基于决策树模型的区域PM_(2.5)污染管控时空识别——以关中地区为例

Optimal time period for PM_(2.5) control based on decision tree model:A case study of Guanzhong, China

以关中地区为研究区域,基于时空聚类和决策树模型提出一种简易的PM_(2.5)污染管控时空识别方法。首先使用时空聚类算法对冬防期PM_(2.5)浓度进行聚类,识别不同的PM_(2.5)污染区域,基于不同区域的气象数据分别构建决策树模型,识别不同区域影响PM_(2.5)浓度最不利扩散的气象条件,分析最不利气象条件下的PM_(2.5)浓度变化情况,以此确定各区域需要进行污染管控的时间段。结果表明:(1)时空聚类方法识别出关中地区PM_(2.5)分布主要呈现出低海拔平原区域和海拔相对较高的山脉区域。(2)决策树模型分析结果显示:高海拔区域在Ⅰ-10(1.57 h≤日照时数<7.88 h、最大风速<3.72 m·s^(-1))和Ⅰ-11(日照时数<1.57 h、最大风速<3.72 m·s^(-1))两类气象条件下,区域的PM_(2.5)浓度保持较高水平;低海拔区域在Ⅱ^(-1)0(小型蒸发量≥0.96 mm、平均相对湿度≥45.38%、日照时数<8.55 h、平均风速≥2.43 m·s^(-1))和Ⅱ-11(小型蒸发量<0.96 mm)两类气象条件下,区域的PM_(2.5)浓度保持较高水平。(3)回归结果显示,关中地区低海拔区域和高海拔区域在最不利气象条件下,PM_(2.5)浓度平均会持续上升4.76 d,直至最高浓度。

A simple spatiotemporal identification method for PM_(2.5) pollution control based on a machine learning model is proposed. A spatiotemporal clustering algorithm was used to cluster PM_(2.5) in the winter prevention period, and different PM_(2.5) polluted areas were identified. Furthermore, a decision tree model was constructed using the meteorological data of different areas to identify the most unfavorable influences on PM_(2.5) concentration in different areas. The changes in PM_(2.5) under the most unfavorable meteorological conditions were analyzed, and the optimal time period for PM_(2.5) pollution control in the different study areas was determined. Using the Guanzhong area as an example, the correlation analysis of the spatial clustering of mean daily PM_(2.5) in winter showed that the Guanzhong area is mainly divided into a low- altitude plain area (the Guanzhong Plain) and a relatively high mountain range. The classification tree model was then constructed, using the meteorological data of the plain and mountain areas. The analysis showed that the mountain elevations have a higher PM_(2.5) concentrations under Ⅰ-10 (1.57 h ≤ sunshine hours < 7.88 h;maximum wind speed < 3.72 m·s^(-1)) and Ⅰ-11 (sunshine hours < 1.57 h;maximum wind speed < 3.72 m·s^(-1)) meteorological conditions;low-altitude areas have a higher PM_(2.5) concentrations under Ⅱ^(-1)0 (small- scale evaporation ≥ 0.96 mm;average relative humidity ≥ 45.38%;sunshine hours < 8.55 h;average wind speed ≥ 2.43 m·s^(-1)) and Ⅱ-11 (small evaporation < 0.96 mm) meteorological conditions. Lastly, the regression analysis results showed that in the mountain elevations area in the Ⅰ-10, Ⅰ-11 category and the plain altitude area under the most unfavorable meteorological conditions (Ⅱ- 10 and Ⅱ- 11 category), the PM_(2.5) concentration will continue to increase for 4.76 days on average, before reaching a maximum concentration.