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高精度综合景观格局的中国人口暴露风险评价OA

Assessment of population exposure risks in China's high-precision comprehensive landscape pattern

中文摘要英文摘要

基于PM2.5、人口格网数据,采用暴露风险评价模型,在高精度时空分辨率下,研究中国2000-2020年PM2.5人口暴露风险的时空演变,对中国的PM2.5人口暴露风险进行评价.并结合土地利用数据,在最佳移动窗口的基础上测算景观格局指数,以皮尔逊相关性分析揭示景观格局对PM2.5浓度及其人口暴露风险关系.中国PM2.5人口暴露风险趋势为先增加后减少,胡焕庸线东西侧PM2.5人口暴露风险值相差较大.从空间变化上看,中国PM2.5人口暴露风险高污染聚集区域主要分布在京津冀、长三角以及中原地区.研究发现,边缘密度(ED)、形状指数(LSI)有利于缓和PM2.5人口暴露风险等问题.边缘密度、形状指数和边缘形状复杂度降低可以有效地减少PM2.5人口暴露风险,而高破碎度的景观类型会导致削减PM2.5人口暴露风险作用进一步弱化.

Based on PM2.5 and population grid data,an exposure risk assessment model was employed,and the spatiotemporal evolution of population exposure risks to PM2.5 in China from 2000 to 2020 was investigated.In addition,the population exposure risks to PM2.5 in China was evaluated.According to land use data,this paper calculated landscape pattern indexes on the basis of the optimal moving window and employed Pearson correlation analysis to reveal the relationship among landscape patterns,PM2.5 concentrations,and population exposure risks.The population exposure risks to PM2.5 in China show a trend of an initial increase followed by a decrease.There is significant variation in the population exposure risks to PM2.5 between the eastern and western sides of the Heihe-Tengchong Line.Spatially,regions with high population exposure risks to PM2.5 in China are predominantly located in the Beijing-Tianjin-Hebei region,Yangtze River Delta,and Central China.The study finds that edge density(ED)and landscape shape index(LSI)can help mitigate population exposure risks to PM2.5.Lower ED,LSI,and edge shape complexity can effectively reduce population exposure risks to PM2.5,while highly fragmented landscape types can further weaken the reduction of population exposure risks to PM2.5.

李琳;张希光

江西理工大学 土木与测绘工程学院,江西 赣州 341400

测绘与仪器

暴露风险评价景观指数评价模型高精度PM2.5

exposure risk assessmentlandscape indexassessment modelhigh precisionPM2.5

《北京测绘》 2024 (009)

1335-1340 / 6

10.19580/j.cnki.1007-3000.2024.09.017

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