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PM2.5 Air Quality Index Prediction Using an Ensemble Learning Model

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Web-Age Information Management (WAIM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8597))

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Abstract

PM2.5 has a significant influence on human health. And with the modern society developing, PM2.5 has been becoming a severe problem for people. In this paper, an ensemble learning method for PM 2.5 prediction is proposed. The assumption is that the information inside the historical data of PM2.5 in the selected station and other stations orderly from the one can be beneficial for the prediction of PM2.5. The results show that the more information, the more accurate the predictions are. Moreover, there are a balance between the good performance and the costs of modelling.

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References

  1. He, L.Y., Hu, M., Huang, X.F., Yu, B.D., Zhang, Y.H., Liu, D.Q.: Measurement of emissions of fine particulate organic matter from Chinese cooking. Atmos. Environ. 38(38), 6557–6564 (2004)

    Article  Google Scholar 

  2. Kleeman, M.J., Schauer, J.J., Cass, G.R.: Size and composition distribution of fine particulate matter emitted from wood burning, meat charbroiling, and cigarettes. Environ. Sci. Technol. 33(20), 3516–3523 (1999)

    Article  Google Scholar 

  3. Pope III, C.A.: Epidemiology of fine particulate air pollution and human health: biologic mechanisms and who’s at risk? Environ. Health Perspect. 108(Suppl. 4), 713 (2000)

    Article  MathSciNet  Google Scholar 

  4. Pope III, C.A., Ezzati, M., Dockery, D.W.: Fine-particulate air pollution and life expectancy in the United States. N. Engl. J. Med. 360(4), 376–386 (2009)

    Article  Google Scholar 

  5. Guo, Y., Jia, Y., Pan, X., Liu, L., Wichmann, H.: The association between fine particulate air pollution and hospital emergency room visits for cardiovascular diseases in Beijing, China. Sci. Total Environ. 407(17), 4826–4830 (2009)

    Article  Google Scholar 

  6. Cobourn, W.G.: An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations. Atmos. Environ. 44(25), 3015–3023 (2010)

    Article  Google Scholar 

  7. Kloog, I., Koutrakis, P., Coull, B.A., Lee, H.J., Schwartz, J.: Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements. Atmos. Environ. 45(35), 6267–6275 (2011)

    Article  Google Scholar 

  8. Ross, Z., Jerrett, M., Ito, K., Tempalski, B., Thurston, G.D.: A land use regression for predicting fine particulate matter concentrations in the New York City region. Atmos. Environ. 41(11), 2255–2269 (2007)

    Article  Google Scholar 

  9. Moore, D.K., Jerrett, M., Mack, W.J., Künzli, N.: A land use regression model for predicting ambient fine particulate matter across Los Angeles, CA. J. Env. Monit. 9(3), 246–252 (2007)

    Article  Google Scholar 

  10. Henderson, S.B., Beckerman, B., Jerrett, M., Brauer, M.: Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter. Environ. Sci. Technol. 41(7), 2422–2428 (2007)

    Article  Google Scholar 

  11. Ordieres, J.B., Vergara, E.P., Capuz, R.S., Salazar, R.E.: Neural network prediction model for fine particulate matter (PM2.5) on the US–Mexico border in El Paso (Texas) and Ciudad Juárez (Chihuahua). Environ. Model Softw. 20(5), 547–559 (2005)

    Article  Google Scholar 

  12. McKendry, I.G.: Evaluation of artificial neural networks for fine particulate pollution (PM10 and PM2.5) forecasting. J. Air Waste Manag. Assoc. 52(9), 1096–1101 (2002)

    Article  Google Scholar 

  13. Sun, W., Zhang, H., Palazoglu, A., Singh, A., Zhang, W., Liu, S.: Prediction of 24-hour-average PM2.5 concentrations using a hidden Markov model with different emission distributions in Northern California. Sci. Total Environ. 443, 93–103 (2013)

    Article  Google Scholar 

  14. Dong, M., Yang, D., Kuang, Y., He, D., Erdal, S., Kenski, D.: PM2.5 concentration prediction using hidden semi-Markov model-based times series data mining. Expert Syst. Appl. 36(5), 9046–9055 (2009)

    Article  Google Scholar 

Download references

Acknowledgment

This research work was partly supported by 973 Project (Grant No. 2012CB316205), National Natural Science Foundation of China (Grant No. 71001103, 91224008, 91324015), Beijing Social Science Fund (No. 13JGB035), Beijing Natural Science Foundation (No. 9122013), Beijing Nova Program (No. Z131101000413058), and Program for Excellent Talents in Beijing.

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Authors and Affiliations

  1. School of Information, Renmin University of China, Beijing, 100872, China

    Wei Xu, Cheng Cheng & Hui Yuan

  2. Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100872, China

    Danhuai Guo & Xin Chen

  3. Department of Engineering Physics, Institute of Public Safety Research, Tsinghua University, Beijing, 100084, China

    Rui Yang & Yi Liu

Authors
  1. Wei Xu
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  2. Cheng Cheng
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  3. Danhuai Guo
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  4. Xin Chen
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  5. Hui Yuan
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  6. Rui Yang
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  7. Yi Liu
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Corresponding author

Correspondence to Wei Xu .

Editor information

Editors and Affiliations

  1. DEKE Lab., Renmin University of China, Beijing, China

    Yueguo Chen

  2. Institute for Information Systems, Technical University Braunschweig, Braunschweig, Germany

    Wolf-Tilo Balke

  3. Hong Kong Baptist University Dept. Computer Science, Kowloon Tong, Hong Kong SAR

    Jianliang Xu

  4. School of Information, Renmin University of China, Beijing, China

    Wei Xu

  5. School of Computer Science and Technology, Hefei, China

    Peiquan Jin

  6. Department of Computer Science, East China Normal University, Shanghai, China

    Xin Lin

  7. Department of Computer Science, Kean University, Wenzhou, China

    Tiffany Tang

  8. School of Electrical Engineering, Korea University, Seoul, Korea, Republic of (South Korea)

    Eenjun Hwang

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Xu, W. et al. (2014). PM2.5 Air Quality Index Prediction Using an Ensemble Learning Model. In: Chen, Y., et al. Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science(), vol 8597. Springer, Cham. https://doi.org/10.1007/978-3-319-11538-2_12

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  • DOI: https://doi.org/10.1007/978-3-319-11538-2_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11537-5

  • Online ISBN: 978-3-319-11538-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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