Random forest model based fine scale spatiotemporal O3 trends in the Beijing-Tianjin-Hebei region in China, 2010 to 2017

Forecasting O3 and NO2 concentrations with spatiotemporally continuous coverage in southeastern China using a Machine learning approach

Ozone (O3) is a significant contributor to air pollution and the main constituent ofphotochemical smog that plagues China. Nitrogen dioxide (NO2) is a significant air pollutant and a critical trace gas in the Earth's atmosphere. The presence of O3 and NO2 has detrimental effects on human health, the ecosystem, and agricultural production. Forecasting accurate ambient O3 and NO2 concentrations with full spatiotemporal coverage is pivotal for decision-makers to develop effective mitigation strategies and prevent harmful public exposure. Existing methods, including chemical transport models (CTMs) and time series at air monitoring sites, forecast O3 and NO2 concentrations either with nontrivial uncertainty or without spatiotemporally continuous coverage. In this research, we adopted a forecasting model that integrates the random forest algorithm with NASA's Goddard Earth Observing System "Composing Forecasting" (GEOS-CF) product. This approach offers spatiotemporally continuous forecasts of O3 and NO2 concentrations across southeastern China for up to five days in advance. Both overall validation and spatial cross-validation revealed that our forecast framework significantly surpassed the initial GEOS-CF model for all validation metrics, substantially reducing the errors in the GEOS-CF forecast data. Our model could provide accurate near-real-time O3 and NO2 forecasts with continuous spatiotemporal coverage.

High-resolution full-coverage ozone (O3) estimates using a data-driven spatial random forest model in Beijing-Tianjin-Hebei region, China

The Beijing-Tianjin-Hebei (BTH) region is severely polluted by ozone (O3). Accurate O3 estimates are essential for identifying high-polluted zones and developing targeted interventions to relieve the burden of diseases. Although many studies have estimated high-resolution O3 concentrations in BTH, the estimation accuracies are still insufficient. In this study, we incorporated data-driven spatial weight matrices (DDWs) into a random forest (RF) model to fully utilize both the spatial homogeneity and heterogeneity of maximum daily 8-h ozone concentration (MDA8O3), and obtained full-coverage MDA8O3 concentrations at 1 km×1 km in BTH from 2014 to 2022. DDW-RF exhibited satisfactory accuracy (10-fold cross-validation R2 =0.937, RMSE=13.919 μg/m3). Overall O3 level presented a spatial pattern of lower in the north and higher in the southeast and showed a distinct temporal trend, i.e., first increasing and then decreasing during 2014-2021 and increasing slightly in 2022. The accurate MDA8O3 estimates indicates that more attention and resources should be poured into the areas adjacent to Bohai Rim, Shandong and Henan. Regulated operation of factories under specific meteorological conditions and upgrading industrial structure and production modes are recommended to mitigate the formation of O3 precursors and reduce O3 generation. Our findings provide evidence and reference for environmental cleaning policies and targeted interventions.

Long-term spatiotemporal variation of benzo[a]pyrene in Japan: Significant decrease in ambient concentrations, human exposure, and health risk

Although Benzo[a]pyrene (BaP) is considered carcinogenic to humans, the health effects of exposure to ambient levels have not been sufficiently investigated. This study estimated the long-term spatiotemporal variation of BaP in Japan over nearly two decades at a fine spatial resolution of 1 km. This study aimed to obtain an accurate spatiotemporal distribution of BaP that can be used in epidemiological studies on the health effects of ambient BaP exposure. The annual BaP concentrations were estimated using an ensemble machine learning approach using various predictors, including the concentrations and emission intensities of the criteria air pollutants, and meteorological, land use, and traffic-related variables. The model performance, evaluated by location-based cross-validation, exhibited satisfactory accuracy (R2 of 0.693). Densely populated areas showed higher BaP levels and greater temporal reduction, whereas BaP levels remained higher in some industrial areas. The population-weighted BaP in 2018 was 0.12 ng m-3, a decrease of approximately 70% from its 2000 value of 0.44 ng m-3, which was also reflected in the estimated excess number of lung cancer incidences. Accordingly, the proportion of BaP exposure below 0.12 ng m-3, which is the BaP concentration associated with an excess lifetime cancer risk of 10-5, reached 67% in 2018. Our estimates can be used in epidemiological studies to assess the health effects of BaP exposure at ambient concentrations.

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

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

Identification of response regulation governing ozone formation based on influential factors using a random forest approach

The pursuit of enhanced scientific, refined, and precise ozone and air quality control continues to pose significant challenges. Using data visualization techniques and random forest (RF) algorithms, the temporal distribution of atmospheric pollutants and the interrelationship between O3 concentration and its influential factors were investigated with one-year monitoring data in Deqing county in 2021. The local atmospheric conditions predominantly belonged to NOx-sensitive and transition zone. Extremely high O3 concentration were primarily observed when temperatures (T) exceeded 30 °C, with relative humidity (RH) ranging between 30 and 60 %. NO2, RH and T were identified as the top 3 important factors, and O3 concentration have stronger linearly relationship to RH and T, while stronger nonlinearly relationship to NO2. By employing an optimized RF model, controlling consistent mild and high reaction atmospheric conditions, the O3 concentration response to the change of individual influencing factors was acquired. The O3 concentration increased and then decreased in response to the increasing NO2 concentration, displaying a characteristic inflection point at 10 μg m-3. More reactive radicals produced at higher VOCs concentration and continuing NOx cycle at lower NO2 concentration, resulting in the acceleration in the direction of producing more O3. Therefore, the significant different O3 response to variation of VOCs and NOx concentration between mild and high reaction atmospheric conditions, as well as the existing of oxidant elevation should be considered in local air quality control. This study demonstrates the efficacy of ML methods in simulating nonlinear response of O3, supports the understanding of local O3 formation and quick guidance for precise local O3 pollution control and the related strategies.

Combining physical mechanisms and deep learning models for hourly surface ozone retrieval in China

As surface ozone (O3) gains increasing attention, there is an urgent need for high temporal resolution and accurate O3 monitoring. By taking advantage of the progress in artificial intelligence, deep learning models have been applied to satellite based O3 retrieval. However, the underlying physical mechanisms that influence surface O3 into model construction have rarely been considered. To overcome this issue, we considered the physical mechanisms influencing surface O3 and used them to select relevant variable features for developing a novel deep learning model. We used a wide and deep model architecture to account for linear and non-linear relationships between the variables and surface O3. Using the developed model, we performed hourly inversions of surface O3 retrieval over China from 2017 to 2019 (9:00-17:00, local time). The validation results based on sample-based (site-based) methods yielded an R2 of 0.94 (0.86) and an RMSE of 12.79 (19.13) μg/m3, indicating the accuracy of the models. The average surface O3 concentrations in China in 2017, 2018, and 2019 were 82, 78, and 87 μg/m3, respectively. There was a diurnal pattern in surface O3 in China, with levels rising significantly from 55 μg/m3 at 9:00 a.m. to 96 μg/m3 at 15:00. Between 15:00 and 16:00, the O3 concentration remained stable at 95 μg/m3 and decreased slightly thereafter (16:00-17:00). The results of this study contribute to a deeper understanding of the physical mechanisms of ozone and facilitate further studies on ozone monitoring, thereby enhancing our understanding of the spatiotemporal characteristics of ozone.

A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023

Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.

The spatiotemporal pattern of surface ozone and its impact on agricultural productivity in China

The slowing of agricultural productivity growth globally over the past two decades has brought a new urgency to detect its drivers and potential solutions. We show that air pollution, particularly surface ozone (O3), is strongly associated with declining agricultural total factor productivity (TFP) in China. We employ machine learning algorithms to generate estimates of high-resolution surface O3 concentrations from 2002 to 2019. Results indicate that China's O3 pollution has intensified over this 18-year period. We coupled these O3 estimates with a statistical model to show that rising O3 pollution during nonwinter seasons has reduced agricultural TFP by 18% over the 2002-2015 period. Agricultural TFP is projected to increase by 60% if surface O3 concentrations were reduced to meet the WHO air quality standards. This productivity gain has the potential to counter expected productivity losses from 2°C warming.

Rebuilding high-quality near-surface ozone data based on the combination of WRF-Chem model with a machine learning method to better estimate its impact on crop yields in the Beijing-Tianjin-Hebei region from 2014 to 2019

In recent years, the problem of surface ozone pollution in China has been of great concern. According to observation data from monitoring stations, the concentration of near-surface ozone (O3) in China has gradually increased in recent years, and ozone concentration often exceeds the contaminant limit standard, especially in the Beijing-Tianjin-Hebei (BTH) region. High O3 concentration pollution will adversely affect crop growth, which can cause crop yield losses. Therefore, it is urgent to recognize the situation of ozone pollution in the BTH region and quantitatively evaluate the crop yield losses caused by ozone pollution to develop more effective pollution prevention and control policies. However, the monitoring of ozone concentration in China started relatively late compared with some developed countries, and currently, long-time series data covering the BTH region cannot be obtained, which makes it difficult to evaluate the impact of ozone on crop yield. Therefore, a new method (WRFC-XGB) was proposed in this study to establish a high-precision near-surface O3 concentration dataset covering the whole BTH region from 2014 to 2019 by integrating the Weather Research and Forecasting with Chemistry (WRF-Chem) model with the extreme gradient boosting (XGBoost) machine learning algorithm. Through verification with ground observation station data, the results of WRFC-XGB are satisfactory, and R2 can reach 0.78-0.91. Compared with other algorithms, the accuracy of the near-surface ozone concentration dataset is greatly improved, which can be used to estimate the impact of surface ozone on crop yield. Based on this dataset, the yield loss of winter wheat, rice, and maize caused by O3 pollution was estimated by using the response equation of the relative yield and ozone dose index. The results showed that the total yield losses of winter wheat, rice and maize from 2014 to 2019 were 2659.21 million tons, 49.23 million tons and 1721.56 million tons due to ozone pollution in the BTH region, respectively, and the highest relative yield loss of crops caused by O3 pollution could be 29.37% during 2014-2019, which indicated that the impact of ozone pollution on crop yield cannot be ignored, and effective measures need to be developed to control ozone pollution, prevent crop production loss, and ensure people's food security.

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

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

Assessing uncertainty and heterogeneity in machine learning-based spatiotemporal ozone prediction in Beijing-Tianjin- Hebei region in China

Accurate prediction of spatiotemporal ozone concentration is of great significance to effectively establish advanced early warning systems and regulate air pollution control. However, the comprehensive assessment of uncertainty and heterogeneity in spatiotemporal ozone prediction remains unknown. Here, we systematically analyze the hourly and daily spatiotemporal predictive performances using convolutional long short term memory (ConvLSTM) and deep convolutional generative adversarial network (DCGAN) models over the Beijing-Tianjin-Hebei region in China from 2013 to 2018. In extensive scenarios, our results show that the machine learning-based (ML-based) models achieve better spatiotemporal ozone concentration prediction performance with multiple meteorological conditions. A further comparison to the air pollution model-Nested Air Quality Prediction Modelling System (NAQPMS) and monitoring observations, the ConvLSTM model demonstrates the practical feasibility of identifying high ozone concentration distribution and capturing spatiotemporal ozone variation patterns at a high spatial resolution (here 15 km × 15 km).

VAR-tree model based spatio-temporal characterization and prediction of O3 concentration in China

Ozone (O₃) pollution in the atmosphere is getting worse in many cities. In order to improve the accuracy of O₃ prediction and obtain the spatial distribution of O₃ concentration over a continuous period of time, this paper proposes a VAR-XGBoost model based on Vector autoregression (VAR), Kriging method and XGBoost (Extreme Gradient Boosting). China is used as an example and its spatial distribution of O₃ is simulated. In this paper, the O₃ concentration data of the monitoring sites in China are obtained, and then a spatial prediction method of O₃ mass concentration based on the VAR-XGBoost model is established, and finnally its influencing factors are analyzed. This paper concludes that O₃ features the highest correlation with PM_2.5 and the lowest correlation with SO₂. Among the measurement factors, wind speed and temperature are the most important factors affecting O₃ pollution, which are positively correlated to O₃ pollution. In addition, precipitation is negatively correlated with 8-hour ozone concentration. In this paper, the performance of the VAR-XGBoost model is evaluated based on the ten-fold cross-validation method of sample, site and time, and a comparison with the results of XGBoost, CatBoost (categorical boosting), ExtraTrees, GBDT (gradient boosting decision tree), AdaBoost (adaptive boosting), RF (random forest), Decision tree, and LightGBM (light gradient boosting machine) models is conducted. The result shows that the prediction accuracy of the VAR-XGBoost model is better than other models. The seasonal and annual average R² reaches 0.94 (spring), 0.93 (summer), 0.92 (autumn), 0.93 (winter), and 0.95 (average from 2016 to 2021). The data show that the applicability of the VAR-XGBoost model in simulating the spatial distribution of O₃ concentrations in China performs well. The spatial distribution of O₃ concentrations in the Chinese region shows an obvious feature of high in the east and low in the west, and the spatial distribution is strongly influenced by topographical factors. The mean concentration is clearly low in winter and high in summer within a season. The results of this study can provide a scientific basis for the prevention and control of regional O₃ pollution in China, and can also provide new ideas for the acquisition of data on the spatial distribution of O₃ concentrations within cities.

Summer O3 pollution cycle characteristics and VOCs sources in a central city of Beijing-Tianjin-Hebei area, China

One of the major pollutants influencing urban air quality in China is O₃. O₃ is the second most important pollutant affecting air quality in Shijiazhuang, which is the third largest city in the Beijing-Tianjin-Hebei area and the provincial capital of Hebei province. To fully understand the characteristics of O₃ and volatile organic compounds (VOCs), which are O₃ precursors, and the role of VOCs to ozone formation, we measured the hourly concentrations of O₃ and 85 VOCs in Shijiazhuang continuously from January to November 2020, and the concentration characteristics of both together with the chemical reactivity and sources of VOCs were analyzed from a seasonal perspective. The O₃ concentration in Shijiazhuang showed a phenomenon of high summer and low winter, and the VOCs showed a phenomenon of high winter and low spring. In the summer when the O₃ exceedance rate is the highest, the time-domain variation characteristics of O₃ were analyzed by wavelet analysis model, and the main periods controlling the O₃ concentration variation in Shijiazhuang in summer 2020 were 52 days, 32 days, 19 days and 12 days. The maximum incremental reactivity (MIR) and propylene equivalence method indicated ethene, propylene and 1-pentene were common substances in the top five species of each season. The T/B, Iso-p/N-p, Iso-p/E, N-p/E, and positive matrix factorization (PMF) model showed that industrial source (18.62%-22.03%) and vehicle emission (13.20%-17.69%) were the major VOCs sources in Shijiazhuang. Therefore, to control the O₃ concentration in Shijiazhuang, it is necessary to decrease alkenes emissions as well as VOCs from industrial source and vehicle emission.

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

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

Ambient fine particulate matter and cardiopulmonary health risks in China

ABSTRACT

In China, the level of ambient fine particulate matter (PM 2.5 ) pollution far exceeds the air quality standards recommended by the World Health Organization. Moreover, the health effects of PM 2.5 exposure have become a major public health issue. More than half of PM 2.5 -related excess deaths are caused by cardiopulmonary disease, which has become a major health risk associated with PM 2.5 pollution. In this review, we discussed the latest epidemiological advances relating to the health effects of PM 2.5 on cardiopulmonary diseases in China, including studies relating to the effects of PM 2.5 on mortality, morbidity, and risk factors for cardiovascular and respiratory diseases. These data provided important evidence to highlight the cardiopulmonary risk associated with PM 2.5 across the world. In the future, further studies need to be carried out to investigate the specific relationship between the constituents and sources of PM 2.5 and cardiopulmonary disease. These studies provided scientific evidence for precise reduction measurement of pollution sources and public health risks. It is also necessary to identify effective biomarkers and elucidate the biological mechanisms and pathways involved; this may help us to take steps to reduce PM 2.5 pollution and reduce the incidence of cardiopulmonary disease.

Predicting ozone formation in petrochemical industrialized Lanzhou city by interpretable ensemble machine learning

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.

Study on the spatiotemporal dynamic of ground-level ozone concentrations on multiple scales across China during the blue sky protection campaign

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.

Ozone pollution in the plate and logistics capital of China: Insight into the formation, source apportionment, and regional transport

As the logistics and plate capital of China, the sources and regional transport of O₃ in Linyi are different from those in other cities because of the significant differences in industrial structure and geographical location. Twenty-five ozone pollution episodes (OPEs, 52 days) were identified in 2021, with a daily maximum 8-h moving average O₃ concentration (O_3-MDA8) of 184.5 ± 22.5 μg/m³. Oxygenated volatile organic compounds (OVOCs) and aromatics were the dominant contributors to ozone formation potential (OFP), with contributions of approximately 23.5-52.7% and 20.0-40.8%, respectively, followed by alkenes, alkanes, and alkynes. Formaldehyde, an OVOC with high concentrations emitted from the plate industry and vehicles, contributed the most to OFP (22.7 ± 5.5%), although formaldehyde concentrations only accounted for 9.4 ± 2.7% of the total non-methane hydrocarbon (NMHC) concentrations. The source apportionment results indicated that the plate industry was the dominant O₃ contributor (27.0%), followed by other sources (21.6%), vehicle-related sources (18.0%), solvent use (16.9%), liquefied petroleum gas (LPG)/natural gas (NG) (8.8%), and combustion sources (7.7%). Therefore, there is an urgent need to control the plating industry in Linyi to mitigate O₃ pollution. The backward trajectory, potential source contribution function (PSCF), and concentration weighted trajectory (CWT) models were used to identify the air mass pathways and potential source areas of air pollutants during the OPEs. O₃ pollution was predominantly affected by air masses that originated from eastern and local regions, while trajectories from the south contained the highest O₃ concentrations (207.0 μg/m³). The potential source area was from east and south Linyi during the OPEs. Therefore, it is critical to implement regional joint prevention and control measures to lower O₃ concentrations.

Predicting wetland area and water depth in Barind plain of India

The present study attempts to delineate wetlands in the lower Tangon river basin in the Barind flood plain region using spectral water body extraction indices. The main objectives of this present study are simulating and predicting wetland areas using the advanced artificial neural network-based cellular automata (ANN-CA) model and water depth using statistical (adaptive exponential smoothing) as well as advanced machine learning algorithms such as Bagging, Random Subspace, Random Forest, Support vector machine, etc. The result shows that RmNDWI and NDWI are the representative wetland delineating indices. NDWI map was used for water depth prediction. Regarding the prediction of wetland areas, a remarkable decline is likely to be identified in the upcoming two decades. The small wetland patches away from the master stream are expected to dry out during the predicted period, where the major wetland patches nearer to the master stream with greater water depth are rather sustainable, but their depth of water is predicted to be reduced in the next decades. All models show satisfactory performance for wetland depth mapping, but the random subspace model was identified as the best-suited water depth predicting method with an acceptable prediction accuracy (root mean square error <0.34 in all the years) and the machine learning models explored better result than adaptive exponential smoothing. This recent study will be very helpful for the policymakers for managing wetland landscape as well as the natural environment.

Image-driven hydrological parameter coupled identification of flood plain wetland conservation and restoration sites

A good many works focus on wetland vulnerability; some works also explore restoration sites at a very limited spatial extent. But the satellite image-driven hydrological data-based approach adopted in this work is absolutely new. Moreover, existing work only focused on identifying restoration sites in the present context, but for devising long-term sustainable planning, predicted hydrological parameters based on possible restoration sites may be an effective tool. Considering this, the present work focused on exploring hydrological data (water presence frequency (WPF), hydro-period (HP) and water depth (WD)) from time-series satellite images. This exploration may resolve the hydrological data scarcity of wetland over the wider geographical areas. Using these parameters, we developed wetland restoration and conservation sites for different historical years (2008, 2018) and predicted years (2028) using ensemble machine learning (EML) models. From the analysis, it was found that water depth, hydro-period and WPF became poorer over the period, and the trend may seem to continue in predicted years. Among the applied EML models, Random Subspace (RS) predicted wetland restoration and conservation sites precisely over others. The predicted area under high-priority restoration sites is 34% in 2018, which was 14% in 2008. In 2028, 12% more areas may fall in this priority level. Wetland away from main streams (mainly ortho-fluvial wetland) and fringe wetland parts should be given more priority for restoration. These present and predicted information will effectively help to frame sustainable wetland restoration planning.

Spatiotemporal variations of air pollutants and ozone prediction using machine learning algorithms in the Beijing-Tianjin-Hebei region from 2014 to 2021

China was seriously affected by air pollution in the past decade, especially for particulate matter (PM) and emerging ozone pollution recently. In this study, we systematically examined the spatiotemporal variations of six air pollutants and conducted ozone prediction using machine learning (ML) algorithms in the Beijing-Tianjin-Hebei (BTH) region. The annual-average concentrations of CO, PM₁₀, PM_2.5 and SO₂ decreased at a rate of 141, 11.0, 6.6 and 5.6 μg/m³/year, while a pattern of initial increase and later decrease was observed for NO₂ and O₃_8 h. The concentration of SO₂, CO and NO₂ was higher in Tangshan and Xingtai, while northern BTH region has lower levels of CO, NO₂ and PM. Spatial variations of ozone were relatively small in the BTH region. Monthly variations of PM₁₀ displayed an increase in March probably due to wind-blown dusts from Northwest China. A seasonal and diurnal pattern with summer and afternoon peaks was found for ozone, which was contrast with other pollutants. Further ML algorithms such as Random Forest (RF) model and Decision tree (DT) regression showed good ozone prediction performance (daily: R² = 0.83 and 0.73, RMSE = 30.0 and 37.3 μg/m³, respectively; monthly: R² = 0.93 and 0.88, RMSE = 12.1 and 15.8 μg/m³, respectively) based on 10-fold cross-validation. Both RF model and DT regression relied more on the spatial trend as higher temporal prediction performance was achieved. Solar radiation- and temperature-related variables presented high importance at daily level, whereas sea level pressure dominated at monthly level. The spatiotemporal heterogeneity in variable importance was further confirmed using case studies based on RF model. In addition, variable importance was possibly influenced by the emission reductions due to COVID-19 pandemic. Despite its possible weakness to capture ozone extremes, RF model was beneficial and suggested for predicting spatiotemporal variations of ozone in future studies.

Hourly Seamless Surface O3 Estimates by Integrating the Chemical Transport and Machine Learning Models in the Beijing-Tianjin-Hebei Region

Surface ozone (O3) is an important atmospheric trace gas, posing an enormous threat to ecological security and human health. Currently, the core objective of air pollution control in China is to realize the joint treatment of fine particulate matter (PM2.5) and O3. However, high-accuracy near-surface O3 maps remain lacking. Therefore, we established a new model to determine the full-coverage hourly O3 concentration with the WRF-Chem and random forest (RF) models combined with anthropogenic emission data and meteorological datasets. Based on this method, choosing the Beijing-Tianjin-Hebei (BTH) region in 2018 as an example, full-coverage hourly O3 maps were generated at a horizontal resolution of 9 km. The performance evaluation results indicated that the new model is reliable with a sample (station)-based 10-fold cross-validation (10-CV) R2 value of 0.94 (0.90) and root mean square error (RMSE) of 14.58 (19.18) µg m−3. In addition, the estimated O3 concentration is accurately determined at varying temporal scales with sample-based 10-CV R2 values of 0.96, 0.98 and 0.98 at the daily, monthly, and seasonal scales, respectively, which is highly superior to traditional derivation algorithms and other techniques in previous studies. An initial increase and subsequent decrease, which constitute the diurnal variation in the O3 concentration associated with temperature and solar radiation variations, were captured. The highest concentration reached approximately 112.73 ± 9.65 μg m−3 at 15:00 local time (1500 LT) in the BTH region. Summertime O3 posed a high pollution risk across the whole BTH region, especially in southern cities, and the pollution duration accounted for more than 50% of the summer season. Additionally, 43 and two days exhibited light and moderate O3 pollution, respectively, across the BTH region in 2018. Overall, the new method can be beneficial for near-surface O3 estimation with a high spatiotemporal resolution, which can be valuable for research in related fields.

Long-term exposure to ozone and cardiovascular mortality in a large Chinese cohort

BACKGROUND

Evidence for the association between long-term exposure to ozone (O₃) and cause-specific cardiovascular disease (CVD) mortality is inconclusive, and this association has rarely been evaluated at high O₃ concentrations.

OBJECTIVES

We aim to evaluate the associations between long-term O₃ exposure and cause-specific CVD mortality in a Chinese population.

METHODS

From 2009 to 2018, 744,882 subjects (median follow-up of 7.72 years) were included in the CHinese Electronic health Records Research in Yinzhou (CHERRY) study. The annual average concentrations of O₃ and fine particulate matter (PM_2.5), which were estimated using grids with a resolution up to 1 × 1 km, were assigned to the community address for each subject. The outcomes were deaths from CVD, ischemic heart disease (IHD), myocardial infarction (MI), stroke, and hemorrhagic/ischemic stroke. Time-varying Cox model adjusted for PM_2.5 and individual-level covariates was used.

RESULTS

The mean of annual average O₃ concentrations was 68.05 μg/m³. The adjusted hazard ratio per 10 μg/m³ O₃ increase was 1.22 (95% confidence interval [CI]: 1.13-1.33) for overall CVD mortality, 1.08 (0.91-1.29) for IHD, 1.21 (0.90-1.63) for MI, 1.28 (1.15-1.43) for overall stroke, 1.39 (1.16-1.67) for hemorrhagic stroke and 1.22 (1.00-1.49) for ischemic stroke, respectively. The study showed that subjects without hypertension had a higher risk for CVD mortality associated with long-term O₃ exposure (1.66 vs. 1.15, p = 0.01).

CONCLUSIONS

We observed the association between long-term exposure to high O₃ concentrations and cause-specific CVD mortality in China, independent of PM_2.5 and other CVD risk factors. This suggested an urgent need to control O₃ pollution, especially in developing countries.

Predicting Daily PM2.5 Exposure with Spatially Invariant Accuracy Using Co-Existing Pollutant Concentrations as Predictors

The spatiotemporal variation of PM2.5 should be accurately estimated for epidemiological studies. However, the accuracy of prediction models may change over geographical space, which is not conducive for proper exposure assessment. In this study, we developed a prediction model to estimate daily PM2.5 concentrations from 2010 to 2017 in the Kansai region of Japan with co-existing pollutant concentrations as predictors. The overall objective was to obtain daily estimates over the study domain with spatially homogeneous accuracy. We used random forest algorithm to model the relationship between the daily PM2.5 concentrations and various predictors. The model performance was evaluated via spatial and temporal cross-validation and the daily PM2.5 surface was estimated from 2010 to 2017 at a 1 km × 1 km resolution. We achieved R2 values of 0.91 and 0.92 for spatial and temporal cross-validation, respectively. The prediction accuracy for each monitoring site was found to be consistently high, regardless of the distance to the nearest monitoring location, up to 10 km. Even for distances greater than 10 km, the mean R2 value was 0.88. Our approach yielded spatially homogeneous prediction accuracy, which is beneficial for epidemiological studies. The daily PM2.5 estimates will be used in a related birth cohort study to evaluate the potential impact on human health.

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

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

Identifying the dominant driver of elevated surface ozone concentration in North China plain during summertime 2012-2017

The increasingly serious surface ozone (O₃) pollution in North China Plain (NCP) has received wide attention. However, the contribution of the changes for each emission source to the elevated O₃ concentration, as well as the direct and indirect effect of meteorological condition variation on increased O₃ level have not been comprehensively analyzed. This study applied the Community Multiscale Air Quality (CMAQ) model coupled with the integrated source apportionment method (ISAM) to quantify changes in daily maximum 8-h average O₃ concentration (MDA8 O₃) under different air pollutants emissions and meteorological conditions during summertime 2012-2017. The results showed that incoordinate NOx/VOC emission control sustainably increased MDA8 O₃ by 2.2-36.2 μg/m³ in the NCP, of which emission changes from industrial and transportation sectors were the predominant contributors (-0.6-19.5 μg/m³ for industrial sector and 1.2-18.1 μg/m³ for transportation, respectively). In contrast, MDA8 O₃ decreased by 2.5-9.2 μg/m³ for the power plants. The effect of changes in meteorological condition on MDA8 O₃ exhibited significantly spatial and temporal variation and unfavorable meteorological fields were shown in 2014, 2016, and 2017, which enhanced MDA8 O₃ by -2.5-23.1, -5.3-20.7, and -7.2-25.8 μg/m³, respectively. In addition, the changed meteorological factors indirectly affected the biogenic emission thus prompting the increases of MDA8 O₃ by -3.9-4.9 μg/m³ in the NCP during 2012-2017. The sensitive simulations suggested that more aggressive control measures about VOC reduction in industrial and transportation sectors should be implemented to further mitigate the O₃ pollution under unfavorable meteorological condition.

Evaluating the spatiotemporal ozone characteristics with high-resolution predictions in mainland China, 2013-2019

Evaluating ozone levels at high resolutions and accuracy is crucial for understanding the spatiotemporal characteristics of ozone distribution and assessing ozone exposure levels in epidemiological studies. The national models with high spatiotemporal resolutions to predict ground ozone concentrations are limited in China so far. In this study, we aimed to develop a random forest model by combining ground ozone measurements from fixed stations, ozone simulations from the Community Multiscale Air Quality (CMAQ) modeling system, meteorological parameters, population density, road length, and elevation to predict ground maximum daily 8-h average (MDA8) ozone concentrations at a daily level and 1 km × 1 km spatial resolution. The model cross-validation R² and root mean squared error (RMSE) were 0.80 and 20.93 μg/m³ at daily level in 2013-2019, respectively. CMAQ ozone simulations and near-surface temperature played vital roles in predicting ozone concentrations among all predictors. The population-weighted median concentrations of predicted MDA8 ozone were 89.34 μg/m³ in mainland China in 2013, and reached 100.96 μg/m³ in 2019. However, the long-term temporal variations among regions were heterogeneous. Central and Eastern China, as well as the Southeast Coastal Area, suffered higher ozone pollution and higher increased rates of ozone concentrations from 2013 to 2019. The seasonal pattern of ozone pollution varied spatially. The peak-season ozone pollution with the highest 6-month ozone concentrations occurred in different months among regions, with more than half domain in April-September. The predictions showed that not only the annual mean concentrations but also the percentages of grid-days with MDA8 ozone concentrations higher than 100/160 μg/m³ have been increasing in the past few years in China; meanwhile, majority areas in mainland China suffered peak-season ozone concentrations higher than the air quality guidelines launched by the World Health Organization in September 2021. The proposed model and ozone predictions with high spatiotemporal resolution and full coverage could provide health studies with flexible choices to evaluate ozone exposure levels at multiple spatiotemporal scales in the future.

Estimation of the Near-Surface Ozone Concentration with Full Spatiotemporal Coverage across the Beijing-Tianjin-Hebei Region Based on Extreme Gradient Boosting Combined with a WRF-Chem Model

With the intensification of global warming and economic development in China, the near-surface ozone (O3) concentration has been increasing recently, especially in the Beijing-Tianjin-Hebei (BTH) region, which is the political and economic center of China. However, O3 has been measured in real time only over the past few years, and the observational records are discontinuous. Therefore, we propose a new method (WRFC-XGB) to establish a near-surface O3 concentration dataset in the BTH region by integrating the Weather Research and Forecasting with Chemistry (WRF-Chem) model with the extreme gradient boosting (XGBoost) algorithm. Based on this method, the 8-h maximum daily average (MDA8) O3 concentrations are obtained with full spatiotemporal coverage at a spatial resolution of 0.1° × 0.1° across the BTH region in 2018. Two evaluation methods, sample- and station-based 10-fold cross-validation (10-CV), are used to assess our method. The sample-based (station-based) 10-CV evaluation results indicate that WRFC-XGB can achieve excellent accuracy with a high coefficient of determination (R2) of 0.95 (0.91), low root mean square error (RMSE) of 13.50 (17.70) µg m−3, and mean absolute error (MAE) of 9.60 (12.89) µg m−3. In addition, superb spatiotemporal consistencies are confirmed for this model, including the estimation of high O3 concentrations, and our WRFC-XGB model outperforms traditional models and previous studies in data mining. In addition, the proposed model can be applied to estimate the O3 concentration when it has not been measured. Furthermore, the spatial distribution analysis of the MDA8 O3 in 2018 reveals that O3 pollution in the BTH region exhibits significant seasonality. Heavy O3 pollution episodes mainly occur in summer, and the high O3 loading is distributed mainly in the southern BTH areas, which will pose challenges to atmospheric environmental governance for local governments.

Development of a high-performance machine learning model to predict ground ozone pollution in typical cities of China

High ozone concentrations have adverse effects on human health and ecosystems. In recent years, the ambient ozone concentration in China has shown an upward trend, and high-quality prediction of ozone concentrations has become critical to support effective policymaking. In this study, a novel hybrid model combining wavelet decomposition (WD), a gated recurrent unit (GRU) neural network and a support vector regression (SVR) model was developed to predict the daily maximum 8 h ozone. We used the ground ozone observation data in six representative megacities across China from Jan. 1, 2015 to Jun. 15, 2020 for model training, and we used data from Jun. 15 to Dec. 31, 2020 for model testing. The results show that the developed model performs very well for megacities; against observations, the model obtains an average cross-validated R² (coefficient of determination) ranging from 0.90 for Shanghai to 0.97 for Chengdu in the one-step predictions, thereby indicating that the model outperformed any single algorithm or other hybrid algorithms reported. The developed model can also capture high ozone pollution episodes with an average accuracy of 92% for the next five days in inland cities. This study will be useful for the environmental health community to prevent high ozone exposure more efficiently in megacities in China and shows great potential for accurate ozone prediction using machine learning approaches.

Ozone and SOA formation potential based on photochemical loss of VOCs during the Beijing summer

Volatile organic compounds (VOCs) are easily degraded by oxidants during atmospheric transport. Therefore, the contribution of VOCs to ozone (O₃) and secondary organic aerosol (SOA) formation at a receptor site is different from that in a source area. In this study, hourly concentrations of VOCs and other pollutants, such as O₃, NOx, HONO, CO, and PM_2.5, were measured in the suburbs (Daxing district) of Beijing in August 2019. The photochemical initial concentrations (PICs), in which the photochemical losses of VOCs were accounted for, were calculated to evaluate the contribution of the VOCs to O₃ and SOA formation. The mean (±standard deviation) measured VOC concentrations and the PICs were 11.2 ± 5.7 and 14.6 ± 8.4 ppbv, respectively, which correspond to O₃ formation potentials (OFP) of 57.8 ± 26.3 and 103.9 ± 109.4 ppbv and SOA formation potentials (SOAP) of 8.4 ± 4.1 and 10.3 ± 7.4 μg m^-3, respectively. Alkenes contributed 80.5% of the consumed VOCs, followed by aromatics (13.3%) and alkanes (6.2%). The contributions of the alkenes and aromatics to the OFP_PICs were 56.8% and 30.3%, respectively; while their corresponding contributions to the SOAP_PICs were 1.9% and 97.3%, respectively. The OFP_PICs was linearly correlated with the observed O₃ concentrations (OFP_PICs = 41.5 + 1.40 × c_O3, R² = 0.87). The O₃ formation was associated with a VOC-limited regime at the receptor site based on the measured VOCs and changed to a transition regime and a NOx sensitive regime based on the PIC. Our results suggest that more attention should be paid to biogenic VOCs when studying O₃ formation in summer in Beijing, while the control of anthropogenic aromatic compounds should be given priority in terms of SOA formation.

Control Models and Spatiotemporal Characteristics of Air Pollution in the Rapidly Developing Urban Agglomerations

This paper systematically summarizes the hierarchical cross-regional multi-directional linkage in terms of air pollution control models implemented in the Beijing-Tianjin-Hebei urban agglomeration, including the hierarchical linkage structure of national-urban agglomeration-city, the cross-regional linkage governance of multiple provinces and municipalities, the multi-directional linkage mechanism mainly involving industry access, energy structure, green transportation, cross-regional assistance, monitoring and warning, consultation, and accountability. The concentration data of six air pollutants were used to analyze spatiotemporal characteristics. The concentrations of SO₂, NO₂, PM₁₀, PM_2.5, CO decreased, and the concentration of O₃ increased from 2014 to 2017; the air pollution control has achieved good effect. The concentration of O₃ was the highest in summer and lowest in winter, while those of other pollutants were the highest in winter and lowest in summer. The high pollution ranges of O₃ diffused from south to north, and those of other pollutants decreased significantly from north to south. Finally, we suggest strengthening the traceability and process research of heavy pollution, increasing the traceability and process research of O₃ pollution, promoting the joint legislation of different regions in urban agglomeration, create innovative pollution discharge supervision mechanisms, in order to provide significant reference for the joint prevention and control of air pollution in urban agglomerations.