中国重点区域PM_(2.5)时空变化分析与预测

Analysis and Prediction of the Spatial and Temporal Changes of PM_(2.5) in Key Regions of China

在我国工业化和城市化迅速发展的背景下,细小颗粒物PM_(2.5)污染已经成为当前主要空气污染物。本文使用基于Python编程语言的网络爬虫技术获取了中国京津冀、长三角、珠三角3个重点区域的PM_(2.5)日均值数据,分别基于Excel软件和ArcGIS软件进行PM_(2.5)时空变化特征分析。最后,通过建立分数阶累加灰色预测模型对北京市2018年PM_(2.5)月均值浓度进行预测。结果显示:1)时间上,我国PM_(2.5)浓度表现为“冬高夏低”的“U”形变化趋势;2)空间上,我国PM_(2.5)浓度整体表现为由南北地区向中部地区PM_(2.5)浓度逐渐增加;3)分数阶GM(1,1)模型对PM_(2.5)月均值浓度数据的预测精确度较高,结合预测结果可从长期或短期提出PM_(2.5)污染治理的措施建议。

In the context of the rapid development of industrialization and urbanization in my country,the pollution of fine particulate matter PM_(2.5) has become the current major air pollutant.The web crawler technology based on Python programming language was used to obtain the daily average PM_(2.5) data of the three key regions of China′s Beijing-Tianjin-Hebei,Yangtze River Delta,and Pearl River Delta,and analyzed the characteristics of PM_(2.5) spatial and temporal changes based on Excel software and ArcGIS software,respectively.Finally,through the establishment of a fractional cumulative gray prediction model,the monthly mean concentration of PM_(2.5) in Beijing in 2018 was predicted.The results show that:①In terms of time,our country′s PM_(2.5) concentration shows a"U"-shaped change trend of"high in winter and low in summer";②In terms of space,the overall PM_(2.5) concentration in my country shows gradual increase from the north to the south to the central area;③The prediction accuracy of the fractional GM(1,1)model on the monthly mean concentration data of PM_(2.5) is relatively high,and combined with the prediction results,it can propose measures for PM_(2.5) pollution control in the long or short term.