王勇, 任栋, 刘严萍, 李江波. 融合GNSS PWV、风速与大气污染观测的河北省春季PM2.5浓度模型研究[J]. 武汉大学学报 ( 信息科学版), 2019, 44(8): 1198-1204. DOI: 10.13203/j.whugis20170340
引用本文: 王勇, 任栋, 刘严萍, 李江波. 融合GNSS PWV、风速与大气污染观测的河北省春季PM2.5浓度模型研究[J]. 武汉大学学报 ( 信息科学版), 2019, 44(8): 1198-1204. DOI: 10.13203/j.whugis20170340
WANG Yong, REN Dong, LIU Yanping, LI Jiangbo. Spring PM2.5 Concentration Model in Hebei Province Based on GNSS PWV, Wind Speed and Air Pollution Observation[J]. Geomatics and Information Science of Wuhan University, 2019, 44(8): 1198-1204. DOI: 10.13203/j.whugis20170340
Citation: WANG Yong, REN Dong, LIU Yanping, LI Jiangbo. Spring PM2.5 Concentration Model in Hebei Province Based on GNSS PWV, Wind Speed and Air Pollution Observation[J]. Geomatics and Information Science of Wuhan University, 2019, 44(8): 1198-1204. DOI: 10.13203/j.whugis20170340

融合GNSS PWV、风速与大气污染观测的河北省春季PM2.5浓度模型研究

Spring PM2.5 Concentration Model in Hebei Province Based on GNSS PWV, Wind Speed and Air Pollution Observation

  • 摘要: PM2.5浓度时空演化特征分析有助于大气污染的现状和发展认知,但PM2.5浓度监测积累时间较短,且受到排放强度和气象因素的影响,因此可融合全球导航卫星系统(Global Navigation Satellite System,GNSS)天顶可降水量(precipitable water vapor,PWV)、风速和大气污染物构建PM2.5浓度模型。以河北省为例,首先分别开展PM2.5浓度与大气污染物、GNSS PWV及风速的相关性分析;然后将大气污染物、GNSSPWV和风速作为输入,PM2.5浓度作为输出,利用逆传播(back propagation,BP)神经网络分别构建城市PM2.5浓度模型和区域PM2.5浓度模型;最后进行PM2.5浓度模型可靠性检验。将模型预测值与PM2.5浓度实测值比较发现,预测PM2.5浓度等级准确率高,相对误差较低。该模型可用于区域PM2.5浓度时空演化特征分析。

     

    Abstract: The analysis of temporal and spatial evolution characteristics of PM2.5 concentration is helpful to recognize the development and status of atmospheric pollution. However, the accumulation time of PM2.5 concentration monitoring is short PM2.5 and concentration is affected by emission intensity and meteorological factors. Therefore, it is necessary to study the PM2.5 concentration inversion model based on other existing data. PM2.5 concentration was affected by emission intensity and meteorological factors. Taking Hebei Province as an example, the PM2.5 concentration model is built by integrating Global Navigation Satellite System (GNSS) precipitable water vapor (PWV), wind speed and air pollutants.Firstly, the correlation analysis of PM2.5 concentration with air pollutants, GNSS PWV and wind speed is carried out. Then air pollutants, GNSS PWV and wind speed are used as input, and PM2.5 concentration is used as output, and urban PM2.5 concentration model and regional PM2.5 concentration model are constructed by back propagation (BP) neural network. Finally, the reliability of PM2.5 concentration model is carried out. The results show that the prediction accuracy of PM2.5 concentration level is high and the relative error is low compared with the measured value of PM2.5 concentration. The PM2.5 concentration model, which combines GNSS PWV, wind speed and air pollutants, can be used to analyze the temporal and spatial evolution characteristics of regional PM2.5 concentration. It can be used for reference for government air pollution control, and also can be used to monitor the concentration of PM2.5.

     

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