基于气象和遥感的空气清新度监测技术研究

Air freshness monitoring technology based on meteorology and remote sensing

负氧离子浓度和PM_(2.5)质量浓度是衡量空气新鲜和清洁程度的重要指标。文章利用2018—2022年福建气象部门50个负氧离子观测站资料,以及基于卫星遥感反演的气溶胶、植被指数、地表亮温等生态环境参数,并融合Cubist机器学习方法建立负氧离子浓度和PM_(2.5)质量浓度估算模型,在此基础上提出综合考量负氧离子浓度和PM_(2.5)质量浓度来建立空气清新指数,并采用统计学上数据序列频率的四分位数结合负氧离子的时空变化特征对空气清新指数进行分级,最后实现对区域空气清新度的精细化网格监测。结果表明:负氧离子浓度估算训练模型拟合优度为0.838,测试模型拟合优度为0.526;PM_(2.5)质量浓度估算训练模型拟合优度为0.968,测试模型拟合优度为0.867。基于气象、遥感和机器学习算法的空气清新指数监测结果与实际情况相符。

The concentrations of negative oxygen ions and particulate matter 2.5(PM_(2.5))serve as important indicators in the assessment of the degrees of air freshness and cleanliness.Based on 2018-2022 data from 50 negative oxygen ion observation stations affiliated with the Fujian meteorological departments,along with the ecological parameters such as aerosol,vegetation index,and surface brightness temperature obtained by satellite-based remote sensing inversion,this study built estimation models for the concentrations of negative oxygen ions and PM_(2.5)using the Cubist machine learning method.Accordingly,it developed an air freshness index(AFI),and the fine-scale mesh-based monitoring of regional air freshness was achieved.The results show that the estimation model for the negative oxygen ion concentration yielded goodness of fit of 0.838 and 0.526 for the training and test sets,respectively.In comparison,the estimation model for the PM_(2.5)concentration exhibited goodness of fit of 0.968 and 0.867 for the training and test sets,respectively.Then,this study developed the AFI by comprehensively considering negative oxygen ions and PM_(2.5).Then,this study graded the AFI using the frequency quartiles of the statistical data series combined with the spatiotemporal changes in negative oxygen ions.The results indicate that the AFI monitoring results based on meteorology,remote sensing,and machine learning algorithms are consistent with the actual conditions.