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汉中市PM_(2.5) 时空变化特征研究及影响因素分析

Research of characteristics of temporal and spatial variation and the influence factors of PM_(2.5)in Hanzhong city
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摘要 以汉中市2019-2021年大气颗粒物PM_(2.5)监测数据为基础,通过数据挖掘和空间插值方法,研究了该地区PM_(2.5)的时空分布及变化特征,并分析其主要影响因素。研究结果表明,汉中市PM_(2.5)高浓度时期集中在每年11月至次年2月,并呈现出明显的季节性变化特征:冬季>春季>秋季>夏季;汉中市PM_(2.5)浓度在空间分布上总体上呈现出南高北低的分布特征,高值区集中在中南部的中心城区一带,分别向东和向北逐步递减延伸;该时空变化特征与当地的自然地理环境、工业分布状况、人口密集程度等影响社会发展的多种因素密切相关。研究结果可为准确把握汉中市污染现状,进一步寻求有效的大气污染防控措施提供理论依据。 Based on the monitoring data of the concentration of the particulate matter in the atmosphere named PM_(2.5)of Hanzhong from 2019 to 2021,using the methods of data mining and spatial interpolation,the characteristics of temporal and spatial variation and the influence factors of PM_(2.5)have been analyzed deeply.The research results show that the period of high concentrations of PM_(2.5)occurs from November to February each year and there is a distinct seasonal characteristic,winter>spring>autumn>summer.The spatial distribution of PM_(2.5)is high in the south and low in the north as a whole.The high value area is mainly concentrated in the central and the south of Hanzhong,extending to the east and the north of this area with gradual decrease.These characteristics of temporal and spatial variation are closely related to the local geographical environment,industrial distribution,population density and other factors of social development.The research results can help with accurate comprehension about the general situation of pollution in Hanzhong city,thereby providing a theoretical basis for effective control measures of air pollution prevention.
作者 刘杰 李鹏飞 LIU Jie;LI Pengfei(School of Mathematics and Computer Science,Shaanxi University of Technology,Hanzhong 723000,China;Shaanxi Province Hanzhong Central Station of Environment Monitoring,Hanzhong 723000,China)
出处 《陕西理工大学学报(自然科学版)》 2023年第3期88-92,共5页 Journal of Shaanxi University of Technology:Natural Science Edition
基金 陕西省科技厅创新能力支撑计划项目(2020KRM035) 陕西理工大学科研基金项目(SLGKY2008)。
关键词 PM_(2.5) 数据挖掘 空间插值 时空变化特征 影响因素 PM_(2.5) data mining spatial interpolation characteristics of temporal and spatial variation influence factors
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