Concentrations In China Research Articles

Meteorological and anthropogenic drivers of surface ozone change in the North China Plain in 2015–2021

Surface ozone (O3) concentrations in China have increased largely in the past decade. An accurate understanding of O3 pollution evolution is critical for making effective regulatory policies. Here we integrate data- and process-based models to explore the drivers of the observed summertime surface O3 change in the North China Plain (NCP) over 2015–2021. The data-based model by the deep learning (DL) suggests the reverse of meteorological contributions to the observed O3 change, i.e., 0.14 ppb/y in 2015–2019 and −1.74 ppb/y in 2019–2021. This is mainly resulted from the reversed changes in meteorological variables in surface air temperature and relative humidity. The simulations from a global chemical transport model, GEOS-Chem, also support those results, i.e., the meteorological contribution to O3 changes are 0.26 ppb/y in 2015–2019 and −0.74 ppb/y in 2019–2021. Furthermore, our analysis exhibits possible weakened anthropogenic contributions to surface O3 rise, for example, 1.53 and 0.54 ppb/y by DL in 2015–2019 and 2019–2021, respectively. Similarly, GEOS-Chem simulations suggest an accelerated decrease in surface O3 concentrations driven by the decline in nitrogen dioxide (NO2) concentrations, i.e., approximately 0.4 and 1.2 ppb in 2015–2019 and 2019–2021, respectively. The combined effects of meteorological and anthropogenic contributions led to a significant decrease in surface O3 concentrations by −1.20 ppb/y in 2019–2021. The findings in this work offer valuable insights to mitigate O3 pollution in China.

Analysis of Spatiotemporal Variation and Influencing Factors of PM2.5 in China Based on Multisource Data

The urbanization process over the past decades has resulted in increasing attention being paid to atmospheric pollution by researchers, especially changes in PM2.5 concentration. This study attempted to explore the spatiotemporal changes in PM2.5 concentration in China from 2000 to 2021, as well as their interaction patterns and intensities with temperature, precipitation, vegetation coverage, and land use types. This was carried out by analyzing monthly average PM2.5 concentration data and various meteorological and geographical factors. Suggestions have also been made to reduce PM2.5 concentration and improve air quality. The results show that in the past 22 years, the overall concentration of PM2.5 in China has shown a downward trend, with an average annual rate of 1.42 μg/m3 from 2013 to 2021, accompanied by a clear spatial pattern and significant seasonal changes. The high pollution areas are mainly concentrated in the Tarim Basin, Sichuan Basin, North China Plain, and the Middle and Lower Yangtze Valley Plain, where the PM2.5 concentration in autumn and winter is significantly higher than that in spring and summer. In addition, based on the national spatial scale, PM2.5 concentration is negatively correlated with precipitation and vegetation coverage, while it is significantly positively correlated with arable land and impervious surfaces. Strengthening the control of farmland pollution, accelerating urban greening construction, further expanding the scale of forests and grasslands, and enriching vegetation types will help reduce PM2.5 concentration and improve air quality.

Spatio-temporal variation of O3 concentration and exposure risk assessment in key regions of China, 2015–2021

In recent years, the increasing Ozone (O3) concentration has become a significant concern for human life and health. In this study, O3 concentration data at 10 km resolution and population density data at 1 km resolution for China were utilized. Methods such as the Global Moran's I index, Getis-Ord Gi* index, and population exposure risk index, to analyze the spatial-temporal variation, meteorological influences, aggregation characteristics, exposure risk levels and health impact of O3 in China and six key regions from 2015 to 2021. The results revealed an increasing trend with a bimodal fluctuation pattern, peak in May and August. Only Pearl River Delta (PRD) showed the highest in autumn, five other key regions were highest O3 concentration in the summer. And the spatial autocorrelation in China is obvious among regions. Among the five meteorological factors, temperature and sunshine duration predominantly influenced O3 concentration in China. Relative humidity was the main factor affecting O3 concentration in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Sichuan Basin (SCB), while precipitation had the greatest impact on FenWei Plain (FWP) and Hexi Corridor (HXC) and exhibited a positive correlation. Wind speed in PRD showed a strong negative correlation with O3 concentration. The Ri exhibited significant differences between the eastern and western sides of the Hu Huanyong line, demonstrating a distribution pattern of "high in the east and low in the west". Through this research, we aim to provide valuable insights for public health policies and the prevention and control of regional air pollution.

Spatiotemporal Weighted for Improving the Satellite-Based High-Resolution Ground PM2.5 Estimation Using the Light Gradient Boosting Machine

Surface fine particulate matter (PM) with a diameter of less than 2.5 microns (PM2.5) negatively impacts human health and the economy. However, due to data and model limitations, obtaining high-quality, high-spatial-resolution surface PM2.5 concentration data is a challenging task, and it is difficult to accurately assess the temporal and spatial changes in PM2.5 levels at a small regional scale. Here, we combined multi-angle implementation of atmospheric correction (MAIAC) aerosol products, ERA5 reanalysis data, etc., to construct an STW-LightGBM model that considers the spatiotemporal characteristics of air pollution and estimate the PM2.5 concentration of China’s surface at 1 km resolution from 2015 to 2020. Our model performed well, and the fitting accuracy of the 10-fold cross-validation between years was 0.877–0.917. The fitting accuracy of the model was >0.85 at different time scales (month, season, and year). The average slope of the regression prediction was 0.9 annually. The results showed that PM2.5 pollution improved from 2015 to 2020. The average PM2.5 concentration decreased by 4.55 μg/m3, and the maximum decrease reached 90.51 μg/m3. The areas with high PM2.5 concentrations were predominantly in the North China Plain, Sichuan Basin, and Xinjiang in the west, and the levels in areas with elevated PM2.5 levels were consistent across most study years. The standard deviation ellipse for PM2.5 in China showed a ‘northeast–southwest’ spatial distribution. From an interannual perspective, the average values of the four seasonal stations in the country showed a downward trend from 2015 to 2020, with the most obvious decline in winter, from 70.67 μg/m3 in 2015 to 46.75 μg/m3 in 2020. Compared to earlier inversion studies, this work provides a more stable and accurate method for obtaining high-resolution PM2.5 data, which is necessary for local air governance and environmental ecological construction at a fine scale.

Influence of ENSO on Tropospheric Ozone Variability in East Asia

AbstractAbundance of tropospheric ozone is determined by the interplay among emissions, chemistry, and climate dependent transport. It is important to identify the essential factors that modulate ozone in China and Korea because of increasing ozone trends in this area. In this study, we investigated the correlations of tropospheric ozone in East Asia with El Niño Southern Oscillations (ENSO). The model simulated ozone from 2000 to 2020 with Community Atmosphere Model version 6 with chemistry (CAM6‐chem), ECMWF Atmospheric Composition Reanalysis data, and ozone profiles measured in South Korea, Japan, and Hong Kong. These data were utilized to understand the ozone–ENSO relationship in this region. We found that tropospheric ozone in East Asia is significantly correlated with the wintertime Niño 3.4 index in El Niño/La Niña developing summer and decaying spring, with correlation coefficient up to 0.9. Particularly tropospheric ozone originated from stratosphere is strongly correlated with the Niño 3.4 index. Ozone concentrations in China and the Korean Peninsula are ∼5 ppb higher during El Niño developing summer compared to La Niña in a broad area, because of anomalous cyclonic/anticyclonic flows in the extratropics. In El Niño/La Niña decaying spring, the opposite occurs in the same region. In the decaying spring, stronger variability was observed in the subtropical regions of the lower to mid troposphere, indicating higher ozone concentrations during El Niño compared to La Niña. This variability is associated with the fluctuations in the Walker circulation, large‐scale sinking/rising motions, and the accompanying biomass burning events. The model reproduced the ozone variability associated with ENSO in the ozonesonde observations.

Remote sensing-based spatiotemporal variation and driving factor assessment of chlorophyll-a concentrations in China’s Pearl River Estuary

Climate change and intensive anthropogenic activities have severely challenged the water quality of China’s Pearl River Estuary (PRE). Further investigations into long-term water quality variation and associated driving mechanisms are therefore necessary to support the sustainable development of the PRE’s Greater Bay Area (GBA). This study used remote sensing retrieval to address long-term spatiotemporal chlorophyll-a (Chl-a) variation characteristics in the PRE and the relationship between Chl-a concentrations and socioeconomic/environmental indicators. Three decades of Landsat satellite images and measured data were collected, and a two-band global algorithm was used to retrieve Chl-a concentration data. Results reveal significant spatiotemporal variability in Chl-a concentrations. The space-averaged Chl-a concentration exhibited a slight downward trend during the past three decades, and the multi-year mean value was 5.20 mg/L. Changes to environmental protection policies in recent years have improved overall PRE water quality. The western section of the PRE had the highest Chl-a concentration (i.e., 5.92 mg/L average) while the eastern section had the lowest (i.e., 3.98 mg/L average). This discrepancy was likely caused by the western section’s more intensive industrial activities, resulting in a higher overall wastewater discharge volume. Affected by climatic conditions, winter Chl-a concentrations were evenly distributed while summer concentrations were significantly higher. Additionally, Chl-a concentrations significantly and positively correlated with total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH3-N), and the biotic oxygen demand (BOD5). Chl-a concentrations also correlated with external factors (i.e., climate and anthropogenic activities). Among these factors, industrial wastewater discharge and the proportion of primary industries in coastal cities significantly and positively correlated with water quality. This study is intended to help direct water quality improvement management and urban sustainable development in the GBA.

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.

Reconstructing annual XCO2 at a 1 km×1 km spatial resolution across China from 2012 to 2019 based on a spatial CatBoost method

Long-time-series, high-resolution datasets of the column-averaged dry-air mole fraction of carbon dioxide (XCO2) have great practical importance for mitigating the greenhouse effect, assessing carbon emissions and implementing a low-carbon cycle. However, the mainstream XCO2 datasets obtained from satellite observations have coarse spatial resolutions and are inadequate for supporting research applications with different precision requirements. Here, we developed a new spatial machine learning model by fusing spatial information with CatBoost, called SCatBoost, to fill the above gap based on existing global land-mapped 1° XCO2 data (GLM-XCO2). The 1-km-spatial-resolution dataset containing XCO2 values in China from 2012 to 2019 reconstructed by SCatBoost has stronger and more stable predictive power (confirmed with a cross-validation (R2 = 0.88 and RSME = 0.20 ppm)) than other traditional models. According to the estimated dataset, the overall national XCO2 showed an increasing trend, with the annual mean concentration rising from 392.65 ppm to 410.36 ppm. In addition, the spatial distribution of XCO2 concentrations in China reflects significantly higher concentrations in the eastern coastal areas than in the western inland areas. The contributions of this study can be summarized as follows: (1) It proposes SCatBoost, integrating the advantages of machine learning methods and spatial characteristics with a high prediction accuracy; (2) It presents a dataset of fine-scale and high resolution XCO2 over China from 2012 to 2019 by the model of SCatBoost; (3) Based on the generated data, we identify the spatiotemporal trends of XCO2 in the scale of nation and city agglomeration. These long-term and high resolution XCO2 data help understand the spatiotemporal variations in XCO2, thereby improving policy decisions and planning about carbon reduction.

Spatial-temporal evolution patterns and drivers of PM2.5 chemical fraction concentrations in China over the past 20 years.

The quantitative assessment of the spatial and temporal variability and drivers of fine particulate matter (PM2.5) fraction concentrations are important for pollution control and public health preservation in China. In this study, we investigated the spatial temporal variation of PM2.5 chemical component based on the PM2.5 chemical component datasets from 2000 to 2019 and revealed the driving forces of the differences in the spatial distribution using geodetector model (GD), multi-scale geographically weighted regression model (MGWR), and a two-step clustering approach. The results show that: the PM2.5 chemical fraction concentrations show a trend of first increasing (2000-2007) and then decreasing (2007-2019). From 2000 to 2019, the change rates of PM2.5, organic matter (OM), black carbon (BC), sulfates (SO2- 4), ammonium (NH+ 4), and nitrates (NO- 3) were -0.59, -0.23, -0.07, -0.15, -0.02, and 0.04μg/m3/yr in the entirety of China. The secondary aerosol (i.e., SO2- 4, NO- 3, and NH+ 4; SNA) had the highest fraction in PM2.5 concentrations (55.6-68.1% in different provinces), followed by OM and BC. Spatially, North, Central, and East China are the regions with the highest PM2.5 chemical component concentrations in China; meanwhile, they are also the regions with the most significant decrease in PM2.5 chemical fraction concentrations. The GD and MGWR model shows that among all variables, the number of enterprises, disposable income, private car ownership, and the share of secondary industry non-linearly enhance the differences in the spatial distribution of PM2.5 component concentrations. Electricity consumption has the strongest influence on NH+ 4 emissions in Northwest China and BC and OM emissions in Northeast China.

Drivers of alleviated PM2.5 and O3 concentrations in China from 2013 to 2020

Since 2013, the Chinese Government implemented “Clean Air Action” Phase I (2013–2017) and Phase II (2018–2020) consecutively to improve air quality. However, it remains unclear how these policies have impacted air quality since most studies merely focus on Phase I. In this study, we applied the Community Multiscale Air Quality (CMAQ) model to investigate variations of air quality in China during “Clean Air Action” Phases I and Phase II (2013–2020). Results show that PM2.5 is significantly reduced (-40%) across China except in summer. O3 concentration also decreases, but a 3% increase is found in the Beijing-Tianjin-Hebei (BTH) region. Emission changes play a dominant role in the reduction of PM2.5 (over 95%). The contributions from meteorology and emissions to O3 reduction are 46% and 54%, respectively. Our results suggest that a more effective emission abatement strategy should be formulated, aiming to control O3 and PM2.5 synergistically.

Impact of Clean Air Policy on Criteria Air Pollutants and Health Risks Across China During 2013–2021

AbstractThe impact of clean air policy enacted in China since 2013 on air quality and human health remained poorly understood. For the first time, we developed a full‐coverage air quality data set including six criteria air pollutants (PM2.5, PM10, NO2, SO2, CO, and 8‐hr O3) using a three‐stage model. Then, the LightGBM model was applied to further quantify the respective contributions of emission and meteorology to air quality. The results suggested that the deweathered concentrations of PM2.5, PM10, NO2, SO2, and CO in China decreased by 46.2%, 43.1%, 24.7%, 55.5%, and 37.2%, respectively. However, the 8‐hr O3 concentration in China increased by 19.4%. The variation trends of deweathered PM2.5 and NO2 were much weaker than the absolute concentrations of these species, indicating more strict emission control measures should be imposed in the future. Based on the comparison of the effectiveness during 2013–2017 and 2018–2021, we found that nearly all of the regions suffered from immense difficulty in reducing PM2.5 emissions in recent years. However, the CO emission control has become more efficient since 2018. The increasing rate of deweathered 8‐hr O3 concentration was more dramatic during 2018–2021 compared with 2013–2017, which was in contrast to the absolute 8‐hr O3 concentration. It suggested that the rapid increase of ambient O3 level might be masked by favorable meteorological conditions since 2018. Although the large reduction of PM2.5 and NO2 saved more health benefits (625,362 cases), the increase of O3 exposure aggravated the health costs (20,419 cases) across China. Therefore, collaborative control of PM2.5, NO2, and O3 pollution are still imperative in the future work.

Accumulation Characteristics and Probabilistic Risk Assessment of Cd in Agricultural Soils Across China

To understand cadmium (Cd) pollution status and associated health risks in agricultural soils of China, a database including Cd concentrations of 160446 soil samples from 240 cities (counties) in 31 provinces was constructed based on domestic and foreign literature. The contamination factor and geochemical accumulation index were applied to explore the pollution level of Cd, and then health risks for different population groups were evaluated with the probabilistic risk assessment method based on Monte Carlo simulation. The results indicated that soil Cd concentrations in China ranged from 0.012 to 23.33 mg·kg-1, with the geometric mean of 0.473 mg·kg-1, which was 1.58 times the risk screening value of heavy metals in the soil of agricultural land (GB 15186-2018) (6.5<pH ≤ 7.5). From the perspective of different regions, the highest concentrations of soil Cd were found in central China, followed by southwest, south, northwest, northeast, east, and north China. From the perspective of time, there was no significant difference in soil Cd in different time periods (P>0.05). Cd pollution in agricultural soils was mainly distributed in Yunnan, Hunan, Guangxi, and Gansu provinces in China, which may have been related to the mining and smelting activities. The non-carcinogenic risks for all the population groups could be negligible, whereas there were potential carcinogenic risks for adults and seniors, with 5.81% and 4.49% of carcinogenic risk values exceeding the threshold of 1E-06 set by USEPA for adults and seniors, respectively. The results of this study provided valuable information for the pollution prevention and risk control of soil Cd pollution. To improve the accuracy of health risk assessment results, the exposure ingestion route of agricultural products and bioavailability of soil Cd should be considered in the probabilistic ecological risk assessment.

The effects of socioeconomic factors on particulate matter concentration in China's: New evidence from spatial econometric model

As a result of rapid industrialization and urbanization, China is now facing a host of environmental problems that have serious health implications. Studies of air pollution's impact on human health are vital in many fields, including epidemiology, environmental science, and the social sciences. To ensure the effective growth of socioeconomic sectors, it is critical to investigate the effect of socioeconomic factors on primary air pollutant particulate matter (PM2.5) and the driving mechanism. We conducted group-wise (i.,e. divide data in 5 different periods, D1 (2002–2006), D2 (2007–2011), D3 (2012–2016), D4 (2017–2021) and D5(2002–2021) spatial autocorrelation and spatial panel regression analyses of PM2.5 emissions using panel data from 34 provincial-level administrative units in China from 2002 to 2021 to understand the factors influencing air pollutant emissions. This study adds to the literature by considering comprehensive features and spatial effects in the panel-data econometric framework of the different areas. The spatial features analysis reveals that pollutant emissions in these regions decreased during the study period, although socioeconomic and natural factors are essential sources of PM2.5. PM2.5 emissions also showed significant positive spatial autocorrelations. Several statistical tests were run to examine the spatial autocorrelation among the regions. The results of a random effect regression model and geometric weighted regression (GWR) revealed that both socioeconomic and natural factors were statistically significant for PM2.5, though to varying degrees depending on region type. Positive and statistically significant results were obtained for China when considering the impacts of urban population, urban green space, economic growth, and economic spending. China has a positive and significant link with the exploitation of energy and natural resources. In light of these findings, we have developed several ideas for addressing air pollution and improving environmental sustainability, such as increasing regional collaboration and reforming the economy.

Temporal and Spatial Distribution of Ozone and Its Influencing Factors in China

Tropospheric ozone (O3) pollution has emerged as a significant concern, as it can adversely influence human health, daily activities, and the surrounding environment(The following tropospheric O3 is referred to as O3). Research on the societal contribution to O3 primarily concentrates on the generation mechanisms and chemical processes, with limited studies examining the influence of social and economic activities on O3 at a national scale. In this investigation, spatial econometric models, random forest models, and geographically weighted regression (GWR) were adopted for assessing the effects of meteorological, natural, and socioeconomic factors on O3 concentration throughout the country. The spatial error model (SEM) revealed that precipitation, temperature, wind direction, per capita GDP, RD project funding, and SO2 were the primary factors influencing O3 concentration in China, among which precipitation had the strongest effect on O3, followed by temperature and SO2. Subsequently, the GWR model was utilized to demonstrate the regional differences in the impacts of precipitation, NOx, secondary industry proportion, and electricity consumption. In central and western regions, such as Jiangxi, Guangxi, and Guizhou, precipitation, NOx, and power consumption were the leading factors contributing to severe O3 pollution. The secondary industry proportion substantially affected O3 pollution in the Beijing-Tianjin-Hebei region, indicating that this sector played a crucial role in the region’s economic growth and contributed to elevated O3 concentrations. Meteorological, natural, and socioeconomic factors exhibited a lesser influence on O3 pollution in most eastern regions compared to central and western regions. This study’s findings identified the primary contributors to O3 pollution and provided a scientific basis for developing strategies to mitigate its impact.

Characteristics and Differences of Household Fine Particulate Matter Pollution Caused by Fuel Burning in Urban and Rural Areas in China

Objective To explore the overall level,distribution characteristics,and differences in household fine particulate matter (PM2.5) pollution caused by fuel burning in urban and rural areas in China. Methods The relevant articles published from 1991 to 2021 were retrieved and included in this study.The data including the average concentration of household PM2.5 and urban and rural areas were extracted,and the stoves and fuel types were reclassified.The average concentration of PM2.5 in different areas was calculated and analyzed by nonparametric test. Results The average household PM2.5 concentration in China was (178.81±249.91) μg/m3.The mean household PM2.5 concentration was higher in rural areas than in urban areas[(206.08±279.40) μg/m3 vs. (110.63±131.16) μg/m3;Z=-5.45,P<0.001] and higher in northern areas than in southern areas[(224.27±301.66) μg/m3 vs.(130.11±140.61) μg/m3;Z=-2.38,P=0.017].The north-south difference in household PM2.5 concentration was more significant in rural areas than in urban areas[(324.19±367.94) μg/m3 vs.(141.20±151.05) μg/m3,χ2=-5.06,P<0.001].The PM2.5 pollution level showed differences between urban and rural households using different fuel types (χ2=92.85,P<0.001),stove types (χ2=74.42,P<0.001),and whether they were heating (Z=-4.43,P<0.001).Specifically,rural households mainly used solid fuels (manure,charcoal,coal) and traditional or improved stoves,while urban households mainly used clean fuels (gas) and clean stoves.The PM2.5 concentrations in heated households were higher than those in non-heated households in both rural and urban areas (Z=-4.43,P<0.001). Conclusions The household PM2.5 pollution caused by fuel combustion in China remains a high level.The PM2.5 concentration shows a significant difference between urban and rural households,and the PM2.5 pollution is more serious in rural households.The difference in the household PM2.5 concentration between urban and rural areas is more significant in northern China.PM2.5 pollution in the households using solid fuel,traditional stoves,and heating is serious,and thus targeted measures should be taken to control PM2.5 pollution in these households.

China's Special Ownership Structure and Corporate Governance Research

This research discusses the impact of China's unique ownership structure and corporate governance on ownership concentration. China has a unique ownership structure with a large number of state-owned enterprises and concentrated ownership in the hands of a few controlling shareholders. This ownership structure has significant implications for earnings management and managerial opportunism. This paper aims to investigate the effects of corporate governance on ownership concentration in China, as well as the factors that influence ownership concentration. Numerous studies have shown that good corporate governance can mitigate the negative effects of ownership concentration on corporate performance by enhancing the effectiveness of internal checks and balances. The factors that influence ownership concentration include the level of state ownership, the presence of large controlling shareholders, and the lack of protection for minority shareholders. This paper concludes by discussing the implications of these research findings for policymakers and managers in China, emphasizing the need to strengthen corporate governance and promote equity diversification.

Assessment of Regional Differences in the Implementation of the Air Pollution Prevention and Control Action Plan in China.

Air pollution is still an important risk factor that endangers the health of Chinese people, leading the government to implement a series of policies to address air pollution. This study takes the Air Pollution Prevention and Control Action Plan (APPCAP) proposed in 2013 as the object and uses the combined data set of China's 2000-2019 economic panel data and PM2.5 remote sensing data to analyse the implementation effect of the policy by the multiperiod difference-in-differences method, considering regional heterogeneity. The results show that the implementation of the APPCAP significantly reduced the PM2.5 concentration in China, and the effect was stronger in the Yangtze River Delta region. Future governance policies should further consider local characteristics and determine pollution control goals and measures according to local conditions.

Mongolia Contributed More than 42% of the Dust Concentrations in Northern China in March and April 2023

Dust storms are one of the most frequent meteorological disasters in China, endangering agricultural production, transportation, air quality, and the safety of people’s lives and property. Against the backdrop of climate change, Mongolia’s contribution to China’s dust cannot be ignored in recent years. In this study, we used the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), along with dynamic dust sources and the HYSPLIT model, to analyze the contributions of different dust sources to dust concentrations in northern China in March and April 2023. The results show that the frequency of dust storms in 2023 was the highest observed in the past decade. Mongolia and the Taklimakan Desert were identified as two main dust sources contributing to northern China. Specifically, Mongolia contributed more than 42% of dust, while the Taklimakan Desert accounted for 26%. A cold high-pressure center, a cold front, and a Mongolian cyclone resulted in the transport of dust aerosols from Mongolia and the Taklimakan Desert to northern China, where they affected most parts of the region. Moreover, two machine learning methods [the XGBoost algorithm and the Synthetic Minority Oversampling Technique (SMOTE)] were used to forecast the dust storms in March 2023, based on ground observations and WRF-Chem simulations over East Asia. XGBoost-SMOTE performed well in predicting hourly PM10 concentrations in China in March 2023, with a mean absolute error of 33.8 µg m−3 and RMSE of 54.2 µg m−3.

Accuracy Verification of Satellite Products and Temporal and Spatial Distribution Analysis and Prediction of the CH4 Concentration in China

In this study, the spatiotemporal variations in CH4 concentrations in China from 2003 to 2021 are investigated, and their trends are forecasted over the next decade. Based on the seventh edition standard product retrieved by the atmospheric infrared detector (AIRS) at an altitude of 500 hPa, we verified monthly CH4 products using observational data provided by the World Data Center for Greenhouse Gases (WDCGG) from six ground stations in and around China. The correlation coefficients (R values) between the two data sets ranged from 0.68 to 0.92, signifying the ability of AIRS inversion data to represent temporal and spatial changes in surface CH4 concentrations. Additionally, China was classified into three regions (steps) based on terrain, and the changes in CH4 concentrations were assessed from three perspectives: spatial distribution, interannual variation, and seasonal variation. The results revealed that the CH4 concentration decreased with elevation along a topographic gradient, with high-value areas located in the first and second steps, corresponding to the eastern Qinghai–Tibet Plateau, northern Xinjiang Uygur Autonomous Region, and Inner Mongolia Autonomous Region. Over 19 years, the average increase in CH4 concentration has ranged from 65 to 175 ppb. In addition, the CH4 concentrations were higher during summer and autumn and lower during spring and winter. Finally, a SARIMA model was used to predict the near-surface CH4 concentration trend in China over the next ten years, which indicated a continued seasonal increase.

A Systematic Review and Meta-Analysis of the R778L Mutation in ATP7B With Wilson Disease in China

BackgroundWilson disease (WD) is a hereditary disorder of copper metabolism, caused by mutations in the ATP7B gene. There are more than 1000 pathogenic variants identified in ATP7B. R778L is the most common ATP7B mutation in China. MethodsTo estimate whether R778L is associated with the onset age of WD and other clinical variables. Genotyping results of ATP7B gene were collected in our 22 patients with WD. We then conducted a systematic review and meta-analysis in databases, using the keywords Wilson disease and R778L mutation. ResultsAfter the screening, a total of 23 studies were included, including 3007 patients with WD. Patients with R778L mutation presented at an earlier age (standardized mean difference [SMD] = −0.18 [95% confidence interval, −0.28 to 0.08], P = 0.0004) and had lower ceruloplasmin concentration (SMD = −0.21 [95% confidence interval, −0.40 to −0.02], P = 0.03) than the patients without the R778L mutation. However, sex (odds ratio [OR] = 1.07 [95% confidence interval, 0.89 to 1.29], P = 0.32) and first presentation were not associated with R778L mutation in WD (hepatic: OR = 1.37 [95% confidence interval, 0.87 to 2.16, P = 0.17; neurological: OR = 0.79 [95% confidence interval, 0.48 to 1.30, P = 0.35; mix: OR = 1.04 [95% confidence interval, 0.42 to 2.53, P = 0.87; asymptomatic/others: OR = 1.98 [95% confidence interval, 0.49 to 7.96, P = 0.34). ConclusionsOur results indicated that the R778L mutation is associated with an earlier presentation and lower ceruloplasmin concentration in China.