Risk analysis of veterinary drug residues in aquatic products in the Yangtze River Delta of China

2021 ◽  
Author(s):  
Sijing Xia ◽  
Bing Niu ◽  
Jiahui Chen ◽  
Xiaojun Deng ◽  
Qin Chen
Keyword(s):  
Yangtze River ◽  
Yangtze River Delta ◽  
River Delta ◽  
Veterinary Drug ◽  
Veterinary Drug Residues ◽  
Drug Residues ◽  
Aquatic Products ◽  
Temporal Cluster ◽  
Spatio Temporal ◽  
The Yangtze River Delta

Aquatic products are favored by people all over the world, but the potential quality and safety issues cannot be ignored. In order to determine the risk of veterinary drug residues in aquatic products in the Yangtze River Delta, this paper used the Geographic Information System (GIS) method to analyze Chinese veterinary drugs in aquatic products in Shanghai, Jiangsu, Zhejiang, and Anhui (Yangtze River Delta Urban Agglomerations) from 2017 to 2019. The spatial distribution pattern, hotspot detection and analysis and spatio-temporal cluster analysis of the residual excess rate and detection rate were studied. The results showed that the overall excess rate and detection rate of veterinary drug residues in aquatic products from 2017 to 2019 showed a spatial random distribution. The result of hotspot analysis and spatio-temporal cluster analysis showed that the rate of detection of veterinary drug residues and the rate of detection of residues in excess of regulatory standards were clustered. This study can provide a scientific basis for food safety evaluation and risk management suggestions.

scholarly journals The Influencing Effects of Industrial Eco-Efficiency on Carbon Emissions in the Yangtze River Delta

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 8169
Author(s):  
Zaijun Li ◽  
Xiang Zheng ◽  
Dongqi Sun
Keyword(s):  
Spatial Correlation ◽  
Carbon Emissions ◽  
Yangtze River ◽  
Temporal Correlation ◽  
Yangtze River Delta ◽  
River Delta ◽  
Direct And Indirect Effects ◽  
The Yangtze River ◽  
Spatio Temporal ◽  
The Yangtze River Delta

A low-carbon economy is the most important requirement to realize high-quality integrated development of the Yangtze River Delta. Utilizing the following models: a super-efficiency slacks-based measure model, a spatio-temporal correlation model, a bivariate LISA model, a spatial econometric model, and a geographically weighted random forest model, this study measured urban industrial eco-efficiency (IEE) and then analyzed its influencing effects on carbon emission in the Yangtze River Delta from 2000 to 2017. The influencing factors included spatio-temporal correlation intensity, spatio-temporal association type, direct and indirect impacts, and local importance impacts. Findings showed that: (1) The temporal correlation intensity between IEE and scale efficiency (SE) and carbon emissions exhibited an inverted V-shaped variation trend, while the temporal correlation intensity between pure technical efficiency (PTE) and carbon emissions exhibited a W-shaped fluctuation trend. The negative spatial correlation between IEE and carbon emissions was mainly distributed in the developed cities of the delta, while the positive correlation was mainly distributed in central Anhui Province and Yancheng and Taizhou cities. The spatial correlation between PTE and carbon emissions exhibited a spatial pattern of being higher in the central part of the delta and lower in the northern and southern parts. The negative spatial correlation between SE and carbon emissions was mainly clustered in Zhejiang Province and scattered in Jiangsu and Anhui provinces, with the cities with positive correlations being concentrated around two locations: the junction of Anhui and Jiangsu provinces, and within central Jiangsu Province. (2) The direct and indirect effects of IEE on carbon emissions were significantly negative, indicating that IEE contributed to reducing carbon emissions. The direct impact of PTE on carbon emissions was also significantly negative, while its indirect effect was insignificant. Both the direct and indirect effects of SE on carbon emissions were significantly negative. (3) It was found that the positive effect of IEE was more likely to alleviate the increase in carbon emissions in northern Anhui City. Further, PTE was more conducive to reducing the increase in carbon emissions in northwestern Anhui City, southern Zhejiang City, and in other cities including Changzhou and Wuxi. Finally, it was found that SE played a relatively important role in reducing the increase in carbon emissions only in four cities: Changzhou, Suqian, Lu’an, and Wenzhou.

scholarly journals Analysis of the spatio-temporal network of air pollution in the Yangtze River Delta urban agglomeration, China

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262444
Author(s):  
Chuanming Yang ◽  
Qingqing Zhuo ◽  
Junyu Chen ◽  
Zhou Fang ◽  
Yisong Xu
Keyword(s):  
Air Pollution ◽  
Yangtze River ◽  
Yangtze River Delta ◽  
River Delta ◽  
Urban Agglomeration ◽  
Temporal Network ◽  
The Yangtze River ◽  
The Cross ◽  
Spatio Temporal ◽  
The Yangtze River Delta

The complex correlation between regions caused by the externality of air pollution increases the difficulty of its governance. Therefore, analysis of the spatio-temporal network of air pollution (STN-AP) holds great significance for the cross-regional coordinated governance of air pollution. Although the spatio-temporal distribution of air pollution has been analyzed, the structural characteristics of the STN-AP still need to be clarified. The STN-AP in the Yangtze River Delta urban agglomeration (YRDUA) is constructed based on the improved gravity model and is visualized by UCINET with data from 2012 to 2019. Then, its overall-individual-clustering characteristics are analyzed through social network analysis (SNA) method. The results show that the STN-AP in the YRDUA was overall stable, and the correlation level gradually improved. The centrality of every individual city is different in the STN-AP, which reveals the different state of their interactive mechanism. The STN-AP could be subdivided into the receptive block, overflow block, bidirectional block and intermediary block. Shanghai, Suzhou, Hangzhou and Wuxi could be key cities with an all above degree centrality, betweenness centrality and closeness centrality and located in the overflow block of the STN-AP. This showed that these cities had a greater impact on the STN-AP and caused a more pronounced air pollution spillovers. The influencing factors of the spatial correlation of air pollution are further determined through the quadratic assignment procedure (QAP) method. Among all factors, geographical proximity has the strongest impact and deserves to be paid attention in order to prevent the cross-regional overflow of air pollution. Furthermore, several suggestions are proposed to promote coordinated governance of air pollution in the YRDUA.

Spatio-temporal evolution and driving effects of the ecological intensity of urban well-being in the Yangtze River Delta

2021 ◽  
pp. 0958305X2110693
Author(s):  
Meijuan Hu ◽  
Suleman Sarwar ◽  
Zaijun Li ◽  
Nianxing Zhou
Keyword(s):  
Yangtze River ◽  
Temporal Evolution ◽  
Economic Effect ◽  
Well Being ◽  
Yangtze River Delta ◽  
River Delta ◽  
Sustainable Urban Development ◽  
The Yangtze River ◽  
Spatio Temporal ◽  
The Yangtze River Delta

The fundamental goal of sustainable urban development is to maximize human well-being with minimum ecological consumption. The ecological intensity of urban well-being (EIWB) achieves an effective linkage among economic, social, and ecological systems, and it is an effective indicator for evaluating urban sustainable development. This study analyzed the spatio-temporal evolution characteristics and driving effects of the ecological intensity of urban well-being over 2000–2019 in the Yangtze River Delta. It was found that as the ecological consumption per unit well-being output decreased gradually, the improvement in well-being level and the increase in ecological consumption were increasingly delinked, and regional EIWB and its sub-dimensions tended to fluctuate. Urban EIWB was dominated by low and lower levels, urban economic well-being (ECWB) was increasingly dominated by the lower type, and urban social well-being (SOWB) and environmental well-being (ENWB) were dominated by the low level. The resource consumption, technology, and well-being effects distinctively inhibited the decrease in regional EIWB and the economic effect exerted an obvious boosting function, whereas environmental consumption effect, scale effect, and efficiency effect had no obvious impact. The variation in urban EIWB was mainly driven by two-factor dominance, featuring economic and technological effects.

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