Search Results (20)

This study addresses the limitations of machine learning in regional groundwater dynamics research, particularly the insufficient integration of the hydrogeological background and low simulation accuracy. Focusing on the shallow groundwater in the Hebei Plain south of Beijing and Tianjin, we integrate static data, including hydrogeological parameters, with the commonly used time-series data. A novel regionalization strategy based on depositional systems is proposed to enhance the model’s spatial adaptability. The Long Short-Term Memory (LSTM) model, augmented with an attention mechanism, adjusts the dynamic model weights using static data to reflect geological impacts on groundwater dynamics. Comparative results show that the refined regionalization and the inclusion of static data significantly improve the accuracy of the model. Based on the fitting results, the comparison of shallow groundwater level prediction between 2023 and 2040 under two mining conditions shows that the continuous implementation of the pressure mining policy has accelerated the recovery of water level, and the rise in groundwater level is obviously different between regions. The alluvial fan in the piedmont has the largest rise, and the marine sedimentary plain has the smallest rise. This study provides a new method for analyzing groundwater dynamics under complex hydrogeological conditions and provides a basis for regional groundwater management and sustainable utilization. Full article

Coastal areas are among the most densely populated areas globally and are crucial components of terrestrial and marine ecosystems. Investigating the interplay between population distribution and the ecosystem service value (ESV) in coastal regions, along with their spatial and temporal dynamics, is crucial for safeguarding coastal ecological security, fostering regional sustainable development, and facilitating a harmonious coexistence between humans and nature. This study focuses on China’s coastal areas, utilizing land use and population data from 2000 to 2020 at the county-level scale. Several methods, such as geographic concentration, spatial autocorrelation, and the spatial mismatch index, are employed to reveal the relationships and spatial and temporal characteristics between population and the ESV. The main findings are as follows: (1) The population in China’s coastal areas increased from 580.6632 million to 700.7265 million, with a rising population density. The population distribution core is concentrated in the Beijing–Tianjin–Hebei Urban Agglomeration, the Yangtze River Delta Urban Agglomeration, and the Pearl River Delta Urban Agglomeration, with secondary cores forming near provincial capitals. (2) The ecological geographic concentration in China’s coastal areas is lower than that of the population, displaying a distribution pattern of “low–high–low” from north to south. The ESV in these areas has increased by CNY 121.66 billion, with a significant decline in the per capita ESV. Low values of ecological geographic concentrations are concentrated in the northern part of the research area, particularly across the North China Plain. (3) The correlation between the ESV and population in China’s coastal areas is negative, with relatively good overall coordination. Increased human activities and urbanization in the Yangtze River Delta and Pearl River Delta have led to the degradation of ecological functions. Full article

High-frequency drought hazards have presented persistent challenges for environmental management and sustainable development in the South Hebei Plain, China. In this paper, the assessment of meteorological droughts in the South Hebei Plain was conducted using a multifaceted approach to ensure a comprehensive analysis. Our results demonstrated that distinct timescale cycles, ranging from centennial–semicentennial to interdecadal variations, can be identified over the past few centuries. These cycles aligned with patterns observed in the middle Yangtze basin and corresponded to regional climatic conditions. The drought cycles in the South Hebei Plain showed significant correlations with variations in the monsoon climate, sunspot activity, global changes, and human disturbances. Changes in the frequency, duration, and intensity of droughts have notably impacted hydrological variations. Extreme droughts, in particular, have heightened concerns about their effects on river systems, potentially increasing the risk of channel migration. This study enhanced our understanding of meteorological hazard patterns in the South Hebei Plain and provided valuable insights into different stages of drought management. It thus can offer lessons for improving drought preparedness and resilience and for formulating adaptive measures to mitigate future droughts and promote regional sustainability. 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 measurement of groundwater exchange between neighboring regions is a critical topic in water resource management and can usually be achieved through a combination of field investigations and the use of groundwater flow models. In this study, we employed the water balance and Darcy’s law methods, utilizing downscaled Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) data to assess groundwater exchange patterns in the Beijing-Tianjin-Hebei (BTH) region of China. Additionally, we determined the contributions of human activities and climate factors to the observed variations via residual analysis. The results revealed a consistent decrease in groundwater storage in the study area since 2008, especially in the spring and summer months. The groundwater exchange rates calculated by 1° and 0.05° groundwater storage anomalies (GWSAs) were basically consistent, and the downscaled GWSAs could better reflect the small-scale groundwater exchange characteristics. The groundwater exchange rate showed a decreasing trend from the Piedmont plain to the coastal areas. A notable trend of declining groundwater exchange between the Taihang Mountains and Piedmont plains was observed, and the downward trend gradually intensified from north to south between 2003 and 2007. After 2008, there was an increasing trend, and coastal areas exhibited the smallest amount of groundwater exchange. Human activities emerged as the predominant factor accounting for more than 90.9% of the overall reduction in groundwater storage, while climate change imposed a minimal influence on groundwater storage variations. The insights obtained in this study hold significant implications for groundwater resource planning and management in the region. Full article

To investigate the spatiotemporal patterns of fine particulate matter (PM_2.5) under years of control measures in China, a comprehensive analysis including statistical analysis, geographical analysis, and health impact assessment was conducted on millions of hourly PM_2.5 concentrations data during the period of 2017–2020 in six typical major urban agglomerations. During the period of 2017–2020, PM_2.5 concentrations in the Beijing–Tianjin–Hebei urban agglomeration (BTH-UA), Central Plains urban agglomeration (CP-UA), Yangtze River Delta urban agglomeration (YRD-UA), Triangle of Central China urban agglomeration (TC-UA), Chengdu–Chongqing urban agglomeration (CY-UA), and Pearl River Delta urban agglomeration (PRD-UA) decreased at a rate of 6.69, 5.57, 5.45, 3.85, 4.66, and 4.1 µg/m³/year, respectively. PM_2.5 concentration in BTH-UA decreased by 30.5% over four years, with an annual average of 44.6 µg/m³ in 2020. CP-UA showed the lowest reduction ratio (22.1%) among the six regions, making it the most polluted urban agglomeration. In southern BTH-UA, northeastern CP-UA, and northwestern TC-UA, PM_2.5 concentrations with high levels formed a high–high agglomeration, indicating pollution caused by source emission in these areas was high and hard to control. Atmospheric temperature, pressure, and wind speed have important influences on PM_2.5 concentrations. RH has a positive correlation with PM_2.5 concentration in north China but a negative correlation in south China. We estimated that meteorological conditions can explain 16.7–63.9% of the PM_2.5 changes in 129 cities, with an average of 33.4%, indicating other factors including anthropogenic emissions dominated the PM_2.5 changes. Among the six urban agglomerations, PM_2.5 concentrations in the CP-UA were most influenced by the meteorological change. Benefiting from the reduction in PM_2.5 concentration, the total respiratory premature mortalities in six regions decreased by 73.1%, from 2017 to 2020. The CP-UA had the highest respiratory premature mortality in six urban agglomerations. We suggested that the CP-UA needs more attention and stricter pollution control measures. Full article

With the intensification of human activities, the natural water cycle has a significant nature-society dual feature, and identifying the mutual feedback mechanism between natural and social water cycles is an important basis for a more accurate simulation of the dualistic water cycle. In this study, two indexes of cumulative runoff change rate and social water cycle feedback rate are put forward, representing the degree of change in socio-hydrological unit runoff under the mutual feedback of the natural social water cycle in all upstream regions, and the degree influence of the water intake, consumption, and discharge process of the social water cycle on the natural water cycle in the socio-hydrological unit, respectively. Taking the Beijing–Tianjin–Hebei region, which is marked by strong human activities, as the study area, the 2035 natural-social dualistic water cycles were simulated by a water allocation and simulation (WAS) model. Different water supply types and use structures cause the social water cycle to increase or decrease local runoff in different areas. The social water cycle feedback rate is greater than 1 in Beijing and Tianjin, and less than 0.25 in the mountainous areas and the Hebei plain, indicating that the social water cycle of each unit in the Beijing–Tianjin–Hebei region increases or decreases local runoff due to different water supply types and use structures. The cumulative runoff change rate in this region was 0.66, indicating that the overall runoff was attenuated due to the social water cycle, and runoff attenuation was greater in the south than the north. Full article

Drought-induced risk has attracted the attention of many scholars, but the risk of combined events caused by drought and high-temperature heat waves still needs further study. Based on MODIS products and meteorological data, the spatiotemporal variation characteristics of summer drought and high-temperature heat waves in the North China Plain from 2000 to 2018 were analyzed by the standardized precipitation evapotranspiration index (SPEI), crop water stress index (CWSI) and high-temperature threshold, and their combined-events risk was evaluated. The results showed that (1) from 2000 to 2018, summer drought in the North China Plain became more severe. Especially in Henan, Anhui and Jiangsu Provinces, drought increased significantly. (2) From 2000 to 2018, the frequency and intensity of high-temperature heat wave events in the North China Plain gradually increased at rates of 0.28 times/10 year and 1.6 °C/10 year, respectively. (3) The slightly high risk and high risk caused by summer drought were mainly distributed in Hebei Province and Tianjin Municipality in the north, and the risk change was characterized by a decrease in the north and an increase in the south. (4) The combined-events risk of summer drought and high-temperature heat waves did not increase significantly, with an increase rate of approximately 0.01/10 year. Among them, the increase rate of combined-events risk in Henan Province was the largest (0.14/10 year), followed by the obvious increase in northern Anhui, Jiangsu and southern Shandong, while the risk in Beijing showed a decreasing trend. The research results have scientific guiding significance for formulating disaster prevention and reduction strategies. Full article

Water consumption and energy consumption are inevitable in grain production, but few studies have focused on the integrated assessment of these two indicators and their relationships. To address the research deficiency, taking the North China Plain (NCP) as a case study, this paper quantifies the changes in grain crop planting structure and the accompanying changes in irrigation water consumption (IWC) and energy consumption (EC) in the NCP. On this basis, the water-energy coupling index (CI) is constructed to analyze the water-energy coupling relationship in the context of grain crop planting structure change. The results revealed that the sown area of three of the four main grain crops in the NCP, namely winter wheat, summer maize, and rice, roughly increased in the south and decreased in the north, while the sown area of spring maize increased in most counties where it was planted in the NCP from 2000 to 2015. With the change of grain crop planting structure, IWC and EC of winter wheat in the NCP decreased by 19.87 × 10⁶ m³ and 16.78 × 10⁸ MJ, respectively, mainly distributed in the Beijing-Tianjin-Hebei region, while IWC and EC of other crops all increased. In terms of CI values, although that of spring maize increased, those of winter wheat, summer maize, and rice all decreased, and the overall CI values of grain production in the NCP decreased from 0.442 in 2000 to 0.438 in 2015, indicating that grain crop distribution has been optimized toward a less water- and energy-intensive and more sustainable layout in the NCP. This paper can add case and methodological support to the food-water-energy (FEW) nexus research and can also provide policy suggestions for regional crop optimization layout and conservation of both water and energy resources. Full article

This paper collects and analyzes the 1954–2017 monthly average reanalysis data from the U.S. National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), the 1954–2017 haze days observation data from the National Meteorological Information Center/China Meteorological Administration (NMIC/CMA) and the PM_2.5 daily average mass concentration data in 2013–2017 from the China Air Quality Online Monitoring Platform. The atmospheric apparent heat source Q₁ (negative Q₁ means cold source in winter) over the Tibetan Plateau in December of 1954–2017 is estimated based on thermodynamic equations, and the East Asian winter monsoon index (EAWMI) is calculated. In addition, the discrepancies of the air quality among China’s five typical regions (Beijing–Tianjin–Hebei, Fen-Wei Plain, Yangtze River Delta, Pearl River Delta and Sichuan-Chongqing regions) under the joint influence of the Q₁ and EAWMI are studied. Results show that when it is a strong cold source year, abnormal downdrafts and “temperature inversion covers” occur in areas far from the high terrain, resulting in increased pollution, while the opposite is true in weak cold source years. In strong EAWMI years, there is an abnormal northerly sinking cold flow in the lower layers of mid-high latitudes, which increases the pollution in the area south of 30° N, and the opposite is true in weak EAWMI years. Affected by the combined activities of the Q₁ and the EAWMI, the meteorological conditions of the five typical regions are different, and thus present different air pollution characteristics. Full article

Air pollution has seriously hindered China’s sustainable development. The impact mechanism of industrial upgrading on air pollution is still unclear, given the rapid digital economy. It is necessary to analyze the impact of industrial structure upgrading on air pollution through the digital economy. To investigate the impact of industrial upgrading and the digital economy on air pollution, this paper selected the industrial advanced index and the digital economy index to construct a panel regression model to explore the improvement effect of industrial upgrading on air pollution and selected China’s three typical areas to construct a zonal regression model. The concentrations of air pollutants showed a downward trend during 2013–2020. Among them, the SO₂ concentration decreased by 63%, which is lower than the PM_2.5 and NO₂ concentrations. The spatial pattern of air pollutants is heavier in the north than in the south and heavier in the east than in the west, with the North China Plain being the center of gravity. These air pollutants have significant spatial spillover effects, while local spatial correlation is dominated by high-high and low-low clustering. Industrial upgrading has a stronger suppressive effect on the PM_2.5 concentration than the suppressive effect on the SO₂ and NO₂ concentrations, while the digital economy has a stronger improvement effect on the SO₂ concentration than its improvement effect on the PM_2.5 and NO₂ concentrations. Industrial upgrading has a stronger improvement effect on air pollution in the Yangtze River Delta urban agglomeration than in Beijing–Tianjin–Hebei and its surrounding areas, while the improvement in air pollution attributable to the digital economy in Beijing–Tianjin–Hebei and its surrounding areas is stronger than in the Yangtze River Delta urban agglomeration. There are significant differences in the effects of industrial upgrading and the digital economy on the various types of air pollutants. Full article

Visibility degradation is a pervasive environmental problem in winter in China and its prediction accuracy is therefore important, especially in low visibility conditions. However, current visibility parameterization algorithms tend to overestimate low visibility (<5 km) during haze–fog events. The key point of low visibility calculation and prediction depends on a reasonable understanding of the correlation between visibility, PM_2.5 concentration, and relative humidity (RH). Using the observations of PM_2.5 concentration and meteorology from December 2016 to February 2017, under different RH levels, the relative contribution differences of PM_2.5 concentrations and RH to visibility degradation are investigated in depth. On this basis, new multiple nonlinear regressions for low visibility are developed for eight regions of China. The results show that under relatively low RH conditions (<80% or 85%), PM_2.5 concentration plays a leading role in visibility changes in China. With the increase in RH (80–90% or 85–95%), the PM_2.5 concentration corresponding to the visibility of 10 and 5 km decreases and the contribution of RH becomes increasingly important. When the RH grows to >95%, a relatively low PM_2.5 concentration could also lead to visibility decreasing to <5 km. Within this range, the PM_2.5 concentration corresponding to the visibility of 5 km in Central China (CC), Sichuan Basin (SCB), and Yangtze River Delta (YRD) is approximately 50, 50, and 30 μg m⁻³, and that in Beijing-Tianjin-Hebei (BTH) and Guanzhong Plain (GZP) is approximately 125 μg m⁻³, respectively. Specifically, based on these contribution differences, new multiple nonlinear regression equations of visibility, PM_2.5 concentration, temperature, and dew point temperature of the eight regions (Scheme A) are established respectively after grouping the datasets by setting different RH levels (BTH, GZP, and North Eastern China (NEC): RH < 80%, 80 ≤ RH < 90% and RH ≥ 90%; CC, SCB, YRD, and South China Coastal (SCC): RH < 85%, 85 ≤ RH < 95% and RH ≥ 95%; Xinjiang (XJ): RH < 90% and RH ≥ 90%). According to the previous regression methods, we directly established the multiple regression models between visibility and the same factors as a comparison (Scheme B). Statistical results show that the advantage of Scheme A for 5 and 3 km evaluation is more significant compared with Scheme B. For the five low visibility regions (BTH, GZP, CC, SCB, and YRD), RMSEs of Scheme A under visibility <5 and 3 km are 0.77–1.01 and 0.48–0.95 km, 16–43 and 24–57% lower than those of Scheme B, respectively. Moreover, Scheme A reproduced the winter visibility in BTH, GZP, CC, SCB, YRD, and SCC from 2016 to 2020 well. The MAEs, MBs, and RMSEs under visibility < 5 km are 0.44–1.41, −1.33–1.24, and 0.58–2.36 km, respectively. Overall, Scheme A is confirmed to be reliable and applicable for low visibility prediction in many regions of China. This study provides a new visibility parameterization algorithm for the haze–fog numerical prediction system. Full article

Catalpa bungei C. A. Mey. (C. bungei) is one of the recommended native species for ecological management in China. It is a fast-growing tree of high economic and ecological importance, but its rare resources, caused by anthropogenic destruction and local climatic degradation, have not satisfied the requirements. It has been widely recommended for large-scale afforestation of ecological management and gradually increasing in recent years, but the impact mechanism of climate change on its growth has not been studied yet. Studying the response of species to climate change is an important part of national afforestation planning. Based on combinations of climate, topography, soil variables, and the multiple model ensemble (MME) of CMIP6, this study explored the relationship between C. bungei and climate change, then constructed Maxent to predict its potential distribution under SSP126 and SSP585 and analyzed its dominant environmental factors. The results showed that C. bungei is widely distributed in Henan, Hebei, Hubei, Anhui, Jiangsu, and Shaanxi provinces and others where it covers an area of 2.96 × 10${}^6$ km${}^2$. Under SSP126 and SSP585, its overall habitat area will increase by more than 14.2% in 2080–2100, which mainly indicates the transformation of unsuitable areas into low suitable areas. The center of its distribution will migrate to the north with a longer distance under SSP585 than that under SSP126, and it will transfer from the junction of Shaanxi and Hubei province to the north of Shaanxi province under SSP585 by 2100. In that case, C. bungei shows a large-area degradation trend in the south of the Yangtze River Basin but better suitability in the north of the Yellow River Basin, such as the Northeast Plain, the Tianshan Mountains, the Loess Plateau, and others. Temperature factors have the greatest impact on the distribution of C. bungei. It is mainly affected by the mean temperature of the coldest quarter, followed by precipitation of the wettest month, mean diurnal range, and precipitation of the coldest quarter. Our results hence demonstrate that the increase of the mean temperature of the coldest quarter becomes the main reason for its degradation, which simultaneously means a larger habitat boundary in Northeast China. The findings provide scientific evidence for the ecological restoration and sustainable development of C. bungei in China. Full article

The production gap of soybean (Glycine max L. Merr.) has been expanding in China recently, due to the increasing demand and decreasing production. Identifying soybean production dynamics is contributable to appropriate adjustment of crop rotation system and efficient use of agricultural resources—and thus to ensure food security. Taking the North China plain (NCP) as a case area, this study first analyzed the spatiotemporal dynamics of soybean production during 1998–2015 based on the spatial autocorrelation method, and then calculated contributions to the total production by yield and sown area using the factor decomposition method. The results indicated that total soybean production in the NCP decreased dramatically from 1998 to 2015 and showed a decreasing trend in 80.4% (263) of the counties, mainly (83.9%) contributed by the shrinkage of sown area, largely caused by decreasing benefit. Two regions were found with significantly spatial clustering degree of soybean production. In the south part of NCP, soybean production was highly clustered in Anhui province, and in north it was mainly clustered in western Hebei plain. It was found that soybean production in the NCP was rather sensitive to the return gaps of soybean from maize (Zea mays L.). These imply that the reduced area of soybean production can be restored if the return is improved by adopting appropriate policies such as appropriate subsidies. These findings could be helpful for the policymakers to make soybean production planning in the NCP, contributing to the national revitalization strategy of soybean production. Full article

In this study, we used in situ measurements for the first time to analyze the applicability and effectiveness of evaluating groundwater storage (GWS) changes across China using Gravity Recovery and Climate Experiment (GRACE) satellite products and hydrological data derived from the WaterGap Global Hydrological Model (WGHM), Global Land Data Assimilation System (GLDAS) and eartH2Observe (E2O). The results show that the GWS derived from GRACE JPL Mascons products combined with GLDAS Noah V2.1 data most accurately reflect the overall distribution of GWS changes in China and the correlation coefficient between the in situ measurements reaches 0.538. The empirical orthogonal function decomposition for GWS indicates clear interannual variation and seasonal variation in China. The trends of China’s GWS changes showed a clear regional characteristic from 2003 to 2016. The GWS in the northeast, central-south, and western junction of Xinjiang-Qinghai-Tibet had increased significantly, and the North China Plain (NCP) had a severe decline. The correlation coefficient between the annual trends of precipitation and GWS was 0.57, and it reached 0.73 when four provinces (Beijing, Tianjin, Shanxi, Hebei) that are wholly or partially located in the NCP were excluded. The seasonal variability of GWS in China was obvious and the volatilities in Jiangxi, Hunan and Fujian provinces were the highest, reaching 6.39 cm, 6.33 cm and 5.20 cm, respectively. The empirical orthogonal function decomposition for GWS and precipitation over China indicated seasonal consistency with a correlation coefficient of 0.76. The awareness of areas with significant depletion and large seasonal fluctuation of GWS help adaptations to manage local GWS situation. Full article