Concentrations In China Research Articles

Culture goes East: Mapping the shifting geographies of urban cultural capital through major cultural buildings

Culture has become a major component of policies to put cities on the global map. This article traces the shifting geographies of urban cultural capital using the lens of major cultural buildings, such as the Guggenheim Bilbao Museum and the Louvre Abu Dhabi, which cities often mobilise to compete for attention, reputation, tourists and investment. Based on a custom-built database containing 438 major cultural buildings opened worldwide between 1990 and 2019, this article finds a strong growth in the number and total cost of these buildings throughout the three decades, far exceeding global GDP growth. What is more, there is a geographical shift from the established cities of high culture in North America and Western Europe towards Asia, with a particular concentration in China and the Gulf region. The growth of investment in culture and its fast-changing urban geographies urge a more profound integration of culture in urban studies and a deeper consideration of the role of cultural capital in making global cities.

Diurnal hourly near-surface ozone concentration derived from geostationary satellite in China

Near-surface O3 is a harmful atmospheric pollutant and a key component of urban photochemical pollution. The availability of satellite ozone concentration products is predominantly restricted to daytime, resulting in a lack of understanding of nighttime ozone pollution (e.g. nocturnal ozone enhancement events). This research leverages 5-km bright temperatures derived from Advanced Himawari Images (AHI) on the Himawari-8 satellite, in conjunction with auxiliary data, to estimate 24-h near-surface O3 concentrations in China at a resolution of 5 km for 2020. The model achieves an average 5-fold cross-validation R2 of 0.92. Comparative analysis with on-site observations reveals that the model has low relative errors between 8:00 and 21:00 LT. The estimated O3 maps depict a consistent 24-h variation pattern, characterized by high and most fluctuating concentrations during the daytime, reaching a peak around 16:00 LT, which is primarily due to the increased photochemical reactions and the O3 accumulation in the mid-afternoon. In the daytime of summer, high surface ozone concentrations are mainly contributed by June. The elevated levels of O3 are predominantly found in central China, particularly in the Beijing-Tianjin-Hebei region and Inner Mongolia. It can also be seen that although the highest average daytime surface O3 concentration occurs in summer, the highest nighttime concentration is observed in spring, which may be attributed to the frequent occurrence of horizontal transport and vertical mixing of O3. This study holds promise in providing comprehensive round-the-clock surface O3 data across China, thereby enhancing our understanding of diurnal ground-level O3 variations.

Competition in the labor market: The wage effect of employer concentration in China

AbstractCompetition in the labor market theoretically leads to higher wages, yet empirical evidence to substantiate it, particularly in developing countries, has been sparse. Our study delves into the impact of increased competition in the labor market on workers' wages using a panel dataset from Chinese industrial firms spanning 1998 to 2013. Employing OLS and IV regressions, we demonstrate that a decrease in employer concentration is significantly linked to higher wages. The elasticities of employer concentration on wages fall within the range of −0.034 and −0.107. Additionally, our findings suggest that state‐owned enterprises gained the most from this upswing in competition, primarily due to restructuring. Furthermore, we demonstrate that total factor productivity serves as an important channel linking employer concentration to wages.

PM2.5 estimation and its relationship with NO2 and SO2 in China from 2016 to 2020

ABSTRACT The distribution of surface particulate matter (PM2.5) exhibits significant spatiotemporal patterns, especially with a spatial clustering effect. Therefore, resolving spatial characteristics is essential in the modeling process. This study proposes a space–time varying coefficients (STVC-LG) model by considering spatial effects from the perspective of eigenvector spatial filtering and incorporating spatial effects and temporal features into the LightGBM model to estimate ground PM2.5. A site-based cross-validation result of the estimated daily 1 km PM2.5 concentration in China from 2016 to 2020 shows that the proposed model offers high accuracy, an R2 of 0.88, and an RMSE of 12.21 μg/m3 (17% and 32% enhanced compared to the original LightGBM model). It uses a series of spatial eigenvectors to describe different spatial patterns. It then constructs spatial interaction terms by combining the eigenvectors and influencing variables, allowing the values of the variable to vary spatially with the eigenvectors. The obtained PM2.5, with a decreasing trend, had a clear distributional consistency with NO2 and SO2 according to the co-occurrence distribution maps, particularly in winter. In areas where high concentrations co-occur, both NO2 and SO2 concentrations are Granger-causing PM2.5 concentrations from a statistical perspective.

Spatiotemporal Patterns and Equity Analysis of Premature Mortality Due to Ischemic Heart Disease Attributable to PM2.5 Exposure in China: 2007-2022.

Long-term exposure to PM2.5 pollution increases the risk of cardiovascular diseases, particularly ischemic heart disease (IHD). Current assessments of the health effects related to PM2.5 exposure are limited by sparse ground monitoring stations and applicable disease research cohorts, making accurate health effect evaluations challenging. Using satellite-observed aerosol optical depth (AOD) data and the XGBoost-PM25 model, we obtained 1 km scale PM2.5 exposure levels across China. We quantified the premature mortality caused by PM2.5-exposure-induced IHD using the Global Exposure Mortality Model (GEMM) and baseline mortality data. Furthermore, we employed the Gini coefficient, a measure from economics to quantify inequality, to evaluate the distribution differences in health impacts due to PM2.5 exposure under varying socioeconomic conditions. The results indicate that PM2.5 concentrations in China are higher in the central and eastern regions. From 2007 to 2022, the national overall level showed a decreasing trend, dropping from 47.41 μg/m3 to 25.16 μg/m3. The number of premature deaths attributable to PM2.5 exposure increased from 819 thousand in 2007 to 870 thousand in 2022, with fluctuations in certain regions. This increase is linked to population growth and aging because PM2.5 levels have decreased. The results also indicate disparities in premature mortality from IHD among different economic groups in China from 2007 to 2022, with middle-income groups having a higher cumulative proportion of IHD-related premature deaths compared with high- and low-income groups. Despite narrowing GDP gaps across regions from 2007 to 2022, IHD consistently "favored" the middle-income groups. The highest Gini coefficient was observed in the Northwest (0.035), and the lowest was in the South (0.019). Targeted policy interventions are essential to establish a more equitable atmospheric environment.

Mapping high-resolution XCO2 concentrations in China from 2015 to 2020 based on spatiotemporal ensemble learning model

High-resolution column-averaged dry air mole fraction of CO2 (XCO2) data is crucial for understanding the spatiotemporal patterns of XCO2 and for mitigating carbon emissions. Due to the limited scanning range of sensors and strict inversion conditions, satellite-retrieved XCO2 data are often significantly incomplete. Machine learning models are widely used to fill these gaps in satellite XCO2 data. However, the limitations of individual machine learning models and the complexity of the spatial distribution of XCO₂ mean that the accuracy of XCO2 predictions still needs improvement. In this study, a new spatiotemporal stacked ensemble learning model (STEL) was developed by combining random forest (RF), extremely randomized trees (ERT), extreme gradient boosting (XGBoost), optical gradient boosting (LightGBM), and categorical boosting (CatBoost) using the stacking ensemble learning methodology. Considering the spatiotemporal heterogeneity of XCO2, a novel spatiotemporal weighting feature was constructed as part of the model's input parameters. Finally, the XCO2 observed by Orbiting Carbon Observatory 2 (OCO-2) was reconstructed using STEL, and a monthly mean XCO2 dataset covering China from 2015 to 2020 was generated at a spatial resolution of 0.1°. The results show that STEL exhibits superior performance and generalization capabilities compared to individual machine-learning models. R2 RMSE and MAPE were 0.9624, 1.0023 ppm, and 0.1583 % on the test set, and 0.8970, 1.4213 ppm, and 0.2475 % for R2, RMSE, and MAPE in ground validation, respectively. In 10-fold cross-validation, STEL's RMSE was reduced by 9.52 % compared to the best-performing single model (RF). The spatiotemporal trend of CO2 in China from 2015 to 2020 was analyzed using STEL XCO2 data. The results indicate that this dataset accurately reflects the spatiotemporal heterogeneity of XCO2 distribution at a fine scale. Overall, XCO2 exhibited a spatiotemporal pattern of “high in the east and low in the west” and “high in spring and low in summer.” Except in summer, high XCO₂ values were mainly distributed in the North China Plain. XCO2 trends and hotspots showed considerable spatial variation. The Pearl River Delta and Yangtze River Delta urban agglomerations have the fastest XCO2 growth rates, and the distribution of XCO2 hotspots is consistent with the distribution of population and economic centers. In the sparsely populated northwest of China, XCO2 is growing rapidly due to increased thermal power generation and coal mining. XCO2 hotspots in Northwest China are mainly located in Xinjiang, Ningxia, and Inner Mongolia. The methodology and data presented are useful for further research on carbon emissions, carbon sinks, and climate change.

Retrieving ground-level PM2.5 concentrations in China (2013–2021) with a numerical-model-informed testbed to mitigate sample-imbalance-induced biases

Abstract. Ground-level PM2.5 data derived from satellites with machine learning are crucial for health and climate assessments. However, uncertainties persist due to the absence of spatially covered observations. To address this, we propose a novel testbed using nontraditional numerical simulations to evaluate PM2.5 estimation across the entire spatial domain. The testbed emulates the general machine-learning approach by training the model with grids corresponding to ground monitoring sites and subsequently testing its predictive accuracy for other locations. Our approach enables comprehensive evaluation of various machine-learning methods' performance in estimating PM2.5 across the spatial domain for the first time. Unexpected results are shown in the application in China, with larger absolute PM2.5 biases found in densely populated regions with abundant ground observations across all benchmark models due to the higher baseline concentration, though the relative error (approximately 20 %) is smaller compared to that in rural areas (over 50 %). The imbalance in training samples, mostly from urban areas with high emissions, is the main reason, leading to significant overestimation due to the lack of monitors in downwind areas where PM2.5 is transported from urban areas with varying vertical profiles. Our proposed testbed also provides an efficient strategy for optimizing model structure or training samples to enhance satellite-retrieval model performance. Integration of spatiotemporal features, especially with conventional neural network (CNN)-based deep-learning approaches like the residual neural network (ResNet) model, has successfully mitigated PM2.5 overestimation (by 5–30 µg m−3) and the corresponding exposure (by 3 million people ⋅ µg m−3) in the downwind area over 9 years (2013–2021) compared to the traditional approach. Furthermore, the incorporation of 600 strategically positioned ground monitoring sites identified through the testbed is essential for achieving a more balanced distribution of training samples, thereby ensuring precise PM2.5 estimation and facilitating the assessment of the associated impacts in China. In addition to presenting the retrieved surface PM2.5 concentrations in China from 2013 to 2021, this study provides a testbed dataset derived from physical modeling simulations which can serve to evaluate the performance of data-driven methodologies, such as machine learning, in estimating spatial PM2.5 concentrations for the community (Li et al., 2024a; https://doi.org/10.5281/zenodo.11122294).

Long-term spatiotemporal distribution characterization of atmospheric black carbon MERRA-2 concentration over China

Black carbon (BC), a component of atmospheric aerosols, strongly adsorbs solar radiation, thereby influencing atmospheric stability and air quality. In this study, the spatiotemporal distribution, historical trends, and influencing factors of BC concentration in China from 1980 to 2020 have been investigated using MERRA-2 (Modern-era Retrospective Analysis for Research and Applications, Vision2) reanalysis dataset, population distribution, and industrial statistics data. The annual total MERRA-2 BC concentration in China (averaging 1.08 μg/m3 over the study period) exhibited three distinct phases: slow growth (1980–1999, 0.02 μg/m3/year), rapid growth (2000–2007, 0.61 μg/m3/year), and gradual decline (2008–2020, -0.014 μg/m3/year). The BC concentration was consistently higher in autumn and winter than in spring and summer, being lowest in July (9.6 μg/m3) and highest in December (14 μg/m3). Spatially, the annual average BC concentration from 1980 to 2020 followed the order: central Yangtze River region (3.14 μg/m3) > eastern coastal region (2.72 μg/m3) > northeastern region (1.40 μg/m3) > western riverine region (0.69 μg/m3) > northwestern frontier region (0.31 μg/m3). High BC-concentration areas, mainly in the central and eastern regions, correlate with regions of vigorous human activities and high industrialization levels, confirming that human activity markedly influences BC pollution. Since 2013, the implementation of emission control strategies and adjustments in the energy structure in China had led to a significant decline in BC concentration. By revealing the spatiotemporal distribution and trends of BC concentration in China, this study provides valuable scientific insights for atmospheric science, environmental protection, and air pollution.

Study on Vertical Distribution of Sediment Concentration in the Tidal Estuary

The vertical distribution of sediment concentration in estuaries is the core issue of suspended sediment transport. At present, there are many researches on the vertical distribution of sediment concentration in rivers, and a lot of results have been achieved, but that in estuaries is seldom studied. The Yellow River Estuary (YRE) and the Qiantang River Estuary (QRE) are typical tidal estuaries with strong erosion/deposition and high concentration in China. Under the action of alternating tidal flow, the suspended sediment transport shows significant time-varying and non-equilibrium transport characteristics. The vertical distribution of sediment concentration is quite different from that of ordinary rivers. This paper analyzes the vertical distribution of sediment concentration under the action of tidal currents based on the theory of sediment movement and diffusion. Compared with equilibrium sediment transport, the vertical distribution of sediment concentration only differs by one vertical distribution coefficient. Based on the analysis of the measured data, it is found that the vertical distribution of sediment concentration in the YRE and QRE is mostly exponential. Firstly, the multivariate relationship between the vertical distribution coefficient and the sediment saturation and suspension index is established by means of the measured water and sediment data of the QRE. The correlation coefficient <I>R</I><sup>2</sup> was above 0.64, which has a good correlation. Finally, the established relationship was validated using measured water and sediment data from the YRE, and the results were basically consistent, indicating that the established formula for vertical distribution of sediment concentration is suitable for general tidal estuaries, and has a certain reference value for the simulation of sediment transport in the YRE and QRE.

Combating air pollution significantly reduced air mercury concentrations in China.

In the past decade, China has motivated proactive emission control measures that have successfully reduced emissions of many air pollutants. For atmospheric mercury, which is a globally transported neurotoxin, much less is known about the long-term changes in its concentrations and anthropogenic emissions in China. In this study, over a decade of continuous observations at four Chinese sites show that gaseous elemental mercury (GEM) concentrations continuously increased until the early 2010s, followed by significant declines at rates of 1.8%-6.1% yr-1 until 2022. The GEM decline from 2013 to 2022 (by 38.6%±12.7%) coincided with the decreasing concentrations of criteria air pollutants in China and were larger than those observed elsewhere in the northern hemisphere (5.7%-14.2%). The co-benefits of emission control measures contributed to the reduced anthropogenic Hg emissions and led to the GEM decline in China. We estimated that anthropogenic GEM emissions in China were reduced by 38%-50% (116-151 tons) from 2013 to 2022 using the machine-learning and relationship models.

Monitoring Methane Concentrations with High Spatial Resolution over China by Using Random Forest Model

Atmospheric methane is one of the major greenhouse gases with a drastic impact on climate change. This study developed a random forest model to obtain a daily 5 km resolution atmospheric methane concentration dataset with full spatial coverage (100%) from 2019 to 2021 in mainland China, thereby filling the gap in the methane product data from the Tropospheric Monitoring Instrument (TROPOMI). The coefficients of determination for a sample-based and spatial-based cross-validation are 0.97 and 0.93, respectively. The average deviation of the seamless methane product reconstructed by the random forest model is less than 1%, validated with the measured methane concentration data from the Total Carbon Column Observing Network sites. Methane concentrations in China show a distribution of high in the east and south and low in the west and north. The high-concentration areas include Central China, the Sichuan Basin, the Pearl River Delta, and the Yangtze River Delta. In terms of time scale, the methane concentration has evident seasonal variation, as it is low in spring (average 1852 ppb) and winter (average 1881 ppb) and high in summer (average 1885 ppb) and autumn (average 1886 ppb). This is mainly due to the significant increase in emissions from rice cultivation and wetlands during the summer and autumn. During the COVID-19 pandemic, the methane concentration decreases significantly and then starts to return to normal around 70 days after the Lunar New Year, indicating that the seamless methane product can potentially detect anomalous changes in methane concentration.

Ozone Pollution in China Affected by Climate Change in a Carbon Neutral Future as Predicted by a Process‐Based Interpretable Machine Learning Method

AbstractOzone (O3) pollution is a severe air quality issue in China, posing a threat to human health and ecosystems. The climate change will affect O3 levels by directly changing physical and chemical processes of O3 and indirectly changing natural emissions of O3 precursors. In this study, near‐surface O3 concentrations in China in 2030 and 2060 are predicted using the process‐based interpretable Extreme Gradient Boosting (XGBoost) model integrated with multi‐source data. The results show that the climate‐driven O3 levels over eastern China are projected to decrease by more than 0.4 ppb in 2060 under the carbon neutral scenario (SSP1‐1.9) compared with the high emission scenario (SSP5‐8.5). Among this reduction, 80% is attributed to the changes in physical and chemical processes of O3 related to a cooler climate, while the remaining 20% is attributed to the reduced biogenic isoprene emissions.

Seamless reconstruction and spatiotemporal analysis of satellite-based XCO2 incorporating temporal characteristics: A case study in China during 2015–2020

Carbon dioxide (CO2) is a crucial greenhouse gas, and its concentration and spatiotemporal characteristics are among the principal sources of uncertainty in global warming assessments. Satellite remote sensing is a widely adopted, high-accuracy approach for monitoring atmospheric CO2. However, limited swath width and cloud cover significantly reduce satellite observation coverage. This study addresses the temporal changes in CO2 concentration and utilizes a machine learning-based fusion of multiple data sources to generate daily, full-coverage, 0.05° spatial resolution column-averaged CO2 concentration data for China from 2015 to 2020. Ten-fold cross-validation yielded a determination coefficient R2 of 0.97, root mean square error of 0.92 ppm, and mean absolute error of 0.59 ppm. Compared to other datasets, this study’s dataset exhibits superior accuracy and spatiotemporal detail. Using the produced CO2 concentration data in this study, we conducted a spatiotemporal analysis of CO2 concentrations in China. The results indicate that, in general, the Western region exhibits a higher growth rate in CO2 concentration than the Eastern and Central regions, with areas of lower CO2 concentration experiencing higher growth rates while regions with higher CO2 concentration have lower growth rates. Moreover, the highest increase in CO2 concentration occurred in 2016, with a substantial decrease in CO2 concentration growth observed in 2018. Notably, the reduction in CO2 concentration in the Qinghai-Tibet Plateau region during the summer is considerably smaller than in other regions, possibly due to atmospheric transport from the Indian Peninsula.

Excess deaths and loss of life expectancy attributed to long-term NO2 exposure in the Chinese elderly

BackgroundEvidence linking nitrogen dioxide (NO2) air pollution to life span of high-vulnerability older adults is extensively scarce in low- and middle-income countries. This study seeks to quantify mortality risk, excess deaths, and loss of life expectancy (LLE) associated with long-term exposure to NO2 among elderly individuals in China. MethodsA nationwide dynamic cohort of 20352 respondents ≥65 years old were enrolled from the China Longitudinal Health and Longevity Survey during 2005–2018. Residential exposures to NO2 and co-pollutants were assessed by well-validated spatiotemporal prediction models. A Cox regression model with time-dependent covariates was utilized to quantify the association of all-cause mortality with NO2 exposure, controlling for confounders such as demographics, lifestyle, health status, and ambient temperature. NO2-attributable deaths and LLE were evaluated for the years 2010 and 2020 based on the pooled NO2-mortality relation derived from multi-national cohort investigations. Decomposition analyses were conducted to dissociate net shift in NO2-related deaths between 2010 and 2020 into four primary contributing factors. ResultsA total of 14313 deaths were recorded during follow-up of approximately 100 hundred person-years (median 3.6 years). We observed an approximately linear relationship (nonlinear P = 0.882) of NO2 exposure with all-cause death across a broad range from 6.6 to 95.7 μg/m3. Every 10-μg/m3 rise in yearly average NO2 concentration was linked to a hazard ratio (HR) of 1.045 (95% confidence interval [CI]: 1.031–1.059). In the updated meta-analysis of this study and 9 existing cohorts, we estimated a pooled HR of 1.043 (95% CI: 1.023–1.063) for each 10-μg/m3 growth in NO2. Reaching a 10-μg/m3 counterfactual target of NO2 concentration in China could avoid 0.33 (95% empirical CI: 0.19–0.49) million premature deaths and an LLE of 0.40 (95% empirical CI: 0.23–0.59) years in 2010, which greatly dropped to 0.24 (95% empirical CI: 0.14–0.36) million deaths and 0.21 (95% empirical CI: 0.12–0.31) years of LLE in 2020. The net fall in NO2-attributable deaths (–26.8%) between 2010 and 2020 was primarily driven by the declines in both NO2 concentration (–41.6%) and mortality rate (–27.1%) under population growth (+41.0%) and age structure transition (+0.9%). ConclusionsOur findings provide national evidence for increased risk of premature death and loss of life expectancy attributed to later-life NO2 exposure among the elderly in China. In an accelerated aging society, strengthened clean air actions should be formulated to minimize the health burden and regional inequality in NO2-attributable mortality.

Global health benefits of shipping emission reduction in early 2020

The impact of COVID-19 lockdown on the absolute concentration of air pollutant has been widely investigated, whereas the impact of lockdown on global shipping-related air pollutants still remained poorly understood because the contribution of individual source cannot be observed directly. In our study, we employed an efficient chemical transport model (GEOS-Chem) to quantify the shipping-related PM2.5, NO2, and O3 levels in early 2019 and 2020, and assessed the impact of COVID-19 lockdown on global shipping-related air pollutants and health effects. The result suggested that shipping-related global PM2.5, NO2, and O3 levels reduced by 14%, 85%, and 1%, respectively. Furthermore, the decreasing ratios of air pollutants in difference regions often suffered from large spatial discrepancy. PM2.5 and NO2 levels in China, India, Europe, and the United States decreased by 6.5% (NO2: 9%), 7.4% (8%), 11% (8.6%), and 15% (10%), which could be caused by dense shipping activities during the non-lockdown period. For O3 concentrations, the shipping-related concentrations in China, India, Europe, and the United States showed 0.7%, −12.9%, 16.6%, and −4.8% during lockdown period, respectively. The relatively slight O3 reduction might be linked with more significant decrease of NOx emission than VOCs. The changes of air pollutant concentrations inevitably led to the variations of health benefits. Globally, the all-cause mortalities caused by shipping-related PM2.5 exposures reached 40,464 (95% CI: 26,546, 54,382) and 39,035 (95% CI: 25,135, 52,935) cases in early 2019 and 2020, respectively. The combined health benefits of PM2.5, NO2, and O3 linked with shipping emission reduction in China, India, Europe, and the United States reached 476 (95% CI: 131, 822), 3341 (95% CI: 3,177, 3505), 2194 (95% CI: −970, 5358), and 311 (95% CI: 242, 379), respectively. Our study confirms the potentially considerable effects of shipping emission on multiple air pollutants and health impacts, which could provide valuable information for air quality management.

Using sulfur isotopes to constrain the sources of sulfate in PM2.5 during the winter in Jiaozuo City

Sulfate (SO42−) is one of the main driving forces behind the explosive growth of atmospheric fine particulate matter (PM2.5), and it scatters sunlight, contributing to atmospheric cooling. Investigating the sources and transformation of SO42− is crucial for understanding PM2.5 pollution and its implications for climate change. Despite a continuous decline in PM2.5 concentrations in China since 2015, it remains unclear whether there have been changes in the sources and transformation processes of SO42−. Furthermore, previous studies that utilized isotope techniques for source identification have often overlooked the impact of isotope fractionation. This study focuses on atmospheric PM2.5 in Jiaozuo based on samples collected from December 2017 to February 2018. The analysis encompassed both the water-soluble ions and sulfur isotope compositions of SO42− (δ34S–SO42−). By employing a Bayesian isotope mixing model, quantitative analysis of SO42− sources in winter atmospheric PM2.5 in the study area was conducted. The findings revealed that the δ34S–SO42− values of winter PM2.5 ranged from −0.1‰ to 9.8‰, with an arithmetic mean of 4.9 ± 1.5‰ (n = 74). After considering sulfur isotope fractionation effects and the impact of primary SO42−, the calculated results suggested that on average, the contributions of primary SO42−, coal combustion, vehicle exhaust, biomass burning, and oil combustion to the SO42− in winter PM2.5 were 24.9 ± 15.7%, 41.2 ± 2.6%, 27.9 ± 5.3%, 4.8 ± 3.7%, and 1.2 ± 0.7%, respectively. Compared with the clean period, the contribution of the primary SO42− decreased significantly during haze episodes, while the contribution of the secondary SO42− increased. The contribution of coal combustion to the secondary SO42− increased notably by 21.9%, while the contributions from other secondary sulfate remained relatively stable. The lower δ34S values of the atmospheric PM2.5 in Jiaozuo affirm the significant impact of the anthropogenic SO2 input on the sulfur isotope composition of the atmospheric sulfate. Analysis confirmed that the decrease in the contribution of coal combustion to atmospheric SO42− in the study area, indicating the positive effects of regional air pollution control measures. Nevertheless, continued attention should be paid to the influences of coal combustion and vehicle exhaust on regional air pollution.

Assessing the Nonlinear Relationship between Land Cover Change and PM10 Concentration Change in China

Inhalable particulate matter (PM10) is a major air pollutant that has significant impacts on environmental climate and human health. Land-cover change is also a key factor influencing changes in atmospheric pollution. Changes in land-cover types can lead to changes in the sources and sinks of air pollutants, thus affecting the spatial distribution of PM10, which poses a threat to human health. Therefore, exploring the relationship between PM10 concentration change and land-cover change is of great significance. In this study, we constructed an extreme randomized trees model (ET) based on ground PM10 monitoring data, satellite-based aerosol optical depth (AOD) data, and auxiliary data including meteorological, vegetation, and population data to retrieve ground-level PM10 concentrations across China. The coefficient of determination (R2), the mean absolute error (MAE), and the root mean square error (RMSE) of the model were 0.878, 5.742 μg/m3, and 8.826 μg/m3, respectively. Based on this, we analyzed the spatio-temporal distribution of PM10 concentrations in China from 2015 to 2021. High PM10 values were mainly observed in the desert areas of northwestern China and the Beijing–Tianjin–Hebei urban agglomeration. The majority of China showed a significant decrease in PM10 concentrations. Additionally, we also analyzed the nonlinear response mechanism of the PM10 concentration change to land-cover change. The PM10 concentration is sensitive to forest and barren land change. Therefore, strengthening the protection of forests and desertification control can significantly reduce air pollution. Attention should also be paid to emission management in agricultural activities and urbanization processes.

Uncovering the evolution of ozone pollution in China: A spatiotemporal characteristics reconstruction from 1980 to 2021

Ozone pollution emerged as a significant environmental concern in China in recent years. Given the enduring impact of anthropogenic activities on the atmosphere, understanding the long-term evolutionary patterns of ozone pollution is crucial for effective pollution mitigation strategies. However, there have been limited studies to reconstruct spatiotemporal continuity ozone concentrations in China since 1980. In this research, monthly MDA8 ozone concentrations from 1980 to 2021 were first estimated by reproducing the numerical relations between ozone pollution and the atmospheric environment based on a machine learning approach. The verification results from temporal, spatial, and sample-based cross-validation methods demonstrated the model performance in simulating MDA8 ozone concentration spatiotemporally, with correlation coefficients (R) ranging from 0.76 to 0.79, mean square error (RMSE) ranging from 22.72 μg·m−3 to 24.28 μg·m−3, and mean absolute error (MAE) ranging from 17.06 μg·m−3 to 18.25 μg·m−3. The evolution of ozone pollution in China can be delineated into two phases, characterized by an increasing trend since 2001, coinciding with significant rises in average annual temperature and hot weather frequency over the past four decades. The 90th percentile of ozone concentration from 2001 to 2021 increased by 1.79% compared to the period from 1980 to 2001. Notably, ozone episodes have expanded beyond summer months, partly due to global warming and frequent extreme climate events, contributing to an annual temperature increase of 0.03 °C. Despite several pollutant emission restrictions since 2013, the ozone pollution in China has persisted due to the increased emissions of VOCs. Spatially, severe ozone pollution hotspots are evident in Xinjiang province, the Jing-Jin-Ji urban agglomeration, and Northeast China, with notable increases observed in Tibet and the Guangdong-Hong Kong-Macao Greater Bay Area over the past six years. Moreover, the influence factors were identified by the average reduction of node impurity calculation. The evolution of ozone pollution was inextricably linked to solar radiation and atmospheric temperature, which played pivotal positive roles in ozone formation. Whilst, there is significant heterogeneity in the effect of meteorological conditions on ozone concentrations due to the disparity between land and oceanic climates.

Challenges and perspectives of air pollution control in China

Air pollution is one of the most challenging environmental issues in the world. China has achieved remarkable success in improving air quality in last decade as a result of aggressive air pollution control policies. However, the average fine particulate matter (PM2.5) concentration in China is still about six times of the World Health Organization (WHO) Global Air Quality Guidelines (AQG) and causing significant human health risks. Extreme emission reductions of multiple air pollutants are required for China to achieve the AQG. Here we identify the major challenges in future air quality improvement and propose corresponding control strategies. The main challenges include the persistently high health risk attributed to PM2.5 pollution, the excessively loose air quality standards, and coordinated control of air pollution, greenhouse gases (GHGs) emissions and emerging pollutants. To further improve air quality and protect human health, a health-oriented air pollution control strategy shall be implemented by tightening the air quality standards as well as optimizing emission reduction pathways based on the health risks of various sources. In the meantime, an “one-atmosphere” concept shall be adopted to strengthen the synergistic control of air pollutants and GHGs and the control of non-combustion sources and emerging pollutants shall be enhanced.

Financial development and population growth: Evidence from bank concentration in China

Using 288 cities in China from 2000 to 2018, we show that a one-standard-deviation increase in bank concentration is associated with 7.9% lower population growth relative to the mean. This effect holds robust after controlling for city characteristics. Cities with higher bank concentration have less entrepreneurship and labor income. We use China's 2009 bank deregulation as a quasi-natural experiment, and find that the shock leads to significant increases in population growth for cities with new entry of joint-stock banks and city commercial banks. We argue the deregulation increases credit supply, which relieves local households' financial constraints and improves population growth.