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Xia F, Zhao Z, Niu X, Wang Z. Integrated pollution analysis, pollution area identification and source apportionment of heavy metal contamination in agricultural soil. JOURNAL OF HAZARDOUS MATERIALS 2024; 465:133215. [PMID: 38101021 DOI: 10.1016/j.jhazmat.2023.133215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 11/22/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023]
Abstract
Given the global prevalence of soil heavy metal contamination, knowledge concerning of soil environmental quality assessment, pollution area identification and source apportionment is critical for implementation of soil pollution prevention and safe utilization strategies. In this study, soil static environmental capacity (QI) for heavy metals was selected to evaluate pollution risks in agricultural soils of Wenzhou, southeast China. Combined with geostatistical methods, the pollution area was identified along with uncertainty analysis. Potential sources were quantitatively apportioned using a positive matrix factorization model (PMF). Results showed that agricultural soils in this study were mainly contaminated by Cd and Pb based on both Nemerow and QI indices. The environmental capacity assessment found more than 90% areas were identified as polluted soils for Qi-Zn, Qi-Cd and Qi-Pb, with minor uncertain areas. Cu was identified as having a high proportion of uncertain pollution area status, which was similar to the results of the integrated environmental capacity for all metals. PMF results indicated that industrial discharge, agrochemicals and parent material accounted for 32.1%, 32.2% and 35.7% of heavy metal accumulation in soils, respectively. Implementation of strict policies to reduce anthropogenic source emissions and remediate soil pollution are crucial to minimize metal pollution inputs, improve agricultural soil quality and enhance food safety.
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Affiliation(s)
- Fang Xia
- School of Life and Environmental Science, Shaoxing University, Shaoxing 312000, China; Zhejiang Provincial Key Laboratory of Watershed Sciences and Health, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China
| | - Zefang Zhao
- School of Life and Environmental Science, Shaoxing University, Shaoxing 312000, China
| | - Xiang Niu
- Shaoxing Academy of Agricultural Science, Shaoxing 312003, China
| | - Zhenfeng Wang
- Zhejiang Provincial Key Laboratory of Watershed Sciences and Health, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China.
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Vishwakarma S, Zhang X, Dobermann A, Heffer P, Zhou F. Global nitrogen deposition inputs to cropland at national scale from 1961 to 2020. Sci Data 2023; 10:488. [PMID: 37495587 PMCID: PMC10372001 DOI: 10.1038/s41597-023-02385-8] [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: 03/24/2023] [Accepted: 07/13/2023] [Indexed: 07/28/2023] Open
Abstract
Nitrogen (N) deposition is a significant nutrient input to cropland and consequently important for the evaluation of N budgets and N use efficiency (NUE) at different scales and over time. However, the spatiotemporal coverage of N deposition measurements is limited globally, whereas modeled N deposition values carry uncertainties. Here, we reviewed existing methods and related data sources for quantifying N deposition inputs to crop production on a national scale. We utilized different data sources to estimate N deposition input to crop production at national scale and compared our estimates with 14 N budget datasets, as well as measured N deposition data from observation networks in 9 countries. We created four datasets of N deposition inputs on cropland during 1961-2020 for 236 countries. These products showed good agreement for the majority of countries and can be used in the modeling and assessment of NUE at national and global scales. One of the datasets is recommended for general use in regional to global N budget and NUE estimates.
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Affiliation(s)
- Srishti Vishwakarma
- University of Maryland Center for Environmental Science Appalachian Laboratory, Frostburg, MD, USA
- Currently located at Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD, USA
| | - Xin Zhang
- University of Maryland Center for Environmental Science Appalachian Laboratory, Frostburg, MD, USA.
| | | | | | - Feng Zhou
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China
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Lu S, Liu J, Chen Y, Jiao Y. A multi-attribute decision-making method for the location selection of emergency rescue centers based on improved cumulative prospect theory under the background of ecological sustainable development. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-222556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The location decision of the emergency rescue center, as a necessary link in the early stage to ensure the smooth development of emergency management, has become increasingly important. This paper analyzes the research theories and methods of location problem at home and abroad. By considering the decision-making psychological behavior of decision-makers, based on the idea of cumulative prospect theory, combined with the Pythagorean fuzzy multi-attribute evaluation method, this paper proposes a Pythagorean fuzzy multi-attribute decision-making evaluation method based on improved cumulative prospect theory. This method is to sort the results of each decision-making scheme by calculating the comprehensive cumulative prospect value. Based on the utility curve improved cumulative prospect theory, the research first depicts the psychological behavior characteristics of various decision-making groups under different risk preferences, and then designs a distance measurement method based on the geometric center of Pythagorean fuzzy right triangle. The main core of the distance measurement method is to convert Pythagorean fuzzy numbers into Pythagorean fuzzy right triangles. In the aspect of attribute weight assignment, a subjective and objective weighting method based on the combination of value function and deviation method of improved cumulative prospect theory is proposed. Finally, the Pythagorean fuzzy multi-attribute decision-making method based on the improved cumulative prospect theory is realized through the selection of reference objects, the calculation of value function value, weight function value and cumulative prospect value. This study takes the site selection of emergency rescue center in the construction period of Ya’an Linzhi Section of Sichuan Tibet Railway in China as an example, and proposes 10 emergency rescue centers as alternatives. Considering the different preferences of decision-makers, the improved decision-making method proposed in this paper is used to obtain the most optimal site selection scheme under different decision-making preferences.
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Affiliation(s)
- Su Lu
- School of Civil Engineering, Inner Mongolia University of Technology, Hohhot, China
| | - Jiaxin Liu
- School of Civil Engineering, Inner Mongolia University of Technology, Hohhot, China
| | - Ying Chen
- College of Civil Engineering and Architecture, Shandong University of Science and Technology, Qingdao, China
| | - Yan Jiao
- Shandong Rongxin Whole Process Consulting Service Co. LTD, Jinan, China
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Jiang J. A multi-attribute decision-making method for the location selection of emergency rescue centers based on improved cumulative prospect theory under the background of ecological sustainable development. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-221301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
This research proposes a Pythagorean fuzzy multi-attribute decision-making evaluation method based on the improved cumulative prospect theory. The method ranks the decision-making results by calculating the comprehensive cumulative prospect value. Firstly, the research improves the cumulative prospect theory based on the utility curve, and describes the psychological and behavioral characteristics of various decision-making groups with different risk preferences. Then, a distance measure method based on the geometric center of the Pythagorean fuzzy right triangle is designed. The main core of the distance measure method is that it converts the Pythagorean fuzzy number into a Pythagorean fuzzy right triangle. In terms of attribute weighting, this research proposes a subjective and objective weighting method based on the combination of value function and deviation method of improved cumulative prospect theory. Finally, the Pythagorean fuzzy multi-attribute decision-making method based on the improved cumulative prospect theory is realized through the selection of reference object, the calculation of value function value, weight function value and cumulative prospect value. The results analysis and the comparison with other methods verify the effectiveness and advancement of the proposed decision-making method, especially that the proposed method has good applicability for the decision-making cases where the attribute value is Pythagorean fuzzy number, the attribute weight is unknown, and the psychological behavior of decision makers cannot be reflected.
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Affiliation(s)
- Jian Jiang
- Business School, Xinjiang University, Urumqi, China
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Liu H, Liu Y, Wang C, Zhao W, Liu S. Landscape pattern change simulations in Tibet based on the combination of the SSP-RCP scenarios. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021; 292:112783. [PMID: 34015616 DOI: 10.1016/j.jenvman.2021.112783] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 04/18/2021] [Accepted: 05/12/2021] [Indexed: 06/12/2023]
Abstract
Monitoring landscape pattern change can provide spatial explicit basis for future landscape management. The future socioeconomic and climate change drivers should be systematically combined in landscape pattern monitoring, while they are often regarded as independent parameters in landscape monitoring models. This study sought to project the detailed landscape pattern change based on landscape composition and configuration in Tibet by 2030, and combined the shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs). The results showed area of the unused land and forest will reduce by a minimum standard of 11.42 × 104 and 9.04 × 104 km2 from 2010 to 2030, respectively. Other land use types will increase, and the highest increase in grassland will be 9.30 × 105 km2. Combined SSP1 and RCP2.6 scenario show high landscape aggregation and low edge density on cultivated land, urban land and grassland in Tibet as a whole. However, in typical cultivated and urban landscape, the abovementioned rule is appeared in the combined SSP4 and RCP6.0 scenario. These findings stress the importance of systematically modeling the socioeconomic demand and climate change in landscape pattern monitoring, and using both landscape composition and configuration indexes for scenario evaluation.
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Affiliation(s)
- Hua Liu
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, 100875, Beijing, China; State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, 100875, Beijing, China
| | - Yanxu Liu
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, 100875, Beijing, China.
| | - Chenxu Wang
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, 100875, Beijing, China
| | - Wenwu Zhao
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, 100875, Beijing, China
| | - Shiliang Liu
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, 100875, Beijing, China
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Land Use-Driven Changes in Ecosystem Service Values and Simulation of Future Scenarios: A Case Study of the Qinghai–Tibet Plateau. SUSTAINABILITY 2021. [DOI: 10.3390/su13074079] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Global climate change and land use change arising from human activities affect the ecosystem service values (ESVs). Such impacts have increasingly become significant, especially in the Qinghai–Tibet Plateau (QTP). Major factors impeding the construction of China’s “ecological security barrier” are shifts in land-use patterns under rapid urbanization, irrational crop and animal husbandry activities, and tourism. In the present study, land use changes in the QTP in recent years were analyzed to determine their impacts on ESVs, followed by simulations of the interactive and evolutionary relationships between land use and ESVs under two scenarios: natural development scenarios and ecological protection scenarios. According to the results, the QTP land-use structure has a small change, and the main land use type is alpine grassland, followed by bare land and woodland. The stability of the major land use types is the key factor responsible for the overall increasing ESV trend. Different regions on the QTP had substantially varied ESVs. The northwest and southeast regions are mostly bare land, which is a concentrated area of low value of ecosystem services. A variety of land use types including grassland and woodland have been found in the humid and semi-humid areas of the central region, so the high value of ecosystem services is concentrated in this area to form a hot spot, with a Z value of 0.63–2.84. Simulations under the natural development and ecological protection scenarios revealed that land use changes guided by ecological policies were more balanced and the associated ESVs were relatively higher than those under the natural development scenario. Under a global climate change context, human activities on the QTP should be better managed. Sustainable development in the region could be facilitated by ensuring synchronization between resource availability and adopted socioeconomic activities.
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Spatially Explicit Reconstruction of Anthropogenic Grassland Cover Change in China from 1700 to 2000. LAND 2020. [DOI: 10.3390/land9080270] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Long-term anthropogenic land use and land cover changes (LULCCs) are regarded as an important component of past global change. The past 300 years have witnessed dramatic changes in LULCC in China, and this has resulted in the large-scale conversion of natural vegetation to agricultural landscapes. Studies of past LULCC in China have mainly focused on cropland and forest; however, estimates of grassland cover remain rare due to the scarcity of grassland-related historical documents. Based on a qualitative analysis of trends in grassland cover in China and their driving forces, we devised different reconstruction methods for grassland cover in eastern and western China and then developed a 10 km database of grassland cover in China for the past 300 years. The grassland area in western China decreased from 295.54 × 106 ha in 1700 to 269.78 × 106 ha in 2000 due to the increase in population and cropland, especially in northeastern China (Heilongjiang, Jilin, and Liaoning), Gan-Ning, and Xinjiang. In eastern China, grassland is degraded secondary vegetation characterized by shrub grassland and meadow grassland, which is scattered in the hills and mountains; its area increased from 7.30 × 106 ha in 1700 to 16.43 × 106 ha in 1950 due to the increase in the degraded land caused by deforestation.
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Exploring Spatiotemporal Pattern of Grassland Cover in Western China from 1661 to 1996. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16173160. [PMID: 31470688 PMCID: PMC6747138 DOI: 10.3390/ijerph16173160] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 08/27/2019] [Accepted: 08/27/2019] [Indexed: 11/17/2022]
Abstract
Historical grassland cover change is vital for global and regional environmental change modeling; however, in China, estimates of this are rare, and therefore, we propose a method to reconstruct grassland cover over the past 300 years. By synthesizing remote sensing-derived Chinese land use and land cover change (LULCC) data (1980–2015) and potential natural vegetation data simulated by the relationship between vegetation and environment, we first determined the potential extent of natural grassland vegetation (PENG) in the absence of human activities. Then we reconstructed grassland cover across western China between 1661 and 1996 at 10 km resolution by overlaying the Chinese historical cropland dataset (CHCD) over the PENG. As this land cover type has been significantly influenced by anthropogenic factors, the data show that the proportion of grassland in western China continuously decreased from 304.84 × 106 ha in 1661 to 277.69 × 106 ha in 1996. This reduction can be divided into four phases, comprising a rapid decrease between 1661 and 1724, a slow decrease between 1724 and 1873, a sharp decrease between 1873 and 1980, and a gradual increase since 1980. These reductions correspond to annual loss rates of 7.32 × 104 ha, 2.90 × 104 ha, 17.04 × 104 ha, and −2.37 × 104 ha, respectively. The data reconstructed here show that the decrease in grassland area between 1661 and 1724 was mainly limited to the Gan-Ning region (Gansu and Ningxia) and was driven by the early agricultural development policies of the Qing Dynasty. Grassland was extensively cultivated in northeastern China (Heilongjiang, Jilin, and Liaoning) and in the Xinjiang region between 1724 and 1980, a process which resulted from an exponential increase in immigrants to these provinces. The reconstruction results enable provide crucial data that can be used for modeling long-term climate change and carbon emissions.
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Spatio-Temporal Patterns of Land Use and Cover Change from 1990 to 2010: A Case Study of Jiangsu Province, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16060907. [PMID: 30871205 PMCID: PMC6466119 DOI: 10.3390/ijerph16060907] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2019] [Revised: 03/04/2019] [Accepted: 03/11/2019] [Indexed: 11/17/2022]
Abstract
Land use and cover change (LUCC) is one of the most significant parts of global environmental changes, which reflects the interaction between human society and natural resources. In China, the urbanization process is experiencing a rapid sprawl since the reform and open program in 1978, and there has been a serious change in situation in the human–land relationship. In this paper, taking Jiangsu province located in the eastern coastal developed region as an example, the historic evolution process of the land use situation from 1990 to 2010 was explored. Landsat images from three periods were analyzed, using the land use transition matrix model, the land use dynamic degree model, and the land use degree model to evaluate the LUCC of Jiangsu during two research periods from 1990 to 2000 and from 2000 to 2010. Additionally, logistic regression models and some quantitative analysis were applied to identify the major potential driving factors behind the LUCC during the research period based on different dimensions. The results showed the following: (1) the most obvious change was the continuous increase of built-up area and the decrease of arable land, which reflected the deterioration of the ecological environment and the accelerate of the urbanization trend. (2) The land use change dynamic degree from 2000 to 2010 was much greater than that from 1990 to 2000. (3) Socio-economic elements and human activities were the major driving forces of LUCC in Jiangsu province. Amongst these forces, the driving factors of the population change, GDP, per capita household income, and per capita housing area have an obvious effect on the arable land loss and the built-up area expansion.
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