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Wang J, Chen G, Yuan Y, Fei Y, Xiong J, Yang J, Yang Y, Li H. Spatiotemporal changes of ecological environment quality and climate drivers in Zoige Plateau. Environ Monit Assess 2023; 195:912. [PMID: 37392290 DOI: 10.1007/s10661-023-11506-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 06/10/2023] [Indexed: 07/03/2023]
Abstract
Ecological environment is the essential material basis of human survival and connects regional economy with socially sustainable development. However, climate changes characterized by global climate warming have caused a series of ecological environmental problems in recent years. Few studies have discussed various climate factors affecting the ecological environment, and the spatial non-stationary effects of different climate factors on the ecological environment are still unclear. Dynamically monitoring ecological environment changes in fragile areas and identifying its climate-driving mechanism are essential for ecological protection and environmental repair. Taking Zoige Plateau as a case, this paper simulated the eco-environmental quality during 1987-2020 using remote sensing data, utilized Geodetector method to identify the contributions of various climate drivers to ecological environment quality, and then adopted the Geographically Weighted Regression model to explore the spatial non-stationary impacts of climate factors on ecological environment quality. The results showed that the ecological quality in the middle regions of the Zoige Plateau was slightly better than in the surrounding marginal areas. For the whole area of Zoige Plateau, the average ecological environment quality index was 54.92, 53.99, 56.17, 57.88, 63.44, 56.93, 59.43, and 59.76 in 1987, 1992, 1997, 2001, 2006, 2013, 2016 and 2020, respectively, which indicated that eco-environmental quality witnessed several fluctuations during the study period but showed a generally increasing trend. Among five climate factors, the temperature was the dominant climate factor affecting the ecological environment quality (q value: 0.11-0.19), sunshine duration (0.03-0.17), wind speed (0.03-0.11), and precipitation (0.03-0.08) were the main climate drivers, while the explanatory power of relative humidity to ecological environment quality was relatively small. Such various climate factors impacting the ecological environment quality demonstrated distinct spatial non-stationary and the range of driving impact varied with time. Temperature, sunshine duration, wind speed, and relative humidity promoted ecological environment quality in most regions (regression coefficients > 0), while precipitation mainly had a negative inhibitory impact (regression coefficients < 0). Meanwhile, the greater impacts of these five climate factors were concentrated in high-elevation regions of the south and west or the northern areas. The appropriate enhancement of climate warming and air humidity was beneficial to the improvement of the ecological environment, but the excessive precipitation would result in landslides and exhibit inhibition of vegetation growth. Therefore, selecting cold-tolerant herbs and shrubs, and strengthening climate monitoring and early warning systems (such as drought and excessive precipitation) are essential for ecological restoration.
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Affiliation(s)
- Jiyan Wang
- School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu, 610500, China
| | - Guo Chen
- School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu, 610500, China
| | - Yirong Yuan
- School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu, 610500, China
| | - Yi Fei
- Sichuan Water Resources and Hydroelectric Investigation & Design Institute Co.Ltd, Chengdu, 610500, China
| | - Junnan Xiong
- School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu, 610500, China.
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jiawei Yang
- School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu, 610500, China
| | - Yanmei Yang
- School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu, 610500, China
| | - Hao Li
- School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu, 610500, China
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Ji S, Wang Y, Wang Y. Geographically weighted poisson regression under linear model of coregionalization assistance: Application to a bicycle crash study. Accid Anal Prev 2021; 159:106230. [PMID: 34153640 DOI: 10.1016/j.aap.2021.106230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 04/27/2021] [Accepted: 05/31/2021] [Indexed: 06/13/2023]
Abstract
While cycling benefits individuals and society, cyclists are vulnerable road users, and their safety concerns arouse more macro-level spatial crash studies. Our study intends to investigate the spatial effects of population, land use, and bicycle lane infrastructures on bicycle crashes. This was done by developing a semi-parametric Geographically Weighted Poisson Regression (sGWPR) model which deals with the issue of spatial correlation and spatial non-stationarity simultaneously. It is a model that combines both constant and geographically varying parameters. To determine which parameter is fixed or non-stationary, previous studies suggest monitoring the Akaike Information Criterion (AICc). Yet, relying only on AICc might bury some spatial associations. So, in this study, we propose a Linear Model of Coregionalization (LMC) to assist the decision. Here, we use bicycle crash data across the metropolitan area of Greater Melbourne to establish sGWPR models suggested by AICc and LMC, respectively. Comparing the two sGWPR models, we found the sGWPR model under LMC results performs as well as sGWPR models suggested by AICc from the AICc perspective, and a 22.5% improvement in the mean squared error (MSE). It also uncovers more details about the spatial relationship between bicycle crashes and bicycle lane intersection density (BLID), an effect not suggested under AICc results. The parameters of BLID, a new measurement of bicycle lane facilities proposed by us, vary over space across analysis zones in Greater Melbourne.
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Affiliation(s)
- Shujuan Ji
- Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area, Chang'an University, P.O. Box 487, Xi'an 710064, China
| | - Yuanqing Wang
- Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area, Chang'an University, P.O. Box 487, Xi'an 710064, China.
| | - Yao Wang
- Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area, Chang'an University, P.O. Box 487, Xi'an 710064, China
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