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

QU Yue; QIAN Xu; SONG Hong-qing; HE Jie; LI Jian-hui; XIU Hao

doi:10.13374/j.issn2095-9389.2019.03.014

Volume 41 Issue 3

Mar. 2019

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Article Navigation > Chinese Journal of Engineering > 2019 > 41(3): 401-407

QU Yue, QIAN Xu, SONG Hong-qing, HE Jie, LI Jian-hui, XIU Hao. Machine-learning-based model and simulation analysis of PM2.5 concentration prediction in Beijing[J]. Chinese Journal of Engineering, 2019, 41(3): 401-407. doi: 10.13374/j.issn2095-9389.2019.03.014

Citation:

QU Yue, QIAN Xu, SONG Hong-qing, HE Jie, LI Jian-hui, XIU Hao. Machine-learning-based model and simulation analysis of PM2.5 concentration prediction in Beijing[J]. Chinese Journal of Engineering, 2019, 41(3): 401-407. doi: 10.13374/j.issn2095-9389.2019.03.014

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

doi: 10.13374/j.issn2095-9389.2019.03.014

QU Yue^{1, 2},
QIAN Xu¹,
SONG Hong-qing^{2, 3
,
,},
HE Jie⁴,
LI Jian-hui^{2, 3},
XIU Hao⁴

1.
School of Mechanical Electronic and Information Engineering, China University of Mining and Technology(Beijing), Beijing 100083, China
2.
National and Local Joint Engineering Lab for Big Data Analysis and Computing Technology, Beijing 100190, China
3.
School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
4.
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China

More Information

Corresponding author: SONG Hong-qing, E-mail: songhongqing@ustb.edu.cn
Received Date: 2018-02-21
Publish Date: 2019-03-20

Abstract

Abstract

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.
- machine learning,
- PM2.5,
- meteorological condition,
- neural networks,
- long short-term memory,
- pollution prediction

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References(26)

References

[1]	Tai A P K, Mickley L J, Jacob D J. Correlations between fine particulate matter (PM_2.5) and meteorological variables in the United States: implications for the sensitivity of PM_2.5 to climate change. Atmos Environ, 2010, 44(32): 3976 doi: 10.1016/j.atmosenv.2010.06.060
[2]	Pateraki S, Asimakopoulos D N, Flocas H A, et al. The role of meteorology on different sized aerosol fractions (PM₁₀, PM_2.5, PM_2.5-10). Sci Total Environ, 2012, 419: 124 doi: 10.1016/j.scitotenv.2011.12.064
[3]	Zhao X J, Zhang X L, Xu X F, et al. Seasonal and diurnal variations of ambient PM_2.5 concentration in urban and rural environments in Beijing. Atmos Environ, 2009, 43(18): 2893 doi: 10.1016/j.atmosenv.2009.03.009
[4]	潘本鋒, 趙熠琳, 李健軍, 等. 氣象因素對大氣中PM_2.5的去除效應分析. 環境科技, 2012, 25(6): 41 doi: 10.3969/j.issn.1674-4829.2012.06.012 Pan B F, Zhao Y L, Li J J, et al. Analysis of the scavenging efficiency on PM_2.5 concentration of some kinds of meteorological factors. Environ Sci Technol, 2012, 25(6): 41 doi: 10.3969/j.issn.1674-4829.2012.06.012
[5]	嚴文蓮, 周德平, 王揚峰, 等. 沈陽冬夏季可吸入顆粒物濃度及尺度譜分布特征. 應用氣象學報, 2008, 19(4): 435 doi: 10.3969/j.issn.1001-7313.2008.04.007 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, 2008, 19(4): 435 doi: 10.3969/j.issn.1001-7313.2008.04.007
[6]	劉潔, 張小玲, 徐曉峰, 等. 北京地區SO₂、NO_x、O₃和PM_2.5變化特征的城郊對比分析. 環境科學, 2008, 29(4): 1059 doi: 10.3321/j.issn:0250-3301.2008.04.036 Liu J, Zhang X L, Xu X F, et al. Comparison analysis of variation characteristics of SO₂, NO_x, O₃ and PM_2.5 between rural and urban areas, Beijing. Environ Sci, 2008, 29(4): 1059 doi: 10.3321/j.issn:0250-3301.2008.04.036
[7]	董雪玲, 劉大錳, 袁楊森, 等. 北京市2005年夏季大氣顆粒物污染特征及影響因素. 環境工程學報, 2007, 1(9): 100 doi: 10.3969/j.issn.1673-9108.2007.09.022 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, 2007, 1(9): 100 doi: 10.3969/j.issn.1673-9108.2007.09.022
[8]	Sillman S. The relation between ozone, NO_x and hydrocarbons in urban and polluted rural environments. Atmos Environ, 1999, 33(12): 1821 doi: 10.1016/S1352-2310(98)00345-8
[9]	周江興. 北京市幾種主要污染物濃度與氣象要素的相關分析. 應用氣象學報, 2005, 16(增刊): 123 https://www.cnki.com.cn/Article/CJFDTOTAL-YYQX2005S1016.htm Zhou J X. Correlative analysis on relationship between changes of several main contaminations and some meteorological elements. J Appl Meteorol Sci, 2005, 16(Suppl): 123 https://www.cnki.com.cn/Article/CJFDTOTAL-YYQX2005S1016.htm
[10]	周麗, 徐祥德, 丁國安, 等. 北京地區氣溶膠PM_2.5粒子濃度的相關因子及其估算模型. 氣象學報, 2003, 61(6): 761 doi: 10.3321/j.issn:0577-6619.2003.06.013 Zhou L, Xu X D, Ding G A, et al. The correlation factors and pollution forecast model for PM_2.5 concentration in Beijing Area. Acta Meteorol Sin, 2003, 61(6): 761 doi: 10.3321/j.issn:0577-6619.2003.06.013
[11]	Ong B T, Sugiura K, Zettsu K. Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM_2.5. Neural Comput Appl, 2016, 27(6): 1553 doi: 10.1007/s00521-015-1955-3
[12]	Lary D J, Faruque F S, Malakar N, et al. Estimating the global abundance of ground level presence of particulate matter (PM_2.5). Geospatial Health, 2014, 8(3): 611 doi: 10.4081/gh.2014.292
[13]	Ong B T, Sugiura K, Zettsu K. Dynamic pre-training of deep recurrent neural networks for predicting environmental monitoring data //2014 IEEE International Conference on Big Data (Big Data). Washington, DC, 2014: 760
[14]	鄭毅, 朱成璋. 基于深度信念網絡的PM_2.5預測. 山東大學學報(工學版), 2014, 44(6): 19 https://www.cnki.com.cn/Article/CJFDTOTAL-SDGY201406004.htm Zheng Y, Zhu C Z. A prediction method of atmospheric PM_2.5 based on DBNs. J Shandong Univ Eng Sci, 2014, 44(6): 19 https://www.cnki.com.cn/Article/CJFDTOTAL-SDGY201406004.htm
[15]	戴李杰, 張長江, 馬雷鳴. 基于機器學習的PM_2.5短期濃度動態預報模型. 計算機應用, 2017, 37(11): 3057 https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY201711004.htm Dai L J, Zhang C J, Ma L M. Dynamic forecasting model of shortterm PM_2.5 concentration based on machine learning. J Comput Appl, 2017, 37(11): 3057 https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY201711004.htm
[16]	Li X, Peng L, Hu Y, et al. Deep learning architecture for air quality predictions. Environ Sci Pollut Res, 2016, 23(22): 22408 doi: 10.1007/s11356-016-7812-9
[17]	Rumelhart D E, McClelland J L. Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1. Foundations. Cambridge: MIT Press, 1986
[18]	Hubel D H, Wiesel T N. Receptive fields of single neurones in the cat's striate cortex. J Physiol, 1959, 148(3): 574 doi: 10.1113/jphysiol.1959.sp006308
[19]	Pigou L, Van Den Oord A, Dieleman S, et al. Beyond temporal pooling: Recurrence and temporal convolutions for gesture recognition in video. Int J Comput Vision, 2018, 126(2-4): 430 doi: 10.1007/s11263-016-0957-7
[20]	Yang J B, Nguyen M N, San P P, et al. Deep convolutional neural networks on multichannel time series for human activity recognition//Proceedings of the Twenty-Fourth International Conference on Artificial Intelligence. Buenos Aires, 2015: 3995
[21]	Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9(8): 1735 doi: 10.1162/neco.1997.9.8.1735
[22]	賈夢唯, 康娜, 趙天良. 南京秋冬季典型霾污染過程及邊界層特征分析. 環境科學與技術, 2014, 37(120): 105 https://www.cnki.com.cn/Article/CJFDTOTAL-FJKS2014S2024.htm Jia M W, Kang N, Zhao T L. Characteristics oftypical autumn and winter haze pollution episodes and their boundary layer in Nanjing. Environ Sci Technol, 2014, 37(120): 105 https://www.cnki.com.cn/Article/CJFDTOTAL-FJKS2014S2024.htm
[23]	張繼紅, 徐盛榮, 趙淑敏. 逆溫天氣對大氣污染的影響分析. 黑龍江環境通報, 2008, 32(2): 24 https://www.cnki.com.cn/Article/CJFDTOTAL-HLJO200802013.htm Zhang J H, Xu S R, Zhao S M. Analysis of effect of temperature inversion to atmosphere pollution. Heilongjiang Environ J, 2008, 32(2): 24 https://www.cnki.com.cn/Article/CJFDTOTAL-HLJO200802013.htm
[24]	寧海文. 西安市大氣污染氣象條件分析及空氣質量預報方法研究[學位論文]. 南京: 南京信息工程大學, 2006 Ning H W. An Analysis on Meteorological Factors and Study on Predicting Air Pollution in Xi'an City[Dissertation]. Nanjing: Nanjing University of Information Science and Technology, 2006
[25]	肖同玉, 任紅玉, 張興文. 哈爾濱冬季逆溫規律分析及預報. 東北農業大學學報, 2001, 32(2): 139 doi: 10.3969/j.issn.1005-9369.2001.02.007 Xiao T Y, Ren H Y, Zhang X W. Analysis and prediction of the winter inversion in Harbin. J Northeast Agric Univ, 2001, 32(2): 139 doi: 10.3969/j.issn.1005-9369.2001.02.007
[26]	曹紅麗, 陳奇. 西安邊界層逆溫特征及其與空氣污染的關系. 陜西氣象, 2014(2): 13 doi: 10.3969/j.issn.1006-4354.2014.02.005 Cao H L, Chen Q. Inverse temperature characteristics of boundary layer and its relationship with air pollution in Xi'an. J Shaanxi Meteorol, 2014(2): 13 doi: 10.3969/j.issn.1006-4354.2014.02.005