Lake surface water temperature (LSWT) has a significant impact on aquatic ecosystem. This study aimed to reveal the variations and driving factors of LSWT in Tonle Sap Lake, the largest freshwater lake in South Asia. For this purpose, the datasets of LSWT-day, atmospheric temperature (AT), lake surface area (LSA), watershed land use and cover change (WLUCC) were extracted based on MODIS products and AT images by remote sensing technology. Then, through GIS geospatial analysis technology and mathematical statistical methods, the spatial-temporal variations of LSWT-day, AT, LSA and WLUCC were analyzed, and the relationships between LSWT-day and AT, LSA, WLUCC were further discussed. Results showed that: 1) from 2001 to 2018, the annual mean LSWT-day and AT showed a significant warming trend. AT was a main driving factor affecting LSWT in Tonle Sap Lake.2) LSA showed a slightly decreasing tendency and had an obvious negative correlation with LSWT-day, which was an important factor affecting LSWT. 3) WLUCC had undergone drastic changes and its influence on LSWT could not be ignored. In summary, this paper fills the gap of the long-term series of LSWT research in Tonle Sap Lake and enhances the understanding of LSWT changing mechanism for similar lakes.
To solve the problem that traditional highway route selection method cannot fully consider the complex geographical environment in the permafrost region of Qinghai-Tibet Plateau, AHP (Analytic Hierarchy Process) and fuzzy comprehensive evaluation were introduced into the highway route comparison and selection in permafrost region. Influence degrees of various factors on different route schemes in permafrost regions of the Qinghai-Tibet Plateau were analyzed by the AHP. The annual average temperature, engineering reliability and engineering capacity were selected as the characteristic parameters and their weights were determined. Variation range of the various influencing factors in permafrost regions of the Qinghai-Tibet Plateau were analyzed by the fuzzy comprehensive evaluation, 5 evaluation grades of highway route scheme in permafrost regions were established, and the actual highway route schemes between Xiushui River and Yamarle River were compared and selected. The results show that the temperature and frozen soil diseases are the main factors affecting the highway route plan in the permafrost regions of the Tibetan plateau, outstanding route plan needs comprehensive consideration of stability of highway engineering and environmental protection.
The central Yunnan is an important development area in Yunnan Province. Studying the pattern of vegetation change in this region and the relationship between vegetation change and climate factors can provide information support for the management of the ecological environment and promote sustainable economic development. This study used the meteorological dataset of the Climate Date Store (ECMWF) and the vegetation dataset of MOD13Q1, combined with spatial autocorrelation analysis, simple linear regression analysis and Pearson correlation coefficient analysis to analyze the change of the NDVI and the distribution characteristics of spatial autocorrelation. The results showed that the NDVI value in central Yunnan increased slowly from 2000 to 2019 with the growth rate of 0.027/decade, but the increase was not significant. On the seasonal scale, the growth rate was highest in autumn (0.051/decade). In the past 20 years, the overall Moran I of NDVI was greater than 0.71, and the vegetation in central Yunnan showed a significant positive spatial autocorrelation.
Particulate matter with a diameter of less than 2.5 µm (PM 2.5 ) has a significant impact on air pollution, atmospheric visibility, and human health. The most basic and important step of regional air pollution control is to obtain air pollution data in different seasons from both satellite sensors and ground-level observations. The aim of this paper is to accurately estimate the PM 2.5 concentration in the Beijing-Tianjin-Hebei urban area in different seasons by establishing a seasonal geographically and temporally weighted regression (S-GTWR) model that integrates multiple complex factors. Using a greedy algorithm, the model results were optimized by selecting the characteristic variables that contributed to the accuracy of the model in different seasons. The measured and estimated PM 2.5 concentrations were compared and the cross-validation results were used as a basis for evaluating the accuracy of the model. The results showed that the accuracy of the S-GTWR model that combined the optimal characteristic variables was higher than that of the geographically weighted regression (GWR) model and the kriging method. The mean prediction error (ME), relative prediction error (RPE), and root mean square error (RMSE) of the S-GTWR model were small, and the coefficient of determination (R 2 ) of the model exceeded 0.86 for each season. The accuracy of the S-GTWR model in estimating the PM 2.5 concentration was highest in summer and lowest in winter. In addition, the proposed model can accurately estimate PM 2.5 concentrations in areas without monitoring sites. The results can provide a scientific basis for the study of pollution control and PM 2.5 exposure in large urban agglomerations.
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