Vulnerability assessment of atmospheric environment driven by human impacts

Zhang, Yang; Shen, Jing; Ding, Feng; Liu, Yu; He, Li

doi:10.1016/j.scitotenv.2016.07.051

Cited by 19 publications

(8 citation statements)

References 49 publications

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“…They found that Beijing obtained the most health benefits, and Hebei bore the highest cost. Similar to the above-mentioned studies, Zhang et al, 2016 , Wang et al, 2019b also proposed key policy directions of pollution mitigation in the BTH region. The forecasting results in these studies are obtained through a scenario analysis.…”

Section: Literature Reviewmentioning

confidence: 78%

Has air pollution emission level in the Beijing–Tianjin–Hebei region peaked? A panel data analysis

Meng

Zhou

2020

Ecological Indicators

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Highlights Air pollution emission level of the Beijing–Tianjin–Hebei region is fitted. Fitting curve to air pollution emission level reached the peak in 2015. Air pollution emissions in 2025 will decrease to the level of 2007. Industrial waste gas emissions play a decisive role for the pollution level change. Air quality is sensitive to the operation of building material industries.

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Section: Literature Reviewmentioning

confidence: 78%

Has air pollution emission level in the Beijing–Tianjin–Hebei region peaked? A panel data analysis

Meng

Zhou

2020

Ecological Indicators

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“…The index system construction included four steps. First, an in-depth literature review guided by the QI and EEQ research above was applied to discern relevant data sets for consideration [ 20 , 21 , 22 , 23 ]. Data were collected from a yearbook of general administration of quality supervision, inspection, and quarantine of the People’s Republic of China; a statistical communique on national economic and social development; a Chinese urban construction statistical yearbook, and a statistical yearbook on environment.…”

Section: Methodsmentioning

confidence: 99%

Coupling Relationship Analysis between Quality Infrastructure and Ecological Environment Quality for Policy Implications

Shen

Zhang

Guo

et al. 2020

IJERPH

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Long-term improvement of ecological environment quality (EEQ) is a hotspot and urgent topic in the context of high-quality and sustainable development. It is urgent to look for methods that could support EEQ improvement in a high-quality and sustainable way. Owing to its natural supporting and guaranteeing functions for enhancing quality, quality infrastructure (QI) is a fundamental and critical element in promoting EEQ, but a neglected one. In this paper, we analyzed the coupling structure between QI and EEQ and applied an improved coupling model to recognize contributed and weakened indicators that affected the coupling relationship. We also examined this coupling relationship in the Yangtze River Delta (YRD) from 2012 to 2017, which proved the current situation where QI construction cannot satisfy the practical needs of EEQ improvement. Results showed that the important supporting role of QI in EEQ improvement should be valued for long-term sustainable development. Meanwhile, equilibrium and consistency of indicators in the QI and EEQ systems determined the coupling state. EEQ improvement countermeasures were also provided according to the coupling relationship analysis results. This study provided a scientific basis and guidance for EEQ improvement and sustainable development.

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“…The adaptive capacity index reflects the ability of the atmospheric environment to cope, i.e., accommodate environmental hazards or policy changes [ 30 ]. Considering expert opinion, literature investigations [ 20 , 31 , 32 ], data availability and local concrete conditions, 15 parameters were chosen to assess atmospheric environment vulnerability in this paper, as shown in Table 1 . The exposure index is positively correlated with atmospheric environment vulnerability, meaning higher exposure results in increased atmospheric environment vulnerability, and so does the sensitivity index.…”

Section: Methodsmentioning

confidence: 99%

Atmospheric Environment Vulnerability Cause Analysis for the Beijing-Tianjin-Hebei Metropolitan Region

Zhang

Shen

Liu

2018

IJERPH

Self Cite

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Assessing and quantifying atmospheric vulnerability is a key issue in urban environmental protection and management. This paper integrated the Analytical hierarchy process (AHP), fuzzy synthesis evaluation and Geographic Information System (GIS) spatial analysis into an Exposure-Sensitivity-Adaptive capacity (ESA) framework to quantitatively assess atmospheric environment vulnerability in the Beijing-Tianjin-Hebei (BTH) region with spatial and temporal comparisons. The elaboration of the relationships between atmospheric environment vulnerability and indices of exposure, sensitivity, and adaptive capacity supports enable analysis of the atmospheric environment vulnerability. Our findings indicate that the atmospheric environment vulnerability of 13 cities in the BTH region exhibits obvious spatial heterogeneity, which is caused by regional diversity in exposure, sensitivity, and adaptive capacity indices. The results of atmospheric environment vulnerability assessment and the cause analysis can provide guidance to pick out key control regions and recognize vulnerable indicators for study sites. The framework developed in this paper can also be replicated at different spatial and temporal scales using context-specific datasets to support environmental management.

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Vulnerability assessment of atmospheric environment driven by human impacts

Cited by 19 publications

References 49 publications

Has air pollution emission level in the Beijing–Tianjin–Hebei region peaked? A panel data analysis

Has air pollution emission level in the Beijing–Tianjin–Hebei region peaked? A panel data analysis

Coupling Relationship Analysis between Quality Infrastructure and Ecological Environment Quality for Policy Implications

Atmospheric Environment Vulnerability Cause Analysis for the Beijing-Tianjin-Hebei Metropolitan Region

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