Methods:We developed a machine learning model with a satellite-derived boundary-layer column, precursors, meteorological conditions, land-use information, and proxies of anthropogenic emissions as predictors.Results:The random, spatial, and temporal cross-validation of our model were 0.87, 0.86, and 0.76, respectively. Model-predicted spatial distribution of ground-level concentrations showed significant differences across seasons. The highest summer peak of occurred in the North China Plain, whereas southern regions were the most polluted in winter. Most large urban centers showed elevated levels, but their surrounding suburban areas may have even higher concentrations owing to nitrogen oxides titration. The annual trend of concentrations fluctuated over 2005–2013, but a significant nationwide increase was observed afterward.Discussion:The present model had enhanced performance in predicting ground-level concentrations in China. This national data set of concentrations would facilitate epidemiological studies to investigate the long-term health effect of in China. Our results also highlight the importance of controlling in China’s next round of the Air Pollution Prevention and Control Action Plan. https://doi.org/10.1289/EHP9406

Quantifying ecological and health risks of ground-level O3 across China during the implementation of the “Three-year Action Plan for Cleaner Air”

After China implemented the Air Pollution Prevention and Control Action Plan (APPCAP), PM2.5 concentrations decreased but were still higher than national standards in major areas and ozone (O3) concentration increased unintentionally. To further decrease PM2.5 concentrations and reduce days with severe air pollution, the government promulgated the “Three-year (2018–2020) Action Plan for Cleaner Air” (the Three-year Action Plan) in 2018. During the three-year Action Plan, a few studies reported a continuous decline in PM2.5, but it is unclear whether O3 and its effects also increase with the decrease of PM2.5 like during APPCAP. In this study, for the first time, we systematically assessed changes in ground-level O3 concentrations and related ecological and health risks during the period of the Three-year Action Plan using nationwide O3 measurements. The national MDA8, Exceedance, and SOMO35 indicators were reduced by 3.8%, 28.5%, and 12.6%, respectively, ecological risk indicators of M12, M7, SUM06, AOT40, and W126 were reduced by 5.4%, 5.6%, 19.5%, 15.4%, and 18.6%, respectively, from 2018 to 2020. Spatially, the greatest reduction in all the indicators except MDA8 occurred in Pearl River Delta, followed by Fen Wei Plains, while Beijing-Tianjin-Hebei, Chengdu-Chongqing, and Yangtze River Delta presented relatively small reductions. Between 2018 and 2020, the production losses caused by O3 for wheat and rice decreased by 21.4% and 17.6%, respectively. Long-term exposure to O3 across China over 2020 was estimated to cause about 160,795 (95% CI: 81,515–312,983) for all-cause mortality, 107,128 (95% CI: 36,703–173,823) for cardiovascular mortality, and 34,444 (95% CI: 0–72,609) for respiratory mortality, indicating decreases of 9.93%, 9.86%, and 9.78%, respectively, compared to the year 2018. Taken together, our results provided the first direct evidence for China's efforts to control O3 pollution in recent years.

Spatiotemporal estimation of hourly 2-km ground-level ozone over China based on Himawari-8 using a self-adaptive geospatially local model

Ground-level ozone (O3) is a primary air pollutant, which can greatly harm human health and ecosystems. At present, data fusion frameworks only provided ground-level O3 concentrations at coarse spatial (e.g., 10 km) or temporal (e.g., daily) resolutions. As photochemical pollution continues increasing over China in the last few years, a high-spatial–temporal-resolution product is required to enhance the comprehension of ground-level O3 formation mechanisms. To address this issue, our study creatively explores a brand-new framework for estimating hourly 2-km ground-level O3 concentrations across China (except Xinjiang and Tibet) using the brightness temperature at multiple thermal infrared bands. Considering the spatial heterogeneity of ground-level O3, a novel Self-adaptive Geospatially Local scheme based on Categorical boosting (SGLboost) is developed to train the estimation models. Validation results show that SGLboost performs well in the study area, with the R2s/RMSEs of 0.85/19.041 μg/m3 and 0.72/25.112 μg/m3 for the space-based cross-validation (CV) (2017–2019) and historical space-based CV (2019), respectively. Meanwhile, SGLboost achieves distinctly better metrics than those of some widely used machine learning methods, such as eXtreme Gradient boosting and Random Forest. Compared to recent related works over China, the performance of SGLboost is also more desired. Regarding the spatial distribution, the estimated results present continuous spatial patterns without a significantly partitioned boundary effect. In addition, accurate hourly and seasonal variations of ground-level O3 concentrations can be observed in the estimated results over the study area. It is believed that the hourly 2-km results estimated by SGLboost will help further understand the formation mechanisms of ground-level O3 in China.

Application of network theory to study spatio-temporal evolution in the weekend effect in urban areas

The occurrence of higher ground-level O3 concentrations on weekends rather than on weekdays, despite reduced anthropogenic activity in urban areas, is known as the O3 weekend effect (OWE). Here, we present an approach to analyse OWE spatio-temporal variations in urban areas, integrated by the trend, prediction and network representation. We used data from ten monitoring sites geographically distributed within the Mexico City Metropolitan Area (MCMA) recorded during 1994-2018. The OWE occurrence within the MCMA ranged typically between 40 and 60 % of the total weeks per year. The annual differences between weekday and weekend O3 peaks (magnitudes) showed were most significant on Sundays. Naive, Linear and Auto-regressive Integrated Moving Average models were tested for predicting the OWE annual occurrences and magnitudes. There was no single model that outperformed significantly for predicting OWE at all sites. The proposed concept of generalised OWE (GOWE) implies that at least half of the sites under study exhibited simultaneous OWE occurrence. GOWE is represented as a network and its integration with prediction models is useful to determinate the OWE spread over the MCMA in the following years. The GOWE occurrence showed an increasing trend interpreted as the spread of VOC-limited conditions over most of the MCMA. Predicted data suggest that, with the current emission control policies, the GOWE will continue occurring. The integrated methodology presented permits the acquisition of valuable insights into the design of potential air quality control strategies.

Exploring the drivers of the increased ozone production in Beijing in summertime during 2005–2016

Abstract. In the past decade, average PM2.5 concentrations decreased rapidly under the strong pollution control measures in major cities in China; however, ozone (O3) pollution emerged as a significant problem. Here we examine a unique (for China) 12-year data set of ground-level O3 and precursor concentrations collected at an urban site in Beijing (PKUERS, campus of Peking University), where the maximum daily 8 h average (MDA8) O3 concentration and daytime Ox (O3+NO2) concentration in August increased by 2.3±1.2 ppbv (+3.3±1.8 %) yr−1 and 1.4±0.6 (+1.9±0.8 %) yr−1, respectively, from 2005 to 2016. In contrast, daytime concentrations of nitrogen oxides (NOx) and the OH reactivity of volatile organic compounds (VOCs) both decreased significantly. Over this same time, the decrease of particulate matter (and thus the aerosol optical depth) led to enhanced solar radiation and photolysis frequencies, with near-surface J(NO2) increasing at a rate of 3.6±0.8 % yr−1. We use an observation-based box model to analyze the combined effect of solar radiation and ozone precursor changes on ozone production rate, P(O3). The results indicate that the ratio of the rates of decrease of VOCs and NOx (about 1.1) is inefficient in reducing ozone production in Beijing. P(O3) increased during the decade due to more rapid atmospheric oxidation caused to a large extent by the decrease of particulate matter. This elevated ozone production was driven primarily by increased actinic flux due to PM2.5 decrease and to a lesser extent by reduced heterogeneous uptake of HO2. Therefore, the influence of PM2.5 on actinic flux and thus on the rate of oxidation of VOCs and NOx to ozone and to secondary aerosol (i.e., the major contributor to PM2.5) is important for determining the atmospheric effects of controlling the emissions of the common precursors of PM2.5 and ozone when attempting to control these two important air pollutants.