Landscape ecological risk assessment and driving factor analysis in Dongjiang river watershed

Karimian, Hamed; Zou, Wenmin; Chen, Youliang; Xia, Jiaqin; Wang, Zhaoru

doi:10.1016/j.chemosphere.2022.135835

Cited by 91 publications

(41 citation statements)

References 34 publications

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“…For the long-term plan, landscape ecological risk assessment could be used to comprehensively evaluate the ecological environment condition and topographic and socioeconomic aspects of Eastern Ukraine from the effect of the war 35 . Furthermore, various new technology can be utilized to determine the consequence of war and the presence of pollutants, such as remote sensing technology, electrochemical techniques, magnetic sensors for monitoring hazardous pollutants in water resources, machine learning approaches and aerosol depth in air pollution prediction [36][37][38][39] .…”

Section: National Level (Technical and Operational)mentioning

confidence: 99%

Novel Google Maps and Google Earth application for chemical industry disaster risk assessment during complex emergencies in Eastern Ukraine

Gan

Bruni

Delgado

et al. 2023

Sci Rep

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The war in Ukraine has led to complex emergencies, humanitarian crises, and other severe consequences, such as chemical industry disasters. The chemical industry is one of the principal sectors of Ukraine’s economy. In 2019, Ukraine had a total volume of hazardous chemical accumulation of more than a 5.1billion tons. Therefore, an attack on chemical industrial facilities will lead to catastrophic consequences such as chemical disasters. This paper aims to study the disaster risk of chemical industrial facilities and its effects on public health and the environment during complex emergencies in Eastern Ukraine. Observational cross-sectional risk assessment method was utilized to assess hazard, vulnerability, and exposure of the chemical industry in Eastern Ukraine in Donetsk Oblast and Luhansk Oblast. Data on chemical factories in Eastern Ukraine was collected on Google Maps and Google Earth on May 2022. Lastly, the semi-quantitative risk assessment method was utilized to describe the risk from the perspective of consequences for life and health, the environment, property, and speed of development. Our disaster risk assessment found more than 1 million people (1,187,240 people) in Donetsk Oblast and more than 350 thousand people (353,716 people) in Luhansk Oblast are exposed to potential hazards from the chemical facilities clusters. The aggregation risk of bombardment of chemical facilities cluster in Eastern Ukraine is also high due to ongoing war. Therefore, the chemical industry disaster risks for Eastern Ukraine during complex emergencies in Donetsk Oblast and Luhansk Oblast are high in terms of likelihood and consequences to life and health, environment, property, and speed of development.

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Section: National Level (Technical and Operational)mentioning

confidence: 99%

Novel Google Maps and Google Earth application for chemical industry disaster risk assessment during complex emergencies in Eastern Ukraine

Gan

Bruni

Delgado

et al. 2023

Sci Rep

View full text Add to dashboard Cite

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“…Its main purpose is to reveal the internal reasons that affect the spatio-temporal changes of ecological risk, and provide the basis for the scientificity and accuracy of spatio-temporal simulation of ecological risk in the future. The research methods mainly include logistic regression (Tian et al, 2022), Geographic detector (Karimian et al, 2022), principal component analysis (Kumar et al, 2020) and geographically weighted regression model (Yu et al, 2020), etc. Compared with other methods, geographic detector can better detect the influencing factors and explain their interaction, so it is widely used in the research of driving factors.…”

Section: Introductionmentioning

confidence: 99%

Simulation, prediction and driving factor analysis of ecological risk in Savan District, Laos

Peng

Qin³

2023

Front. Environ. Sci.

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Introduction: In recent years, Laos, with its unique geographical advantages, has vigorously developed overseas trade and cultural tourism industry, which has promoted local economic development and introduced numerous ecological and environmental problems.Method: Therefore, this research took Savan District as the research area, built the landscape ecological risk assessment model to reveal the spatiotemporal evolution characteristics of Savan District’s ecological risk from 2000 to 2020. Then, the Geodetector model was utilized to explain the driving forces behind changes in the landscape’s ecological risk. Finally, the Markov-PLUS coupling model was used to simulate and predict the changes in the land use pattern and ecological risk in the Savan District in 2030.Results: The following results are presented. 1) The landscape types in Savan District are mainly cultivated land and forest land, with the lowest degree of landscape fragmentation and loss, and the highest degree of landscape loss in unused land. 2) From 2000 to 2020, the average ecological risk index of the whole study area was low and showed a decreasing trend yearly, with the area of low-risk areas accounting for more than 90%. 3) In 2030, the average ecological risk index of the entire Savan District and the two provinces will rise, meanwhile the low-risk areas in the midwest regions and high-risk areas in the northwest will increase.Discussion: This study can guide the future coordinated development of the social economy and ecological environment in Savan District, Laos, and has good reference significance for ecological construction in similar areas in Southeast Asia.

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“…In contrast, special assessments of ecological risk have considered only the loss degree of an ecosystem when it is disturbed and have neglected the benefits ecosystems provide for human beings. At the same time, there is currently a lack of comprehensive consideration of ESV and the ERI together (Jr et al, 2009;Forbes and Calow, 2013;Xu et al, 2016;Wang, 2021;Karimian et al, 2022), especially for desert ecosystems. Due to the natural conditions of drought and a lack of rain, desert ecosystems are extremely fragile and face ecological risks such as sandstorms and landslides.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal evolution and driving forces of ecosystem service value and ecological risk in the Ulan Buh Desert

Liu

Wang

Sun

et al. 2023

Front. Environ. Sci.

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A clear understanding of the spatiotemporal evolution and driving factors of ecosystem service value (ESV) and the landscape ecological risk index (ERI) can effectively link human well-being and ecosystem security, which is essential for sustainable ecosystem management. Based on multitemporal land use data (1990, 2000, 2010, and 2018), the spatiotemporal evolution of ESV and the ERI in the Ulan Buh Desert was evaluated. The driving forces of ESV and the ERI were quantitatively evaluated by the Geodetector model. The results show that 1) from 1990 to 2018, total ESV in the Ulan Buh Desert increased from 7.00×108 yuan to 11.09×108 yuan. Low-ESV areas accounted for approximately 72.28% of the study area. High-ESV and moderate-high-ESV areas were mainly distributed along the Yellow River and the northeastern region. 2) During the study period, the ecological risk of the Ulan Buh Desert generally decreased; only the ecological risk of the northeastern region improved significantly, and high-risk areas were mainly distributed across sand dunes located in the middle of the desert. 3) There was a negative correlation between ESV and the ERI in the study area, and the main relationship was low value-high risk. 4) Driving force analysis results show that natural and human impact factors jointly affected the spatiotemporal differentiation of ESV and the ERI in the Ulan Buh Desert. Among the influencing factors, the interaction between the distance to a highway and annual precipitation had the strongest impact. The implementation of relevant policies in the study area should be guided by ESV and the ERI, and the protection and restoration of various ecosystems in the study area must be strengthened.

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Landscape ecological risk assessment and driving factor analysis in Dongjiang river watershed

Cited by 91 publications

References 34 publications

Novel Google Maps and Google Earth application for chemical industry disaster risk assessment during complex emergencies in Eastern Ukraine

Novel Google Maps and Google Earth application for chemical industry disaster risk assessment during complex emergencies in Eastern Ukraine

Simulation, prediction and driving factor analysis of ecological risk in Savan District, Laos

Spatiotemporal evolution and driving forces of ecosystem service value and ecological risk in the Ulan Buh Desert

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