Ozone pollution in Chinese cities: Assessment of seasonal variation, health effects and economic burden

A deep learning model integrating a wind direction-based dynamic graph network for ozone prediction

Ozone pollution is an important environmental issue in many countries. Accurate forecasting of ozone concentration enables relevant authorities to enact timely policies to mitigate adverse impacts. This study develops a novel hybrid deep learning model, named wind direction-based dynamic spatio-temporal graph network (WDDSTG-Net), for hourly ozone concentration prediction. The model uses a dynamic directed graph structure based on hourly changing wind direction data to capture evolving spatial relationships between air quality monitoring stations. It applied the graph attention mechanism to compute dynamic weights between connected stations, thereby aggregating neighborhood information adaptively. For temporal modeling, it utilized a sequence-to-sequence model with attention mechanism to extract long-range temporal dependencies. Additionally, it integrated meteorological predictions to guide the ozone forecasting. The model achieves a mean absolute error of 6.69 μg/m3 and 18.63 μg/m3 for 1-h prediction and 24-h prediction, outperforming several classic models. The model's IAQI accuracy predictions at all stations are above 75 %, with a maximum of 81.74 %. It also exhibits strong capabilities in predicting severe ozone pollution events, with a 24-h true positive rate of 0.77. Compared to traditional static graph models, WDDSTG-Net demonstrates the importance of incorporating short-term wind fluctuations and transport dynamics for data-driven air quality modeling. In principle, it may serve as an effective data-driven approach for the concentration prediction of other airborne pollutants.

Impact of ozone pollution on crop yield, human health, and associated economic costs in the Indo-Gangetic plains

Ozone pollution is a growing problem in many developing countries posing challenges not only to air quality but also affecting agricultural productivity and human well-being. This is the first study in the Indo-Gangetic Plain exploring how the spatial variation and severity of tropospheric ozone affect both wheat yield and all-cause mortality. We estimated that ozone-related cumulative crop production loss for wheat in selected districts of IGP was 3.4 million tonnes during the study period (2019-2021), which amounted to 923 million USD. The production-weighted Relative Yield Loss (RYL) for wheat in the IGP was 9.3 % in 2019, 12.8 % in 2020, and 11.3 % in 2021. The losses incurred in 2021 could contribute to fulfilling the wheat requirements of 11.4 million people. We also assess the health and economic gains resulting from the attainment of the World Health Organization Air Quality Guidelines (WHO AQG) for ozone concentrations. It is estimated that interventions that achieve AQG would have averted 11,407 premature deaths in 2021 translating into an impressively large health and economic gain. The annual benefits in 2021 totaled to 34 billion USD. We observe that Uttar Pradesh experienced the highest losses, both in terms of crop damage and premature deaths. Our study observes that implementing policies to prepone the planting of wheat enhances food security by mitigating yield losses. Mitigating the health impact of ambient ozone necessitates a reduction in anthropogenic emissions and to attain this objective, we propose adopting an exposure-integrated source reduction approach.

Exploring the nexus of urban form, transport, environment and health in large-scale urban studies: A state-of-the-art scoping review

BACKGROUND

As the world becomes increasingly urbanised, there is recognition that public and planetary health relies upon a ubiquitous transition to sustainable cities. Disentanglement of the complex pathways of urban design, environmental exposures, and health, and the magnitude of these associations, remains a challenge. A state-of-the-art account of large-scale urban health studies is required to shape future research priorities and equity- and evidence-informed policies.

OBJECTIVES

The purpose of this review was to synthesise evidence from large-scale urban studies focused on the interaction between urban form, transport, environmental exposures, and health. This review sought to determine common methodologies applied, limitations, and future opportunities for improved research practice.

METHODS

Based on a literature search, 2958 articles were reviewed that covered three themes of: urban form; urban environmental health; and urban indicators. Studies were prioritised for inclusion that analysed at least 90 cities to ensure broad geographic representation and generalisability. Of the initially identified studies, following expert consultation and exclusion criteria, 66 were included.

RESULTS

The complexity of the urban ecosystem on health was evidenced from the context dependent effects of urban form variables on environmental exposures and health. Compact city designs were generally advantageous for reducing harmful environmental exposure and promoting health, with some exceptions. Methodological heterogeneity was indicative of key urban research challenges; notable limitations included exposure and health data at varied spatial scales and resolutions, limited availability of local-level sociodemographic data, and the lack of consensus on robust methodologies that encompass best research practice.

CONCLUSION

Future urban environmental health research for evidence-informed urban planning and policies requires a multi-faceted approach. Advances in geospatial and AI-driven techniques and urban indicators offer promising developments; however, there remains a wider call for increased data availability at local-levels, transparent and robust methodologies of large-scale urban studies, and greater exploration of urban health vulnerabilities and inequities.

Improving photochemical indicators for attributing ozone sensitivities in source apportionment analysis

Comprehensive Air Quality Model with extensions (CAMx)-Decoupled Direct Method (DDM) simulations of first-order ozone (O3) sensitivity to nitrogen oxides (NOx) and volatile organic compounds (VOCs) emissions were performed and combined with modelled [Formula: see text] ratios to obtain a range of thresholds for determining O3-sensitivity regimes for different areas of China. Utilising the new threshold ranges for photochemical indicators, the method for determining O3 formation in the Ozone Source Apportionment Technology (OSAT) module within CAMx was improved by a dynamically varied threshold of [Formula: see text] ratio. The O3 concentration contributions in the newly added transition regime were apportioned to NOx and VOCs emissions in proportion to the relationship between the [Formula: see text] ratio and first-order O3 sensitivity. The source contributions of O3 concentrations from different emission sectors from June to September 2019 were compared using the original and improved CAMx-OSAT. The results showed that the O3 concentration contributions changed significantly in the NOx-limited regime, with a maximum decrease of 21.89%, while the contributions increased by up to 7.57% in the VOC-limited regime, and were within 15 µg/m3 in the transition regime. The modified OSAT module enabled a more sophisticated attribution of O3 to precursor emissions and may have far-reaching implications for informing O3 pollution control policy.

Exposure to ambient ozone and sperm quality among adult men in China

BACKGROUND

Limited evidence exists regarding the association between ozone exposure and adverse sperm quality. We aimed to assess the association between ozone exposure and sperm quality, and identify susceptible exposure windows.

METHODS

We recruited 32,541 men aged between 22 and 65 years old attending an infertility clinic in Wuhan, Hubei Province, China from 2014 to 2020. Ozone data were obtained from a satellite-based spatiotemporal model. Generalized linear models were used to estimate the association between ozone exposure and sperm quality parameters, including sperm concentration, sperm count, sperm total motility, and sperm progressive motility during the entire stage of sperm development (0-90 days before ejaculation) and three crucial stages (0-9 days, 10-14 days and 70-90 days before ejaculation). Stratified analyses were performed to evaluate whether associations varied by age, body mass index, and education levels.

RESULTS

The final analysis included 27,854 adult men. A 10 μg/m3 increase in ozone concentrations during the entire stage of sperm development was associated with a -4.17 % (95 % CI: -4.78 %, -3.57 %) decrease in sperm concentration, -6.54 % (95 % CI: -8.03 %, -5.60 %) decrease in sperm count, -0.50 % (95 % CI: -0.66 %, -0.34 %) decrease in sperm total motility, and -0.07 % (95 % CI: -0.22 %, 0.09 %) decrease in sperm progressive motility. The associations were stronger during 70-90 days before ejaculation and among men with middle school and lower education for sperm concentration.

CONCLUSIONS

Ozone exposure was associated with decreased sperm quality among Chinese adult men attending an infertility clinic. These results suggest that ozone may be a risk factor contributing to decreased sperm quality in Chinese men.

Synergistic effects of copper and oxygen vacancies in enhancing the efficacy of partially crystalline CuMnxOy catalyst for ozone decomposition

To tackle the challenge of ground-level ozone pollution, this study proposed a potential catalytic design approach for ozone decomposition using Cu-Mn bimetallic oxide. This approach is grounded in an understanding of the intrinsic reactivity for catalyst and incorporates a novel potassium-driven low-temperature oxidation process for catalyst synthesis. The research highlights the creation of a highly reactive Cu-Mn oxide phase with extensive defect coverage, leading to significantly increased reaction rates. It also identifies the MnO2(100) facet as a crucial active phase, where oxygen vacancies simultaneously enhance O3 adsorption and decomposition, albeit with a concurrent risk of O2 poisoning due to the stabilization of adsorbed O2. Crucially, the incorporation of Cu offsets the effects of oxygen vacancies, influencing conversion rates and lessening O2 poisoning. The synergistic interplay between Cu and oxygen vacancies elevates the performance of the defect-rich Cu-Mn oxide catalyst. By combining computational and experimental methods, this study not only advances the understanding of the Cu-Mn oxide system for ozone decomposition but also contributes valuable insights into developing more efficient catalysts to mitigate ozone pollution.

Parsimonious estimation of hourly surface ozone concentration across China during 2015-2020

Surface ozone is an important air pollutant detrimental to human health and vegetation productivity, particularly in China. However, high resolution surface ozone concentration data is still lacking, largely hindering accurate assessment of associated environmental impacts. Here, we collected hourly ground ozone observations (over 6 million records), remote sensing products, meteorological data, and social-economic information, and applied recurrent neural networks to map hourly surface ozone data (HrSOD) at a 0.1° × 0.1° resolution across China during 2015-2020. The coefficient of determination (R2) values in sample-based, site-based, and by-year cross-validations were 0.72, 0.65 and 0.71, respectively, with the root mean square error (RMSE) values being 11.71 ppb (mean = 30.89 ppb), 12.81 ppb (mean = 30.96 ppb) and 11.14 ppb (mean = 31.26 ppb). Moreover, it exhibits high spatiotemporal consistency with ground-level observations at different time scales (diurnal, seasonal, annual), and at various spatial levels (individual sites and regional scales). Meanwhile, the HrSOD provides critical information for fine-resolution assessment of surface ozone impacts on environmental and human benefits.

Relationship between ozone and air temperature in future conditions: A case study in sichuan basin, China

The Sichuan Basin (SCB) is located in southwestern China and has a unique topography where ozone (O3) pollution is frequent during summer. Few studies have clarified the relationship between O3 and air temperature in SCB. Here, the SCB was divided into four major urban agglomerations. The weather research and forecasting model-community multiscale air quality model (WRF-CMAQ) was used to analyze the meteorology, spatial distribution characteristics of pollutants, and interactions among the urban agglomerations in the SCB. WRF-CMAQ was used to study the historical changes in the climate penalty factor (CPF) from 2015 to 2020 and the climate pathways under the SSP2-4.5 CPF in values in 2030 for the ambitious pollution NDC-goal scenario (NDC) and current-goals scenario (Current). The results show that the SCB is warmer in the summer months with prevailing northeasterly winds. Ozone accumulated in the western part of the SCB, and a high CPF of O3 concentration was most prominent in NW urban agglomeration, where the O3 concentration increased by 4.12-5.40 ppb for every 1 °C increase in air temperature. The observed CPF in the SCB in 2020 averaged 3.64 ppb/°C. The average CPF in the SCB in 2030 was 1.152 ppb/°C under the NDC scenario and 1.269 ppb/°C under the current scenario. This study is critical for understanding the relationship between O3 concentration and air temperature in China.

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.

Estimating morning and evening commute period O3 concentration in Taiwan using a fine spatial-temporal resolution ensemble mixed spatial model with Geo-AI technology

Elevated levels of ground-level ozone (O3) can have harmful effects on health. While previous studies have focused mainly on daily averages and daytime patterns, it's crucial to consider the effects of air pollution during daily commutes, as this can significantly contribute to overall exposure. This study is also the first to employ an ensemble mixed spatial model (EMSM) that integrates multiple machine learning algorithms and predictor variables selected using Shapley Additive exExplanations (SHAP) values to predict spatial-temporal fluctuations in O3 concentrations across the entire island of Taiwan. We utilized geospatial-artificial intelligence (Geo-AI), incorporating kriging, land use regression (LUR), machine learning (random forest (RF), categorical boosting (CatBoost), gradient boosting (GBM), extreme gradient boosting (XGBoost), and light gradient boosting (LightGBM)), and ensemble learning techniques to develop ensemble mixed spatial models (EMSMs) for morning and evening commute periods. The EMSMs were used to estimate long-term spatiotemporal variations of O3 levels, accounting for in-situ measurements, meteorological factors, geospatial predictors, and social and seasonal influences over a 26-year period. Compared to conventional LUR-based approaches, the EMSMs improved performance by 58% for both commute periods, with high explanatory power and an adjusted R2 of 0.91. Internal and external validation procedures and verification of O3 concentrations at the upper percentile ranges (in 1%, 5%, 10%, 15%, 20%, and 25%) and other conditions (including rain, no rain, weekday, weekend, festival, and no festival) have demonstrated that the models are stable and free from overfitting issues. Estimation maps were generated to examine changes in O3 levels before and during the implementation of COVID-19 restrictions. These findings provide accurate variations of O3 levels in commute period with high spatiotemporal resolution of daily and 50m * 50m grid, which can support control pollution efforts and aid in epidemiological studies.

A comprehensive review on endocrine toxicity of gaseous components and particulate matter in smog

Smog is a form of extreme air pollution which comprises of gases such as ozone, sulfur dioxide, nitrogen and carbon oxides, and solid particles including particulate matter (PM2.5 and PM10). Different types of smog include acidic, photochemical, and Polish. Smog and its constituents are hazardaous to human, animals, and plants. Smog leads to plethora of morbidities such as cancer, endocrine disruption, and respiratory and cardiovascular disorders. Smog components alter the activity of various hormones including thyroid, pituitary, gonads and adrenal hormones by altering regulatory genes, oxidation status and the hypothalamus-pituitary axis. Furthermore, these toxicants are responsible for the development of metabolic disorders, teratogenicity, insulin resistance, infertility, and carcinogenicity of endocrine glands. Avoiding fossil fuel, using renewable sources of energy, and limiting gaseous discharge from industries can be helpful to avoid endocrine disruption and other toxicities of smog. This review focuses on the toxic implications of smog and its constituents on endocrine system, their toxicodynamics and preventive measures to avoid hazardous health effects.

Changing Responses of PM2.5 and Ozone to Source Emissions in the Yangtze River Delta Using the Adjoint Model

China's industrial restructuring and pollution controls have altered the contributions of individual sources to varying air quality over the past decade. We used the GEOS-Chem adjoint model and investigated the changing sensitivities of PM2.5 and ozone (O3) to multiple species and sources from 2010 to 2020 in the central Yangtze River Delta (YRDC), the largest economic region in China. Controlling primary particles and SO2 from industrial and residential sectors dominated PM2.5 decline, and reducing CO from multiple sources and ≥C3 alkenes from vehicles restrained O3. The chemical regime of O3 formation became less VOC-limited, attributable to continuous NOX abatement for specific sources, including power plants, industrial combustion, cement production, and off-road traffic. Regional transport was found to be increasingly influential on PM2.5. To further improve air quality, management of agricultural activities to reduce NH3 is essential for alleviating PM2.5 pollution, while controlling aromatics, alkenes, and alkanes from industry and gasoline vehicles is effective for O3. Reducing the level of NOX from nearby industrial combustion and transportation is helpful for both species. Our findings reveal the complexity of coordinating control of PM2.5 and O3 pollution in a fast-developing region and support science-based policymaking for other regions with similar air pollution problems.

Assessment of tropospheric ozone simulations in a regional chemical transport model using GEOS-Chem outputs as chemical boundary conditions

Regional chemical transport models (e.g., Community Multiscale Air Quality (CMAQ) Modeling System) are widely used to simulate the physical and chemical process of regional ozone (O3) pollution and its variation trend in recent years. However, chemical boundary condition (CBC) is an important input of these models and contributes to the model bias against observations. In this study, we develop a tool named GC2CMAQ that provides the CMAQ model with the CBCs from the GEOS-Chem simulation. Two experiments using different CBCs were conducted to evaluate their effect on seasonal O3 simulation in China. The Default experiment utilized the model-default static condition (the relatively clean atmosphere in the eastern United States), and the GC experiment employed the GEOS-Chem simulation results. Compared with the observation, the GC experiment has a much better performance in reproducing elevated O3 levels in the higher troposphere and lower stratosphere during different seasons. Near the earth's surface, the simulated concentrations of pollutants O3 (and PM2.5) in the GC experiment were also closer to the observation in April and July. The accuracy of simulation results in provinces close to the boundary was improved by approximately 20 %-30 % relative to the Default experiment. The CBCs provided by GEOS-Chem enabled a better simulation of stratosphere-troposphere O3 exchange in late spring and early summer, which then affected the pollutant concentration near surfaces through vertical transport. This finding was confirmed by a case study in southwestern Tibet on April 28, 2017, in which we quantified the contributions of different physical and chemical processes to O3 variations at different altitudes using the process analysis method. This study highlights the importance of using a reliable CBC for the regional chemical transport model to derive a better performance of O3 simulation.

Increased ground-level O3 during the COVID-19 pandemic in China aggravates human health risks but has little effect on winter wheat yield

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

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.

Drivers of ozone-related premature mortality in China: Implications for historical and future scenarios

Long-term exposure to ambient ozone (O3) poses a severe public health threat in China. However, the drivers of premature mortality caused by O3 pollution are still poorly constrained, despite being prerequisites for addressing the threat. Here, we demonstrate the contributions of historical and future changes in peak-season O3, population size, age structure, and baseline mortality to China's O3-related mortality using decomposition analysis. From 2013 to 2021, O3-related mortality decreased dramatically from 78.8 (40.8-124.6) to 68.7 (36.0-107.2) thousand, especially in densely populated areas with high pollution. Variations in peak-season O3, population size, age structure, and baseline mortality led to changes in O3-related mortality of +27.3 (14.8-41.3), +2.6 (1.4-4.1), +22.3 (11.5-35.2), and -40.3 (20.9-63.7) thousand, respectively. The influence of peak-season O3 on O3-related mortality shifted from positive during 2013-2019 (+8.4% per year) to negative during 2019-2021 (-8.8% per year), which highly regulated the interannual trend of mortality. From 2021 to 2035, O3-related mortality is expected to increase by 31% in the current context of peak-season O3 levels, primarily caused by increased aging. Even reducing peak-season O3 to the WHO interim target 1 (IT-1) would only reduce O3-related mortality by 3.9%, while a more rigorous standard (IT-2) would prevent 83.7% of mortality. These findings suggest that improving ambient O3 can lead to significant health benefits, but substantial mitigation strategies are merited given the future trend of population aging.

Estimation of health risk and economic loss attributable to PM2.5 and O3 pollution in Jilin Province, China

Ambient pollutants, particularly fine particulate matter (PM2.5) and ozone (O3), pose significant risks to both public health and economic development. In recent years, PM2.5 concentration in China has decreased significantly, whereas that of O3 has increased rapidly, leading to considerable health risks. In this study, a generalized additive model was employed to establish the relationship of PM2.5 and O3 exposure with non-accidental mortality across 17 districts and counties in Jilin Province, China, over 2015-2016. The health burden and economic losses attributable to PM2.5 and O3 were assessed using high-resolution satellite and population data. According to the results, per 10 µg/m3 increase in PM2.5 and O3 concentrations related to an overall relative risk (95% confidence interval) of 1.004 (1.001-1.007) and 1.009 (1.005-1.012), respectively. In general, the spatial distribution of mortality and economic losses was uneven. Throughout the study period, a total of 23,051.274 mortalities and 27,825.015 million Chinese Yuan (CNY) in economic losses were attributed to O3 exposure, which considerably surpassing the 5,450.716 mortalities and 6,553,780 million CNY in economic losses attributed to PM2.5 exposure. The O3-related health risks and economic losses increased by 3.75% and 9.3% from 2015 to 2016, while those linked to PM2.5 decreased by 23.33% and 18.7%. Sensitivity analysis results indicated that changes in pollutant concentrations were the major factors affecting mortality rather than baseline mortality and population.

Development of a recurrent spatiotemporal deep-learning method coupled with data fusion for correction of hourly ozone forecasts

Ambient ozone (O3) predictions can be very challenging mainly due to the highly nonlinear photochemistry among its precursors, and meteorological conditions and regional transport can further complicate the O3 formation processes. The emission-based chemical transport models (CTM) are broadly used to predict O3 formation, but they may deviate from observations due to input uncertainties such as emissions and meteorological data, in addition to the treatment of O3 nonlinear chemistry. In this study, an innovative recurrent spatiotemporal deep-learning (RSDL) method with model-monitor coupled convolutional recurrent neural networks (ConvRNN) has been developed to improve O3 predictions of CTM. The RSDL method was first used to build the ConvRNN within a 24-h scale to characterize the spatiotemporal relationships between the monitored O3 data and CTM simulations, and then incorporated the recurrent pattern to achieve 72-h multi-site forecasts based on a pilot study over the Pearl River Delta (PRD) region of China. The results showed that the RSDL method predicted O3 with high accuracy over this case study, with an increase of 27.54% in the correlation coefficient (R) average for all sites as well as an increase in R of 0.14-0.21 for all cities compared to CTM. Moreover, the regional distribution of CTM was further improved by the RSDL predictions with the data fusion technique, which greatly reduced the underpredictions of O3 concentrations, particularly in high O3-level areas (concentrations >160 μg/m3), with a 33.55% reduction in the mean absolute error (MAE).

An assessment of O3-related health risks and economic losses in typical regions of China

Introduction

As one of the key areas for air pollution prevention and control in China, the Fenwei Plain is experiencing serious near-surface O3 pollution, which is a key issue that needs to be solved urgently.

Methods

Based on pollutant concentration monitoring data and meteorological and health data over the same period, this study analyzed the temporal and spatial characteristics, the relationships with meteorological factors of O3 pollution, and the health effects and economic losses caused by exposure to O3 pollution using environmental health risk and environmental value assessment methods in 11 cities on the Fenwei Plain in China from 2014 to 2020.

Results

The results showed that O3 pollution has become increasingly serious on the Fenwei Plain in recent years. The annual average concentration of O3_8h_max showed an overall upwards trend, with an increase of 32.39% in 2020 compared to 2014. The mean concentrations observed in summer were the highest, followed by spring and autumn, and the lowest was in winter. The O3 concentration had a significant positive correlation with air temperature and sunshine hours. The evaluation results of the impact of air pollution on population health showed that the number of premature deaths caused by O3 pollution fluctuated and increased during 2014-2020. In 2020, the numbers of total, cardiovascular and respiratory deaths attributable to O3 pollution on the Fenwei Plain were 6,867 (95% CI: 3,739-9,965), 3,652 (95% CI: 1,363-5,905), and 1,257 (95% CI: 747-2,365), respectively, and the total number of premature deaths related to O3 exposure increased by 48.05% compared with 2014. The health and economic losses attributed to O3 pollution on the Fenwei Plain during the study period were 44.22 (95% CI: 22.17-69.18), 47.16 (95% CI: 23.64-73.77), 68.28 (95% CI: 34.27-106.31), 114.44 (95% CI: 57.42-177.76), 110.85 (95% CI: 55.45-172.52), 116.41 (95% CI: 58.24-180.74), and 116.81 (95% CI: 58.00-180.88) billion yuan, respectively. In Linfen City, the increasing rate of the number of premature deaths reached 283.39% because the O3 concentration increased greatly.

Discussion

Due to high O3 concentrations and obvious population growth in Xi'an, the problems of premature death and health and economic losses attributed to O3 concentrations exceeding the standard value are prominent.

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.

Contributions of local emissions and regional background to summertime ozone in central China

Source contributions and regional transport of maximum daily average 8-h (MDA8) O3 during a high O3 month (June 2019) in Henan province in central China are explored using a source-oriented Community Multiscale Air Quality (CMAQ) model. The monthly average MDA8 O3 exceeds ∼70 ppb in more than half of the areas and shows a clear spatial gradient, with lower O3 concentrations in the southwest and higher in the northeast. Significant contributions of anthropogenic emissions to monthly average MDA8 O3 concentrations of more than 20 ppb are predicted in the provincial capital Zhengzhou, mostly due to emissions from the transportation sector (∼50%) and in the areas in the north and northeast regions where industrial and power generation-related emissions are high. Biogenic emissions in the region only contribute to approximately 1-3 ppb of monthly average MDA8 O3. In industrial areas north of the province, their contributions reach 5-7 ppb. Two CMAQ-based O3-NOx-VOCs sensitivity assessments (the local O3 sensitivity ratios based on the direct decoupled method and the production ratio of H2O2 to HNO3) and the satellite HCHO to NO2 column density ratio consistently show that most of the areas in Henan are in NOx-limited regime. In contrast, the high O3 concentration areas in the north and at the city centers are in the VOC-limited or transition regimes. The results from this study suggest that although reducing NOx emissions to reduce O3 pollution in the region is desired in most areas, VOC reductions must be applied to urban and industrial regions. Source apportionment simulations with and without Henan anthropogenic emissions show that the benefit of local anthropogenic NOx reduction might be lower than expected from the source apportionment results because the contributions of Henan background O3 increase in response to the reduced local anthropogenic emissions due to less NO titration. Thus, collaborative O3 controls in neighboring provinces are needed to reduce O3 pollution problems in Henan effectively.

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.

Deep learning mapping of surface MDA8 ozone: The impact of predictor variables on ozone levels over the contiguous United States

The limited number of ozone monitoring stations imposes uncertainty in various applications, calling for accurate approaches to capturing ozone values in all regions, particularly those with no in-situ measurements. This study uses deep learning (DL) to accurately estimate daily maximum 8-hr average (MDA8) ozone and examines the spatial contribution of several factors on ozone levels over the contiguous U.S. (CONUS) in 2019. A comparison between in-situ observations and DL-estimated MDA8 ozone values shows a correlation coefficient (R) of 0.95, an index of agreement (IOA) of 0.97, and a mean absolute bias (MAB) of 2.79 ppb, highlighting the promising performance of the deep convolutional neural network (Deep-CNN) at estimating surface MDA8 ozone. Spatial cross-validation also confirms the high spatial accuracy of the model, which obtains an R of 0.91, and IOA of 0.96 and an MAB of 3.46 ppb when it is trained and tested on separate stations. To interpret the black-box nature of our DL model, we use Shapley additive explanations (SHAP) to generate a spatial feature contribution map (SFCM), the results of which confirm an advanced ability of Deep-CNN to capture the interactions between most predictor variables and ozone. For instance, the model shows that solar radiation (SRad) SFCM, with higher values, enhances the formation of ozone, particularly in the south and southwestern CONUS. As SRad triggers ozone precursors to produce ozone via photochemical reactions, it increases ozone concentrations. The model also shows that humidity, with its low values, increases ozone concentrations in the western mountainous regions. The negative correlation between humidity and ozone levels can be attributed to factors such as higher ozone decomposition resulting from increased levels of humidity and OH radicals. This study is the first to introduce the SFCM to investigate the spatial role of predictor variables on changes in estimated MDA8 ozone levels.

A Geo-AI-based ensemble mixed spatial prediction model with fine spatial-temporal resolution for estimating daytime/nighttime/daily average ozone concentrations variations in Taiwan

High levels of ground level ozone (O₃) are associated with detrimental health concerns. Most of the studies only focused on daily average and daytime trends due to the presence of sunlight that initiates its formation. However, atmospheric chemical reactions occur all day, thus, nighttime concentrations should be given equal importance. In this study, geospatial-artificial intelligence (Geo-AI) which combined kriging, land use regression (LUR), machine learning, an ensemble learning, was applied to develop ensemble mixed spatial models (EMSMs) for daily, daytime, and nighttime periods. These models were used to estimate the long-term O₃ spatio-temporal variations using a two-decade worth of in-situ measurements, meteorological parameters, geospatial predictors, and social and season-dependent factors. From the traditional LUR approach, the performance of EMSMs improved by 60% (daytime), 49% (nighttime), and 57% (daily). The resulting daily, daytime, and nighttime EMSMs had a high explanatory power with and adjusted R² of 0.91, 0.91, and 0.88, respectively. Estimation maps were produced to examine the changes before and during the implementation of nationwide COVID-19 restrictions. These results provide accurate estimates and its diurnal variation that will support pollution control measure and epidemiological studies.

Ozone exposure and health risks of different age structures in major urban agglomerations in People's Republic of China from 2013 to 2018

High concentration of surface ozone (O₃) will cause health risks to people. In order to analyze the spatiotemporal characteristics of O₃ and assess O₃ exposure and health risks for different age groups in China, we applied multiple methods including standard deviation ellipse, spatial autocorrelation, and exposure-response functions. Results show that O₃ concentrations increased in 64.5% of areas in China from 2013 to 2018. The central plain urban agglomeration (CPU), Beijing-Tianjin-Hebei (BTH), and Yangtze River Delta (YRD) witnessed the greatest incremental rates of O₃ by 16.7%, 14.3%, and 13.1%. Spatially, the trend of O₃ shows a significant positive autocorrelation, and high trend values primarily in central and east China. The proportion of the total population exposed to high O₃ (above 160 μg/m³) increased annually. Compared to 2013, the proportion of the young, adult, and old populations exposed to high O₃ increased to different extents in 2018 by 26.8%, 29.6%, and 27.2%, respectively. The extent of population exposure risk areas in China expanded in size, particularly in north and east China. The total premature respiratory mortalities attributable to long-term O₃ exposure in six urban agglomerations were about 177,000 in 2018 which has increased by 16.4% compared to that in 2013. Among different age groups, old people are more vulnerable to O₃ pollution, so we need to strengthen their relevant health protection of them.

Association analysis between socioeconomic factors and urban ozone pollution in China

Ozone pollution in China has gradually increased, attracting extensive attention. Existing studies on ozone pollution typically take environmental and chemical perspectives. As air pollution is closely related to social and economic activities, it is also important to study ozone pollution from a socioeconomic perspective. Using the association rule mining technique, we uncovered hidden patterns between ozone variance and socioeconomic factors in macro-, meso-, and micro-scenarios in 297 Chinese cities. We found that the acceleration of urbanization and industrialization has indeed aggravated urban ozone pollution. The supply of water and power resources may be a significant factor influencing urban ozone pollution. Transportation hub cities with more developed economies and industries are more likely to suffer from ozone pollution in summer and autumn. Human behavior is a critical factor influencing the weekly variance in ozone concentration during weekdays and weekends. The influence of plant-derived VOC emissions on the formation of ozone cannot be overlooked. Our results deepen the understanding of ozone pollution in Chinese cities, and we provide corresponding policy recommendations to alleviate ozone pollution and improve air quality.

Modification effects of ambient temperature on associations of ambient ozone exposure before and during pregnancy with adverse birth outcomes: A multicity study in China

BACKGROUND

Epidemiological studies suggest that both ambient ozone (O₃) and temperature were associated with increased risks of adverse birth outcomes. However, very few studies explored their interaction effects, especially for small for gestational age (SGA) and large for gestational age (LGA).

OBJECTIVES

To estimate the modification effects of ambient temperature on associations of ambient O₃ exposure before and during pregnancy with preterm birth (PTB), low birth weight (LBW), SGA and LGA based on multicity birth cohorts.

METHODS

A total of 56,905 singleton pregnant women from three birth cohorts conducted in Tianjin, Beijing and Maoming, China, were included in the study. Maximum daily 8-h average O₃ concentrations of each pregnant woman from the preconception period to delivery for every day were estimated by matching their home addresses with the Tracking Air Pollution in China (TAP) datasets. We first applied the Cox proportional-hazards regression model to evaluate the city-specific effects of O₃ exposure before and during pregnancy on adverse birth outcomes at different temperature levels with adjustment for potential confounders, and then a meta-analysis across three birth cohorts was conducted to calculate the pooled associations.

RESULTS

In pooled analysis, significant modification effects of ambient temperature on associations of ambient O₃ with PTB, LBW and LGA were observed (P_interaction < 0.05). For a 10 μg/m³ increase in ambient O₃ exposure at high temperature level (> 75^th percentile), the risk of LBW increased by 28 % (HR: 1.28, 95% CI: 1.13-1.46) during the second trimester and the risk of LGA increased by 116% (HR: 2.16, 95%CI: 1.16-4.00) during the entire pregnancy, while the null or weaker association was observed at corresponding low (≤ 25^th percentile) and medium (> 25^th and ≤ 75^th percentile) temperature levels.

CONCLUSION

This multicity study added new evidence that ambient high temperature may enhance the potential effects of ambient O₃ on adverse birth outcomes.

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 52.13 µg/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 96.18 µg/m³ (exceeding 1.60 times the WHO Guidelines 2021) and 94.86 µg/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, NO_x and NMVOCs, are the main contributors to O₃ pollution, with the largest contribution coming from zone 1 (FAZ) with 91.5 thousand tons of NO_x/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.

Comparison of the association between different ozone indicators and daily respiratory hospitalization in Guangzhou, China

Background

Epidemiological studies have widely proven the impact of ozone (O₃) on respiratory mortality, while only a few studies compared the association between different O₃ indicators and health.

Methods

This study explores the relationship between daily respiratory hospitalization and multiple ozone indicators in Guangzhou, China, from 2014 to 2018. It uses a time-stratified case-crossover design. Sensitivities of different age and gender groups were analyzed for the whole year, the warm and the cold periods. We compared the results from the single-day lag model and the moving average lag model.

Results

The results showed that the maximum daily 8 h average ozone concentration (MDA8 O₃) had a significant effect on the daily respiratory hospitalization. This effect was stronger than for the maximum daily 1 h average ozone concentration (MDA1 O₃). The results further showed that O₃ was positively associated with daily respiratory hospitalization in the warm season, while there was a significantly negative association in the cold season. Specifically, in the warm season, O₃ has the most significant effect at lag 4 day, with the odds ratio (OR) equal to 1.0096 [95% confidence intervals (CI): 1.0032, 1.0161]. Moreover, at the lag 5 day, the effect of O₃ on the 15-60 age group was less than that on people older than 60 years, with the OR value of 1.0135 (95% CI: 1.0041, 1.0231) for the 60+ age group; women were more sensitive than men to O₃ exposure, with an OR value equal to 1.0094 (95% CI: 0.9992, 1.0196) for the female group.

Conclusion

These results show that different O₃ indicators measure different impacts on respiratory hospitalization admission. Their comparative analysis provided a more comprehensive insight into exploring associations between O₃ exposure and respiratory health.

Associations between Different Ozone Indicators and Cardiovascular Hospital Admission: A Time-Stratified Case-Crossover Analysis in Guangzhou, China

Epidemiological studies reported that ozone (O₃) is associated with cardiovascular diseases. However, only few of these studies examined the impact of multiple O₃ indicators on cardiovascular hospital admissions. This study aimed to explore and compare the impacts of different O₃ indicators on cardiovascular hospital admissions in Guangzhou, China. Based upon the data on daily cardiovascular hospital admissions, air pollution, and meteorological factors in Guangzhou from 2014 to 2018, a time-stratified case-crossover design model was used to analyze the associations between different O₃ indicators and cardiovascular hospital admissions. Moreover, the sensitivities of different age and gender groups were analyzed for the whole year and different seasons (i.e., warm and cold). During the warm season, for the single-pollutant model, the odds ratio (OR) value of cardiovascular hospital admissions was 1.0067 (95% confidence interval (CI): 1.0037, 1.0098) for every IQR increase in MDA8 O₃ at a lag of five days. The effect of O₃ on people over 60 year was stronger than that on the 15-60 years age group. Females were more sensitive than males to O₃ exposure. These results provided valuable references for further scientific research and environmental improvement in Guangzhou. Given that short-term O₃ exposure poses a threat to human health, the government should therefore pay attention to prevention and control policies to reduce and eliminate O₃ pollution and protect human health.

Effects of chemical boundary conditions on simulated O3 concentrations in China and their chemical mechanisms

Chemical boundary conditions (BCs) are important inputs for regional chemical transport models. In this study, we use the brute-force method (BFM), process analysis (PA) and response surface model (RSM) to quantify the effects of BCs on simulated O₃ concentrations in different regions of China by the weather research and forecasting with chemistry (WRF-Chem) model. We combine the model with an integrated gas-phase reaction rate (IRR) tool to further analyze the changes in the O₃ chemical mechanisms. Our results show that the simulated O₃ concentrations in western cities are significantly affected by the O₃ in the BCs (BC-O₃), which can increase the maximum simulated O₃ concentration, such as in Lanzhou (36.6 μg/m³, 26.3 %), Wuhai (30.1 μg/m³, 25.5 %) and Urumqi (50.7 μg/m³, 41.2 %). In contrast, O₃ generation in the eastern region is dominated by emissions. Subsequently, we compare the reaction rate changes in O₃ generation and consumption under the effects of BC-O₃ in the western city of Urumqi and the eastern city of Beijing. The results show that in Beijing, the O₃ concentration and the related chemical reaction rates undergo little change, while in Urumqi, the concentration and reaction rates have significant differences. The BC-O₃ significantly accelerates the O₃ photochemical reaction process in Urumqi, resulting in increased O₃ generation and consumption reaction rates; additionally, there may be a chemical reaction pathway for the formation of O₃: BC-O₃ + NO → NO₂ + hv → O + O₂ → O₃. BC-O₃ transmission is the main pathway of changes in the simulated O₃ concentration in the study area, and the chemical reactions between BC-O₃ and local pollutants are primarily characterized by O₃ consumption. In conclusion, the study shows the importance of BCs for regional model simulation while providing supporting information for O₃ formation in model studies.

Surface ozone pollution in China: Trends, exposure risks, and drivers

Introduction

Within the context of the yearly improvement of particulate matter (PM) pollution in Chinese cities, Surface ozone (O₃) concentrations are increasing instead of decreasing and are becoming the second most important air pollutant after PM. Long-term exposure to high concentrations of O₃ can have adverse effects on human health. In-depth investigation of the spatiotemporal patterns, exposure risks, and drivers of O₃ is relevant for assessing the future health burden of O₃ pollution and implementing air pollution control policies in China.

Methods

Based on high-resolution O₃ concentration reanalysis data, we investigated the spatial and temporal patterns, population exposure risks, and dominant drivers of O₃ pollution in China from 2013 to 2018 utilizing trend analysis methods, spatial clustering models, exposure-response functions, and multi-scale geographically weighted regression models (MGWR).

Results

The results show that the annual average O₃ concentration in China increased significantly at a rate of 1.84 μg/m³/year from 2013 to 2018 (160 μg/m³) in China increased from 1.2% in 2013 to 28.9% in 2018, and over 20,000 people suffered premature death from respiratory diseases attributed to O₃ exposure each year. Thus, the sustained increase in O₃ concentrations in China is an important factor contributing to the increasing threat to human health. Furthermore, the results of spatial regression models indicate that population, the share of secondary industry in GDP, NOx emissions, temperature, average wind speed, and relative humidity are important determinants of O₃ concentration variation and significant spatial differences are observed.

Discussion

The spatial differences of drivers result in the spatial heterogeneity of O₃ concentration and exposure risks in China. Therefore, the O₃ control policies adapted to various regions should be formulated in the future O₃ regulation process in China.

Tracking long-term population exposure risks to PM2.5 and ozone in urban agglomerations of China 2015-2021

China has experienced severe air pollution in the past decade, especially PM2.5 and emerging ozone pollution recently. In this study, we comprehensively analyzed long-term population exposure risks to PM2.5 and ozone in urban agglomerations of China during 2015-2021 regarding two-stage clean-air actions based on the Ministry of Ecology and the Environment (MEE) air monitoring network. Overall, the ratio of the population living in the regions exceeding the Chinese National Ambient Air Quality Standard (35 μg/m3) decreases by 29.9 % for PM2.5 from 2015 to 2021, driven by high proportions in the Middle Plain (MP, 42.3 %) and Lan-Xi (35.0 %) regions. However, this ratio almost remains unchanged for ozone and even increases by 1.5 % in the MP region. As expected, the improved air quality leads to 234.7 × 103 avoided premature mortality (ΔMort), mainly ascribed to the reduction in PM2.5 concentration. COVID-19 pandemic may influence the annual variation of PM2.5-related ΔMort as it affects the shape of the population exposure curve to become much steeper. Although all eleven urban agglomerations share stroke (43.6 %) and ischaemic heart disease (IHD, 30.1 %) as the two largest contributors to total ΔMort, cause-specific ΔMort is highly regional heterogeneous, in which ozone-related ΔMort is significantly higher (21 %) in the Tibet region than other urban agglomeration. Despite ozone-related ΔMort being one order of magnitude lower than PM2.5-related ΔMort from 2015 to 2021, ozone-related ΔMort is predicted to increase in major urban agglomerations initially along with a continuous decline for PM2.5-related ΔMort from 2020 to 2060, highlighting the importance of ozone control. Coordinated controls of PM2.5 and O3 are warranted for reducing health burdens in China during achieving carbon neutrality.

Air pollution impacts on public health: Evidence from 110 cities in Yangtze River Economic Belt of China

This study intends to further reveal the relationship between air pollution and public health on a city scale in China and explore the spillover effect among cities. On the basis of collecting the panel data of 110 cities in the Yangtze River Economic Belt from 2010 to 2018, we establish a spatial econometric model to analyze the impacts of air pollution, economic development, and other factors on public health. According to the results, a significant spatial correlation exists between the public health and air pollution levels in all of the cities in the Yangtze River Economic Belt. Air pollution also shows a spillover effect among these cities; the relationships between the industrial fume (dust) emissions, industrial sulfur dioxide emissions, and particulate matter (PM 2.5) concentration and the public health level are not simple linear relationships, but instead U-shaped curvilinear relationships. The economic development in recent years has contributed to the improvement of the public health level of the cities in the Yangtze River Economic Belt. The economic development of their neighboring cities, however, has adversely affected the public health levels of these cities.

Diagnosing the Model Bias in Simulating Daily Surface Ozone Variability Using a Machine Learning Method: The Effects of Dry Deposition and Cloud Optical Depth

Machine learning methods are increasingly used in air quality studies to predict air pollution levels, while few applied them to diagnose and improve the underlying mechanisms controlling air pollution represented in chemical transport models (CTMs). Here, we use the random forest (RF) method to diagnose high biases of surface daily maximum 8 h average (MDA8) ozone concentrations in the GEOS-Chem CTM evaluated against measurements from the nationwide monitoring network in summer 2018 over China. The feature importance results show that cloud optical depth (COD), relative humidity, and precipitation are the top three factors affecting CTM high biases. Such results indicate that the high ozone biases in summer over China mainly occur on wet/cloudy days (∼40% biased high), while biases on dry/clear days are small (within 5%). We link the important features with model parameterizations and variables, identifying model underestimates in the dry deposition velocity and COD on wet/cloudy days. By accounting for the enhanced dry deposition on wet plant cuticles and using satellite observation constrained COD, we find that CTM high ozone biases can be halved with an improved agreement in the temporal variability, highlighting the effects of dry deposition and COD on ozone, as suggested by the RF outcomes.

Mathematical modeling in the health risk assessment of air pollution-related disease burden in China: A review

This review paper covers an overview of air pollution-related disease burden in China and a literature review on the previous studies which have recently adopted a mathematical modeling approach to demonstrate the relative risk (RR) of air pollution-related disease burden. The associations between air pollution and disease burden have been explored in the previous studies. Therefore, it is necessary to quantify the impact of long-term exposure to ambient air pollution by using a suitable mathematical model. The most common way of estimating the health risk attributable to air pollution exposure in a population is by employing a concentration-response function, which is often based on the estimation of a RR model. As most of the regions in China are experiencing rapid urbanization and industrialization, the resulting high ambient air pollution is influencing more residents, which also increases the disease burden in the population. The existing RR models, including the integrated exposure-response (IER) model and the global exposure mortality model (GEMM), are critically reviewed to provide an understanding of the current status of mathematical modeling in the air pollution-related health risk assessment. The performances of different RR models in the mortality estimation of disease are also studied and compared in this paper. Furthermore, the limitations of the existing RR models are pointed out and discussed. Consequently, there is a need to develop a more suitable RR model to accurately estimate the disease burden attributable to air pollution in China, which contributes to one of the key steps in the health risk assessment. By using an updated RR model in the health risk assessment, the estimated mortality risk due to the impacts of environment such as air pollution and seasonal temperature variation could provide a more realistic and reliable information regarding the mortality data of the region, which would help the regional and national policymakers for intensifying their efforts on the improvement of air quality and the management of air pollution-related disease burden.

Effects of air pollution on cardiovascular health in patients with type 2 diabetes mellitus: Evidence from a large tertiary hospital in Shandong Province, China

Air pollution has posed serious threats to human health. Based on the microdata of a large tertiary hospital in Shandong Province from 2016 to 2021, combined with the macro data such as air quality monitoring data, meteorological data, and city-level regional socio-economic data, this paper empirically tests the impact of air pollution instrumented by thermal inversions on the cardiovascular health of patients with type 2 diabetes mellitus (T2DM) and its group differences. The results show that: (1) Air pollution has a negative impact on the cardiovascular health of patients with T2DM, that is, the cardiovascular health of patients with T2DM will decline in regions with high air pollution; (2) The impact of air pollution on cardiovascular health in T2DM patients is heterogeneous, with males and older patients bearing greater air pollution health losses; (3) From the perspective of the external environment, the negative effects of environmental pollution on patients' health were significantly reduced in areas with higher environmental regulation intensity and better public health conditions, indicating the necessity of strengthening environmental governance and increasing public health expenditure.

Association between prenatal exposure to ambient ozone, birth weight, and macrosomia in healthy women

Studies have shown that prenatal ozone exposure is associated with an increased risk of adverse pregnancy outcomes, among which abnormal birth weight is a detrimental factor for diseases in adulthood, but the association between birth weight and ozone is inconclusive. Herein, we conducted this study by enrolling 407 couples of pregnant women and collected their demographical materials, their exposure to ambient ozone was assessed according to the place of their residence. The hourly monitored ozone was first averaged to the daily level, then monthly and whole-gestationally levels. After adjusting confounders, we processed a multivariate generalized addictive analysis to predict the association between prenatal ozone exposure and birth weight. We also divided the cohort into two categories according to whether the infant met the standard of macrosomia, and the occurrence of macrosomia was studied via univariate and multivariate logistic regression analyses as extreme conditions of the effects of ozone exposure on birth weight. We found that the ground-level ozone in Jinan changed with temperature periodically, higher in summer and lower in winter. Over the past 8 years from 2014, the ambient ozone increased by 1.74 μg/m³ per year. Of the 407 singleton-pregnant women, 21 infants were diagnosed with macrosomia. After adjusting confounders, we found that each unit increase in prenatal ozone exposure caused 8.80% [OR_ozone90%CI: 0.912 (0.850, 0.978)] decreased risk of macrosomia, but the splined ambient ozone exposure data was not statistically associated with birth weight, which is probably due to the limited sample size. In conclusion, prenatal ozone exposure is associated with decreased risk of macrosomia but is weakly linked to birth weight.

Related Factors and Economic Burden Evaluation of Nosocomial Infection in Patients with Chronic Kidney Disease

Kidney disease in China has the characteristics of “three highs and one low“, namely high incidence, high disability rate, high treatment cost and low awareness. The main outcome is a progressive deterioration of renal function that is not easily corrected, and patients are often hospitalized repeatedly due to factors such as infection, fatigue, lack of medication, water and electrolyte imbalances. It not only accelerates the deterioration of renal function, but also easily causes psychological problems and seriously affects the quality of life of patients. Kidney is an organ with dual functions of endocrine and material metabolism in the human body, and plays an important role in both physiological and pathological processes. Its main functions include urine generation, renal tubular reabsorption, regulation of water and electrolyte metabolism, and endocrine functions. Chronic kidney disease is defined by an unexplained decrease in GFR over three months. At present, clinicians usually take drug corrective measures only when CKD patients have intermediate and advanced PEM with abnormal index examinations (such as decreased plasma albumin, anemia, etc.), and the clinical efficacy is not stable. At present, there are few studies on the related factors and economic burden of nosocomial infection in patients with chronic kidney disease. This study understands the nutrient intake of patients in each disease process, and makes individualized nutritional dietary guidance programs more targeted. Starting from the second day of the patient's hospitalization, the food eaten by the patient was weighed by professionals using the weighing method for 3 consecutive days. By recording and entering into the analysis system, it performs the calculation and analysis of food composition and nutrient intake, and analyzes it using statistical methods. The experimental results showed that patients aged 31-60 years had a higher risk of preoperative infection.

Evaluation of the Street Canyon Level Air Pollution Distribution Pattern in a Typical City Block in Baoding, China

Urban traffic pollution, which is strongly influenced by the complex urban morphology, has posed a great threat to human health. In this study, we performed a high-resolution simulation of traffic pollution in a typical city block in Baoding, China, based on the Parallelized Large-eddy simulation Model (PALM), to examine the distribution patterns of traffic-related pollutants and explore their relationship with urban morphology. Based on the model results, we conducted a multi-linear regression (MLR) analysis and found that the distribution of air pollutants inside the city block was dominated by both traffic emissions and urban morphology, which explained about 70% of the total variance in spatial distribution of air pollutants. Excluding the contribution of emissions, over 50% of the total variance can still be explained by the urban morphology. Among these urban morphological factors, the key factors determining the spatial distribution of air pollution are "Distance from the road" (DR), "Building Coverage Ratio" (BCR) and "Aspect Ratio" (H/W) of the street canyon. Specifically, urban areas with lower Aspect Ratio, lower BCR and larger DR are less affected by traffic pollution. Compiling these individual factors, we developed a complex Urban Morphology Pollution Index (UMPI). Each unit increase in UMPI is associated with a one percent increase of nearby traffic pollution contribution. This index can help urban planners to semi-quantitatively evaluate building groups which tend to trap or ventilate traffic pollution and thus help to reduce human exposure to street canyon level pollution through either traffic emission control or urban morphology amelioration.

Spatiotemporal variation of ozone pollution and health effects in China

With the rapid urbanization and industrialization in China, ozone pollution has become increasingly serious and poses a greater threat to human health. In this study, the spatiotemporal distribution of ozone pollution in China's cities and urban agglomerations from 2015 to 2019 was analyzed. The health effects and health economic costs of ozone pollution in China were estimated by applying the environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) model. The results are as follows: (1) ozone pollution was more serious in Chinese urban agglomerations from 2015 to 2019; (2) the hot spots of ozone concentration mainly distributed in the North China Plain, expanding from north to south; the cold spots decreased year by year and were located in the northeast, northwest, and southwest of China, shifting from northwest to southwest; (3) the seasonal average of ozone concentration in China was the highest in summer, followed by spring and autumn, and the lowest in winter; (4) the number of all-cause premature deaths of ozone pollution in China increased slowly from 2015 to 2019, and the average of urban agglomerations was significantly higher than cities, with similar spatial distribution characteristics as ozone concentration; (5) the health economic costs of ozone pollution from 2015 to 2019 slowly expanded to surrounding cities with Beijing, Shanghai, Xi'an, and Chongqing as the centers of high values, while the low value areas decreased year by year and were mainly concentrated in southwest and northeast China. The health economic costs of ozone pollution at urban agglomerations scale were higher in the eastern coastal regions and lower in the northwest inland regions. Thus, this study presents policy recommendations to provide decision-making reference for realizing the inter-regional prevention and control of ozone pollution.

Assessment of PM2.5-related health effects: A comparative study using multiple methods and multi-source data in China

In China, PM_2.5 pollution has caused extensive death and economic loss. Thus, an accurate assessment of the spatial distribution of these losses is crucial for delineating priority areas for air pollution control in China. In this study, we assessed the PM_2.5 exposure-related health effects according to the integrated exposure risk function and non-linear power law (NLP) function in 338 prefecture-level cities in China by utilizing online monitoring data and the PM_2.5 Hindcast Database (PHD). Our results revealed no significant difference between the monitoring data and PHD (p value = 0.66 > 0.05). The number of deaths caused by PM_2.5-related Stroke (cerebrovascular disease), ischemic heart disease, chronic obstructive pulmonary disease, and lung cancer at the national level estimated through the NLP function was 0.27 million (95% CI: 0.06-0.50), 0.23 million (95% CI: 0.08-0.38), 0.31 million (95% CI: 0.04-0.57), and 0.31 million (95% CI: 0.16-0.40), respectively. The total economic cost at the national level in 2016 was approximately US$80.25 billion (95% CI: 24.46-132.25). Based on a comparison of Z statistics, we propose that the evaluation results obtained using the NLP function and monitoring data are accurate. Additionally, according to scenario simulations, Beijing, Chongqing, Tianjin, and other cities should be priority areas for PM_2.5 pollution control to achieve considerable health benefits. Our statistics can help improve the accuracy of PM_2.5-related health effect assessments in China.

Ambient ozone exposure combined with residential greenness in relation to serum sex hormone levels in Chinese rural adults

BACKGROUND

Long-term exposure to ambient ozone (O₃) and residential greenness independently relate to altered hormones levels in urban settings and developed countries. However, independent and their joint associations with progestogen and androgen were sparsely studied in rural regions.

MATERIALS AND METHODS

A total of 6211 individuals were recruited in this study. Random forest model was applied to predict the daily average concentrations of O₃ using the satellites data. Residential greenness was reflected by the normalized difference vegetation index (NDVI). Liquid chromatography-tandem mass spectrometry was used to measure serum progestogen and androgen concentrations. Gender and menopausal status modified associations of long-term exposure to O₃ and residential greenness with hormones levels were analyzed by generalized linear models.

RESULTS

Long-term exposure to O₃ was negatively related to 17-hydroxyprogesterone, testosterone, and androstenedione in both men and women (premenopausal and postmenopausal); the estimated β and 95% CI of ln-progesterone in response to per 10 μg/m³ increment in O₃ concentration was -0.560 (-0.965, -0.155) in postmenopausal women. Association of long-term exposure to O₃ with serum androgen levels in premenopausal and postmenopausal women were alleviated by residing in places with higher greenness. Additionally, a prominent effect of long-term exposure to O₃ related to decreased serum progestogen and androgen levels was found in participants with middle- or high-level of physical activity or lower education level.

CONCLUSIONS

The results suggested that long-term exposure to high levels of O₃ related to decreased serum androgen levels was attenuated by living in high greenness places in women regardless of menopause status. Future studies are needed to confirm the positive health effects of residential greenness on the potential detrimental effects due to exposure to O₃.

Assessing the impact of green nudges on ozone concentration: Evidence from China's night refueling policy

Ozone (O₃) pollution poses health risks and premature mortality, and gas stations are one of the largest sources of urban volatile organic compounds (VOCs, the main precursor to O₃). This paper investigates whether the government's call for night refueling, which can be regarded as a green nudge, can guide changes in consumer behavior and consequently improve environmental quality. Using a difference-in-differences (DID) estimation and weekly monitoring site air quality panel data, we analyze the effect of the Night Refueling Preferential Policy on O₃ concentrations. We find that the policy can reduce O₃ concentrations by 10% by encouraging consumers to refuel at night. The reduction in O₃ has brought great benefits to human health, leading to a 4-5‰ reduction in non-accidental mortality and a 6-8‰ reduction in cardiovascular mortality in Jiangsu province. The economic benefits of this policy would be approximately 62-189 billion Chinese Yuan (CNY) if it were implemented nationwide. The findings of this study suggest that the government can influence consumer behavior to promote environmental quality.

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

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

Impacts of the differences in PM2.5 air quality improvement on regional transport and health risk in Beijing-Tianjin-Hebei region during 2013-2017

Due to the implementation of different air pollution control measures in the Beijing-Tianjin-Hebei (BTH) region during 2013-2017, the air quality exhibited varied improvements in each province, indicating substantial changes in the interprovincial regional transport of PM_2.5. In this study, we investigated these changes by using the Community Multiscale Air Quality (CMAQ) model coupled with the integrated source apportionment method (ISAM) during this period. The results showed that the concentrations of primary particles, SO₄^2-, and NO₃^- decreased by 41.5, 40.8, and 1.8%, respectively due to the air pollutants emission reduction. Local air pollutant emissions were the predominant contributors of PM_2.5 for each region in BTH, accounting for 41.3-47.6, 38.1-40.6, 50.6-53.6, and 54.0-57.1% of PM_2.5 in Beijing, Tianjin, and northern and southern Hebei, respectively. Total PM_2.5 has been mitigated by 7.1-12.3 and 5.1-11.7 μg/m³ from local and regional emission reduction, respectively in the BTH. Moreover, diverse local meteorological conditions variation increased the PM_2.5 concentration by 5.3 μg/m³ in Tianjin and decreased it by 7.6, 2.0, and 4.9 μg/m³ in Beijing, and northern and southern Hebei, respectively. Estimation by integrated exposure-response function revealed that the number of premature deaths attributable to PM_2.5 exposure decreased by approximately 3000 in the BTH region during 2013-2017. Additional policies that focus on PM_2.5-O₃ coordinated control and stringent regional joint air pollution regulation are required to substantially reduce the health impacts, especially in southern Hebei.

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), PM_2.5 concentrations decreased but were still higher than national standards in major areas and ozone (O₃) concentration increased unintentionally. To further decrease PM_2.5 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 PM_2.5, but it is unclear whether O₃ and its effects also increase with the decrease of PM_2.5 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.

A Health Impact and Economic Loss Assessment of O3 and PM2.5 Exposure in China From 2015 to 2020

China is in a critical air quality management stage. Rapid industrial development and urbanization has resulted in non-ignorable air pollution, which seriously endangers human health. Assessment of the health impacts and economic losses of air pollution is essential for the prevention and control policy formulation. Based on ozone (O3) and fine particulate matter concentration (PM2.5) monitoring data in 331 Chinese cities from 2015 to 2020, this study evaluated the health effects and the corresponding economic losses of O3 and PM2.5 pollution on three health endpoints. The ratio of population exposed to O3 levels that exceeded the Chinese Ambient Air Quality Standards (CAAQS) increased from 13.35% in 2015 to 14.15% in 2020, which resulted in 133,415 (2015) - 156,173 (2020) all-cause deaths, 88,941 (2015) - 104,051 (2020) cardiovascular deaths, and 28,614 (2015) - 33,456 (2020) respiratory deaths. The ratio of population exposed to PM2.5 levels that exceeded the CAAQS decreased, but in many regions, especially in North China and the Yangtze River Delta, the PM2.5 concentration remained high. By 2020, nearly half of the population in China was still exposed to PM2.5 levels that exceeded the CAAQS, and the corresponding economic losses reached CNY 3.46 and 3.05 billion, respectively. These results improved the understanding of the spatial-temporal variation trends of major air pollutants at city scale in China, and emphasize the continued coordination urgently needed for controlling O3 and PM2.5 following the implementation of the 2013 policy to mitigate air pollution to protect human health.

Ground-level ozone simulation using ensemble WRF/Chem predictions over the Southeast United States

Being detrimental to human health and vegetation growth, ground-level ozone (O₃) is becoming a huge concern as an air pollutant. The processes of formation, diffusion, transformation, and transport of O₃ in the atmosphere are highly affected by meteorological conditions such as solar radiation, temperature, precipitation, and wind. Chemical transport models (CTMs) are widely used in simulating O₃ pollution with two main inputs of the meteorological condition and emission inventory. Meteorological inputs play a crucial role in the model simulation accuracy especially in areas where emission has been well constrained such as the United States (U.S.). However, most O₃ simulations today still use only one set of meteorological input, which leaves room for model performance improvement by using ensemble meteorological conditions. In this study, O₃ over the Southeast U.S. was simulated for one week in the summer of each year from 2016 to 2018 by using ensemble meteorological inputs offered by Short Range Ensemble Forecast products. The predictions were conducted through the Weather Research and Forecasting model coupled with Chemistry. The calculated ensemble prediction results got at least 66.7% improvement in agreement with O₃ observations compared with single runs in the three selected cities (Miami, Atlanta, and Baton Rouge) from 2016 to 2018. This study emphasized the accuracy and provided a new idea of using ensemble meteorological inputs to improve O₃ prediction than using traditional single meteorology by CTMs.

Quantification of the global and regional impacts of gas flaring on human health via spatial differentiation

Globally, gas flaring caused 350 million tonnes of CO₂ emissions in 2018. In addition to climate change, this burning practice also has other negative consequences for humans (e.g., respiratory problems) and the environment. The aim of this paper is to quantify the impact of flaring on human health (at the global and country level) via the calculation of the number of disability-adjusted life years (DALYs) caused by the combustion of associated gas. For this quantification, gas flaring emissions were linked with midpoint indicators (e.g., climate change) in life cycle assessment (LCA) for all selected countries. Regionalised characterisation factors (CFs) were applied in the impact assessment to allow for spatial differentiation. The global impact on human health caused by flaring was obtained by taking the sum for all countries. The results show that these flaring emissions globally cause 4.83 × 10⁵ DALYs or 6.19 × 10^-5 DALYs/person on an annual basis. This amounts to 0.12% of the total DALYs related to air pollution (from PM_2.5) caused by all polluting sectors and 6.51% of the total DALYs related to climate change. To quantify these impacts, this study uses a country perspective rather than considering local characteristics. Thus, if more precise information at a more local level (e.g., city level) is sought, additional factors (e.g., meteorological conditions) should be taken into account. Finally, future research should also focus on the benefits of gas flaring reduction techniques to enable the selection of the most promising technologies for the elimination of gas flaring and its effects.