Machine-learning-based model and simulation analysis of PM2.5 concentration prediction in Beijing

被引：0

作者：

Qu Y. ^{[1
,2
]}

Qian X. ^{[1
]}

Song H.-Q. ^{[2
,3
]}

He J. ^{[4
]}

Li J.-H. ^{[2
,3
]}

Xiu H. ^{[4
]}

机构：

[1] School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing

[2] National and Local Joint Engineering Lab for Big Data Analysis and Computing Technology, Beijing

[3] School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing

[4] School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing

来源：

Gongcheng Kexue Xuebao/Chinese Journal of Engineering | 2019年 / 41卷 / 03期

关键词：

Long short-term memory; Machine learning; Meteorological condition; Neural networks; PM2.5; Pollution prediction;

D O I：

10.13374/j.issn2095-9389.2019.03.014

中图分类号：

学科分类号：

摘要：

In recent years, the air quality in China has become a matter of serious concern. Among the available indicators for evaluating air quality, PM2.5 is one of the most important. It comprises a complex mixture of extremely small particles and liquid droplets emitted into the air, whose diameters are no more than 2.5 μm. Environments with a high PM2.5 index are extremely harmful to human health. Once inhaled, these particles can affect the heart and lungs and cause serious health problems. Air pollution is closely related to meteorological conditions such as wind speed, wind direction, atmospheric stability, temperature, and air humidity. With the development of various machine learning methods, deep learning models based on neural networks are increasingly applied in air pollution research. In this study, the temperature, humidity, wind velocity data at different pressure altitudes from 8 locations around Beijing and average of PM2.5 data in Beijing were analyzed and normalized. Multi-dimensional data was ideal for research applications using machine learning methods. and three neural network models were built, including the back propagation (BP), convolutional neural network (CNN), and long short-term memory (LSTM) models, and trained them using the meteorological and PM2.5 data. The results indicate that the accuracies of the back propagation and convolutional neural network models in predicting the PM2.5 pollution level in the next hour is much lower than that of the long short-term memory model. The PM2.5 pollution index predicted for the next hour by the long short-term memory model is very close to the actual value. This result reveals the strong relationship between the PM2.5 pollution index of Beijing and the local meteorological conditions. The long short-term memory model is trained using meteorological data from different pressure altitudes, and found it to be more accurate in predicting pollution levels when using near-surface meteorological data than that obtained from multiple altitudes. © All right reserved.

引用

页码：401 / 407

页数：6

共 26 条

[1] Tai A.P.K., Mickley L.J., Jacob D.J., Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States: implications for the sensitivity of PM2.5 to climate change, Atmos Environ, 44, 32, (2010)
[2] Pateraki S., Asimakopoulos D.N., Flocas H.A., Et al., The role of meteorology on different sized aerosol fractions (PM10, PM2.5, PM2.5-10), Sci Total Environ, 419, (2012)
[3] Zhao X.J., Zhang X.L., Xu X.F., Et al., Seasonal and diurnal variations of ambient PM2.5 concentration in urban and rural environments in Beijing, Atmos Environ, 43, 18, (2009)
[4] Pan B.F., Zhao Y.L., Li J.J., Et al., Analysis of the scavenging efficiency on PM2.5 concentration of some kinds of meteorological factors, Environ Sci Technol, 25, 6, (2012)
[5] Yan W.L., Zhou D.P., Wang Y.F., Et al., Concentrations and size distributions of inhalable particles in summer and winter in Shenyang, J Appl Meteorol Sci, 19, 4, (2008)
[6] Liu J., Zhang X.L., Xu X.F., Et al., Comparison analysis of variation characteristics of SO2, NOx, O3 and PM2.5 between rural and urban areas, Beijing, Environ Sci, 29, 4, (2008)
[7] Dong X.L., Liu D.M., Yuan Y.S., Et al., Pollution characteristics and influencing factors of atmospheric particulates in Beijing during the summer of 2005, Chin J Environ Eng, 1, 9, (2007)
[8] Sillman S., The relation between ozone, NOx and hydrocarbons in urban and polluted rural environments, Atmos Environ, 33, 12, (1999)
[9] Zhou J.X., Correlative analysis on relationship between changes of several main contaminations and some meteorological elements, J Appl Meteorol Sci, 16, (2005)
[10] Zhou L., Xu X.D., Ding G.A., Et al., The correlation factors and pollution forecast model for PM2.5 concentration in Beijing Area, Acta Meteorol Sin, 61, 6, (2003)

← 1 2 3 →