Distribution and partitioning of heavy metals in sediments of the Xinjiang River in Poyang Lake Region, China

2014 ◽  
Vol 34 (3) ◽  
pp. 713-723 ◽  
Author(s):  
Yong Ji ◽  
Jie Zhang ◽  
Ronghui Li ◽  
Baozhu Pan ◽  
Liwei Zhang ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Poyang Lake ◽  
Poyang Lake Region ◽  
Lake Region

scholarly journals Source Apportionment of Heavy Metal Pollution in Agricultural Soils around the Poyang Lake Region Using UNMIX Model

2021 ◽  
Vol 13 (9) ◽  
pp. 5272
Author(s):  
Yanhong Li ◽  
Huifen Kuang ◽  
Chunhua Hu ◽  
Gang Ge
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Metal Pollution ◽  
Agricultural Soils ◽  
Poyang Lake ◽  
Pollution Sources ◽  
Average Content ◽  
Natural Background ◽  
Poyang Lake Region ◽  
Lake Region

Rapid urbanization and industrialization have caused the continuous discharge of heavy metals into the soils of China’s Poyang Lake region, where they pose a major threat to human health. Yet, the spatial characteristics of these heavy metals in farmland soils and their pollution sources in this region remain unclear. This study was conducted to document the pollution caused by heavy metals in the Poyang Lake region through sampling that consisted of the collection of 215 soil samples from agricultural fields. The UNMIX model provided identification of the sources causing heavy metal pollution and source contributions to soil pollution. ArcGIS was used to study the spatial distribution of the eleven heavy metals and to validate the apportionment of pollution sources provided by the UNMIX model. Soil concentrations of heavy metals were above the local background concentrations. The average content of eight heavy metals, including Cd, Mo, Zn, Cu, Sb, W, Pb, and Ni, was approximately 1–6 times greater than natural background levels (6.91, 2.0, 1.67, 1.53, 1.23, 1.38, 1.11, and 1.24, respectively), while the average content of V, Cr, and Co was lower than natural background levels. The average contents of Cr, Ni, Cu, Zn, Cd, and Pb were all lower than the screening levels for unacceptable risks in agricultural land soils. The percentage of Cd content exceeded the risk screening value in all sampling sites, up to 55%, indicating that agricultural soils may significantly be affected by cadmium contamination. Five pollution sources of heavy metals were identified: natural sources, copper mine tailings, agricultural activities, atmospheric depositions, and industrial activities. The contribution rates of the pollution sources were 7%, 13%, 20%, 29%, and 31%, respectively. The spatial pattern of heavy metals was closely aligned with the outputs of the UNMIX model. The foregoing supports the utility of the UNMIX model for the identification of pollution sources of heavy metals, apportionment study, and its implementation in agricultural soils in the Poyang Lake region.

scholarly journals Assessing habitat suitability and habitat fragmentation for endangered Siberian cranes in Poyang Lake region, China

2021 ◽  
Vol 125 ◽  
pp. 107594
Author(s):  
Zhengtao Zhu ◽  
Wenxin Huai ◽  
Zhonghua Yang ◽  
Da Li ◽  
Yisen Wang
Keyword(s):  
Habitat Fragmentation ◽  
Habitat Suitability ◽  
Poyang Lake ◽  
Poyang Lake Region ◽  
Lake Region

Eco-Efficiency Evaluation of Poyang Lake Region Based on DEA Method

2011 ◽  
Vol 181-182 ◽  
pp. 118-123
Author(s):  
Hai Tao Su ◽  
Hai Qing Guo ◽  
Jin Feng Hu ◽  
Hui Zeng
Keyword(s):  
Technical Efficiency ◽  
Poyang Lake ◽  
Scale Efficiency ◽  
Maximum Output ◽  
Efficiency Evaluation ◽  
Economic Region ◽  
Poyang Lake Region ◽  
Lake Region ◽  
Decision Making Unit ◽  
Dea Method

The eco-efficiency and sustainable development have become the focus of world and the issues to be resolved urgently. In this paper, the recent research status of eco-economic region of Poyang Lake in China is analyzed, and the multi-level evaluation index system of eco-efficiency of Poyang Lake is constructed. The minimum input and maximum output method based on DEA(Data Envelopment Analysis) is proposed, the mathematical model of validity evaluation of eco-economic region of Poyang Lake is set up and programmed by MATLAB. Efficiency evaluation of a complex system with the cases from nine districts of Poyang Lake region in China is realized, which is more than one homogeneous decision-making unit of multi-input and multi-output. The MDEA (Modified DEA) method resolves the problems of ranking DEA efficient units of Poyang Lake, The DEAP2.1 software differentiates the technical efficiency and scale efficiency of eco-economic region of Poyang Lake, and adjusts the DEA inefficient units to become technical efficiency. The model can be used to analyze efficiency and diagnose different units at the same time or same unit at different time. It can be more accurate and convenient for the management process of eco-economic region of Poyang Lake and the similar eco-economic region.

Identification of a novel reassortant A (H9N6) virus in live poultry markets in Poyang Lake region, China

2017 ◽  
Vol 162 (12) ◽  
pp. 3681-3690 ◽  
Author(s):  
Heng Zhang ◽  
Mingbin Liu ◽  
Xiaoxu Zeng ◽  
Xiang Zhao ◽  
Zhiqiang Deng ◽  
...  
Keyword(s):  
Poyang Lake ◽  
Poyang Lake Region ◽  
Lake Region ◽  
Live Poultry ◽  
Live Poultry Markets

scholarly journals Flooding hazard mapping for Poyang Lake Region with remote sensing and water level records

2009 ◽  
Vol 21 (5) ◽  
pp. 720-724 ◽  
Author(s):  
QI Shuhua ◽  
◽  
SHU Xiaobo ◽  
Daniel Brown ◽  
JIANG Luguang
Keyword(s):  
Remote Sensing ◽  
Water Level ◽  
Poyang Lake ◽  
Hazard Mapping ◽  
Flooding Hazard ◽  
Poyang Lake Region ◽  
Lake Region

scholarly journals Potential Impact of Flood on Schistosomiasis in Poyang Lake Regions Based on Multi-Source Remote Sensing Images

2020 ◽  
Author(s):  
Jing-Bo Xue ◽  
Xin-Yi Wang ◽  
Li-Juan Zhang ◽  
Yu-Wan Hao ◽  
Zhe Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Body ◽  
Poyang Lake ◽  
Transmission Risk ◽  
Schistosomiasis Japonica ◽  
Landsat 8 ◽  
Remote Sensing Images ◽  
Poyang Lake Region ◽  
Lake Region ◽  
Dual Polarized

Abstract BackgroundFlooding may be the most important factors contributing to the rebound of Oncomelania hupensis in endemic foci. This study aimed to assess the risk of schistosomiasis japonica transmission impacted by flooding around the Poyang Lake region using multi-source remote sensing images.MethodsNormalized Difference Vegetation Index (NDVI) data collected by the Landsat 8 satellite was used as an ecological and geographical suitability indicator of O. hupensis snail habitats in the Poyang Lake region. The flood-affected water body expansion was estimated using dual polarized threshold calculations based on the dual polarized synthetic aperture radar (SAR). The image data were captured from Sentinel-1B satellite in May 2020 before the flood and in July 2020 during the flood. The spatial database of snail habitats distribution was created by using the 2016 snail survey in Jiangxi Province. The potential spread of O. hupensis snails after the flood was predicted by an overlay analysis of the NDVI maps of flood-affected water body areas. In addition, the risk of schistosomiasis transmission was classified based on O. hupensis snail density data and the related NDVI. ResultsThe surface area of Poyang Lake was approximately 2,207 km2 in May 2020 before the flood and 4,403 km2 in July 2020 during the period of the flood peak, and the flood-caused expansion of water body was estimated as 99.5%. After the flood, the potential snail habitats were predicted to be concentrated in areas neighboring the existing habitats in marshlands of the Poyang Lake. The areas with high risk of schistosomiasis transmission were predicted to be mainly distributed in Yongxiu, Xinjian, Yugan and Poyang (District) along Poyang Lake. By comparing the predictive results and actual snail distribution, the predictive accuracy of the model was estimated as 87%, which meant the 87% of actual snail distribution were correctly identified as the snail habitats in the model predictions. ConclusionsFlood-affected water body expansion and environmental factors pertaining to snail breeding may be rapidly extracted from Landsat 8 and Sentinel-1B remote sensing images. The applications of multi-source remote sensing data are feasible for the timely and effective assessment of the potential schistosomiasis transmission risk caused by snail spread during the flood disaster, which is of great significance for precision control of schistosomiasis.

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