基于遥感技术和机器学习的城市街区PM2.5空间分布特征研究——以合肥市滨湖新区为例

doi:10.16258/j.cnki.1674-5906.2024.09.010

生态环境学报 ›› 2024, Vol. 33 ›› Issue (9): 1426-1437.DOI: 10.16258/j.cnki.1674-5906.2024.09.010

• 研究论文【环境科学】 • 上一篇下一篇

基于遥感技术和机器学习的城市街区PM_2.5空间分布特征研究——以合肥市滨湖新区为例

王薇¹^,²^,^*(), 夏宇轩³

1.合肥工业大学建筑与艺术学院，安徽合肥 230601
2.徽州古村落数字化保护与传承创意安徽省重点实验室，安徽合肥 230601
3.合肥城市学院，安徽合肥 238076

收稿日期:2023-12-11 出版日期:2024-09-18 发布日期:2024-10-18
通讯作者: ^*
作者简介:王薇（1975年生），女，教授，博士，博士研究生导师，研究方向为建筑技术和人居环境。E-mail: vivi.gan@126.com
基金资助:
国家自然科学基金项目(52478016);安徽省自然科学基金项目(2308085ME182);徽州古村落数字化保护与传承创意安徽省重点实验室“自主创新专项”(PA2023GDSK0116);安徽省重点研究与开发计划项目(2023g07020003)

Spatial Distribution Characteristics of PM_2.5 in Urban Block Based on Remote Sensing Technology and Machine Learning: Taking Binhu New District of Hefei City as an Example

WANG Wei¹^,²^,^*(), XIA Yuxuan³

1. College of Architecture and Art, Hefei University of Technology, Hefei 230601, P. R. China
2. Anhui Provincial Key Laboratory of Digitalized Conservation and Innovative Revitalization of Ancient Huizhou Villages, Hefei 230601, P. R. China
3. Hefei City University, Hefei 238076, P. R. China

Received:2023-12-11 Online:2024-09-18 Published:2024-10-18

摘要/Abstract

摘要：

为获取合肥市滨湖新区地面PM_2.5浓度，利用地面风速对2022年7月31日－2023年7月31日MODIS传感器MCD19A2数据进行风速订正，并与同期气象数据和合肥市10个国控监测站点的PM_2.5数据建立AOD-PM_2.5反演模型。结果表明：BP网络训练到第10代时训练结果最为理想，均方误差MSE值约为0.027。训练集、验证集、测试集和总体结果回归值r均在0.88以上，总体结果高达0.93。通过BP预测工具箱建立模型，拟合优度达到0.807。选择滨湖新区7个典型测点，测量2023年7月12－16日每日平均PM_2.5浓度、地面风速和温湿度，结合气象数据代入反演模型对比发现，估算值和实测值曲线高度吻合，证明该模型可用于反演滨湖新区地面PM_2.5浓度。对PM_2.5浓度进行估算和分析，结果表明：1）滨湖新区西南侧PM_2.5浓度最高，东南侧最低；2）功能区PM_2.5浓度呈现为工业区>商业区>居住区>生态园区；3）工业园区废弃物的排放会导致PM_2.5浓度升高，对工厂设备的优化使用以及合理处理生产过程中的废弃物可降低PM_2.5浓度；4）商业区和居住区中的尾气排放、扬尘、工业排风等均会导致PM_2.5浓度升高，优化交通组织，推动新能源汽车的发展可降低PM_2.5浓度；5）产业园区的发展模式对城市PM_2.5浓度有显著影响，加快产业结构调整，推动产业绿色技术创新可降低PM_2.5浓度；6）城市绿地空间对PM_2.5有显著调节作用，增加绿地空间连通度、聚集度，加强大型绿地建设可发挥较强的PM_2.5消减作用。该研究所建立的AOD-PM_2.5反演模型旨在为城市街区PM_2.5空间分布特征研究提供可靠方法，具有重要的实践意义。

关键词: 遥感技术, PM_2.5, 气溶胶, 城市街区, 风速订正, 空间分布, 机器学习

Abstract:

In order to obtain the ground-level PM_2.5 concentration in Binhu New District of Hefei City, the ground wind speed was used to correct the wind speed of the MCD19A2 data of the MODIS sensor from July 31, 2022, to July 31, 2023, and the AOD-PM_2.5 inversion model was established with the same period of meteorological data and the PM_2.5 data from 10 state-controlled monitoring stations in Hefei City. The test results were most satisfactory when the BP network was set up to the 10th generation, and the Mean Square Error (MSE) value was 0.027. The regression values of R for the training, validation, and test sets were greater than 0.88, and the overall result was as high as 0.93. The model was built using the BP prediction toolbox and the goodness-of-fit was 0.807. Seven typical measuring points were selected in the Binhu New District to measure the daily average PM_2.5, ground wind speed, temperature, and humidity from July 12, 2023, to July 16, 2023. After combining the meteorological data into the inversion model, it was found that the estimated and measured value curves were highly consistent, which proves that the model can be used to reverse the ground PM_2.5 concentration in Binhu New District. The results of the estimation and analysis of the PM_2.5 concentration showed that: 1) the highest PM_2.5 concentrations were found on the southwest side of the Binhu New District and the lowest on the southeast side. 2) In terms of land use, PM_2.5 concentration was presented as industrial area>commercial area>residential area > ecological park. 3) Waste emissions in industrial zones led to an increase in PM_2.5. Optimizing the use of factory equipment and proper treatment of waste in the production process can reduce PM_2.5. 4) Tailpipe emissions, dust, and industrial exhaust in commercial and residential areas led to higher PM_2.5. Optimizing traffic organization and promoting the development of energy vehicles can reduce PM_2.5. 5) The development mode of industrial parks has a significant impact on the concentration of PM_2.5; accelerating the adjustment of industrial structure and promoting the innovation of industrial green technology can reduce the concentration of PM_2.5. 6) Urban green spaces had a significant regulatory effect on PM_2.5. Increasing the connectivity and aggregation of green spaces and strengthening the construction of large-scale green spaces can play a stronger role in PM_2.5 reduction. The AOD-PM_2.5 inversion model provides a reliable method for the study of the spatial distribution characteristics of PM_2.5 in urban neighborhoods, which has important practical significance.

Key words: remote sensing technology, PM_2.5, aerosol, urban block, revised wind speed, spatial distribution, machine learning

中图分类号:

王薇, 夏宇轩. 基于遥感技术和机器学习的城市街区PM_2.5空间分布特征研究——以合肥市滨湖新区为例[J]. 生态环境学报, 2024, 33(9): 1426-1437.

WANG Wei, XIA Yuxuan. Spatial Distribution Characteristics of PM_2.5 in Urban Block Based on Remote Sensing Technology and Machine Learning: Taking Binhu New District of Hefei City as an Example[J]. Ecology and Environment, 2024, 33(9): 1426-1437.

图/表 14

图1 AOD反演PM2.5浓度技术路线图

Figure 1 Technology roadmap for AOD inversion of PM2.5 concentration

图2 MATLAB建模流程图

Figure 2 MATLAB modeling flow chart

图3 合肥市国控监测站点分布图

Figure 3 Distribution of state-controlled monitoring stations in Hefei City

图4 MCD19A2数据预处理步骤图

Figure 4 Step-by-step diagram of MCD19A2 data preprocessing

图5 研究区域TIF图像

Figure 5 TIF image of the study area

表1 自变量共线性检验结果

Table 1 Table of collinearity test results for independent variables

变量		未标准化系数		标准化系数	检验值	系统因素	共线性统计
模型		B	标准错误	Beta	t	显著性	容差	VIF
1	(常量)	31.4	4.35		7.22	<0.001
	AOD	58.0	4.58	0.481	12.6	<0.001	0.770	1.30
	温度	−1.00	0.080	−0.467	−12.4	<0.001	0.792	1.26
	湿度	0.097	0.080	0.046	1.20	0.229	0.767	1.30
	经向风速	−0.184	0.432	−0.014	−0.427	0.670	0.970	1.03
	纬向风速	2.23	0.412	0.201	5.42	<0.001	0.812	1.23

图6 模型精确度验证结果图

Figure 6 Plot of model accuracy validation results

图7 BP预测结果图

Figure 7 BP prediction result chart

图8 合肥市滨湖新区测点分布图

Figure 8 Distribution of measuring points in Binhu New District, Hefei City

图9 估算值与实测值对比曲线图

Figure 9 Estimated versus measured values graph

图10 估算区域TIF图像

Figure 10 TIF image of the estimation area

图11 滨湖新区估算点方位图

Figure 11 Directions to the estimated points of the Binhu New District

图12 滨湖新区估算点PM2.5质量浓度分布点位图

Figure 12 Distribution of PM2.5 Concentration at the Estimation Points in Binhu New District

图13 滨湖新区估算点PM2.5质量浓度分布区域图

Figure 13 Distribution area map of PM2.5 concentration at estimated points in Binhu New District

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基于遥感技术和机器学习的城市街区PM_2.5空间分布特征研究——以合肥市滨湖新区为例

Spatial Distribution Characteristics of PM_2.5 in Urban Block Based on Remote Sensing Technology and Machine Learning: Taking Binhu New District of Hefei City as an Example

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