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Urban-rural inequality in soil heavy metal health risks: Insights from Baoding, China

Ecotoxicology and environmental safety 2025年 300卷 118458页

作者： Lingzhi Luo Liang Wang You Li Hongying Cao Yanling Guo Xiaoyong Liao Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences (CAS) Beijing 100101 China Beijing Key Laboratory of Environmental Damage Assessment and Remediation Beijing 100101 China University of Chinese Academy of Sciences Beijing 100049 China. Beijing Key Laboratory of Environmental Damage Assessment and Remediation Beijing 100101 China. Electronic address: wangliang@***. Beijing Key Laboratory of Environmental Damage Assessment and Remediation Beijing 100101 China. Beijing Key Laboratory of Environmental Damage Assessment and Remediation Beijing 100101 China. Electronic address: liaoxy@***.

Soil heavy metal contamination poses serious health risks, but few studies have quantitatively assessed disparities in these risks between urban and rural populations. To address this gap, we introduce a novel framework integrating machine learning and spatially explicit risk models to assess individual- and population-level health risks from soil heavy metals in Baoding City, China. We used random forest models to predict high-resolution soil metal concentration maps, Positive Matrix Factorization for source apportionment, and spatial exposure models to estimate human health risks under multiple exposure pathways. This is the first study to combine high-resolution machine learning mapping, source apportionment, and multi-scale risk assessment in an urban-rural context. key findings reveal risk contrasts: urban soils exhibited a 51 % higher ecological risk index than rural soils, reflecting concentrated pollution hotspots. However, individual-level risk assessments indicate that rural residents face 3 higher health hazards than urban residents. By contrast, aggregated non-carcinogenic and carcinogenic population risks were 1.8 and 1.7 times higher in urban areas. These contrasting results reveal an overlooked rural vulnerability at the individual scale versus greater aggregate risk in urban populations. Combining machine learning with spatially explicit risk modeling, our study quantifies previously undetected urban-rural health risk inequalities from soil contamination. This integrated approach advances scientific understanding of how urbanization shapes spatial health risk patterns and provides actionable insights for targeted environmental management to protect vulnerable communities, inform mitigation strategies, and identify priority intervention areas.

关键词： Health risk Heavy metals Machine learning Soil pollution Urban and rural residents

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Households’ Climate Change Adaptation Strategies for Agriculture on the Eastern Tibetan Plateau

Research Square

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Research Square 2021年

作者： He, Xinjun Yan, Jianzhong Huang, Anyi Zhou, Hong Wu, Ya Yang, Liang Emlyn Paudel, Basanta State Cultivation Base of Eco-Agriculture for Southwest Mountainous Land College of Resources and Environment Southwest University Chongqing400715 China Department of Geography Ludwig Maximilian University of Munich Munich Germany Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China

How agriculture can better adapt to climate change has been a key topic of interest for scholars. This study provides insights from the eastern agricultural region of the Tibetan Plateau (TP). The Yellow River-Huangshui River valley (YHV) is an important food-producing region on the TP. The agricultural production of small households has been affected by significant climate change and by a series of interventions on the part of the local government. Five main adaptation strategies adopted by households include crop rotation (86.71%), increasing agricultural inputs (74.21%), changing the sowing time of crops (61.51%), expanding cropland area (32.94%) and raising more livestock (16.27%) to adapt to the effects of climate change. Regression analysis revealed that households’ perceptions of climate change and five types of livelihood capital are important factors influencing their adoption of various livelihood strategies. In addition, the adaptation strategies used by households in the YHV are incremental adjustments to their existing production, while transformative adaptation strategies (e.g., irrigation facilities, improved crop varieties, agricultural insurance), which are larger in scale and could fundamentally reduce households’ vulnerability to climate change have been planned by the government. Due to the presence of government interventions, households in the YHV area are more proactive in adapting to climate change. Finally, the results of this paper are conducive to guiding the local government to enhance its intervention role to promote households’ climate change adaptation behavior. © 2021, CC BY.

关键词： Climate change

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Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield

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International Journal of Digital Earth 2020年第4期13卷 504-526页

作者： Jianxi Huang Wen Zhuo Ying Li Ran Huang Fernando Sedano Wei Su Jinwei Dong Liyan Tian Yanbo Huang Dehai Zhu Xiaodong Zhang College of Land Science and Technology China Agricultural UniversityBeijingPeople’s Republic of China Key Laboratory of Remote Sensing for Agri-Hazards Ministry of AgricultureBeijingPeople’s Republic of China CMA·Henan Key Laboratory of Agrometeorological Support and Applied Technique ZhengzhouPeople’s Republic of China Henan Institute of Meteorological Sciences ZhengzhouPeople’s Republic of China Department of Geographical Sciences University of MarylandCollege ParkMDUSA Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingPeople’s Republic of China Department of Geography College of GeosciencesTexas A&M UniversityCollege StationTXUSA United States Department of Agriculture Agricultural Research ServiceCrop Production Systems Research UnitStonevilleMSUSA

Agricultural drought threatens food *** remote-sensing drought indices have been developed,but their different principles,assumptions and physical quantities make it necessary to compare their suitability for drought monitoring over large ***,we analyzed the performance of three typical remote sensing-based drought indices for monitoring agricultural drought in two major agricultural production regions in Shaanxi and Henan provinces,northern China(predominantly rain-fed and irrigated agriculture,respectively):vegetation health index(VHI),temperature vegetation dryness index(TVDI)and drought severity index(DSI).We compared the agreement between these indices and the standardized precipitation index(SPI),soil moisture,winter wheat yield and National Meteorological Drought Monitoring(NMDM)*** average,DSI outperformed the other indices,with stronger correlations with SPI and soil *** also corresponded better with soil moisture and NMDM *** jointing and grain-filling stages of winter wheat are more sensitive to water stress,indicating that winter wheat required more water during these ***,the correlations between the drought indices and SPI,soil moisture,and winter wheat yield were generally stronger in Shaanxi province than in Henan province,suggesting that remote-sensing drought indices provide more accurate predictions of the impacts of drought in predominantly rain-fed agricultural areas.

关键词： Agricultural drought remote sensing drought index winter wheat yield

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AN IMPROVED ALGORITHM FOR TERRAIN RENDERING

AN IMPROVED ALGORITHM FOR TERRAIN RENDERING

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IEEE International Geoscience and Remote Sensing Symposium

作者： Wei Cao Lin Huang Yunfeng Hu Duanyang Xu Hongyan Ren Junxing Yang Key Laboratory of Land Surface Pattern and Simulation Chinese Academy of Sciences Beijing China

The real-time visualization of large-scale terrain is a very complex and challenging problem, as the increasing resolution of geometry and texture has greatly exceeded the rendering capabilities of the available hardware. This is an active research area, and has attracted the interest of researchers for decades. To solve this problem, LOD-based algorithms of various forms have been proposed. These aim to reduce the complexity of the geometry and decrease the number of triangles that must be rendered. In this paper we will mainly focus on LOD generation, data scheduling and terrain popping, and put forward a new approach for terrain rendering. To promote rendering efficiency, we will take advantage of the programmability of GPU and immigrate some algorithms from CPU to GPU.

关键词： Rendering (computer graphics) Indexes Graphics processing units Geometry Real-time systems Hardware Testing

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Risk Identification of Seismic Landslides by Joint Newmark and RockFall Analyst Models: A Case Study of Roads Affected by the Jiuzhaigou Earthquake

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International Journal of Disaster Risk Science 2018年第3期9卷 392-406页

作者： Xiliu Yue Shaohong Wu Yunhe Yin Jiangbo Gao Jingyun Zheng Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Key Laboratory of Land Surface Pattern and Simulation University of Chinese Academy of Sciences

Geological disasters are a great threat to people's lives and property. At present, it is difficult to evaluate quantitatively the cascading effects of regional geological disasters, and the development of new methods for such evaluation is much needed. In this study, the authors have developed a joint procedure that couples the Newmark model and the RockFall Analyst model based on a GIS platform in order to identify the impact of seismic landslides on roads. The new method effectively combines two processes—seismic landslide occurrence probability analysis and mass movement trajectory simulation. The permanent displacement derived from the Newmark model is used to identify potential source areas of *** on the RockFall Analyst model, the possible impact of mass movement on the roads can be simulated. To verify the reliability of the method, the landslides induced by the 2017 Jiuzhaigou Earthquake were taken as a case *** results suggest that about 21.37% of the study area is at high risk of seismic landslides, and approximately 3.95 km of road sections are at extremely high risk of large landslides. The simulated area is consistent with the distribution of disasters revealed by post-earthquake remote sensing image interpretation and field investigation in existing studies. This indicates that the procedure, which joins the Newmark and RockFall models, has a high reliability for risk identification and can be applied to seismic landslide risk assessment and prediction in similar areas.

关键词： China Jiuzhaigou earthquake Newmark model Risk identification RockFall analyst model Seismic landslide

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Mapping the optimal regions for wind farm construction in China

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Resources, Conservation and Recycling 2025年 222卷

作者： Dazhi Yang Xuyang Zhang Wei Song Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China University of Chinese Academy of Sciences Beijing 100049 China School of Earth and Environment Anhui University of Science and Technology Huainan 232001 Anhui China Hebei Collaborative Innovation Center for Urban-Rural Integration Development Shijiazhuang 050061 China

Against the backdrop of accelerating global energy transition, wind energy has emerged as a strategically critical clean energy source, yet its deployment confronts dual challenges of suboptimal site selection efficiency and multifaceted environmental constraints. This research addresses the inherent limitations of unidimensional siting methodologies through the development of an innovative multi-factor coupled assessment framework. By synthesizing multidimensional geospatial datasets spanning nature reserves, land use patterns, wind resource availability, transportation infrastructure, and grid connectivity, a comprehensive evaluation system was established. This system quantitatively integrates five critical dimensions: wind energy generation potential, demographic-economic electricity demand profiles, transportation network accessibility, power transmission capacity, and geomorphological characteristics. The proposed model achieves unprecedented macro-scale synergistic optimization of these interdependent factors, systematically elucidating the spatial configuration patterns of wind farm suitability across China. Furthermore, it provides a novel decision-support mechanism for scientifically-based site selection processes.

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Sensitivity of arid/humid patterns in China to future climate change under a high-emissions scenario

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Journal of Geographical Sciences 2019年第1期29卷 29-48页

作者： MA Danyang DENG Haoyu YIN Yunhe WU Shaohong ZHENG Du Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research CAS University of Chinese Academy of Sciences Henan Province Development and Reform Commission

Changes in regional moisture patterns under the impact of climate change are an important focus for science. Based on the five global climate models (GCMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5), this paper projects trends in the area of arid/humid climate regions of China over the next 100 years. It also identifies the regions of arid/humid patterns change and analyzes their temperature sensitivity of responses. Results show that future change will be characterized by a significant contraction in the humid region and an expansion of arid/humid transition zones. In particular, the sub-humid region will expand by 28.69% in the long term (2070-2099) relative to the baseline period (1981-2010). Under 2°C and 4°C warming, the area of the arid/humid transition zones is projected to increase from 10.17% to 13.72% of the total of China. The humid region south of the Huaihe River Basin, which is affected mainly by a future increase in evapotranspiration, will retreat southward and change to a sub-humid region. In general, the sensitivity of responses of arid/humid patterns to climate change in China will intensify with accelerating global warming.

关键词： arid/humid patterns climate change sensitivity aridity index China

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Crop Model Ensemble Averaging: A Large But Underappreciated Uncertainty Source for Global Crop Yield Projections Under Climate Change

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Earth's Future 2025年第6期13卷

作者： Xiaomeng Yin Guoyong Leng Jiali Qiu Xiaoyong Liao Shengzhi Huang Jian Peng School of Geographical Sciences Hebei Normal University Shijiazhuang China Hebei Key Laboratory of Environmental Change and Ecological Construction Shijiazhuang China Key Laboratory of Water Cycle and Related Land Surface Processes Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China Xi'an University of Technology Xi'an China Department of Remote Sensing Helmholtz Centre for Environmental Research - UFZ Leipzig Germany Remote Sensing Centre for Earth System Research—RSC4Earth Leipzig University Leipzig Germany

Using an ensemble of crop models have been encouraged for global crop yield projections, which would, however, introduce additional uncertainty from the choice of ensemble averaging methods. Here, we use seven ensemble averaging methods including simple average, regression, Support Vector Regressor, Bayesian model average (BMA), Random Forest (RF), Artificial neural network (ANN) and Long-short term memory to derive the ensemble mean of eight process-based crop models for global maize yield projections. Results show that the choice of ensemble averaging methods has a large impact on the projection of long-term mean yield and year-to-year yield variability, with a range of −19.79%–16.62% and −47.92%–55.39% for the globe, respectively. Regionally, the largest uncertainties from the choice of ensemble averaging methods are observed in Indonesia and Canada. Further uncertainty decomposition analysis shows that ensemble averaging methods contributes to 39%–87% of total uncertainties for global yield projections, which is even higher than climate models. These results imply that although using an ensemble of crop models is valuable for informing risk-based policy-makings, how we choose to combine and derive the best estimates of crop model ensembles has large influence on the assessment outcomes. This study highlights an important but not well recognized uncertainty source for global yield predictions which arises from the choice of ensemble averaging methods. Seven ensemble averaging methods are evaluated for global yield projections Choice of ensemble averaging methods cause large uncertainties for yield projections Uncertainty from ensemble averaging methods is comparable to climate model uncertainty Process-based crop models are widely used for projecting future crop yield under climate change, but often show large uncertainties. Therefore, an ensemble of crop models is increasingly applied for global yield projections. Here, we show that the choice of ensemble averag

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A Sharpe ratio-integrated indicator system for land use changes ecological risks: Within coupled disaster-economic assessment frameworks

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Ecological Indicators 2025年 176卷

作者： Xuyang Zhang Wei Song He Gao Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China School of Earth and Environment Anhui University of Science and Technology Huainan 232001 Anhui China Hebei Collaborative Innovation Center for Urban-Rural Integration Development Shijiazhuang 050061 China School of Geosciences Yangtze University Wuhan 430100 China

Global climate change and human-induced land-use changes have threatened the stability of ecosystem service provision and intensified ecological risks. However, the ecological risk of land-use change (ERLUC) remains an emerging concept with unclear definitions and assessment methodologies. This study proposes a novel Sharpe ratiobased Ecological Risk Index (ERI) that integrates the probability of land use change occurrence with expected ecological benefits, applied to the North China Plain (NCP). key findings include: (1) Probability of land-use change can be defined as the likelihood of future transitions, while expected ecological benefits represent potential ecosystem service gains or losses from such changes. The Sharpe ratio effectively couples these parameters for risk assessment. (2) Under three scenarios – natural development, urban expansion, and ecological conservation – the mean ERI values were 0.4718, 0.4719, and 0.4703 respectively, indicating highest overall risk in urban expansion. (3) Spatial analysis revealed similar risk gradation patterns across the plain, dominated by medium–high risk areas (20.33%), followed by medium-risk (30.21%), with relatively smaller proportions of high-risk (13.77%) and medium–low risk (14.23%) zones. (4) Future projections show escalating risks in eastern regions, particularly along urban peripheries. This framework advances ecological risk science by integrating financial risk theory into land system analysis, providing policymakers with spatial targeting tools to balance developmental priorities and ecosystem security in rapidly urbanizing regions.

关键词： Land-use change Ecological risks Ecosystem services Sharpe ratio CA-Markov model North China Plain

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Progress in Research on the Influences of Climatic Changes on the Industrial Economy in China

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Journal of Resources and Ecology 2020年第1期11卷 1-12页

作者： LIU Yuanzhe SONG Wei ZHAO Dongsheng GAO Jiangbo College of Geography and Environment Shandong Normal UniversityJinan 250358China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China

Global climate changes have led to ocean acidification,ice and snow melting,a continuous rise in temperature,and an increasing frequency of extreme weather events,with profound impacts on the social economic *** the aggravation from climate changes,even the industrial fields with a relatively strong resistance to climatic changes have also suffered serious *** present,the vulnerability of the industrial field is growing,and the absolute economic losses are *** quantitative evaluation of these industrial economic losses is therefore an important basis for formulating policies to tackle global climate change,and analyzing the current research progress can provide ideas and methods for the effective evaluation of the industrial ***,in this paper,we summarized both the positive and negative effects of climate changes on the industrial fields and found that the influences of climatic changes on different industrial sectors are slightly *** example,while the mining industry,so far,has positively responded to the changing climate,severe weather events such as storms,drought,and rain could severely impede the normal production and business operation activities of the mining industry in the *** manufacturing industry mostly involves indoor jobs,which are relatively resistant to extreme weather events,and some industries have complex response *** terms of the construction industry,its losses are mainly indirect through increased electricity *** production and supply industries for electricity,heat,and water would suffer transmission supply losses in extreme weather events;and as the largest carbon emission industries,the costs of emission reduction would affect the economic growth of this sector in the short ***,the industrial sectors pay relatively high costs for climate change mitigation and adaptation,and therefore,the quantitative evaluation of industrial economic losses through models i

关键词： climate change industrial economy extreme climate events carbon emission reduction

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