Search Results (31)

In recent years, coal-combustion-related air pollution has declined markedly, whereas tropospheric ozone (O₃) pollution has emerged as a growing environmental concern. Long-term exposure to O₃ can severely impact human health and ecosystems, constraining socioeconomic development. The Fenwei Plain has complex topographical conditions and a relatively simple industrial structure, and at present, O₃ is one of the main pollutants affecting air quality in this region. Therefore, studying the distribution of O₃ pollution in the Fenwei Plain can provide a reference for developing plans to control O₃ pollution in the area, which is important for safeguarding local public health and economic development. Currently, the number of pollutant monitoring stations in China is limited, spatially discontinuous, and significantly affected by environmental factors, making it difficult to obtain high-precision, large-scale observational data. Satellite-based remote sensing provides broad spatial coverage and is free from topographic constraints, thereby serving as an effective complement to ground-based monitoring networks. This provides important technical support for studying the distribution characteristics of O₃ pollution and its associated health risks. This study focuses on the Fenwei Plain, utilizing machine learning models to estimate continuous O₃ concentrations from 2015 to 2022 and analyze the spatiotemporal distribution of O₃. Based on this, an assessment and analysis of the health risks associated with near-surface O₃ exposure in the study area will be conducted, incorporating the population exposed in the Fenwei Plain and individuals with chronic obstructive pulmonary disease (COPD). Full article

The Fenwei Plain (FWP), one of China’s most polluted regions, has experienced severe ozone (O₃) pollution in recent years. Volatile organic compounds (VOCs), key O₃ precursors, undergo significant photochemical degradation, yet their loss and the implications for source apportionment and ozone formation potential (OFP) in this region remain unclear. This study conducted summertime VOC measurements in two industrial cities in the FWP, Hancheng (HC) and Xingping (XP), to quantify photochemical losses of VOCs and assessed their impact on source attribution and OFP with photochemical age-based parameterization methods. Significant VOC photochemical losses were observed, averaging 3.6 ppbv (7.1% of initial concentrations) in HC and 1.9 ppbv (5.6%) in XP, with alkenes experiencing the highest depletion (22–30%). Source apportionment based on both initial (corrected) and observed concentrations revealed that industrial sources (e.g., coking, coal washing, and rubber manufacturing) dominated ambient VOCs. Ignoring photochemical losses underestimated contributions from natural gas combustion and biogenic sources, while it overestimated the secondary source. OFP calculated with lost VOCs (OFP_loss) reached 34 ppbv in HC and 15 ppbv in XP, representing 20% and 25% of OFP based on observed concentrations, respectively, with reactive alkenes accounting for over 90% of OFPloss. The results highlight the importance of accounting for VOC photochemical losses for accurate source identification and developing effective O₃ control strategies in the FWP. Full article

Understanding the relationship between regional vegetation dynamics and water resources is essential for improving integrated vegetation–water management, enhancing ecosystem services, and advancing the sustainable development of ecological–economic–social systems. As China’s second largest river basin, the Yellow River Basin (YRB) is ecologically fragile and experiences severe water scarcity. Vegetation changes further intensify conflicts between water supply and demand. To investigate the evolution and interaction mechanisms between vegetation and water resources in the YRB, this study uses the InVEST model to simulate annual water yield (Wyield) from 1982 to 2020 and applies the Dimidiate Pixel Model (DPM) to estimate fractional vegetation cover (FVC). The Theil–Sen method is applied to quantify the spatiotemporal trends of Wyield and FVC. A pixel-based second-order partial correlation analysis is performed to clarify the intrinsic relationship between FVC and Wyield at the grid scale. The main conclusions are as follows: (1) During the statistical period (1982–2020), the multi-year average annual Wyield in the YRB was 73.15 mm. Interannual Wyield showed a clear fluctuating trend, with an initial decline followed by a subsequent increase. Wyield showed marked spatial heterogeneity, with high values in the southern upper reaches and low values in the Longzhong Loess Plateau and Hetao Plain. During the same period, about 68.74% of the basin experienced increasing Wyield, while declines were concentrated in the upper reaches. (2) The average FVC across the basin was 0.51, showing a significant increasing trend during the statistical period. The long-term average FVC showed significant spatial heterogeneity, with high values in the Fenwei Plain, Shanxi Basin, and Taihang Mountains, and low values in the Loess Plateau and Hetao Plain. Spatially, 68.74% of the basin exhibited significant increases in FVC, mainly in the middle and lower reaches, while decreases were mostly in the upper reaches. (3) Areas with significant FVC–Wyield correlations covered a small portion of the basin: trade-off regions made up 10.35% (mainly in the southern upper reaches), and synergistic areas accounted for 5.26% (mostly in the Hetao Plain and central Loess Plateau), both dominated by grasslands and croplands. Mechanistic analysis revealed spatiotemporal heterogeneity in FVC–Wyield relationships across the basin, influenced by both natural drivers and anthropogenic activities. This study systematically explores the patterns and interaction mechanisms of FVC and Wyield in the YRB, offering a theoretical basis for regional water management, ecological protection, and sustainable development. Full article

Although China has achieved substantial reductions in particulate matter pollution, continually rising ground-level ozone now constitutes the primary challenge to further air-quality improvements. A systematic assessment of the long-term spatiotemporal behavior of ozone (O₃) and its links to meteorology and emissions is still lacking. Here, we develop a novel framework that couples Kolmogorov–Zurbenko (KZ) filtering with an optimized random forest (RF) regression model to examine daily maximum 8 h average ozone (O₃-8h) in 372 Chinese cities from 2013 to 2023. The approach quantitatively disentangles meteorological and emission contributions at the national scale, overcoming the limitations of traditional linear methods in capturing non-linear processes. Long-term components explain, in general, <40% of total O₃ variance. In eastern urban agglomerations, long-term meteorological factors—particularly temperature and surface ultraviolet radiation—account for up to 80% of the trend, whereas long-term emission contributions remain modest (≈5–6%), with pronounced signals in the Beijing–Tianjin–Hebei and Fenwei Plain regions. Empirical orthogonal function analysis further reveals distinct spatial patterns of emission influence: long-term O₃ trends in mega-cities such as Beijing, Tianjin, and Shanghai are driven mainly by local emissions (1.5–3% contribution), while key transport hubs including Xi’an, Tangshan, and Langfang are markedly affected by traffic-related emissions (>2%). These findings clarify the heterogeneous mechanisms governing O₃ formation across China and provide a scientific basis for designing and implementing the next phase of region-specific, joint prevention-and-control policies. Full article

The Taihang Mountains serve as a critical geographical barrier in northern China, delineating two major 2.5-micrometer particulate matter (PM_2.5) pollution hotspots in the Beijing–Tianjin–Hebei region and the Fenwei Plain. This study examines the underlying mechanisms and interregional dynamic transport pathways of a severe PM_2.5 pollution event that occurred in the urban agglomerations of the Central Taihang Mountains (CTHM) from 8–13 December 2021. The WRF-HYSPLIT simulation was employed to analyze a broader range of potential pollution sources and transport pathways. Additionally, a new river network analysis module was developed and integrated with the Atmospheric Pollutant Transport Quantification Model (APTQM). This module is capable of identifying localized, small-scale (interplot) pollution transport processes, thereby enabling more accurate identification of potential source areas and transport routes. The findings indicate that the persistence of low temperatures, high humidity, and stagnant atmospheric conditions facilitated both the local accumulation and cross-regional transport of PM_2.5. The eastern urban agglomerations, such as Shijiazhuang and Xingtai, were predominantly influenced by northwesterly air masses originating from Inner Mongolia and Shanxi, with pollution levels intensified due to topographic blocking and subsidence effects east of the Taihang Mountains. In contrast, western urban centers, including Taiyuan and Yangquan, experienced pollution primarily from short-range transport within the Fen River Basin, central Inner Mongolia, and Shaanxi, compounded by basin-induced stagnation. Three principal transport pathways were identified: (1) a northwestern pathway from Inner Mongolia to Hebei, (2) a southwestern pathway following the Fen River Basin, and (3) a southward inflow from Henan. The trajectory analysis revealed that approximately 68% of PM_2.5 in eastern receptor cities was transported through topographic channels within the Taihang Transverse Valleys, whereas 43% of pollution in the western regions originated from intra-basin emissions and basin-capture circulation. Furthermore, APTQM-PM_2.5 identified major pollution source regions, including Ordos and Chifeng in Inner Mongolia, as well as Taiyuan and the Fen River Basin. This study underscores the synergistic effects of basin topography, regional circulation, and anthropogenic emissions in shaping pollution distribution patterns. The findings provide a scientific basis for formulating targeted, regionally coordinated air pollution mitigation strategies in complex terrain areas. Full article

PM_2.5 and volatile organic compounds (VOCs) have been identified as the primary air pollutants affecting the Fenwei Plain (FWP), necessitating urgent measures to improve its air quality. To gain a deeper understanding of the formation mechanisms of these pollutants, this study employed various methods such as HYSPLIT, PCT, and PMF for analysis. Our results indicate that the FWP is primarily impacted by PM_2.5 from the southern Shaanxi air mass and the northwestern air mass during winter. In contrast, during summer, it is mainly influenced by O₃ originating from the southern air mass. Specifically, high-pressure fronts are the dominant weather pattern affecting PM_2.5 pollution in the FWP, while high-pressure backs predominately O₃ pollution. Regarding the sources of PM_2.5, secondary nitrates, vehicle exhausts, and secondary sulfates are major contributors. As for volatile organic compounds, liquefied petroleum gas sources, vehicle exhausts, solvent usage, and industrial emissions are the primary sources. This study holds crucial scientific significance in enhancing the regional joint prevention and control mechanism for PM_2.5 and O₃ pollution, and it provides scientific support for formulating effective strategies for air pollution prevention and control. Full article

Solar radiation is the fundamental energy source of climate change, which has a significant impact on the generations of secondary fine particulate matter (PM_2.5) and ozone (O₃) in the atmosphere. Additionally, surface solar radiation is also affected by the concentration of PM_2.5, which in turn affects the generation of O₃. To clarify the relationships among the O₃, PM_2.5 and the total radiation intensity, this study analyzes their temporal and spatial variation trends from 2017 to 2019. Meanwhile, as a common precursor of O₃ and PM_2.5, concentration variations in nitrogen dioxide (NO₂) are discussed as well in this study. The results showed the following: (1) There are significant positive correlations between the O₃-8 h concentrations and the total radiation intensities in critical regions, especially in the “2 + 26” cities, Fen-Wei Plain and Yangtze River Delta. (2) The decrease in PM_2.5 concentrations is in good agreement with the trend of NO₂ concentrations, while the response of O₃ concentration to the NO₂ concentration variation differs in different regions, except in the Pearl River Delta. (3) In addition to the meteorological factors, changes in the concentrations and ratios of precursors such as NO₂ and volatile organic compounds (VOCs) likely contribute to the observed fluctuations in O₃ concentrations in recent years. Full article

The coordinated development of atmospheric pollution and socio-economic growth plays a core role in the sustainable development of cities and regions. The relationship between socio-economic growth and air pollution can be described using decoupling analysis. The seven key regions of China (168 cities), including Beijing–Tianjin–Hebei and its surrounding areas (BTHSR), the Yangtze River Delta region (YRDR), the Fen-Wei Plain (FWP), the Chengdu–Chongqing region (CCR), the urban agglomeration of the middle reaches of the Yangtze River (MLRYR), the Pearl River Delta region (PRDR), and other provincial capitals and municipalities with specialized plans (OPCCSP) were taken as targets to investigate the spatiotemporal evolution characteristics of AQI values and PM_2.5 concentrations from 2014 to 2022. Then, the decoupling relationship between the AQI/PM_2.5 and the socio-economic growth index (SEGI) in these key regions was deeply researched by the Tapio decoupling model. The main results were as follows: (1) Although the continuous improvement in air quality was observed in these seven key regions in China, the PM_2.5 concentration in the BTHSR and FWP was still higher than 35 μg·m⁻³. The AQI showed a spatial pattern of high in the north and low in the south, and the distribution of PM_2.5 in China was high in the east and low in the west. (2) The decoupling degree between air pollution and socio-economic growth was relatively high in the PRDR and YRDR. In contrast, the degree of decoupling was poor in the FWP and OPCCSP. The decoupling states were primarily influenced by industrial structure, energy consumption, and urbanization. (3) The decoupling of air pollution from socio-economic growth was in a strong decoupling state throughout the majority of the study period, achieving a comparatively ideal decoupling state in 2018. However, the overall decoupling states of the seven regions were not sustainable, and the decoupling stability was relatively poor. During the research period, the decoupling state between socio-economic growth and air pollution changed and was unstable. Full article

The study of the temporal and spatial characteristics of air pollutants in heavily polluted cities is extremely important for analyzing the causes of pollution and achieving a viable means of control. Such characteristics in the case of Xi’an, a typical heavily polluted city in Fenwei Plain, China, have remained unclear due to limitations in data accuracy and research methods. The monthly, daily, and hourly patterns of O₃ and particulate matter (PM_2.5 and PM₁₀) are analyzed in this study using on-site data provided by an urban air quality monitoring network. The analysis of variance (ANOVA) method was used to compare differences in pollutant concentrations during different seasons and time periods. The spatial distributions of O₃, PM_2.5, and PM₁₀ at different time points following interpolation of the air quality monitoring sites have been analyzed. The results show that the O₃ concentration from 12 p.m. to 3 p.m. was significantly higher than that in the morning and evening, and the concentrations of PM_2.5 and PM₁₀ from 7 p.m. to 10 p.m. were significantly higher than those in the morning and afternoon. The number of qualified days for PM_2.5 was less than 30 and unqualified days for O₃ was more than 100 in 2019. There is a potential risk of exposure to pollution with associated health risks. Even on the same day, the spatial pollutant distributions at different time points can differ significantly. This study provides a scientific basis for reducing O₃ and particulate matter exposure. Outdoor activities in the morning in summer are more beneficial to reduce O₃ exposure, and outdoor activities should be curtailed in the evening in winter to reduce particulate exposure. This study provides a scientific basis for the government to formulate public health policies to reduce pollution exposure from outdoor activities. Full article

Regional haze often occurs after the New Year holiday. To explore the characteristics of PM_2.5 pollutions under the influence of the New Year’s Day effect, this study analyzed the spatiotemporal changes relating to PM_2.5 during and around the New Year’s Day holiday in China from 2015 to 2022, and used the Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ) model to study the effects of human activities and meteorological factors on PM_2.5 pollutions, as well as the differences in the contributions of different industries to PM_2.5 pollutions. The results show that for the entire study period (i.e., before, during, and after the New Year’s Day holiday) from 2015 to 2022, the average concentrations of PM_2.5 in China decreased by 41.9% overall. In 2019~2022, the New Year’s Day effect was significant, meaning that the average concentrations of PM_2.5 increased by 18.9~46.8 μg/m³ from before to after the New Year’s Day holiday, with its peak occurring (64.3~74.9 μg/m³) after the holiday. In terms of spatial differences, the average concentrations of PM_2.5 were higher in the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and central China. Moreover, the Beijing–Tianjin–Hebei region and its surrounding areas, the Chengdu–Chongqing region, the Fenwei Plain, and the middle reaches of the Yangtze River region were greatly affected by the New Year’s Day effect. Human activities led to higher increases in PM_2.5 in Henan, Hubei, Hebei, and Anhui on 3 and 4 January 2022. If the haze was accompanied by cloudy days or weak precipitation, the accumulation of surface water vapor and atmospheric aerosols further increased the possibility of heavy pollution. It was found that, for the entire study period, PM_2.5 generated by residential sources contributed the vast majority (60~100 μg/m³) of PM_2.5 concentrations, and that the main industry sources that caused changes in time distributions were industrial and transportation sources. Full article

Hyperspectral remote sensing technology is an advanced and powerful tool that enables fine identification of the numerous soil reflectance spectrum characteristics. Heavy metal(loid)s (HMs) are the primary pollutants affecting the soil biodiversity and ecosystem services. Estimating HMs’ concentrations in soils using hyperspectral data is an effective method but is challenging due to the effects of varied soil properties and measurement-related errors inflicted by atmospheric effects. This study focused on typical mining areas in the Fenwei Plain (FWP), China. Soil-related data were collected by leveraging airborne- and ground-based integrated remote sensing observations. The concentrations of eight HMs, namely copper (Cu), lead (Pb), zinc (Zn), nickel (Ni), chromium (Cr), cadmium (Cd), arsenic (As), and mercury (Hg), were measured by laboratory analysis from 100 in situ soil samples. Soil reflectance spectra were processed based on resampling and envelope methods. The combination datasets of the concentrations and optimal soil reflectance spectra were used to build the soil-related parameter retrieval models using three machine learning (ML) methods, and the feasibility of applying the high-performance retrieval model to estimate the HM concentrations in mining areas was evaluated and explored. Spectral analysis results show that four hundred and twenty-eight bands of five wavelength ranges are of high quality and obviously demonstrate the spectral characteristics selected to build the soil-related parameter models. The evaluation results of eight combination data subsets and three methods show that the preprocessing of spectral data (ground- and airborne-based reflectance) and soil samples with the random forest (RF) method can obtain higher accuracy than support vector machine (SVM) and partial least squares (PLS) methods, denoted as the AER-ACS-RF and GER-GCS-RF models (the average RMSE values of eight HMs were 2.61 and 2.53 mg/kg, respectively). The highest R² values were observed in Cd and As, with an equal value of 0.98, followed by that of Pb (R² = 0.97). The relative prediction deviation (RPD) values of Cu and AS were greater than 1.9. Moreover, the airborne-based AER-ACS-RF model presents a good mapping effect about the concentrations (mg/kg) of eight HMs in mining areas, ranging from 21.65 to 31.25 (Cu), 16.38 to 30.45 (Pb), 62.02 to 109.48 (Zn), 23.33 to 32.47 (Ni), 49.81 to 66.56 (Cr), 0.09 to 0.23 (Cd), 7.31 to 12.24 (As), and 0.03 to 0.17 (Hg), respectively. Full article

Efficiently mitigating the severe air pollution resulting from rapid progress is crucial for the sustainable development of the socio-ecological system. Recently, concerns about nature-based solutions have emerged in the research on the treatment of air pollution. Studies on the purification of PM_2.5 using vegetation currently concentrate on the individual scale of tree species or urban vegetation, ignoring the regional scale, which could better assist ecological governance. Therefore, taking the Fenwei Plain of China as the study area, an assessment framework of the air purification service’s spatial distribution reflecting regional vegetation was constructed. The dry deposition model and GeoDetector were used to quantify the spatial-temporal pattern and explore natural driving factors on the removal of PM_2.5. The results showed that (1) the PM_2.5 purification services offered by various types of vegetation exhibit notable variations. The average removal rates of PM_2.5 by vegetation were 0.186%, 0.243%, and 0.435% in 2000, 2010, and 2021, respectively. (2) Meanwhile, a wide range of spatial mismatch exists between the PM_2.5 concentration and PM_2.5 removal. Insufficient supply regions of PM_2.5 purification services account for 50% of the Fenwei Plain. (3) PM_2.5 removal was strongly influenced by the types of vegetation and the Normalized Vegetation Index (NDVI), followed by the Digital Elevation Model (DEM), and less affected by meteorological factors; a strong joint effect was shown among the factors. The findings in this research provide a new perspective on regional air pollution management at the regional scale. Full article

Reference evapotranspiration (ET₀), an essential variable used to estimate crop evapotranspiration, is expected to change significantly under climate change. Detecting and attributing the change trend in ET₀ to underlying drivers is therefore important to the adoption of agricultural water management under climate change. In this study, we focus on a typical agricultural region of the Fenwei Plain in northern China and use the Mann–Kendall test and contribution rate to detect the change and trend in ET₀ at annual and seasonal scales and determine the major contribution factors to ET₀ change for the baseline period (1985–2015) and the future period (2030–2060) based on high-resolution gridded data and climatic data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results indicate that the annual ET₀ of the Fenwei Plain showed a significant decreasing trend in the baseline period but insignificant and significant increasing trends in the future period under the SSP245 and SSP585 scenarios, respectively. The annual ET₀ of the plain under the SSP245 and SSP585 scenarios increase by 4.6% and 3.0%, respectively, compared to the baseline period. The change and trend in ET₀ between the four seasons are different in the baseline and future periods. Winter and autumn show clear increases in ET₀. VPD is the major contribution factor to the ET₀ change in the plain. The change in ET₀ is mainly driven by the climatic variables that change the most rather than by the climatic variables that are the most sensitive to the ET₀ change. The change and trend in ET₀ in the plain showed clear spatial differences, especially between the eastern and western area of the plain. To adapt to the impact of climate change on ET₀, the irrigation schedule of the crops cultivated in the plain, the cropping system and management of the irrigation district in the plain need to be adjusted according to the change characteristics of spatial and temporal ET₀ in the future. These results contribute to understanding the impacts of climate change on evapotranspiration in the study region and provide spatial and temporal references for adaptation in managing agricultural water use and crop cultivation under climate change. Full article

In order to improve air quality, China has implemented a series of the most stringent control measures ever in recent years. Quantitatively analyzing the contribution of emissions to the trend change in air pollutants is an essential scientific basis for verifying the effectiveness of air pollution control. We based our study on the air quality online monitoring data and meteorological element data of 11 cities in the Fenwei Plain from 2018 to 2022. We quantitatively investigated the changing patterns of NO₂, O₃, PM₁₀, and PM_2.5 and their influencing factors in the major cities of the Fenwei Plain by using the KZ filtering and MLR modeling analysis methods. The results show the following: (1) The long-term fractions of NO₂, PM₁₀, and PM_2.5 in the Fenwei Plain decreased by 10.5, 33.1, and 17.1 μg·m⁻³, with decreases of 25.8%, 29%, and 28.8%, respectively, from 2018 to 2022. The long-term fractions of O₃ showed the characteristics of decreasing and then increasing, with 2020 as the dividing line. (2) The short-term components of NO₂, PM₁₀, and PM_2.5 contributed the most to the total variance, with the proportion of short-term components ranging from 34.7% to 69.8%, 53% to 73%, and 43% to 58%, respectively. The seasonal components of O₃ contributed the most to the total variance, with the proportion of short-term components ranging from 54% to 70.7%. (3) Most cities in the Fenwei Plain had unfavorable meteorological conditions with regard to NO₂, PM₁₀, and PM_2.5 in 2018–2020 and favorable meteorological conditions in terms of NO₂, PM₁₀, and PM_2.5 in 2020–2022. O₃ showed different characteristics from the other three pollutants. Most cities in the Fenwei Plain had meteorological conditions in 2018–2019 that were unfavorable for improving O₃ levels. In 2019–2021, meteorological conditions were favorable for improving O₃ levels, while in 2021–2022, meteorological conditions were unfavorable for improving O₃ levels. Full article

As the market subject of China’s economic development, private enterprises play an important role in fighting against pollution and solving ecological and environmental problems. It is especially important to evaluate the green development progress of Chinese private enterprises in the epidemic era. This paper conducts a questionnaire survey on 10,623 private enterprises in 31 provinces of China, and based on 6223 industrial survey results, it focuses on the production and operation status of private enterprises in terms of pollution reduction performance, energy saving, and carbon reduction intensity in order to construct a green development progress index. The spatial Moran index test and the spatial Durbin model are used to analyze the regional correlations and influencing factors of green development progress in China. The results show that the green development of enterprises with a main business income of more than 100 million CNY and key areas such as Fenwei Plain have improved significantly in 2021, especially with the increase in a private enterprise scale, the carbon reduction regime, the pollution abatement regime, and the pollution control manner, and because the investment, profitability, and pollution discharge of private enterprises is more significant. The indexes of the provinces in the southeast coastal area and the northeastern region of China are the highest and lowest, respectively, in terms of pollution, which is demonstrated by the spatial aggregation effect on the surrounding areas by Moran local index analysis. The urbanization level and government financial support for environmental protection are just two of the negative factors regarding this issue, while the economic development level and industrial structure are positive factors that have a spatial spillover effect. Full article