Wang Mengjie, Wang Yanjun, Teng Fei, Li Shaochun, Lin Yunhao, Cai Hengfan
Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, China.
National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China.
Int J Environ Res Public Health. 2022 Apr 3;19(7):4306. doi: 10.3390/ijerph19074306.
Rapid economic and social development has caused serious atmospheric environmental problems. The temporal and spatial distribution characteristics of PM concentrations have become an important research topic for sustainable social development monitoring. Based on NPP-VIIRS nighttime light images, meteorological data, and SRTM DEM data, this article builds a PM concentration estimation model for the Chang-Zhu-Tan urban agglomeration. First, the partial least squares method is used to calculate the nighttime light radiance, meteorological elements (temperature, relative humidity, and wind speed), and topographic elements (elevation, slope, and topographic undulation) for correlation analysis. Second, we construct seasonal and annual PM concentration estimation models, including multiple linear regression, support random forest, vector regression, Gaussian process regression, etc., with different factor sets. Finally, the accuracy of the PM concentration estimation model that results in the Chang-Zhu-Tan urban agglomeration is analyzed, and the spatial distribution of the PM concentration is inverted. The results show that the PM concentration correlation of meteorological elements is the strongest, and the topographic elements are the weakest. In terms of seasonal estimation, the spring estimation results of multiple linear regression and machine learning estimation models are the worst, the winter estimation results of multiple linear regression estimation models are the best, and the annual estimation results of machine learning estimation models are the best. At the same time, the study found that there is a significant difference in the temporal and spatial distribution of PM concentrations. The methods in this article overcome the high cost and spatial resolution limitations of traditional large-scale PM concentration monitoring, to a certain extent, and can provide a reference for the study of PM concentration estimation and prediction based on satellite remote sensing technology.
快速的经济和社会发展引发了严重的大气环境问题。PM浓度的时空分布特征已成为可持续社会发展监测的重要研究课题。基于NPP-VIIRS夜间灯光图像、气象数据和SRTM DEM数据，本文构建了长株潭城市群PM浓度估算模型。首先，采用偏最小二乘法计算夜间灯光辐射度、气象要素（温度、相对湿度和风速）和地形要素（海拔、坡度和地形起伏）进行相关性分析。其次，构建季节和年度PM浓度估算模型，包括具有不同因子集的多元线性回归、支持随机森林、向量回归、高斯过程回归等。最后，分析长株潭城市群得到的PM浓度估算模型的精度，并反演PM浓度的空间分布。结果表明，气象要素的PM浓度相关性最强，地形要素最弱。在季节估算方面，多元线性回归和机器学习估算模型的春季估算结果最差，多元线性回归估算模型的冬季估算结果最好，机器学习估算模型的年度估算结果最好。同时，研究发现PM浓度的时空分布存在显著差异。本文方法在一定程度上克服了传统大规模PM浓度监测成本高和空间分辨率限制的问题，可为基于卫星遥感技术的PM浓度估算和预测研究提供参考。
