基于分布式感知深度神经网络的高分辨率PM2.5值估算

doi:10.11821/dlxb202101015

摘要/Abstract

摘要：

细颗粒物（PM_2.5）个体暴露水平是健康效应研究中的关键问题,然而历史数据缺失和地面监测点覆盖范围小阻碍了相关研究。基于美国国家航空航天局遥感数据提供的气溶胶光学厚度（AOD）,融合地面监测、气象等多源数据进行建模来估算近地面PM_2.5浓度,所得结果的空间覆盖范围广、时间连续性强、方法成本低。本文基于2018年京津冀鲁地区,引入气象、NDVI、时间节点、空间标识等50个特征分析AOD-PM_2.5关系。鉴于传统插补方法单一所造成的信息损失,运用时空多视图插补方法来提高插补的精度和广度。考虑到特征的滞后作用、特征间相关性与偏相关性所导致的复杂关系,运用分布式感知深度神经网络模型来分别捕捉多源特征间的高阶特性。结果表明：① 时空多视图插补方法的相对误差为27.5%,数据平均缺失52.1%降至4.84%。② 分布式感知深度神经网络模型在时间预测上平均绝对误差、相对误差、均方误差、均方根误差分别为17.7 μg/m ³、46.8%、766.2 μg ²/m ⁶、26.9 μg/m ³,空间上,为16.6 μg/m ³、41.8%、691.5 μg ²/m ⁶、26.6 μg/m ³,从精度、稳健性、泛化能力和耗时方面综合来看,结果优于线性统计模型和常见深度学习架构。

关键词: 气溶胶光学厚度, PM_2.5, 多视图插补, 分布式感知, 深度学习, 时空迁移预测

Abstract:

The individual exposure level of fine particulate matter (PM_2.5) is a key issue in the study of health effects. However, the lack of historical data and the small coverage of ground monitoring stations have hindered the development of related research. Based on the aerosol optical depth (AOD) provided by NASA remote sensing data, multi-source data such as ground monitoring and meteorological data were integrated for modeling to estimate near-ground PM_2.5 concentration. The results have wide spatial coverage, strong time continuity and low method cost. Based on the Beijing-Tianjin-Hebei-Shandong region in 2018, this paper introduces 50 features such as meteorological elements, NDVI, time nodes and spatial markers to analyze the relationship between AOD and PM_2.5. In view of the information loss caused by the single traditional interpolation method, the spatiotemporal multi-view interpolation method is used to improve the accuracy and coverage of interpolation. Considering the complex relationship caused by hysteresis of features, and the correlation and partial correlation between features, this paper uses a distributed perception deep neural network model (DP-DNN) to separately capture higher-order features between multiple-source features. The results show that: (1) The relative error of the spatiotemporal multi-view interpolation method is 27.5%, and the average proportion of missing data decreases from 52.1% to 4.84%. (2) In terms of time prediction, mean absolute error, relative error, mean square error and root mean square error of DP-DNN are 7.7 μg/m ³, 46.8%, 766.2 μg ²/m ⁶ and 26.9 μg/m ³, respectively. In terms of space prediction, they are 16.6 μg/m ³, 41.8%, 691.5 μg ²/m ⁶ and 26.6 μg/m ³, respectively. In aspects of accuracy, robustness, generalization ability and time consuming, the results are superior to linear statistical models and common deep learning architecture.

Key words: aerosol optical depth (AOD), PM_2.5 prediction, multi-view interpolation, distributed perception, deep learning, prediction of spatiotemporal migration

刘基伟, 闵素芹, 金梦迪. 基于分布式感知深度神经网络的高分辨率PM_2.5值估算[J]. 地理学报, 2021, 76(1): 191-205.

LIU Jiwei, MIN Suqin, JIN Mengdi. High resolution PM_2.5 estimation based on the distributed perception deep neural network model[J]. Acta Geographica Sinica, 2021, 76(1): 191-205.

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类别	特征	单位	特征描述
PM_2.5特征	PM_2.5	μg/m³	近地面细颗粒物空气动力学粒径小于等于2.5 μm的颗粒物质量浓度的全日平均值
AOD特征	AOD		MODIS暗像元与深蓝算法融合提取0.55 μm处气溶胶光学厚度
NDVI特征	NDVI		MODIS近红外波段反射值与红光波段反射值之差比上两者之和
气象特征	温度	℃	近地面空气气温的全日平均值
	体感温度^[43]	℃	人体所感受到的冷暖程度,转换成同等温度的全日平均值
	温差	℃	给定日期内近地面空气气温最大温差
	日间温差	℃	给定日期内白天时段近地面空气气温最大温差
	体感温差	℃	给定日期内体感气温最大温差
	日间体感温差	℃	给定日期内白天时段体感气温最大温差
	云量		介于0和之间1的被云遮挡的天空百分比的全日平均值
	露点	℃	空气中所含的气态水达到饱和而凝结成液态水所需要降至的温度的全日平均值
	相对湿度		近地面相对湿度的全日平均值
	日照长度	h	给定日期的日照长度
	能见度	km	平均能见度的全日平均值
	阵风速度	m/s	近地面的阵风速度的全日平均值
	风速	m/s	近地面的风速的全日平均值
	风速角	°	以角度为单位的风的来向,取全日平均风向
	气压	hPa	面积上从海平面到大气上界空气柱的重量的全日平均值
	积雪强度	cm	积雪强度的全日平均值
	降雨强度	cm/h	降雨强度的全日平均值
时滞特征			滞后一日的气溶胶光学厚度、气温、云量、露点、相对湿度、日照长度、能见度、风速、气压、积雪强度、降雨强度
空间标识	经纬度		监测点站点经纬度
时间节点	月份		给定日期所在月份

评价标准	多视图		时间视图		空间视图		局部视图		全局视图
评价标准	RE(%)	MAE(μg/m³)	RE(%)	MAE(μg/m³)	RE(%)	MAE(μg/m³)	RE(%)	MAE(μg/m³)	RE(%)	MAE(μg/m³)
PM_2.5	21.3	7.3	68.0	24.7	19.1	6.8	17.8	5.9	16.2	5.2
AOD	24.6	113.4	76.8	283.9	12.3	60.2	20.9	110.6	22.6	76.2
AODs	52.7	136.6	104.5	357.2	50.6	116.7	33.7	116.4	87.6	151.2
NDVI	26.4	0.05	49.8	0.12	12.9	0.02	20.0	0.03	12.3	0.02
气象	12.5	\	36.3	\	15.5	\	15.5	\	21.8	\
平均结果	27.5	\	67.8	\	22.1	\	21.5	\	32.1	\

插补方法	插补前	多视图	时间视图	空间视图	局部视图	全局视图
AOD缺失	71.7	0.0	0.4	1.5	2.8	0.0
AODs平均缺失	66.3	0.0	2.9	21.6	42.1	0.0
NDVI缺失	58.6	1.0	24.3	52.2	55.5	34.4
气象数据平均缺失	48.3	22.7	27.3	29.2	37.5	29.4
PM_2.5缺失	15.9	0.5	10.5	3.8	15.5	10.5
平均结果	52.1	4.84	13.1	21.7	30.7	14.9

		DP-DNN			LSTM	GWR	B-OLSR	EN
		地域迁移检验
MAE	16.6		17.2	18.5	22.1	29.1	19.2	21.3
MAE_std	1.1		1.2	1.6	0.9	41.0	0.4	0.6
RE(%)	41.80		44.7	44.6	76.1	141.1	56.6	67.0
RE_std(%)	4.90		4.72	5.0	13.7	141.7	4.3	5.7
MSE	691.5		744.2	901.2	1026.6	63086.4	788.6	1008.9
MSE_std	100.4		103.7	181.1	176.1	275336.9	49.0	81.0
RMSE	26.6		27.2	29.9	31.9	100.3	28.1	31.7
RMSE_std	1.9		1.9	2.9	2.6	230.3	0.9	1.3
		时间预测检验
MAE	17.7		18.9	22.4	25.7	33.7	53.8	21.9
MAE_std	3.7		5.0	7.4	4.7	2.7	12.0	4.3
RE(%)	46.8		50.9	61.9	87.4	108.3	211.2	70.7
RE_std(%)	7.2		11.7	20.8	18.8	26.9	71.3	10.9
MSE	766.2		837.4	1126.8	1645.7	2228.3	3966.7	1038
MSE_std	355.6		446.7	789.5	712.1	532.6	1300	489.7
RMSE	26.9		27.4	31.9	39.6	46.9	62.1	31.3
RMSE_std	6.1		5.0	10.3	8.9	5.1	10.5	7.6
平均耗时(s)	4.2		3.1	3.3	26.4	3389.0	1.3	0.5