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Yin C, Chen R, Xiao X, Van de Voorde T, Qin Y, Guo X, Meng F, Pan L, Yao Y, Li Y. Thirty years of 3-D urbanization in the Yangtze River Delta, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 949:174909. [PMID: 39059646 DOI: 10.1016/j.scitotenv.2024.174909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 07/15/2024] [Accepted: 07/18/2024] [Indexed: 07/28/2024]
Abstract
Accurately capturing the urbanization process is essential for planning sustainable cities and realizing the United Nations Sustainable Development Goal 11. However, until recently, most of the studies on urban expansion in the world have focused on area growth but have little knowledge of height dynamics. This study mapped the spatial distribution of urban built-up areas (UBA) in the Yangtze River Delta (YRD), one of the most urbanized regions in China, to investigate the spatio-temporal evolution in both the horizontal and vertical directions from 1990 to 2020. We coupled and analyzed the horizontal and vertical urban expansion from the 3-D perspective and identified the dominant types. The results showed that 30 cities (73.17 % of the total number of cities) were increasing in the 3-D combined expansion intensity. The decreasing cities were mainly located in Anhui Province. Despite the increasing number of skyscrapers, horizontal growth has dominated urban expansion over the past three decades. The UBA area of the YRD has grown from 4,855.30 km2 to 44,447.15 km2, while the average building height has slowly decreased by 1.26 m. Significant unevenness and differences existed in horizontal and vertical expansions of varying provinces and cities. Our study can accurately grasp the 3-D urban expansion process in the YRD and could promote the efficient development and sustainable utilization of urban land resources in China and beyond.
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Affiliation(s)
- Chenglong Yin
- School of Geographic Sciences, East China Normal University, Shanghai 200241, China; School of Biological Sciences, Center for Earth Observation and Modeling, University of Oklahoma, Norman, OK 73019, USA.
| | - Ruishan Chen
- School of Geographic Sciences, East China Normal University, Shanghai 200241, China; School of Design, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Xiangming Xiao
- School of Biological Sciences, Center for Earth Observation and Modeling, University of Oklahoma, Norman, OK 73019, USA.
| | | | - Yuanwei Qin
- School of Biological Sciences, Center for Earth Observation and Modeling, University of Oklahoma, Norman, OK 73019, USA
| | - Xiaona Guo
- School of Design, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Fei Meng
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
| | - Li Pan
- School of Biological Sciences, Center for Earth Observation and Modeling, University of Oklahoma, Norman, OK 73019, USA
| | - Yuan Yao
- School of Biological Sciences, Center for Earth Observation and Modeling, University of Oklahoma, Norman, OK 73019, USA
| | - Yinshuai Li
- School of Design, Shanghai Jiao Tong University, Shanghai 200240, China
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2
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Zhang Z, Wang S, Yu W, Wang P, Zhang H. Health Impacts of Fine Particulate Matter Shift Due to Urbanization in China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:15732-15740. [PMID: 39141343 DOI: 10.1021/acs.est.4c05146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Abstract
Rapid urbanization and industrialization have resulted in diverse anthropogenic activities and emissions between urban and non-urban regions, leading to varying levels of exposure to air pollutants and associated health risks. However, endeavors to mitigate air pollution and health benefits have displayed considerable heterogeneity across different regions. Therefore, comprehending the changes in air pollutant concentrations and health impacts within an urbanization context is imperative for promoting environmental equity. This paper uses gross domestic product (GDP)- and population-weighted methods to distinguish anthropogenic emissions from urban and non-urban areas in China and quantified their contributions to fine particulate matter (PM2.5) using the Community Multiscale Air Quality (CMAQ) model in 2010 and 2019. Anthropogenic emissions from urban and non-urban (outside urban) regions decreased by 26 and 44% from 2010 to 2019, respectively, resulting in 31 and 28% reductions of PM2.5 in China. PM2.5-related premature mortality attributed to non-urban and urban anthropogenic emission decreases by 8%. Non-urban anthropogenic activities are the main contributor to PM2.5 (56% in 2010 and 2019) and its associated premature mortality (59%), which also predominantly affects non-urban premature mortality (37-42% in 2010-2019). Population changes increase the proportion of premature mortality in urban populations (7-19%) from 2010 to 2019. This study emphasizes the shift of affected populations due to urbanization and population changes.
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Affiliation(s)
- Zhaolei Zhang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Fudan University, Shanghai 200438, People's Republic of China
| | - Shuai Wang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Fudan University, Shanghai 200438, People's Republic of China
| | - Wenxuan Yu
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Fudan University, Shanghai 200438, People's Republic of China
| | - Peng Wang
- Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai 200438, People's Republic of China
- Integrated Research on Disaster Risk (IRDR) International Center of Excellence (ICoE) on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai 200438, People's Republic of China
- Shanghai Key Laboratory of Ocean-Land-Atmosphere Boundary Dynamics and Climate Change, Shanghai 200438, People's Republic of China
| | - Hongliang Zhang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Fudan University, Shanghai 200438, People's Republic of China
- Integrated Research on Disaster Risk (IRDR) International Center of Excellence (ICoE) on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai 200438, People's Republic of China
- Institute of Eco-Chongming, Shanghai 200438, People's Republic of China
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3
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Cai Z, Chen R, Yang M, La Sorte FA, Chen Y, Wu J. Addressing critical gaps in protected area coverage for bird habitats in China. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 368:122263. [PMID: 39180820 DOI: 10.1016/j.jenvman.2024.122263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Revised: 08/07/2024] [Accepted: 08/20/2024] [Indexed: 08/27/2024]
Abstract
Currently, protected areas cover approximately 14% of the Earth's land surface, yet 12.2% of the world's bird species remain unprotected by any designated areas and face significant threats. This study investigates the current status of bird conservation in China, aiming to evaluate the effectiveness of existing protected areas, analyze why certain bird species are not adequately protected, and propose strategies for optimizing protected area configurations. Utilizing citizen science data and the zonation optimization algorithm, we comprehensively assessed the conservation value of birds in China. We then employed anthropogenic stressor data to evaluate the conservation of threatened bird habitats through a binary conflict intensity model. Finally, we conducted a spatial overlap analysis to determine the coverage and effectiveness of Chinese nature reserves in regions with high conservation value and high conflict risk. Our findings indicate that only 10.0% of the highest conservation value bird habitats are covered by protected areas, and just 7.3% of these protected areas effectively safeguard these critical habitats. Additionally, only 5.9% of bird habitats impacted by human activity conflicts are within protected areas, and merely 22.0% of the total protected areas can effectively conserve high conflict risk habitats. Overall, China's current protected area system has substantial shortcomings in safeguarding bird habitats and requires further optimization and expansion to maximize conservation benefits.
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Affiliation(s)
- Zhizheng Cai
- College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, Zhejiang Province, PR China; Center for Balanced Architecture, Zhejiang University, Hangzhou, 310058, Zhejiang Province, PR China.
| | - Runnig Chen
- College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, Zhejiang Province, PR China
| | - Mengxia Yang
- College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, Zhejiang Province, PR China
| | - Frank A La Sorte
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, 06511, USA; Center for Biodiversity and Global Change, Yale University, New Haven, CT, 06511, USA.
| | - Yu Chen
- Center for Balanced Architecture, Zhejiang University, Hangzhou, 310058, Zhejiang Province, PR China; The Architectural Design & Research Institute of Zhejiang University Co., Ltd., Zhejiang University, Hangzhou, 310028, Zhejiang Province, PR China
| | - Jiayu Wu
- Institute of Landscape Architecture, Zhejiang University, Hangzhou, 310058, Zhejiang Province, PR China.
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Li Z, Liu W, Zhang X, Liu L. Carbon emissions from global impervious surface expansion between 1985 and 2020. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 952:175856. [PMID: 39216764 DOI: 10.1016/j.scitotenv.2024.175856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 08/10/2024] [Accepted: 08/26/2024] [Indexed: 09/04/2024]
Abstract
Impervious surface expansion (ISE) refers to the phenomenon that natural surfaces are covered by artificial materials, such as cement, asphalt, bricks, etc., usually due to human activities. Over recent decades, the rapid growth of the global population, economic development, and continuous urbanization have contributed to extensive ISE, which caused significant losses in terrestrial ecosystem carbon pools. A global assessment effort is lacking because of limited comprehensive data on carbon pools and uncertainties surrounding the extent of ISE. In this study, we aimed to quantify the carbon emissions resulting from global ISE between 1985 and 2020, following the method provided by the Intergovernmental Panel on Climate Change (IPCC) Guidelines. We first divided global land into 87 fine geographical zones by overlaying continental boundary information with an ecological zone map. Then, multiple time-series impervious surface data products, land cover dynamic monitoring product, global biomass data, and topsoil organic carbon (TSOC) information were integrated to build a lookup table (LUT) of biomass and TSOC density for these fine geographical zones. Last, we employed the IPCC method to estimate carbon emissions from ISE between 1985 and 2020. Our findings indicated a global ISE encompassing a total area of 58.12 Mha, with cropland encroachment accounting for 67.30 %. Over the past 35 years, cumulative committed carbon emissions from global ISE reached 1.14 ± 0.38 PgC, with TSOC representing 54.55 % and biomass carbon contributing 45.45 %.
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Affiliation(s)
- Zhehua Li
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wendi Liu
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiao Zhang
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
| | - Liangyun Liu
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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5
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Xie J, Wei N, Gao Q. Assessing spatiotemporal population density dynamics from 2000 to 2020 in megacities using urban and rural morphologies. Sci Rep 2024; 14:14166. [PMID: 38898070 PMCID: PMC11187102 DOI: 10.1038/s41598-024-63311-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 05/27/2024] [Indexed: 06/21/2024] Open
Abstract
Rapid urbanization has resulted in the substantial population growth in metropolitan areas. However, existing research on population change of the cities predominantly draws on grid statistical data at the administrative level, overlooking the intra-urban variegation of population change. Particularly, there is a lack of attention given to the spatio-temporal change of population across different urban forms and functions. This paper therefore fills in the lacuna by clarifying the spatio-temporal characteristics of population growth in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2000 to 2020 through the methods of local climate zone (LCZ) scheme and urban-rural gradients. The results showed that: (1) High population density was observed in the compact high-rise (LCZ 1) areas, with a noticeable decline along urban-rural gradients. (2) The city centers of GBA experienced the most significant population growth, while certain urban fringes and rural areas witnessed significant population shrinkage. (3) The rate of growth tended to slow down after 2010, but the uneven development of population-based urbanization was also noticeable, as urbanization and industrialization varied across different LCZ types and cities in GBA. This paper therefore contributes to a deeper understanding of population change and urbanization by clarifying their spatio-temporal contingences at landscape level.
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Affiliation(s)
- Jing Xie
- School of Geography and Planning, Sun Yat-Sen University, Guangzhou, China
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
| | - Nan Wei
- School of Geography and Planning, Sun Yat-Sen University, Guangzhou, China
| | - Quan Gao
- School of Geography and Planning, Sun Yat-Sen University, Guangzhou, China.
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China.
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Xia N, Du E, Wu X, Tang Y, Guo H, Wang Y. Distinct latitudinal patterns and drivers of topsoil nitrogen and phosphorus across urban forests in eastern China. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2024; 34:e2951. [PMID: 38357775 DOI: 10.1002/eap.2951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 12/07/2023] [Indexed: 02/16/2024]
Abstract
Nitrogen (N) and phosphorus (P) are the two most important macronutrients supporting forest growth. Unprecedented urbanization has created growing areas of urban forests that provide key ecosystem services for city dwellers. However, the large-scale patterns of soil N and P content remain poorly understood in urban forests. Based on a systematic soil survey in urban forests from nine large cities across eastern China, we examined the spatial patterns and key drivers of topsoil (0-20 cm) total N content, total P content, and N:P ratio. Topsoil total N content was found to change significantly with latitude in the form of an inverted parabolic curve, while total P content showed an opposite latitudinal pattern. Variance partition analysis indicated that regional-scale patterns of topsoil total N and P contents were dominated by climatic drivers and partially regulated by time and pedogenic drivers. Conditional regression analyses showed a significant increase in topsoil total N content with lower mean annual temperature (MAT) and higher mean annual precipitation (MAP), while topsoil total P content decreased significantly with higher MAP. Topsoil total N content also increased significantly with the age of urban park and varied with pre-urban soil type, while no such effects were found for topsoil total P content. Moreover, topsoil N:P ratio showed a latitudinal pattern similar to that of topsoil total N content and also increased significantly with lower MAT and higher MAP. Our findings demonstrate distinct latitudinal trends of topsoil N and P contents and highlight a dominant role of climatic drivers in shaping the large-scale patterns of topsoil nutrients in urban forests.
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Affiliation(s)
- Nan Xia
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China
- School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Enzai Du
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China
- School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Xinhui Wu
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China
- School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Yang Tang
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China
- School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Hongbo Guo
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China
- School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Yang Wang
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China
- School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
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7
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Ahn Y, Leyk S, Uhl JH, McShane CM. An Integrated Multi-Source Dataset for Measuring Settlement Evolution in the United States from 1810 to 2020. Sci Data 2024; 11:275. [PMID: 38453937 PMCID: PMC10920637 DOI: 10.1038/s41597-024-03081-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 02/16/2024] [Indexed: 03/09/2024] Open
Abstract
Understanding changes in the built environment is vital for sustainable urban development and disaster preparedness. Recent years have seen the emergence of a variety of global, continent-level, and nation-wide datasets related to the current state and the evolution of the built environment, human settlements or building stocks. However, such datasets may face limitations like incomplete coverage, sparse building information, coarse resolution, and limited timeframes. This study addresses these challenges by integrating three spatial datasets to create an extensive, attribute-rich sequence of settlement layers spanning 200 years for the contiguous U.S. This integration process involves complex data processing, merging property-level real estate, parcel, and remote sensing-based building footprint data, and creating gridded multi-temporal settlement layers. This effort unveils the latest edition (Version 2) of the Historical Settlement Data Compilation for the U.S. (HISDAC-US), which includes the latest land use and structural information as of the year 2021. It enables detailed research on urban form and structure, helps assess and map the built environment's risk to natural hazards, assists in population modeling, supports land use analysis, and aids health studies.
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Affiliation(s)
- Yoonjung Ahn
- University of Kansas, Department of Geography & Atmospheric Science, Lawrence, KS, USA.
- Institute of Behavioral Science, University of Colorado Boulder, Boulder, CO, USA.
| | - Stefan Leyk
- Institute of Behavioral Science, University of Colorado Boulder, Boulder, CO, USA
- University of Colorado Boulder, Department of Geography, Boulder, CO, USA
| | - Johannes H Uhl
- Institute of Behavioral Science, University of Colorado Boulder, Boulder, CO, USA
- European Commission, Joint Research Centre, Ispra, VA, Italy
| | - Caitlin M McShane
- University of Colorado Boulder, Department of Geography, Boulder, CO, USA
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Yang M, Xue L, Liu Y, Liu S, Han Q, Yang L, Chi Y. Asymmetric response of vegetation GPP to impervious surface expansion: Case studies in the Yellow and Yangtze River Basins. ENVIRONMENTAL RESEARCH 2024; 243:117813. [PMID: 38043893 DOI: 10.1016/j.envres.2023.117813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 11/22/2023] [Accepted: 11/27/2023] [Indexed: 12/05/2023]
Abstract
Terrestrial gross primary production (GPP) changes due to impervious surfaces significantly impact ecosystem services in watersheds. Understanding the asymmetric response of vegetation GPP to impervious surface expansion is essential for regional development planning and ecosystem management. However, the asymmetric response of vegetation GPP to the impacts of impervious surface expansion is unknown in different watersheds. This paper selected the Yellow River and Yangtze River basins as case studies. We characterized the overall change in GPP based on changes in impervious surface ratio (ISR), determined impervious surface expansion's direct and indirect impacts on GPP in the two watersheds, and further analyzed the asymmetric response of the compensatory effects of indirect influences on the impervious surface expansion in different watersheds. The results showed that: (1) The vegetation GPP decreased with increasing ISR in the Yangtze River Basin, while that in the Yellow River Basin first increased and then reduced. (2) The direct impacts of increased ISR reduced vegetation GPP, while the indirect impacts both had a growth-compensating effect. Growth compensation stabilized at approximately 0.40 and 0.30 in the Yellow and Yangtze River Basins. (3) When the ISR was 0.34-0.56, the growth compensation could offset the reduction of GPP due to direct impact and ensure that the background vegetation GPP was not damaged in the Yellow River Basin. In contrast, the background vegetation GPP was inevitably impaired with increased ISR in the Yangtze River Basin. Therefore, this study suggests that the ISR should be ensured to be between 0.34 and 0.56 to maximize the impervious surface of the Yellow River Basin without compromising the background vegetation GPP. While pursuing impervious surface expansion in the Yangtze River Basin, other programs should be sought to compensate for the loss to GPP.
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Affiliation(s)
- Mingjie Yang
- College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, China
| | - Lianqing Xue
- College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, China; School of Hydraulic Engineering, Wanjiang University of Technology, Ma'anshan, 243031, China.
| | - Yuanhong Liu
- College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, China
| | - Saihua Liu
- College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, China
| | - Qiang Han
- College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, China
| | - Lijuan Yang
- College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, China
| | - Yixia Chi
- School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212100, China
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Xue T, Wang R, Wang M, Wang Y, Tong D, Meng X, Huang C, Ai S, Li F, Cao J, Tong M, Ni X, Liu H, Deng J, Lu H, Wan W, Gong J, Zhang S, Zhu T. Health benefits from the rapid reduction in ambient exposure to air pollutants after China's clean air actions: progress in efficacy and geographic equality. Natl Sci Rev 2024; 11:nwad263. [PMID: 38213522 PMCID: PMC10776362 DOI: 10.1093/nsr/nwad263] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 09/13/2023] [Accepted: 10/08/2023] [Indexed: 01/13/2024] Open
Abstract
Clean air actions (CAAs) in China have been linked to considerable benefits in public health. However, whether the beneficial effects of CAAs are equally distributed geographically is unknown. Using high-resolution maps of the distributions of major air pollutants (fine particulate matter [PM2.5] and ozone [O3]) and population, we aimed to track spatiotemporal changes in health impacts from, and geographic inequality embedded in, the reduced exposures to PM2.5 and O3 from 2013 to 2020. We used a method established by the Global Burden of Diseases Study. By analyzing the changes in loss of life expectancy (LLE) attributable to PM2.5 and O3, we calculated the gain of life expectancy (GLE) to quantify the health benefits of the air-quality improvement. Finally, we assessed the geographic inequality embedded in the GLE using the Gini index (GI). Based on risk assessments of PM2.5 and O3, during the first stage of CAAs (2013 to 2017), the mean GLE was 1.87 months. Half of the sum of the GLE was disproportionally distributed in about one quarter of the population exposed (GI 0.44). During the second stage of CAAs (2017 to 2020), the mean GLE increased to 3.94 months and geographic inequality decreased (GI 0.18). According to our assessments, CAAs were enhanced, from the first to second stages, in terms of not only preventing premature mortality but also ameliorating health inequalities. The enhancements were related to increased sensitivity to the health effects of air pollution and synergic control of PM2.5 and O3 levels. Our findings will contribute to optimizing future CAAs.
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Affiliation(s)
- Tao Xue
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
- Advanced Institute of Information Technology, Peking University, Hangzhou311215, China
| | - Ruohan Wang
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Meng Wang
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY14214, USA
| | - Yanying Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
| | - Dan Tong
- Department of Earth System Science, Tsinghua University, Beijing100084, China
| | - Xia Meng
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education, and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai200433, China
| | - Conghong Huang
- College of Land Management, Nanjing Agricultural University, Nanjing 210095, China
- National & Local Joint Engineering, Research Center for Rural Land Resources Use and Consolidation, Nanjing 210095, China
| | - Siqi Ai
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
| | - Fangzhou Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
| | - Jingyuan Cao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
| | - Mingkun Tong
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Xueqiu Ni
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Hengyi Liu
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Jianyu Deng
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Hong Lu
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing100191, China
| | - Wei Wan
- Clean Air Asia, Beijing100600, China
| | - Jicheng Gong
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
| | - Shiqiu Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
| | - Tong Zhu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing100871, China
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing100871, China
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10
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Mi J, Han X, Cao M, Pan Z, Guo J, Huang D, Sun W, Liu Y, Xue T, Guan T. The Association Between Urbanization and Electrocardiogram Abnormalities in China: a Nationwide Longitudinal Study. J Urban Health 2024; 101:109-119. [PMID: 38216823 PMCID: PMC10897075 DOI: 10.1007/s11524-023-00816-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/29/2023] [Indexed: 01/14/2024]
Abstract
The health effects of urbanization are controversial. The association between urbanization and reversible subclinical risks of cardiovascular diseases (e.g., electrocardiogram (ECG) abnormalities) has rarely been studied. This study aimed to assess the association between urbanization and ECG abnormalities in China based on the China National Stroke Screening Survey (CNSSS). We used changes in the satellite-measured impervious surfaces rate and nighttime light data to assess the level of urbanization. Every interquartile increment in the impervious surfaces rate or nighttime light was related to a decreased risk of ECG abnormalities, with odds ratios of 0.894 (95% CI, 0.869-0.920) or 0.809 (95% CI, 0.772-0.847), respectively. And we observed a U-shaped nonlinear exposure-response relationship curve between the impervious surfaces rate and ECG abnormalities. In conclusion, the current average level of urbanization among the studied Chinese adults remains a beneficial factor for reducing cardiovascular risks.
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Affiliation(s)
- Jiarun Mi
- School of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Xueyan Han
- School of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Man Cao
- School of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Zhaoyang Pan
- School of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Jian Guo
- Department of Cardiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
- Medical Research Center, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Dengmin Huang
- School of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Wei Sun
- School of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Yuanli Liu
- School of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Tao Xue
- Institute of Reproductive and Child Health, National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Epidemiology of Major Diseases (PKU), School of Public Health, Peking University Health Science Centre, Beijing, 100191, China.
- State Environmental Protection Key Laboratory of Atmospheric Exposure, and Health Risk Management and Center for Environment and Health, Peking University, Beijing, 100871, China.
- Advanced Institute of Information Technology, Peking University, Hangzhou, 311215, China.
| | - Tianjia Guan
- School of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
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11
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Su F, Liu Y, Ling F, Zhang R, Wang Z, Sun J. Epidemiology of Hemorrhagic Fever with Renal Syndrome and Host Surveillance in Zhejiang Province, China, 1990-2021. Viruses 2024; 16:145. [PMID: 38275955 PMCID: PMC10818760 DOI: 10.3390/v16010145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/02/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024] Open
Abstract
Hemorrhagic fever with renal syndrome (HFRS) is caused by hantaviruses (HVs) and is endemic in Zhejiang Province, China. In this study, we aimed to explore the changing epidemiology of HFRS cases and the dynamics of hantavirus hosts in Zhejiang Province. Joinpoint regression was used to analyze long-term trends in the incidence of HFRS. The comparison of animal density at different stages was conducted using the Mann-Whitney Test. A comparison of HV carriage rates between stages and species was performed using the chi-square test. The incidence of HFRS shows a continuous downward trend. Cases are widely distributed in all counties of Zhejiang Province except Shengsi County. There was a high incidence belt from west to east, with low incidence in the south and north. The HFRS epidemic showed two seasonal peaks in Zhejiang Province, which were winter and summer. It showed a marked increase in the age of the incidence population. A total of 23,073 minibeasts from 21 species were captured. Positive results were detected in the lung tissues of 14 rodent species and 1 shrew species. A total of 80% of the positive results were from striped field mice and brown rats. No difference in HV carriage rates between striped field mice and brown rats was observed (χ2 = 0.258, p = 0.611).
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Affiliation(s)
- Fan Su
- Health Science Center, Ningbo University, Ningbo 315211, China;
| | - Ying Liu
- Key Lab of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China (R.Z.)
| | - Feng Ling
- Key Lab of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China (R.Z.)
| | - Rong Zhang
- Key Lab of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China (R.Z.)
| | - Zhen Wang
- Key Lab of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China (R.Z.)
| | - Jimin Sun
- Key Lab of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China (R.Z.)
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12
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Ahmad MN, Shao Z, Javed A. Mapping impervious surface area increase and urban pluvial flooding using Sentinel Application Platform (SNAP) and remote sensing data. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:125741-125758. [PMID: 38006477 DOI: 10.1007/s11356-023-30990-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 11/03/2023] [Indexed: 11/27/2023]
Abstract
Expansion of urban impervious surface (UIA) and increased urban pluvial flooding (UPF) have an impact on urban dynamics, socioeconomic activities, and our environment. Therefore, monitoring the increase in UIS and its effect on UPF is essential. The notion of this research is based on the mapping of impervious surface area increase in three major cities of Pakistan. There were two key objectives: (i) Mapping impervious surface area growth using the global impervious surface area index (GISAI) on Google Earth Engine from 1992 to 2022 and (ii) mapping the pluvial flood extent in selected urban areas using Sentinel-1 Ground Range Detected (GRD) data. Thus, we have utilized the GISAI for mapping urban impervious surface area (UISA) using Landsat time-series data on GEE. Our research findings revealed that about 16.8%, 23.5%, and 16.4% of the impervious surface have been increased in Islamabad, Lahore, and Karachi, respectively. Also, Lahore city has the highest overall accuracy, aiming at the GISAI of 93%, followed by Karachi and Islamabad with an overall accuracy of 86% and 85%, respectively. The results indicated that urban flooding has occurred in those areas where the ISA has grown during the last three decades. It shows significant changes in the impervious surface area that cause enhanced urban pluvial flooding in major cities of Pakistan. Also, Sentinel-1 data and the SNAP tool significantly mapped flooded areas in the selected zones. So, providing cities and local governments with increased quick flood detection capabilities is essential. It can also provide feasible policy recommendations for Pakistan decision-makers in city management. Therefore, we suggest a modeling-based solution to identify high-risk locations in major cities for upcoming UPF events.
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Affiliation(s)
- Muhammad Nasar Ahmad
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 43007, Hubei, China.
| | - Zhenfeng Shao
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 43007, Hubei, China
| | - Akib Javed
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 43007, Hubei, China
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13
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Yan R, Liu L, Liu W, Wu S. Quantitative flood disaster loss-resilience with the multilevel hybrid evaluation model. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 347:119026. [PMID: 37816280 DOI: 10.1016/j.jenvman.2023.119026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 08/23/2023] [Accepted: 09/15/2023] [Indexed: 10/12/2023]
Abstract
The severity of global flood disasters is growing, causing loss of human life and property. Building a high-resilience social system, an important means of sustainable flood control, can address these flood-related issues. Numerous studies have carried out disaster resilience evaluations and explored the correlation between flood disaster loss and intensity, but neglected to analyze the role of resilience construction in disaster loss reduction. This study proposed a research route for linking flood loss and disaster loss to quantify the relationship between the two. Take Guangdong Province, China as a study case, the mixed-effects (ME) model and multilevel hybrid evaluation model (MHEM) were established to assess disaster loss and resilience of cities, respectively. Subsequently, disaster resilience curves were built to quantitatively evaluate disaster resilience and corresponding disaster loss. The results show that (1) the ME model can concurrently build the disaster intensity-loss curves of multiple cities with high fitting accuracy. The MHEM combines multiple methods to determine the evaluation result with the highest consistency, showing high reliability. (2) The central and southern regions of Guangdong Province have low disaster loss and high resilience, while the northern regions have high disaster loss and low resilience. (3) With the improvement of disaster resistance, the reduction in disaster loss gradually decreases. Disaster loss in low-resilience cities exhibits greater randomness than that in high-resilience cities, and increasing their resilience can more significantly reduce their level of loss. This study provides a quantitative basis and available methods for comprehensive responses to natural disasters and adaptation to global climate change.
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Affiliation(s)
- Rui Yan
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lulu Liu
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, 100101, China
| | - Wanlu Liu
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Shaohong Wu
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China.
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14
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Shi S, Wang W, Li X, Xu C, Lei J, Jiang Y, Zhang L, He C, Xue T, Chen R, Kan H, Meng X. Evolution in disparity of PM 2.5 pollution in China. ECO-ENVIRONMENT & HEALTH (ONLINE) 2023; 2:257-263. [PMID: 38435353 PMCID: PMC10902506 DOI: 10.1016/j.eehl.2023.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/06/2023] [Accepted: 08/28/2023] [Indexed: 03/05/2024]
Abstract
The spatial disparity of air pollutants is one of the key influential factors for environmental inequality. We quantitatively evaluated the evolution of PM2.5 spatial disparity in China during 2013-2020, and investigated the associations between PM2.5 spatial disparity and economic indicators. Differences in PM2.5 between more- and less-polluted cities declined over time, suggesting decreased absolute disparity. However, the more polluted cities in 2013 remained so in 2017 and 2020, and vice versa, indicating persistent relative disparity. PM2.5 pollution levels increased with higher GDP per capita in less-developed areas of China, but such negative effects weakened over time, while economic development tended to promote cleaner air in developed areas of China. Therefore, policies to improve air quality and promote economic development simultaneously are needed in China to reduce the disparity of air pollution and promote all people to enjoy environmental equality.
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Affiliation(s)
- Su Shi
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Weidong Wang
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Xinyue Li
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Chang Xu
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Jian Lei
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Yixuan Jiang
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Lina Zhang
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Cheng He
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
- Institute of Epidemiology, Helmholtz Zentrum München–German Research Center for Environmental Health (GmbH), Munich D-85764, Germany
| | - Tao Xue
- Institute of Reproductive and Child Health/Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
| | - Renjie Chen
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Haidong Kan
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Xia Meng
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
- Shanghai Typhoon Institute/CMA, Shanghai Key Laboratory of Meteorology and Health, Shanghai 200030, China
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15
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Liu X, Li ZL, Li Y, Wu H, Zhou C, Si M, Leng P, Duan SB, Yang P, Wu W, Tang R, Liu M, Shang GF, Zhang X, Gao M. Local temperature responses to actual land cover changes present significant latitudinal variability and asymmetry. Sci Bull (Beijing) 2023; 68:2849-2861. [PMID: 37852823 DOI: 10.1016/j.scib.2023.09.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 08/23/2023] [Accepted: 08/24/2023] [Indexed: 10/20/2023]
Abstract
Land cover changes (LCCs) affect surface temperatures at local scale through biophysical processes. However, previous observation-based studies mainly focused on the potential effects of virtual afforestation/deforestation using the space-for-time assumption, while the actual effects of all types of realistic LCCs are underexplored. Here, we adopted the space-and-time scheme and utilized extensive high-resolution (1-km) satellite observations to perform the first such assessment. We showed that, from 2006 to 2015, the average temperature in the areas with LCCs increased by 0.08 K globally, but varied significantly across latitudes, ranging from -0.05 to 0.18 K. Cropland expansions dominated summertime cooling effects in the northern mid-latitudes, whereas forest-related LCCs caused warming effects elsewhere. These effects accounted for up to 44.6% of overall concurrent warming, suggesting that LCC influences cannot be ignored. In addition, we revealed obvious asymmetries in the actual effects, i.e., LCCs with warming effects occurred more frequently, with stronger intensities, than LCCs with cooling effects. Even for the mutual changes between two covers in the same region, warming LCCs generally had larger magnitudes than their cooling counterparts due to asymmetric changes in transition fractions and driving variables. These novel findings, derived from the assessment of actual LCCs, provide more realistic implications for land management and climate adaptation policies.
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Affiliation(s)
- Xiangyang Liu
- State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Zhao-Liang Li
- State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China; State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
| | - Yitao Li
- State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Hua Wu
- State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Chenghu Zhou
- Center for Ocean Remote Sensing of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
| | - Menglin Si
- State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Pei Leng
- State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Si-Bo Duan
- State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Peng Yang
- State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Wenbin Wu
- State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Ronglin Tang
- State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Meng Liu
- State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Guo-Fei Shang
- School of Land Science and Space Planning, Hebei GEO University, Shijiazhuang 050031, China
| | - Xia Zhang
- School of Land Science and Space Planning, Hebei GEO University, Shijiazhuang 050031, China
| | - Maofang Gao
- State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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16
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Peng X, Jiang S, Liu S, Valbuena R, Smith A, Zhan Y, Shi Y, Ning Y, Feng S, Gao H, Wang Z. Long-term satellite observations show continuous increase of vegetation growth enhancement in urban environment. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 898:165515. [PMID: 37451465 DOI: 10.1016/j.scitotenv.2023.165515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 07/10/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023]
Abstract
Urbanization shows continuous expansion and development, ushering in the co-evolution of urban environments and vegetation over time. Recent remote sensing-based studies have discovered prevalent vegetation growth enhancement in urban environments. However, whether there is a temporal evolution of the growth enhancement remains unknown and unexplored. Here we expanded the existing framework for assessing the long-term impact of urbanization on vegetation greenness (enhanced vegetation index, EVI) using long time series of remote sensing images and applied it in Changsha, the capital city of Hunan province in China. Results showed that vegetation growth experienced widespread enhancement from 2000 to 2017, and increased 1.8 times from 2000 to 2017, suggesting strong continuous adaptive capability of vegetation to urban conditions. Although the overall impact of urbanization was negative due to the replacement of vegetated surfaces, the growth enhancement nevertheless offset or compensated the direct loss of vegetated cover during urbanization in the magnitude of 28 % in 2000 to 44 % in 2017. Our study also revealed large spatial heterogeneity in vegetation growth response among various districts at different urbanization levels and found an emergent trend under the observed spatial heterogeneity toward an asymptotic maximum with urbanization, showing EVI converges to 0.22 in highly urbanized areas. We further found that the positive effect of urbanization on vegetation growth is a function of urbanization intensity and time, which implies that the effect of the urban environment on vegetation can be simulated and predicted, and can be verified in more cities in the future. Our study is the first to successfully quantify long-term spatial patterns on the co-evolution of urbanization and vegetation, providing a new understanding of the continuous adaptive responses of vegetation growth to urbanization and shedding light on predicting biological responses to future environmental change.
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Affiliation(s)
- Xi Peng
- College of Life Science and Technology, National Engineering Laboratory for Applied Technology in Forestry & Ecology in South China, Central South University of Forestry and Technology, Changsha 410004, China
| | - Shucheng Jiang
- College of Life Science and Technology, National Engineering Laboratory for Applied Technology in Forestry & Ecology in South China, Central South University of Forestry and Technology, Changsha 410004, China
| | - Shuguang Liu
- College of Life Science and Technology, National Engineering Laboratory for Applied Technology in Forestry & Ecology in South China, Central South University of Forestry and Technology, Changsha 410004, China.
| | - Rubén Valbuena
- School of Natural Sciences, Bangor University, Bangor, UK
| | - Andy Smith
- School of Natural Sciences, Bangor University, Bangor, UK
| | - Yang Zhan
- College of Life Science and Technology, National Engineering Laboratory for Applied Technology in Forestry & Ecology in South China, Central South University of Forestry and Technology, Changsha 410004, China
| | - Yi Shi
- College of Life Science and Technology, National Engineering Laboratory for Applied Technology in Forestry & Ecology in South China, Central South University of Forestry and Technology, Changsha 410004, China
| | - Ying Ning
- College of Life Science and Technology, National Engineering Laboratory for Applied Technology in Forestry & Ecology in South China, Central South University of Forestry and Technology, Changsha 410004, China
| | - Shuailong Feng
- College of Life Science and Technology, National Engineering Laboratory for Applied Technology in Forestry & Ecology in South China, Central South University of Forestry and Technology, Changsha 410004, China
| | - Haiqiang Gao
- College of Life Science and Technology, National Engineering Laboratory for Applied Technology in Forestry & Ecology in South China, Central South University of Forestry and Technology, Changsha 410004, China
| | - Zhao Wang
- College of Life Science and Technology, National Engineering Laboratory for Applied Technology in Forestry & Ecology in South China, Central South University of Forestry and Technology, Changsha 410004, China
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17
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Xia N, Du E, Tang Y, Guo H. A distinctive latitudinal trend of nitrogen isotope signature across urban forests in eastern China. GLOBAL CHANGE BIOLOGY 2023; 29:5666-5676. [PMID: 37555694 DOI: 10.1111/gcb.16899] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 08/10/2023]
Abstract
Rapid urbanization has greatly altered nitrogen (N) cycling from regional to global scales. Compared to natural forests, urban forests receive much more external N inputs with distinctive abundances of stable N isotope (δ15 N). However, the large-scale pattern of soil δ15 N and its imprint on plant δ15 N remain less well understood in urban forests. By collecting topsoil (0-20 cm) and leaf samples from urban forest patches in nine large cities across a north-south transect in eastern China, we analyzed the latitudinal trends of topsoil C:N ratio and δ15 N as well as the correlations between tree leaf δ15 N and topsoil δ15 N. We further explored the spatial variation of topsoil δ15 N explained by corresponding climatic, edaphic, vegetation-associated, and anthropogenic drivers. Our results showed a significant increase of topsoil C:N ratio towards higher latitudes, suggesting lower N availability at higher latitudes. Topsoil δ15 N also increased significantly at higher latitudes, being opposite to the latitudinal trend of soil N availability. The latitudinal trend of topsoil δ15 N was mainly explained by mean annual temperature, mean annual precipitation, and atmospheric deposition of both ammonium and nitrate. Consequently, tree leaf δ15 N showed significant positive correlations with topsoil δ15 N across all sampled plant species and functional types. Our findings reveal a distinctive latitudinal trend of δ15 N in urban forests and highlight an important role of anthropogenic N sources in shaping the large-scale pattern of urban forest 15 N signature.
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Affiliation(s)
- Nan Xia
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China
- School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Enzai Du
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China
- School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Yang Tang
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China
- School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Hongbo Guo
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China
- School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
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18
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Geng M, Li X, Mu H, Yu G, Chai L, Yang Z, Liu H, Huang J, Liu H, Ju Z. Human footprints in the Global South accelerate biomass carbon loss in ecologically sensitive regions. GLOBAL CHANGE BIOLOGY 2023; 29:5881-5895. [PMID: 37565368 DOI: 10.1111/gcb.16900] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 07/10/2023] [Indexed: 08/12/2023]
Abstract
Human activities have placed significant pressure on the terrestrial biosphere, leading to ecosystem degradation and carbon losses. However, the full impact of these activities on terrestrial biomass carbon remains unexplored. In this study, we examined changes in global human footprint (HFP) and human-induced aboveground biomass carbon (AGBC) losses from 2000 to 2018. Our findings show an increasing trend in HFP globally, resulting in the conversion of wilderness areas to highly modified regions. These changes have altered global biomes' habitats, particularly in tropical and subtropical regions. We also found accelerated AGBC loss driven by HFP expansion, with a total loss of 19.99 ± 0.196 PgC from 2000 to 2018, especially in tropical regions. Additionally, AGBC is more vulnerable in the Global South than in the Global North. Human activities threaten natural habitats, resulting in increasing AGBC loss even in strictly protected areas. Therefore, scientifically guided planning of future human activities is crucial to protect half of Earth through mitigation and adaptation under future risks of climate change and global urbanization.
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Affiliation(s)
- Mengqing Geng
- College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Xuecao Li
- College of Land Science and Technology, China Agricultural University, Beijing, China
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Haowei Mu
- School of Geography and Ocean Science, Nanjing University, Nanjing, China
| | - Guojiang Yu
- College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Li Chai
- International College, China Agricultural University, Beijing, China
| | - Zhongwen Yang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Haimeng Liu
- Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Jianxi Huang
- College of Land Science and Technology, China Agricultural University, Beijing, China
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Han Liu
- Key Laboratory of Land Consolidation and Rehabilitation, Land Consolidation and Rehabilitation Center, Ministry of Natural Resources, Beijing, China
| | - Zhengshan Ju
- Key Laboratory of Land Consolidation and Rehabilitation, Land Consolidation and Rehabilitation Center, Ministry of Natural Resources, Beijing, China
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19
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Tang H, Hou K, Wu S, Liu J, Ma L, Li X. Interpretation of the coupling mechanism of ecological security and urbanization based on a Computation-Verification-Coupling framework: Quantitative analysis of sustainable development. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 263:115294. [PMID: 37499388 DOI: 10.1016/j.ecoenv.2023.115294] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 07/16/2023] [Accepted: 07/21/2023] [Indexed: 07/29/2023]
Abstract
In recent decades, China's rapid urbanization has produced numerous economic benefits while simultaneously creating substantial risks to ecological security. China's 14th Five-Year Plan and the United Nations Sustainable Development Goals (SDGs) have recently explicitly called for the coordinated development of ecological security and urbanization. Given this context, it is important to explore the mechanism by which ecological security and urbanization are coupled and coordinated to promote sustainable development. In this study, an index of the relationship between ecological security and urbanization was established via high-resolution data, and a "Computation-Verification-Coupling" (CVC) framework was constructed. The accuracy of the ecological security index was verified using a linear regression model, and the coordination level between ecological security and urbanization was analyzed via a coupled coordination model (CCM). The results revealed a steady increase in the ecological security index from 2010 to 2020; the proportion of the area above the medium level increased from 63.1 % to 74.1 %. The urbanization index in core counties exhibited rapid growth, with level V urbanized areas expanding from 5.5 % to 9.9 %. The ecological security verification model produced a coefficient of determination (R²) of 0.75685, indicating a satisfactory degree of predictive capability. From 2010-2020, the coupled coordination improved, with the high coordination area accounting for 48.8 % and the extreme discoordination area decreasing from 1.8 % to 1.0 %. Coordinated development exhibited a stable progression, characterized by a cyclical evolution from initial coupling to antagonistic coupling and finally to coordinated development. This framework can be used not only to investigate the relationship between ecological security and urbanization but also to provide a quantifiable measure of progress toward achieving the SDGs.
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Affiliation(s)
- Haojie Tang
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an 710048, China
| | - Kang Hou
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an 710048, China.
| | - Siqi Wu
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an 710048, China
| | - Jiawei Liu
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an 710048, China
| | - Lixia Ma
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an 710048, China
| | - Xuxiang Li
- School of Human Settlements and Civil Engineering, Xi'an Jiao Tong University, Xi'an 710049, China
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20
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Sun G, Li Z, Zhang A, Wang X, Yan K, Jia X, Liu Q, Li J. A 10-m resolution impervious surface area map for the greater Mekong subregion from remote sensing images. Sci Data 2023; 10:607. [PMID: 37689803 PMCID: PMC10492804 DOI: 10.1038/s41597-023-02518-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 08/30/2023] [Indexed: 09/11/2023] Open
Abstract
High-resolution and multi-temporal impervious surface area maps are crucial for capturing rapidly developing urbanization patterns. However, the currently available relevant maps for the greater Mekong subregion suffer from coarse resolution and low accuracy. Addressing this issue, our study focuses on the development of accurate impervious surface area maps at 10-m resolution for this region for the period 2016-2022. To accomplish this, we present a new machine-learning framework implemented on the Google Earth Engine platform that merges Sentinel-1 Synthetic Aperture Radar images and Sentinel-2 Multispectral images to extract impervious surfaces. Furthermore, we also introduce a training sample migration strategy that eliminates the collection of additional training samples and automates multi-temporal impervious surface area mapping. Finally, we perform a quantitative assessment with validation samples interpreted from Google Earth. Results show that the overall accuracy and kappa coefficient of the final impervious surface area maps range from 92.75% to 92.93% and 0.854 to 0.857, respectively. This dataset provides comprehensive measurements of impervious surface coverage and configuration that will help to inform urban studies.
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Affiliation(s)
- Genyun Sun
- College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, 266580, China
- Laboratory for Marine Resources Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266237, China
| | - Zheng Li
- College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, 266580, China
| | - Aizhu Zhang
- College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, 266580, China
| | - Xin Wang
- College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, 266580, China
| | - Kai Yan
- Center for GeoData and Analysis, State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.
| | - Xiuping Jia
- School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT2600, Australia
| | - Qinhuo Liu
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
| | - Jing Li
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China.
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21
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Chen B, Kayiranga A, Ge M, Ciais P, Zhang H, Black A, Xiao X, Yuan W, Zeng Z, Piao S. Anthropogenic activities dominated tropical forest carbon balance in two contrary ways over the Greater Mekong Subregion in the 21st century. GLOBAL CHANGE BIOLOGY 2023; 29:3421-3432. [PMID: 36949006 DOI: 10.1111/gcb.16688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 02/11/2023] [Indexed: 05/16/2023]
Abstract
The tropical forest carbon (C) balance threatened by extensive socio-economic development in the Greater Mekong Subregion (GMS) in Asia is a notable data gap and remains contentious. Here we generated a long-term spatially quantified assessment of changes in forests and C stocks from 1999 to 2019 at a spatial resolution of 30 m, based on multiple streams of state-of-the-art high-resolution satellite imagery and in situ observations. Our results show that (i) about 0.54 million square kilometers (21.0% of the region) experienced forest cover transitions with a net increase in forest cover by 4.3% (0.11 million square kilometers, equivalent to 0.31 petagram of C [Pg C] stocks); (ii) forest losses mainly in Cambodia, Thailand, and in the south of Vietnam, were also counteracted by forest gains in China due mainly to afforestation; and (iii) at the national level during the study period an increase in both C stocks and C sequestration (net C gain of 0.087 Pg C) in China from new plantation, offset anthropogenetic emissions (net C loss of 0.074 Pg C) mainly in Cambodia and Thailand from deforestation. Political, social, and economic factors significantly influenced forest cover change and C sequestration in the GMS, positively in China while negatively in other countries, especially in Cambodia and Thailand. These findings have implications on national strategies for climate change mitigation and adaptation in other hotspots of tropical forests.
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Affiliation(s)
- Baozhang Chen
- School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, 210044, Nanjing, China
- State Key Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 100101, Beijing, China
- University of Chinese Academy of Sciences, 100190, Beijing, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, 210023, Nanjing, China
| | - Alphonse Kayiranga
- State Key Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 100101, Beijing, China
- University of Chinese Academy of Sciences, 100190, Beijing, China
| | - Mengyu Ge
- School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, 210044, Nanjing, China
| | - Philippe Ciais
- Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191, Gif-sur-Yvette, France
| | - Huifang Zhang
- State Key Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 100101, Beijing, China
| | - Andy Black
- Faculty of Land and Food Systems, University of British Columbia, British Columbia, Vancouver, Canada
| | - Xiangming Xiao
- Department of Microbiology and Plant Biology, Center for Earth Observation and Modeling, University of Oklahoma, Oklahoma, Norman, USA
| | - Wenping Yuan
- School of Atmospheric Sciences, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Zhuhai Key Laboratory of Dynamics Urban Climate and Ecology, Sun Yat-sen University, 510245, Guangdong, Zhuhai, China
| | - Zhenzhong Zeng
- State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Shilong Piao
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, 100871, Beijing, China
- Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, 100085, Beijing, China
- Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, 100085, Beijing, China
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22
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Li X, Wang P, Wang W, Zhang H, Shi S, Xue T, Lin J, Zhang Y, Liu M, Chen R, Kan H, Meng X. Mortality burden due to ambient nitrogen dioxide pollution in China: Application of high-resolution models. ENVIRONMENT INTERNATIONAL 2023; 176:107967. [PMID: 37244002 DOI: 10.1016/j.envint.2023.107967] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 04/07/2023] [Accepted: 05/07/2023] [Indexed: 05/29/2023]
Abstract
BACKGROUND A large gap exists between the latest Global Air Quality Guidelines (AQG 2021) and Chinese air quality standards for NO2. Assessing whether and to what extent air quality standards for NO2 should be tightened in China requires a comprehensive understanding of the spatiotemporal characteristics of population exposure to ambient NO2 and related health risks, which have not been studied to date. OBJECTIVE We predicted ground NO2 concentrations with high resolution in mainland China, explored exposure characteristics to NO2 pollution, and assessed the mortality burden attributable to NO2 exposure. METHODS Daily NO2 concentrations in 2019 were predicted at 1-km spatial resolution in mainland China using random forest models incorporating multiple predictors. From these high-resolution predictions, we explored the spatiotemporal distribution of NO2, population and area percentages with NO2 exposure exceeding criterion levels, and premature deaths attributable to long- and short-term NO2 exposure in China. RESULTS The cross-validation R2and root mean squared error of the NO2 predicting model were 0.80 and 7.78 μg/m3, respectively,at the daily level in 2019.The percentage of people (population number) with annual NO2 exposure over 40 μg/m3 in mainland China in 2019 was 10.40 % (145,605,200), and it reached 99.68 % (1,395,569,840) with the AQG guideline value of 10 μg/m3. NO2 levels and population exposure risk were elevated in urban areas than in rural. Long- and short-term exposures to NO2 were associated with 285,036 and 121,263 non-accidental deaths, respectively, in China in 2019. Tightening standards in steps gradually would increase the potential health benefit. CONCLUSION In China, NO2 pollution is associated with significant mortality burden. Spatial disparities exist in NO2 pollution and exposure risks. China's current air quality standards may no longer objectively reflect the severity of NO2 pollution and exposure risk. Tightening the national standards for NO2 is needed and will lead to significant health benefits.
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Affiliation(s)
- Xinyue Li
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200302, China
| | - Peng Wang
- Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai 200438, China
| | - Weidong Wang
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200302, China
| | - Hongliang Zhang
- Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai 200438, China
| | - Su Shi
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200302, China
| | - Tao Xue
- Institute of Reproductive and Child Health/National Health Commission Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, China
| | - Jintai Lin
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Yuhang Zhang
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Mengyao Liu
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Renjie Chen
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200302, China
| | - Haidong Kan
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200302, China
| | - Xia Meng
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200302, China; Shanghai Typhoon Institute/CMA, Shanghai Key Laboratory of Meteorology and Health, Shanghai 200030, China.
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23
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Dong J, Li B, Li Y, Zhou R, Gan C, Zhao Y, Liu R, Yang Y, Wang T, Liao H. Atmospheric ammonia in China: Long-term spatiotemporal variation, urban-rural gradient, and influencing factors. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 883:163733. [PMID: 37116808 DOI: 10.1016/j.scitotenv.2023.163733] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 03/16/2023] [Accepted: 04/21/2023] [Indexed: 05/03/2023]
Abstract
In recent years, atmospheric ammonia (NH3) concentrations have increased in China. Ammonia control has become one of the next hot topics in air pollution mitigation with the increasing cost of acid gas emission reduction. In this study, using Infrared Atmospheric Sounding Interferometer (IASI) satellite observations, we analyzed the spatiotemporal distribution, the urban-rural gradient of the vertical column densities (VCDs) of NH3 and the contribution of influencing factors (meteorology, social, atmospheric acid gases, and NH3 emissions) in China from 2008 to 2019 using hotspot analysis, circular gradient analysis, geographical and temporal weighted regression, and some other methods. Our results showed that NH3 VCDs in China have significantly increased (31.88 %) from 2008 to 2019, with the highest occurring in North China Plain. The average NH3 VCDs in urban areas were significantly higher than those in rural areas, and the urban-rural gap in NH3 VCDs was widening. The results of circular gradient analysis showed an overall decreasing trend in NH3 VCDs along the urban-rural gradient. We used a geographically and temporally weighted regression model to analyze the contribution of various influencing factors to NH3 VCDs: meteorology (30.13 %), social (27.40 %), atmospheric acid gases (23.20 %), and NH3 emissions (19.28 %) factors. The results showed substantial spatiotemporal differences in the influencing factors. Atmospheric acid gas was the main reason for the increase in NH3 VCDs from 2008 to 2019. A more thorough understanding of the spatiotemporal distribution, urban-rural variations, and factors influencing NH3 in China will aid in developing control strategies to reduce PM2.5.
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Affiliation(s)
- Jinyan Dong
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Baojie Li
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China.
| | - Yan Li
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Rui Zhou
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Cong Gan
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Yongqi Zhao
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Rui Liu
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Yating Yang
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Teng Wang
- College of Oceanography, Hohai University, Nanjing 210098, China
| | - Hong Liao
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
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24
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Xue T, Tong M, Wang M, Yang X, Wang Y, Lin H, Liu H, Li J, Huang C, Meng X, Zheng Y, Tong D, Gong J, Zhang S, Zhu T. Health Impacts of Long-Term NO 2 Exposure and Inequalities among the Chinese Population from 2013 to 2020. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:5349-5357. [PMID: 36959739 DOI: 10.1021/acs.est.2c08022] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Nitrogen dioxide (NO2) is associated with mortality and many other adverse health outcomes. In 2021, the World Health Organization established a new NO2 air quality guideline (AQG) (annual average <10 μg/m3). However, the burden of diseases attributable to long-term NO2 exposure above the AQG is unknown in China. Nitrogen oxide is a major air pollutant in populous cities, which are disproportionately impacted by NO2; this represents a form of environmental inequality. We conducted a nationwide risk assessment of premature deaths attributable to long-term NO2 exposure from 2013 to 2020 based on the exposure-response relationship, high-resolution annual NO2 concentrations, and gridded population data (considering sex, age, and residence [urban vs rural]). We calculated health metrics including attributable deaths, years of life lost (YLL), and loss of life expectancy (LLE). Inequality in the distribution of attributable deaths and YLLs was evaluated by the Lorenz curve and Gini index. According to the health impact assessments, in 2013, long-term NO2 exposure contributed to 315,847 (95% confidence interval [CI]: 306,709-319,269) premature deaths, 7.90 (7.68-7.99) million YLLs, and an LLE of 0.51 (0.50-0.52) years. The high-risk subgroup (top 20%) accounted for 85.7% of all NO2-related deaths and 85.2% of YLLs, resulting in Gini index values of 0.81 and 0.67, respectively. From 2013 to 2020, the estimated health impact from NO2 exposure was significantly reduced, but inequality displayed a slightly increasing trend. Our study revealed a considerable burden of NO2-related deaths in China, which were disproportionally frequent in a small high-risk subgroup. Future clean air initiatives should focus not only on reducing the average level of NO2 exposure but also minimizing inequality.
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Affiliation(s)
- Tao Xue
- Institute of Reproductive and Child Health/National Health Commission Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing 100191, China
- Center for Environment and Health, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Mingkun Tong
- Institute of Reproductive and Child Health/National Health Commission Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing 100191, China
| | - Meng Wang
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, New York 14214, United States
- Research and Education in Energy, Environment and Water Institute, University at Buffalo, Buffalo, New York 14214, United States
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington 98115, United States
| | - Xinyue Yang
- College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Yanying Wang
- College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Huan Lin
- College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Hengyi Liu
- Institute of Reproductive and Child Health/National Health Commission Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing 100191, China
| | - Jiajianghui Li
- Institute of Reproductive and Child Health/National Health Commission Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing 100191, China
| | - Conghong Huang
- College of Land Management, Nanjing Agricultural University, Nanjing 210095, China
- National & Local Joint Engineering, Research Center for Rural Land Resources Use and Consolidation, Nanjing 210095, China
| | - Xia Meng
- School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education, and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China
| | - Yixuan Zheng
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing 100012, China
| | - Dan Tong
- Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Jicheng Gong
- SKL-ESPC, College of Environmental Sciences and Engineering, Center for Environment and Health, Peking University, Beijing 100871, China
| | - Shiqiu Zhang
- College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Tong Zhu
- SKL-ESPC, College of Environmental Sciences and Engineering, Center for Environment and Health, Peking University, Beijing 100871, China
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25
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Zhao F, Yang L, Tang J, Fang L, Yu X, Li M, Chen L. Urbanization-land-use interactions predict antibiotic contamination in soil across urban-rural gradients. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 867:161493. [PMID: 36634779 DOI: 10.1016/j.scitotenv.2023.161493] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 01/05/2023] [Accepted: 01/05/2023] [Indexed: 06/17/2023]
Abstract
Antibiotics ubiquitously occur in soils and pose a potential threat to ecosystem health. Concurrently, urbanization and land-use intensification have transformed soil ecosystems, but how they affect antibiotic contamination remain largely unknown. Therefore, we profiled a broad-scale pattern of antibiotics in soil from agricultural lands and green spaces across urbanization gradients, and explored the hypothetical models to verify the effects of urbanization and land-use intensity on antibiotic contamination. The results showed that antibiotic concentrations and seasonality were higher in agricultural soil than in green spaces, which respectively showed linear or hump-shaped declines along with the increasing distance to urban centers. However, the response of antibiotic pollution to land-use intensity depended strongly on the urbanization level. More importantly, interactions between urbanization and land-use explained, on average, 59.6 % of the variation in antibiotic concentrations in soil across urbanization gradients. The proposed interactions can predict the non-linear changes in soil vulnerability to antibiotic contamination. Our study revealed that the urbanization can modulate the effects of land-use intensity on antibiotic concentration and seasonality in the soil environment, and that there is high stress on peri-urban soil ecosystems due to ongoing land-use changes arising from rapid urbanization processes.
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Affiliation(s)
- Fangkai Zhao
- School of Ecology and Environmental Sciences, Yunnan University, Kunming 650500, China; State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Lei Yang
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jianfeng Tang
- CAS Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Li Fang
- Key Laboratory of Health Risk Factors for Seafood of Zhejiang Province, Zhoushan Municipal Center for Disease Control and Prevention, Zhoushan 316021, China
| | - Xinwei Yu
- Key Laboratory of Health Risk Factors for Seafood of Zhejiang Province, Zhoushan Municipal Center for Disease Control and Prevention, Zhoushan 316021, China
| | - Min Li
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liding Chen
- School of Ecology and Environmental Sciences, Yunnan University, Kunming 650500, China; State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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26
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Xiong H, Ma C, Li M, Tan J, Wang Y. Landslide susceptibility prediction considering land use change and human activity: A case study under rapid urban expansion and afforestation in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 866:161430. [PMID: 36623663 DOI: 10.1016/j.scitotenv.2023.161430] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 01/01/2023] [Accepted: 01/03/2023] [Indexed: 06/17/2023]
Abstract
China has been subject to rapid urban expansion and afforestation since the economic reform in 1978. However, the influence of land use and cover changes (LUCCs) and human activities on landslide occurrence is often ignored in landslide susceptibility mapping and zonation (LSMZ). In this study, Enshi City, China, was selected as the study area because of dramatic LUCCs during the last two decades. This study divided landslide affecting factors (AFs) into base affecting factors (BAFs) and land-related affecting factors (LAFs), and 15 landslide susceptibility maps were created by three different types of models. The results showed that the combination 6 of heuristic multi-layer perceptron model with LAFs (HMLP-LAFC6) model obtained the highest model performance. In addition, any factor combinations of HMLP-LAF model outperformed other two types of models, and the use of land use and cover (LULC) in different periods as well as LUCCs may significantly impact the model performance. Given that land policy adjustments are normally core drivers of LUCC in China, a land planning based LSMZ framework was proposed, which is suitable for LSMZ in rapid LUCC regions with radical land policies. Finally, this paper strongly suggests developing more hybrid models that coupling dynamic AFs, clarifying the quantitative boundaries of time-irrelevant and dynamic AFs, increasing the accuracy of LULC prediction, and improving the abilities of bilateral understanding for effective, integrated, and systematic management of land planning and landslide hazards.
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Affiliation(s)
- Hanxiang Xiong
- School of Environmental Studies, China University of Geosciences, Wuhan 430074, China.
| | - Chuanming Ma
- School of Environmental Studies, China University of Geosciences, Wuhan 430074, China.
| | - Minghong Li
- School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
| | - Jiayao Tan
- School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
| | - Yuzhou Wang
- School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
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Li T, Dong Y. Phased and polarized development of ecological quality in the rapidly-urbanized Pearl River Delta, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:36176-36189. [PMID: 36547841 DOI: 10.1007/s11356-022-24852-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 12/15/2022] [Indexed: 06/17/2023]
Abstract
Urbanization is one of the most significant human activities in the Anthropocene, with profound impacts on environmental quality. The lack of an understanding about the relationship between urbanization and ecological quality limits the effectiveness of urban planning and ecological policies in alleviating urban ecological problems. Based on the integrated ecological index RSEI (remote sensing ecological index), this study attempts to clarify the spatio-temporal characteristics of ecological quality in an urbanization process through an empirical study in China's Pearl River Delta (PRD) and explores the relationship between urbanization and ecological quality. Our results show that the ecological development of the PRD in the period of 1986 to 2019 was a phased and polarized process. Two periods are distinguished, based on RSEI dispersion: the period of 1986 to 2003, with slight dispersion, and the period of 2004 to 2019, with higher dispersion. Plain areas show evidence of ecological degradation, whereas a considerable improvement was observed in hilly areas. Industrialization and consummation of legal system were the driving factors behind the phased development of ecological quality, while the differences in landform and land management were the fundamental reasons for the spatial differentiation of ecological quality. The findings of this study provide experience and enlightenment for ecological management and sustainable development strategies in regions seeking rapid growth in their prosperity.
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Affiliation(s)
- Ting Li
- School of Environmental and Chemical Engineering, Foshan University, Foshan, 528011, China
- Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-Sen University, Guangzhou, 510275, China
| | - Yuxiang Dong
- Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-Sen University, Guangzhou, 510275, China.
- School of Resources and Planning, Xinhua College of Guangzhou, Guangzhou, 510520, China.
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28
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Li S, Zhu L, Zhang L, Zhang G, Ren H, Lu L. Urbanization-Related Environmental Factors and Hemorrhagic Fever with Renal Syndrome: A Review Based on Studies Taken in China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3328. [PMID: 36834023 PMCID: PMC9960491 DOI: 10.3390/ijerph20043328] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 02/03/2023] [Accepted: 02/08/2023] [Indexed: 06/18/2023]
Abstract
Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease that has threatened Chinese residents for nearly a century. Although comprehensive prevent and control measures were taken, the HFRS epidemic in China presents a rebounding trend in some areas. Urbanization is considered as an important influencing factor for the HFRS epidemic in recent years; however, the relevant research has not been systematically summarized. This review aims to summarize urbanization-related environmental factors and the HFRS epidemic in China and provide an overview of research perspectives. The literature review was conducted following the PRISMA protocol. Journal articles on the HFRS epidemic in both English and Chinese published before 30 June 2022 were identified from PubMed, Web of Science, and Chinese National Knowledge Infrastructure (CNKI). Inclusion criteria were defined as studies providing information on urbanization-related environmental factors and the HFRS epidemic. A total of 38 studies were included in the review. Changes brought by urbanization on population, economic development, land use, and vaccination program were found to be significantly correlated with the HFRS epidemic. By changing the ecological niche of humans-affecting the rodent population, its virus-carrying rate, and the contact opportunity and susceptibility of populations-urbanization poses a biphasic effect on the HFRS epidemic. Future studies require systematic research framework, comprehensive data sources, and effective methods and models.
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Affiliation(s)
- Shujuan Li
- National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Lingli Zhu
- National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Lidan Zhang
- Department of Public Health, Faculty of Medicine, Imperial College London, London W2 1PG, UK
| | - Guoyan Zhang
- Beijing Dong Cheng Center for Disease Control and Prevention, Beijing 100010, China
| | - Hongyan Ren
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Liang Lu
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
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29
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Zhao A, Liu X, Zheng Z. Evaluation of urban expansion and the impacts on vegetation in Chinese Loess Plateau: a multi-scale study. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:6021-6032. [PMID: 35986853 DOI: 10.1007/s11356-022-22633-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 08/16/2022] [Indexed: 06/15/2023]
Abstract
Vegetation degradation caused by rapid urban expansion is a pressing global challenge. Focusing on the Chinese Loess Plateau (CLP), we use satellite observations from 2000 to 2017 to evaluate the spatiotemporal pattern of urban expansion, and its imprint on vegetation across old urban, new urban, urban, non-urban areas as well as the entire urbanization intensity (UI) gradient (from 0 to 100%). We found a massive increase of urban impervious surface area (UISA) in the CLP from 2000 to 2017, and an uneven expansion of UISA at different urban agglomerations and cities. Less green were found in urban and new urban areas, while old urban and non-urban areas generally showed an improved greening pattern. In addition, the annual maximum EVI (EVImax) differences between urban and non-urban areas were - 0.0995 on average from 2000 to 2017. The Guanzhong Plain urban agglomeration (GPUA) witnessed the most significant EVImax differences (- 0.120), and the Ningxia Yanhuang urban agglomeration (NYUA) witnessed the lowest EVImax differences (- 0.012). The EVImax showed significantly decreased trends along the entire spectrum of urbanization gradient for 97.4% (38 of 39) cities and five urban agglomerations. The most significant decrease was found in the GUPA (slope = - 0.0197/10a, p < 0.01), while the smallest drop was found in the NYUA (slope = - 0.011/10a, p < 0.01). This study offered a fundamental support for understanding the vegetation variation along the urban-rural gradient, which may help stakeholders to make better ecological management policies for urban vegetation in ecologically fragile areas.
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Affiliation(s)
- Anzhou Zhao
- College of Mining and Geomatics, Hebei University of Engineering, Handan, 056038, China.
| | - Xiaoqian Liu
- College of Applied Arts and Science, Beijing Union University, Beijing, 100191, China.
| | - Zhoutao Zheng
- Key Laboratory of Ecosystem Network Observation and Modeling, Beijing, China
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30
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Wang Y, Li B, Yang G. Stream water quality optimized prediction based on human activity intensity and landscape metrics with regional heterogeneity in Taihu Basin, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:4986-5004. [PMID: 35978234 DOI: 10.1007/s11356-022-22536-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 08/10/2022] [Indexed: 06/15/2023]
Abstract
The driving effects of landscape metrics on water quality have been acknowledged widely, however, the guiding significance of human activity intensity and landscape metrics based on reference conditions for water environment management remains to be explored. Thus, we used the self-organized map, long- and short-term memory (LSTM) algorithm, and geographic detectors to simulate the response of human activity intensity and landscape metrics to water quality in Taihu Lake Basin, China. Fitting results of LSTM displayed that the accuracy was acceptable, and scenario 2 (regional heterogeneity) was more efficient than scenario 1 (regional consistent) in the improvement of water quality. In the driving analysis for the reference conditions, clusters I and II (urban agglomeration areas) were mainly affected by the amount of production wastewater per unit of developed land and the amount of livelihood wastewater per unit of developed land, respectively. Their optimal values were 0.09 × 103 t/km2 (reduction of 35.71%) and 0.2 × 103 t/km2 (reduction of 4.76%). Cluster III (agricultural production areas) was mainly affected by interference intensity, and the optimal value was 2.17 (increased 38.22%), and cluster IV (ecological forest areas) was mainly affected by the fragmentation of cropland, and the optimal value was 1.14 (reduction of 1.72%). The research provides a reference for the prediction of water quality response and presents an ecological and economic sustainability way for watershed governance.
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Affiliation(s)
- Ya'nan Wang
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
- College of Nanjing, University of Chinese Academy of Sciences, Nanjing, 211135, China
| | - Bing Li
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
- College of Nanjing, University of Chinese Academy of Sciences, Nanjing, 211135, China
| | - Guishan Yang
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China.
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China.
- College of Nanjing, University of Chinese Academy of Sciences, Nanjing, 211135, China.
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31
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Zhou D, Sun S, Li Y, Zhang L, Huang L. A multi-perspective study of atmospheric urban heat island effect in China based on national meteorological observations: Facts and uncertainties. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 854:158638. [PMID: 36089010 DOI: 10.1016/j.scitotenv.2022.158638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 09/05/2022] [Accepted: 09/06/2022] [Indexed: 06/15/2023]
Abstract
The atmospheric urban heat island (AUHI) effect, traditionally measured by in-situ sensors mounted on fixed meteorological stations, has been extensively studied by different and imperfect methods. However, facts and uncertainties of the AUHI estimates revealed by the different methods are not well understood at a large scale. Here we examined the spatial-temporal variations of the AUHI effects from multiple perspectives in China's 86 large cities as revealed by national-level meteorological observations at 2-m height from 1981 to 2017. We find relatively consistent patterns of larger urban heating effects in daily minimum temperature, winter, and Northeast China than their counterparts in terms of multiyear mean intensity (AUHII), long-term trend (△AUHII), and contribution to local warming (according to the CTRUMR "urban minus rural" and CTROMR "observation minus reanalysis" methods). Concurrently, a cooling impact or a reduction in the heating effect has been observed in some cities randomly, especially in daily maximum temperature. On average across cities, the AUHII, △AUHI, CTRUMR, and CTROMR for the daily mean temperature amount to 0.33 °C, 0.013 °C 10a-1, 53 %, and 23 % at an annual mean time scale, respectively. Nevertheless, the poor representativeness of weather stations, discrepancies among the quantification methods, nonlinearity of the long-term tendencies, and coupling effects with rural crop land use activities lead to large uncertainties of the AUHI estimates. Our results emphasize the limitations of national-level meteorological stations in characterizing AUHI in China and suggest that the urban heat island remains a "well described but rather poorly understood" phenomenon warranting further investigation by a combined uses of multiple techniques like high-density sensor networks, remote sensing techniques, and high-resolution numerical models.
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Affiliation(s)
- Decheng Zhou
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science &Technology, Nanjing 210044, China
| | - Shanlei Sun
- International Joint Laboratory on Climate and Environment Change, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Yu Li
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science &Technology, Nanjing 210044, China
| | - Liangxia Zhang
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science &Technology, Nanjing 210044, China
| | - Lin Huang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
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32
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Wu Y, Han Z, Faisal Koko A, Zhang S, Ding N, Luo J. Analyzing the Spatio-Temporal Dynamics of Urban Land Use Expansion and Its Influencing Factors in Zhejiang Province, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16580. [PMID: 36554460 PMCID: PMC9779644 DOI: 10.3390/ijerph192416580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 11/30/2022] [Accepted: 12/08/2022] [Indexed: 06/17/2023]
Abstract
The 21st century expansion of built-up areas due to rapid urbanization has recently been at the forefront of global land use/land cover research. Knowledge of the changing dynamics of urban land use is crucial for the monitoring of urbanization and the promotion of sustainable urban development. In this paper, Zhejiang Province was selected as the study area. It is a region with rapid urban growth located along the southeastern coast of China, with a highly developed economy but with a shortage of land resources. We employed remotely sensed and socio-economic panel data for the period between 1990 and 2020 to monitor urban land use changes and utilized the spatial Durbin model (SDM) to examine the urbanization process and the various driving factors of rapid urban expansion in Zhejiang Province, China, from 1990 to 2020. The study's results revealed substantial urban growth of about 6899.59 km2, i.e., 6.6%, whereas agricultural land decreased by 4320.68 km2, i.e., 4.19%. The rapid urban development was primarily attributed to the transformation of farmlands, forestlands, and water bodies into built-up areas by nearly 86.9%, 6.94%, and 6.06%, respectively. The built-up areas revealed features of spatial clustering. The study showed that the expansion hotspots were mainly distributed within the urban fabric of cities such as Hangzhou, Ningbo, Jinhua-Yiwu, and Wenzhou-Taizhou. The results further revealed the substantial influence of urban growth on the local areas of the province. As the core explanatory variables, population and economic development significantly promoted local urban expansion. The study's findings indicated a positive spatial spillover effect as regards the influence of economic development on the study area's urban growth, whereas the spatial spillover effect of the population was negative. Therefore, economic development was a major driving factor contributing immensely to the expansion of urban areas in Zhejiang Province, especially in the 26 mountainous counties of the province. The study enriches our understanding of the transformation of LULC and the changing dynamics of urban areas in China and provides the necessary research data that are vital for urban land-use planners and decision-makers to overcome the negative consequences of the expansion of urban areas due to the continuous economic growth of China.
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Affiliation(s)
- Yue Wu
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
- International Center for Architecture and Urban Development Studies, Zhejiang University, Hangzhou 310058, China
| | - Zexu Han
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
- International Center for Architecture and Urban Development Studies, Zhejiang University, Hangzhou 310058, China
| | - Auwalu Faisal Koko
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
- International Center for Architecture and Urban Development Studies, Zhejiang University, Hangzhou 310058, China
| | - Siyuan Zhang
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
- International Center for Architecture and Urban Development Studies, Zhejiang University, Hangzhou 310058, China
| | - Nan Ding
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
- International Center for Architecture and Urban Development Studies, Zhejiang University, Hangzhou 310058, China
| | - Jiayang Luo
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
- International Center for Architecture and Urban Development Studies, Zhejiang University, Hangzhou 310058, China
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33
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Gao M, Gu X, Liu Y, Zhan Y, Wei X, Yu H, Liang M, Weng C, Ding Y. An Improved Spatiotemporal Data Fusion Method for Snow-Covered Mountain Areas Using Snow Index and Elevation Information. SENSORS (BASEL, SWITZERLAND) 2022; 22:8524. [PMID: 36366220 PMCID: PMC9657560 DOI: 10.3390/s22218524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 11/01/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
Remote sensing images with high spatial and temporal resolution in snow-covered areas are important for forecasting avalanches and studying the local weather. However, it is difficult to obtain images with high spatial and temporal resolution by a single sensor due to the limitations of technology and atmospheric conditions. The enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) can fill in the time-series gap of remote sensing images, and it is widely used in spatiotemporal fusion. However, this method cannot accurately predict the change when there is a change in surface types. For example, a snow-covered surface will be revealed as the snow melts, or the surface will be covered with snow as snow falls. These sudden changes in surface type may not be predicted by this method. Thus, this study develops an improved spatiotemporal method ESTARFM (iESTARFM) for the snow-covered mountain areas in Nepal by introducing NDSI and DEM information to simulate the snow-covered change to improve the accuracy of selecting similar pixels. Firstly, the change in snow cover is simulated according to NDSI and DEM. Then, similar pixels are selected according to the change in snow cover. Finally, NDSI is added to calculate the weights to predict the pixels at the target time. Experimental results show that iESTARFM can reduce the bright abnormal patches in the land area compared to ESTARFM. For spectral accuracy, iESTARFM performs better than ESTARFM with the root mean square error (RMSE) being reduced by 0.017, the correlation coefficient (r) being increased by 0.013, and the Structural Similarity Index Measure (SSIM) being increased by 0.013. For spatial accuracy, iESTARFM can generate clearer textures, with Robert's edge (Edge) being reduced by 0.026. These results indicate that iESTARFM can obtain higher prediction results and maintain more spatial details, which can be used to generate dense time series images for snow-covered mountain areas.
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Affiliation(s)
- Min Gao
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xingfa Gu
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China
| | - Yan Liu
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100049, China
| | - Yulin Zhan
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100049, China
| | - Xiangqin Wei
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100049, China
| | - Haidong Yu
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Man Liang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chenyang Weng
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yaozong Ding
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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34
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Xu H, Jia A, Song X, Bai Y. Suitability evaluation of carrying capacity and utilization patterns on tidal flats of Bohai Rim in China. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 319:115688. [PMID: 35834852 DOI: 10.1016/j.jenvman.2022.115688] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 06/29/2022] [Accepted: 07/04/2022] [Indexed: 06/15/2023]
Abstract
Tidal flats in the Bohai Rim are facing threats from human activities. Quantifying the carrying capacity and suitability of tidal flats is of great significance to the regional environment and resource management. In this study, the existing social and natural data were collected and the natural conditions of tidal flats, e.g., the distributions and utilization patterns, were investigated through remote sensing image interpretation and field investigation in the Bohai Rim. Then, a multi-index evaluation system was developed with indexes organized under the framework of the analytic hierarchy process (AHP) and the Drivers-State-Impact (DSI) framework, processed by fuzzy evaluation, and weighted by the entropy method. The studies show that the rapid expansion of industry-port-town, salt pans or aquafarms in the Bohai Rim during 1990-2020 squeezed the space of tidal flats. Despite the limitation of the declining resource condition, the carrying capacity of tidal flats in the Bohai Rim increased slightly during 2000-2018 because of the great improvement in economic and ecological conditions. We estimate 59.93% of the land resources are suitable for economic development while others are temporarily unsuitable for reclamation due to their high ecological importance. The land use data and macro-evaluation system of tidal flat utilization patterns herein can provide references for coastal resource management and ecological restoration.
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Affiliation(s)
- Haijue Xu
- State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300350, China; Institute for Sedimentation on River and Coastal Engineering, Tianjin University, Tianjin 300350, China.
| | - Ao Jia
- State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300350, China.
| | - Xiaolong Song
- State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300350, China; Institute for Sedimentation on River and Coastal Engineering, Tianjin University, Tianjin 300350, China.
| | - Yuchuan Bai
- State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300350, China; Institute for Sedimentation on River and Coastal Engineering, Tianjin University, Tianjin 300350, China.
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35
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Yu Z, Ciais P, Piao S, Houghton RA, Lu C, Tian H, Agathokleous E, Kattel GR, Sitch S, Goll D, Yue X, Walker A, Friedlingstein P, Jain AK, Liu S, Zhou G. Forest expansion dominates China's land carbon sink since 1980. Nat Commun 2022; 13:5374. [PMID: 36100606 PMCID: PMC9470586 DOI: 10.1038/s41467-022-32961-2] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 08/25/2022] [Indexed: 12/04/2022] Open
Abstract
Carbon budget accounting relies heavily on Food and Agriculture Organization land-use data reported by governments. Here we develop a new land-use and cover-change database for China, finding that differing historical survey methods biased China's reported data causing large errors in Food and Agriculture Organization databases. Land ecosystem model simulations driven with the new data reveal a strong carbon sink of 8.9 ± 0.8 Pg carbon from 1980 to 2019 in China, which was not captured in Food and Agriculture Organization data-based estimations due to biased land-use and cover-change signals. The land-use and cover-change in China, characterized by a rapid forest expansion from 1980 to 2019, contributed to nearly 44% of the national terrestrial carbon sink. In contrast, climate changes (22.3%), increasing nitrogen deposition (12.9%), and rising carbon dioxide (8.1%) are less important contributors. This indicates that previous studies have greatly underestimated the impact of land-use and cover-change on the terrestrial carbon balance of China. This study underlines the importance of reliable land-use and cover-change databases in global carbon budget accounting.
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Affiliation(s)
- Zhen Yu
- Institute of Ecology and School of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing, China
- Key Laboratory of Forest Ecology and Environment, China's National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Beijing, China
- Research Center for Global Changes and Ecosystem Carbon Sequestration & Mitigation, Nanjing University of Information Science & Technology, Nanjing, China
| | - Philippe Ciais
- Laboratoire des Sciences du Climat et l'Environnement, CEA CNRS UVSQ Gif-sur-Yvette, Gif-sur-Yvette, France
| | - Shilong Piao
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | | | - Chaoqun Lu
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA
| | - Hanqin Tian
- Schiller Institute for Integrated Science and Society, Department of Earth and Environmental Sciences, Boston College, Chestnut Hill, MA, USA
| | - Evgenios Agathokleous
- Institute of Ecology and School of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing, China
| | - Giri Raj Kattel
- School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing, China
- Department of Infrastructure Engineering, University of Melbourne, Parkville, Melbourne, Australia
- Department of Hydraulic Engineering, Tsinghua University, Beijing, China
| | - Stephen Sitch
- College of Life and Environmental Sciences, University of Exeter, Exeter, UK
| | - Daniel Goll
- Laboratoire des Sciences du Climat et l'Environnement, CEA CNRS UVSQ Gif-sur-Yvette, Gif-sur-Yvette, France
| | - Xu Yue
- School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, China
| | | | - Pierre Friedlingstein
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
| | - Atul K Jain
- University of Illinois, Urbana-Champaign, Urbana, IL, USA
| | - Shirong Liu
- Key Laboratory of Forest Ecology and Environment, China's National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Beijing, China.
| | - Guoyi Zhou
- Institute of Ecology and School of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing, China.
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36
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Liu X, Samat A, Li E, Wang W, Abuduwaili J. Self-Trained Deep Forest with Limited Samples for Urban Impervious Surface Area Extraction in Arid Area Using Multispectral and PolSAR Imageries. SENSORS (BASEL, SWITZERLAND) 2022; 22:6844. [PMID: 36146193 PMCID: PMC9504763 DOI: 10.3390/s22186844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/28/2022] [Accepted: 09/07/2022] [Indexed: 06/16/2023]
Abstract
Impervious surface area (ISA) has been recognized as a significant indicator for evaluating levels of urbanization and the quality of urban ecological environments. ISA extraction methods based on supervised classification usually rely on a large number of manually labeled samples, the production of which is a time-consuming and labor-intensive task. Furthermore, in arid areas, man-made objects are easily confused with bare land due to similar spectral responses. To tackle these issues, a self-trained deep-forest (STDF)-based ISA extraction method is proposed which exploits the complementary information contained in multispectral and polarimetric synthetic aperture radar (PolSAR) images using limited numbers of samples. In detail, this method consists of three major steps. First, multi-features, including spectral, spatial and polarimetric features, are extracted from Sentinel-2 multispectral and Chinese GaoFen-3 (GF-3) PolSAR images; secondly, a deep forest (DF) model is trained in a self-training manner using a limited number of samples for ISA extraction; finally, ISAs (in this case, in three major cities located in Central Asia) are extracted and comparatively evaluated. The experimental results from the study areas of Bishkek, Tashkent and Nursultan demonstrate the effectiveness of the proposed method, with an overall accuracy (OA) above 95% and a Kappa coefficient above 0.90.
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Affiliation(s)
- Ximing Liu
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
- Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Alim Samat
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
- Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Erzhu Li
- School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
| | - Wei Wang
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
- Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jilili Abuduwaili
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
- Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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Yang C, Liu H, Li Q, Wang X, Ma W, Liu C, Fang X, Tang Y, Shi T, Wang Q, Xu Y, Zhang J, Li X, Xu G, Chen J, Su M, Wang S, Wu J, Huang L, Li X, Wu G. Human expansion into Asian highlands in the 21st Century and its effects. Nat Commun 2022; 13:4955. [PMID: 36002452 PMCID: PMC9402921 DOI: 10.1038/s41467-022-32648-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Accepted: 08/09/2022] [Indexed: 11/08/2022] Open
Abstract
Most intensive human activities occur in lowlands. However, sporadic reports indicate that human activities are expanding in some Asian highlands. Here we investigate the expansions of human activities in highlands and their effects over Asia from 2000 to 2020 by combining earth observation data and socioeconomic data. We find that ∼23% of human activity expansions occur in Asian highlands and ∼76% of these expansions in highlands comes from ecological lands, reaching 95% in Southeast Asia. The expansions of human activities in highlands intensify habitat fragmentation and result in large ecological costs in low and lower-middle income countries, and they also support Asian developments. We estimate that cultivated land net growth in the Asian highlands contributed approximately 54% in preventing the net loss of the total cultivated land. Moreover, the growth of highland artificial surfaces may provide living and working spaces for ∼40 million people. Our findings suggest that highland developments hold dual effects and provide new insight for regional sustainable developments.
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Affiliation(s)
- Chao Yang
- MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China
- School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China
| | - Huizeng Liu
- MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, China
| | - Qingquan Li
- MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China.
| | - Xuqing Wang
- Center for Hydrogeology and Environmental Geology, China Geological Survey, Nanjing, 210000, China
| | - Wei Ma
- School of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, China
| | - Cuiling Liu
- MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China
- School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China
| | - Xu Fang
- MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Yuzhi Tang
- MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China
| | - Tiezhu Shi
- MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China
- School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China
| | - Qibiao Wang
- Anhui Zhonghui Urban Planning Survey & Design Institute, Tongling, 244000, China
| | - Yue Xu
- MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China
| | - Jie Zhang
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
| | - Xuecao Li
- College of Land Science and Technology, China Agricultural University, Beijing, 100083, China
| | - Gang Xu
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China
| | - Junyi Chen
- Key Laboratory of Virtual Geographic Environment of the Ministry of Education, Nanjing Normal University, Nanjing, 210000, China
| | - Mo Su
- Shenzhen Urban Planning and Land Resource Research Center, Shenzhen, 518034, China
| | - Shuying Wang
- MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China
| | - Jinjing Wu
- MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China
| | - Leping Huang
- MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China
| | - Xue Li
- MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China
| | - Guofeng Wu
- MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China.
- School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China.
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38
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Wang L, De Boeck HJ, Chen L, Song C, Chen Z, McNulty S, Zhang Z. Urban warming increases the temperature sensitivity of spring vegetation phenology at 292 cities across China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 834:155154. [PMID: 35413347 DOI: 10.1016/j.scitotenv.2022.155154] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 03/31/2022] [Accepted: 04/06/2022] [Indexed: 06/14/2023]
Abstract
Urban spring phenology changes governed by multiple biological and environmental factors significantly impact urban ecosystem functions and services. However, the temporal changes in spring phenology (i.e., the start of the vegetation growing season, SOS) and the magnitude of SOS sensitivity to temperature in urban settings are not well understood compared with natural ecosystems. Therefore, we explored warming impacts on SOS across 292 rural and urban areas from 2001 to 2016. We found that warming occurred in 79.9% of urban areas and 61.3% of rural areas. This warming advanced SOS in 78.3% of the urban settings and 72.8% of the rural areas. The accelerated rate of SOS in urban settings was significantly higher (-0.52 ± 0.86 days/year) than in rural areas (-0.09 ± 0.69 days/year). Moreover, SOS was significantly more sensitive to warming in urban areas (-2.86 ± 3.57 days/°C) than in rural areas (-1.57 ± 3.09 days/°C), driven by urban-rural differences in climatic (precipitation, temperature, and warming speed) and vegetation factors. Precipitation contributed the most had the highest relative importance for controlling SOS, at 45% and 63% for urban and rural areas, respectively. These findings provide a new understanding of the impacts of urbanization and climate change on vegetation phenology. Moreover, our results have implications for urban environment impacts on ecosystems and human health.
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Affiliation(s)
- Liqun Wang
- Jixian National Forest Ecosystem Observation and Research Station, CNERN, School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, PR China; Key Laboratory of Soil and Water Conservation and Desertification Combating, State Forestry and Grassland Administration, Beijing 100083, PR China
| | - Hans J De Boeck
- Research group PLECO (Plants and Ecosystems), Universiteit Antwerpen, Wilrijk 2610, Belgium
| | - Lixin Chen
- Jixian National Forest Ecosystem Observation and Research Station, CNERN, School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, PR China; Key Laboratory of Soil and Water Conservation and Desertification Combating, State Forestry and Grassland Administration, Beijing 100083, PR China
| | - Conghe Song
- Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Institute for the Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Zuosinan Chen
- Jixian National Forest Ecosystem Observation and Research Station, CNERN, School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, PR China; Key Laboratory of Soil and Water Conservation and Desertification Combating, State Forestry and Grassland Administration, Beijing 100083, PR China
| | - Steve McNulty
- Eastern Forest Environment Threat Assessment Center, USDA Forest Service, Research Triangle Park, NC 27709, USA
| | - Zhiqiang Zhang
- Jixian National Forest Ecosystem Observation and Research Station, CNERN, School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, PR China; Key Laboratory of Soil and Water Conservation and Desertification Combating, State Forestry and Grassland Administration, Beijing 100083, PR China.
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39
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Xu N, Chen W, Pan S, Liang J, Bian J. Evolution Characteristics and Formation Mechanism of Production-Living-Ecological Space in China: Perspective of Main Function Zones. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:9910. [PMID: 36011547 PMCID: PMC9407866 DOI: 10.3390/ijerph19169910] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/04/2022] [Accepted: 08/08/2022] [Indexed: 06/15/2023]
Abstract
The main function zone (MFZ) is the major strategy of China's economic development and ecological environment protection. Clarifying the logical relationship between "MFZ strategy" and "territorial spatial layout" is vital to construct regional economic layout and territorial spatial supporting system of high-quality development. However, few studies have revealed the evolution process and formation mechanism of the production-living-ecological space (PLES) structure of China's MFZ over a long period of time. To bridge the gap, based on the land use dataset in China from 1980 to 2020, this study analyzed the evolution patterns of PLES in China's MFZs using multiple methods and measured the formation mechanism of PLES in different types of MFZs with the GeoDetector model. Results showed that the spatial structure of China's national territory has evolved drastically in the past 40 years, showing significant horizontal regional differentiation and vertical gradient differentiation. Ecological space has been continuously decreasing, while production space and living space have been continuously increasing, and the evolution of PLES varied significantly in different MFZs. During the study period, the gravity center of PLES in China all moved westward. The spatial distribution pattern of production space and living space was from northeast to southwest, and the ecological space was from east to west. The evolution of China's territorial spatial structure was subject to the combined effects of natural and socio-economic factors, exhibiting significant differences in different MFZs. Land use intensity had the most prominent influence on the formation of PLES, followed by elevation. The influences of different factors on PLES structure were strengthened mainly through two types of nonlinear enhancement and dual-factor enhancement. This study can provide scientific support for the optimal management and high-quality development of territorial space in China.
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Affiliation(s)
- Ning Xu
- School of Political Science and Public Administration, Henan Normal University, Xinxiang 453007, China
| | - Wanxu Chen
- School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
| | - Sipei Pan
- College of Public Administration, Nanjing Agricultural University, Nanjing 210095, China
| | - Jiale Liang
- School of Geography and Oceanography Sciences, Nanjing University, Nanjing 210023, China
| | - Jiaojiao Bian
- School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
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40
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Urban Sustainability: Integrating Socioeconomic and Environmental Data for Multi-Objective Assessment. SUSTAINABILITY 2022. [DOI: 10.3390/su14159142] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The large concentration of the world’s population in cities, along with rapid urbanization, have brought numerous environmental and socioeconomic challenges to sustainable urban systems (SUS). However, current SUS studies focus heavily on ecological aspects, rely on SUS indicators that are not supported by available data, lack comprehensive analytical frameworks, and neglect SUS regional differences. This paper develops a novel approach to assessing urban sustainability from regional perspectives using commonly enumerated socioeconomic statistics. It integrates land use and land cover change data and ecosystem service values, applies data mining analytics to derive SUS indicators, and evaluates SUS states as trade-offs among relevant SUS indicators. This synthetic approach is called the integrated socioeconomic and land-use data mining–based multi-objective assessment (ISL-DM-MOA). The paper presents a case study of urban sustainability development in cities and counties in Inner Mongolia, China, which face many environmental and sustainable development problems. The case study identifies two SUS types: (1) several large cities that boast well-developed economies, diversified industrial sectors, vital transportation locations, good living conditions, and cleaner environments; and (2) a few small counties that have a small population, small urban construction areas, extensive natural grasslands, and primary grazing economies. The ISL-DM-MOA framework innovatively synthesizes currently available socioeconomic statistics and environmental data as a unified dataset to assess urban sustainability as a total socio-environmental system. ISL-DM-MOA deviates from the current indicator approach and advocates the notion of a data-mining-driven approach to derive urban sustainability dimensions. Furthermore, ISL-DM-MOA diverges from the concept of a composite score for determining urban sustainability. Instead, it promotes the concept of Pareto Front as a choice set of sustainability candidates, because sustainability varies among nations, regions, and locations and differs between political, economic, environmental, and cultural systems.
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41
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Annual Maps of Built-Up Land in Guangdong from 1991 to 2020 Based on Landsat Images, Phenology, Deep Learning Algorithms, and Google Earth Engine. REMOTE SENSING 2022. [DOI: 10.3390/rs14153562] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Accurate mapping of built-up land is essential for urbanization monitoring and ecosystem research. At present, remote sensing is one of the primary means used for real-time and accurate surveying and mapping of built-up land, due to the long time series and multi-information advantages of existing remote sensing images and the ability to obtain highly precise year-by-year built-up land maps. In this study, we obtained feature-enhanced data regarding built-up land from Landsat images and phenology-based algorithms and proposed a method that combines the use of the Google Earth Engine (GEE) and deep learning approaches. The Res-UNet++ structural model was improved for built-up land mapping in Guangdong from 1991 to 2020. Experiments show that overall accuracy of built-up land map in the study area in 2020 was 0.99, the kappa coefficient was 0.96, user accuracy of built-up land was 0.98, and producer accuracy was 0.901. The trained model can be applied to other years with good results. The overall accuracy (OA) of the assessment results every five years was above 0.97, and the kappa coefficient was above 0.90. From 1991 to 2020, built-up land in Guangdong has expanded significantly, the area of built-up land has increased by 71%, and the proportion of built-up land has increased by 3.91%. Our findings indicate that the combined approach of GEE and deep learning algorithms can be developed into a large-scale, long time-series of remote sensing classification techniques framework that can be useful for future land-use mapping research.
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42
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Analysis of the Spatio-Temporal Characteristics of Nanjing’s Urban Expansion and Its Driving Mechanisms. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2022. [DOI: 10.3390/ijgi11070406] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The expansion and evolution of urban areas are the most perceptible manifestations of the transformation of the urban spatial form. This study uses remote sensing images of Nanjing from 2001, 2006, 2011, 2016, and 2021, along with socio-economic data to analyse the spatio-temporal characteristics of the city’s urban expansion. Furthermore, we utilize a binary logistic regression to quantitatively analyse the driving forces in each stage. We find that from 2001 to 2021, Nanjing’s urban area expanded approximately 3.97 times. Notably, the city started moving from a stage of medium-speed development to rapid development in 2006, and then slowed down and returned to medium-speed development in 2011. The urban land mainly expanded in the north, northeast, southeast, and southwest directions in a lopsided cross-shape roughly along the northwest-southeast direction; meanwhile, the city’s centre of gravity continuously moved towards the southeast. Among the driving factors, neighbourhood (distance from planned commercial centres, railways, and highways), topography, and geolocation (distance from the Yangtze River, and elevation) had a greater, albeit inhibitory effect on urban expansion. However, the effects of different socio-economic factors (GDP per capita, resident population, secondary and tertiary industry, etc.) varied across different time periods.
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Li L, Zhu A, Huang L, Wang Q, Chen Y, Ooi MCG, Wang M, Wang Y, Chan A. Modeling the impacts of land use/land cover change on meteorology and air quality during 2000-2018 in the Yangtze River Delta region, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 829:154669. [PMID: 35314237 DOI: 10.1016/j.scitotenv.2022.154669] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 03/08/2022] [Accepted: 03/15/2022] [Indexed: 06/14/2023]
Abstract
The land use/land cover (LULC) change in the fast-developing city clusters of China exhibits impacts on both the meteorology and air quality. However, this effect, especially in the Yangtze River Delta (YRD), has not been well quantified. In this study, the LULC data are extracted from Landsat satellite imageries for year 2000 and 2018 for the YRD region. The Weather Research and Forecasting with Chemistry (WRF/Chem) model is applied to investigate the impact of historical LULC change on regional meteorology and air pollution over the YRD region during the past two decades. Two simulation scenarios are performed with two sets of LULC data to represent the pre-urbanization (LULC of year 2000) and the most recent urban pattern (LULC of year 2018). Results indicate that rapid urbanization leads to an increase of monthly mean 2-m temperature by 0.4-2.1 °C but decrease of the 10-m wind speed by 0.5-1.3 m/s in urban areas; the maximum increase of daytime planetary boundary layer height (PBLH) in July and November is 289 and 132 m, respectively. Affected by favorable changes in the meteorological conditions due to LULC change, the PM2.5 concentrations in most urban areas show a decreasing trend, especially during the nighttime in summer. On the contrary, surface ozone (O3) concentration in urban areas has increased by 7.2-9.8 ppb in summer and 1.9-2.1 ppb in winter. Changes in O3 concentration are inversely proportional to changes in NOx and the spatial distribution of PM2.5. Areas with higher O3 concentration are consistent with areas of higher temperature and lower wind speed. Our findings reveal that LULC changes during the past years bring observable changes in air pollutant concentrations, which should not be neglected in the YRD region regarding air quality trends as well as policy evaluations under the warming threat.
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Affiliation(s)
- Li Li
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
| | - Ansheng Zhu
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
| | - Ling Huang
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
| | - Qing Wang
- Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Yixiao Chen
- Department of Civil Engineering, University of Nottingham Malaysia, Semenyih 43500, Selangor, Malaysia
| | - Maggie Chel Gee Ooi
- Institute of Climate Change (IPI), National University of Malaysia (UKM), 43600 Bangi, Selangor, Malaysia
| | - Min Wang
- Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Yangjun Wang
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China.
| | - Andy Chan
- Department of Civil Engineering, University of Nottingham Malaysia, Semenyih 43500, Selangor, Malaysia.
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44
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Characteristics of Changes in Urban Land Use and Efficiency Evaluation in the Qinghai–Tibet Plateau from 1990 to 2020. LAND 2022. [DOI: 10.3390/land11050757] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The Qinghai–Tibet Plateau has seen decades of changes in land use/cover as a result of urbanization and regional planning policy. Research on the efficiency of social development aids in the pursuit of social and environmental sustainability. Based on CLUD and socioeconomic statistical data, this study systematically analyses the spatiotemporal evolution characteristics of urban land use in the Qinghai–Tibet Plateau and evaluates its social development efficiency from three perspectives—the holistic, the municipal, and urban hierarchy—by using indicators such as the Moran index, land use efficiency, and urban expansion speed and proportion. Results show that the urbanization rate climbed from 21.26% to 54.95%, and the area of urban lands increased from 201.93 km2 to 796.59 km2 from 1990 to 2020, with urban lands expanding from the Lanzhou–Xining City Area to the central and south of the Qinghai–Tibet Plateau. The holistic urban land use efficiency grew from 1.14 to 1.53, while the UPD decreased slightly from 1.44 to 1.31, and the UED increased steadily from 1.40 to 12.97 per decade. Moreover, we should pay attention to the rational allocation of land in human, social and ecosystem terms to comprehensively improve the quality of urbanization across the plateau.
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45
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Impact of Urbanization on Seismic Risk: A Study Based on Remote Sensing Data. SUSTAINABILITY 2022. [DOI: 10.3390/su14106132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
The management of seismic risk is an important aspect of social development. However, urbanization has led to an increase in disaster-bearing bodies, making it more difficult to reduce seismic risk. To understand the changes in seismic risk associated with urbanization and then adjust the risk management strategy, remote-sensing technology is necessary. By identifying the types of earthquake-bearing bodies, it is possible to estimate the seismic risk and then determine the changes. For this purpose, this study proposes a set of algorithms that combine deep-learning models with object-oriented image classification and extract building information using multisource remote sensing data. Following this, the area of the building is estimated, the vulnerability is determined, and, lastly, the economic and social impacts of an earthquake are determined based on the corresponding ground motion level and fragility function. Our study contributes to the understanding of changes in seismic risk caused by urbanization processes and offers a practical reference for updating seismic risk management, as well as a methodological framework to evaluate the effectiveness of seismic policies. Experimental results indicate that the proposed model is capable of effectively capturing buildings’ information. Through verification, the overall accuracy of the classification of vulnerability types reaches 86.77%. Furthermore, this study calculates social and economic losses of the core area of Tianjin Baodi District in 2011, 2012, 2014, 2016, 2018, 2020, and 2021, obtaining changes in seismic risk in the study area. The result shows that for rare earthquakes at night, although the death rate decreased from 2.29% to 0.66%, the possible death toll seems unchanged, due to the increase in population.
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46
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Characterizing the Patterns and Trends of Urban Growth in Saudi Arabia’s 13 Capital Cities Using a Landsat Time Series. REMOTE SENSING 2022. [DOI: 10.3390/rs14102382] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Development and a growing population in Saudi Arabia have led to a substantial increase in the size of its urban areas. This sustained development has increased policymakers’ need for reliable data and analysis regarding the patterns and trends of urban expansion throughout the country. Although previous studies on urban growth in Saudi cities exist, there has been no comprehensive research that focused on all 13 regional capitals within the country. Our study addressed this gap by producing a new annual long-term dataset of 30 m spatial resolution that covered 35 years (1985–2019) and maintained a high overall accuracy of annual classifications across the study period, ranging between 93 and 98%. Utilizing the continuous change detection and classification (CCDC) algorithm and all available Landsat data, we classified Landsat pixels into urban and non-urban classes with an annual frequency and quantified urban land cover change over these 35 years. We implemented a stratified random sampling design to assess the accuracy of the annual classifications and the multi-temporal urban change. The results revealed that Saudi capitals experienced massive urban growth, from 1305.28 ± 348.71 km2 in 1985 to 2704.94 ± 554.04 km2 in 2019 (± values represent the 95% confidence intervals). In addition to the high accuracy of the annual classifications, the overall accuracy of the multi-temporal urban change map was also high and reached 91%. The urban expansion patterns varied from city to city and from year to year. Most capital cities showed clear growth patterns of edge development, that is, a continuous expansion of built-up lands radiating from existing urban areas. This study provides distinct insights into the urban expansion characteristics of each city in Saudi Arabia and a synoptic view of the country as a whole over the past four decades. Our results provided a dataset that can be used as the foundation for future socioeconomic and environmental studies.
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47
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Shao M, Wu L, Li F, Lin C. Spatiotemporal Dynamics of Ecosystem Services and the Driving Factors in Urban Agglomerations: Evidence From 12 National Urban Agglomerations in China. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.804969] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The natural environment provides multiple ecosystem services for urban development and human quality of life. Given that current cities interact with each other and form urban agglomerations, understanding the spatiotemporal changes in ecosystem services and the driving forces is crucial for sustainable urban development. Using 12 national-level urban agglomerations as a case study, this paper quantifies the spatial patterns of multiple ecosystem service values from 2000 to 2015 and assesses how natural and socioeconomic factors contribute to such changes by using ordinary least squares (OLS) and geographically weighted regression (GWR). The results show the following: (1) spatial discrepancies of ecosystem services exist both in and between urban agglomerations, and ecosystem service values are reduced in more than 70% of urban agglomerations at a rate ranging from 0.02 to 4.27%; (2) elevation, precipitation, and fraction of woodland have positive impacts on ecosystem service values in urban agglomerations; while gross domestic product (GDP), population, and proportion of built-up area have negative effects; (3) both natural and social driving factors impact the ecosystem services of different urban agglomeration in different ways, according to the differences in their driving degrees. We categorized 12 urban agglomerations in China into six typical types: natural-factor dominated, socioeconomic-factor dominated, policy dominated, balanced, natural-factor inclined, and socioeconomic-factor inclined. Our results can be used to inform decision makers and urban planners to propose explicit location strategies to balance natural protection and socioeconomic development and ultimately promote sustainable urbanization across the nation.
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48
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Liu S, Wang Y, Zhang GJ, Wei L, Wang B, Yu L. Contrasting influences of biogeophysical and biogeochemical impacts of historical land use on global economic inequality. Nat Commun 2022; 13:2479. [PMID: 35513425 PMCID: PMC9072699 DOI: 10.1038/s41467-022-30145-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 04/11/2022] [Indexed: 11/30/2022] Open
Abstract
Climate change has significant implications for macro-economic growth. The impacts of greenhouse gases and anthropogenic aerosols on economies via altered annual mean temperature (AMT) have been studied. However, the economic impact of land-use and land-cover change (LULCC) is still unknown because it has both biogeochemical and biogeophysical impacts on temperature and the latter differs in latitudes and disturbed land surface types. In this work, based on multi-model simulations from the Coupled Model Intercomparison Project Phase 6, contrasting influences of biogeochemical and biogeophysical impacts of historical (1850-2014) LULCC on economies are found. Their combined effects on AMT result in warming in most countries, which harms developing economies in warm climates but benefits developed economies in cold climates. Thus, global economic inequality is increased. Besides the increased AMT by the combined effects, day-to-day temperature variability is enhanced in developing economies but reduced in developed economies, which further deteriorates global economic inequality.
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Affiliation(s)
- Shu Liu
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, 100084, China
| | - Yong Wang
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, 100084, China.
| | - Guang J Zhang
- Scripps Institution of Oceanography, La Jolla, CA, USA
| | - Linyi Wei
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, 100084, China
| | - Bin Wang
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, 100084, China
- State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
- College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Le Yu
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, 100084, China
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Deng L, Han Z, Pu W, Bao R, Wang Z, Wu Q, Qiao J. Impacts of Human Activities and Climate Change on Water Storage Changes in Shandong Province, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:35365-35381. [PMID: 35060057 DOI: 10.1007/s11356-022-18759-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 01/15/2022] [Indexed: 06/14/2023]
Abstract
The over-exploitation of water resources causes water resource depletion, which threatens water security, human life, and social and economic development. Only by clarifying the spatial pattern, changing trends, and influencing factors of water storage can we promote the rational development of water resources and relieve the pressure on water resources. However, there is still a lack of research on these aspects. In this study, the water-scarce area in Shandong Province, China, was selected to quantify the spatial and temporal changes in the terrestrial water storage (TWS) and groundwater storage (GWS) over the past 30 years. Nighttime light data were used to characterize the urbanization level (UL) and explore the effects of human activities (i.e., UL) and climate change (temperature and precipitation) on the TWS and GWS. The results show that 1) from 1990 to 2018, the overall TWS exhibited a significant decreasing trend (- 0.084 cm yr-1). The change trend of the GWS was consistent with that of the TWS (- 0.516 m3 yr-1). Spatially, there was significant spatial heterogeneity in the trend of the TWS and GWS. At the grid and prefectural scales, the TWS mainly exhibited a downward trend in the central and western regions, and an upward trend in the eastern region of Shandong Province. For the GWS, all cities exhibited a decreasing trend at the prefectural scale, whereas 92% of the regions exhibited a decreasing trend with less spatial heterogeneity at the grid scale. 2) Precipitation was the mean factor controlling the total amount of TWS and GWS in Shandong Province. Precipitation and temperature positively affected water storage, and the UL negatively affected it. At the prefectural scale, except for a few cities which were greatly influenced by the UL, the dominant factor of the TWS and GWS was precipitation in the other cities. At the grid scale, for the TWS, precipitation was the predominant factor in 51.82% of the entire region, followed by the UL (44.14%) and temperature (4.04%). For the GWS, precipitation was the predominant factor in 55.73% of the area, and the other 44.27% of the area was mainly influenced by the UL. Overall, precipitation and the UL were the key factors affecting the TWS and GWS. The results of this study provide a theoretical and decision-making basis for the optimal allocation and sustainable use of regional water resources.
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Affiliation(s)
- Longyun Deng
- College of Geography and Environment, Shandong Normal University, Jinan, 250358, China
| | - Zhen Han
- College of Geography and Environment, Shandong Normal University, Jinan, 250358, China
- QingDao Marine Remote Sensing Information Technology Co.,LTD, 266000, Qingdao, China
| | - Weixing Pu
- College of Geography and Environment, Shandong Normal University, Jinan, 250358, China
| | - Rong Bao
- College of Geography and Environment, Shandong Normal University, Jinan, 250358, China
| | - Zheye Wang
- Kinder Institute for Urban Research, Rice University, Houston, TX, 77005, USA
| | - Quanyuan Wu
- College of Geography and Environment, Shandong Normal University, Jinan, 250358, China.
- , Jinan, China.
| | - Jianmin Qiao
- College of Geography and Environment, Shandong Normal University, Jinan, 250358, China.
- , Jinan, China.
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50
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Mu H, Li X, Wen Y, Huang J, Du P, Su W, Miao S, Geng M. A global record of annual terrestrial Human Footprint dataset from 2000 to 2018. Sci Data 2022; 9:176. [PMID: 35440581 PMCID: PMC9018937 DOI: 10.1038/s41597-022-01284-8] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 02/18/2022] [Indexed: 11/25/2022] Open
Abstract
Human Footprint, the pressure imposed on the eco-environment by changing ecological processes and natural landscapes, is raising worldwide concerns on biodiversity and ecological conservation. Due to the lack of spatiotemporally consistent datasets of Human Footprint over a long temporal span, many relevant studies on this topic have been limited. Here, we mapped the annual dynamics of the global Human Footprint from 2000 to 2018 using eight variables that reflect different aspects of human pressures. The accuracy assessment revealed a good agreement between our mapped results and the previously developed datasets in different years. We found more than two million km2 of wilderness (i.e., regions with Human Footprint values below one) were lost over the past two decades. The biome dominated by mangroves experienced the most significant loss (i.e., above 5%) of wilderness, likely attributed to intensified human activities in coastal areas. The derived annual and spatiotemporally consistent global Human Footprint can be a fundamental dataset for many relevant studies about human activities and natural resources.
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Affiliation(s)
- Haowei Mu
- College of Land Science and Technology, China Agricultural University, Beijing, 100083, China
| | - Xuecao Li
- College of Land Science and Technology, China Agricultural University, Beijing, 100083, China.
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, 100083, China.
| | - Yanan Wen
- College of Land Science and Technology, China Agricultural University, Beijing, 100083, China
| | - Jianxi Huang
- College of Land Science and Technology, China Agricultural University, Beijing, 100083, China
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, 100083, China
| | - Peijun Du
- School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu, 221100, China
| | - Wei Su
- College of Land Science and Technology, China Agricultural University, Beijing, 100083, China
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, 100083, China
| | - Shuangxi Miao
- College of Land Science and Technology, China Agricultural University, Beijing, 100083, China
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, 100083, China
| | - Mengqing Geng
- College of Land Science and Technology, China Agricultural University, Beijing, 100083, China
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