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Predicting Air Quality from Low-Cost Sensor Measurements

  • Hamish Huggard
  • Yun Sing KohEmail author
  • Patricia Riddle
  • Gustavo Olivares
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 996)

Abstract

Urban air pollution poses a significant global health risk, but due to the high expense of measuring air quality, the amount of available data on pollutant exposure has generally been wanting. In recent years this has motivated the development of several cheap, portable air quality monitoring instruments. However, these instruments also tend to be unreliable, and thus the raw measurements require preprocessing to make accurate predictions of actual air quality conditions, making them an apt target for machine learning techniques. In this paper we use a dataset of measurements from a low cost air-quality instrument—the ODIN-SD—to examine which techniques are most appropriate, and the limitations of such an approach. From theoretical and experimental considerations, we conclude that a robust linear regression over measurements of air quality metrics, as well as relative humidity and temperature measurements produces the model with greatest accuracy. We also discuss issues of concept drift which occur in this context, and quantify how much training data is required to strike the right balance between predictive accuracy and efficient data collection.

Keywords

Air quality Polynomial regression Concept drift 

References

  1. 1.
    Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448. SIAM (2007)Google Scholar
  2. 2.
    Air Quality Sensor Performance Evaluation Center. Purpleair PA-ii - summary report. http://www.aqmd.gov/docs/default-source/aq-spec/summary/purpleair-pa-ii---summary-report.pdf?sfvrsn=4. Accessed 20 Feb 2018
  3. 3.
    Cohen, A.J., et al.: The global burden of disease due to outdoor air pollution. J. Toxicol. Environ. Health Part A 68(13–14), 1301–1307 (2005)CrossRefGoogle Scholar
  4. 4.
    Delany, S.J., Cunningham, P., Tsymbal, A., Coyle, L.: A case-based technique for tracking concept drift in spam filtering. Knowl. Based Syst. 18(4–5), 187–195 (2005)CrossRefGoogle Scholar
  5. 5.
    Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 286–295. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-28645-5_29CrossRefGoogle Scholar
  6. 6.
    Zico Kolter, J., Maloof, M.A.: Dynamic weighted majority: an ensemble method for drifting concepts. J. Mach. Learn. Res. 8, 2755–2790 (2007)zbMATHGoogle Scholar
  7. 7.
    Koychev, I.: Gradual forgetting for adaptation to concept drift. In: Proceedings of ECAI 2000 Workshop on Current Issues in Spatio-Temporal Reasoning (2000)Google Scholar
  8. 8.
    Lu, X., Wang, Y., Huang, L., Yang, W., Shen, Y.: Temporal-spatial aggregated urban air quality inference with heterogeneous big data. In: Yang, Q., Yu, W., Challal, Y. (eds.) WASA 2016. LNCS, vol. 9798, pp. 414–426. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-42836-9_37CrossRefGoogle Scholar
  9. 9.
    McKone, T.E., Barry Ryan, P., Özkaynak, H.: Exposure information in environmental health research: current opportunities and future directions for particulate matter, ozone, and toxic air pollutants. J. Expo. Sci. Environ. Epidemiol. 19(1), 30 (2009)CrossRefGoogle Scholar
  10. 10.
    Olivares, G., Edwards, S.: The outdoor dust information node (ODIN)-development and performance assessment of a low cost ambient dust sensor. Atmos. Meas. Tech. Discuss. 8, 7511–7533 (2015)CrossRefGoogle Scholar
  11. 11.
    Shalizi, C.: Advanced Data Analysis from an Elementary Point of View. Cambridge University Press, Cambridge (2013)Google Scholar
  12. 12.
    Snyder, E.G., et al.: The changing paradigm of air pollution monitoring (2013)CrossRefGoogle Scholar
  13. 13.
    Su, B., Shen, Y.-D., Xu, W.: Modeling concept drift from the perspective of classifiers. In: 2008 IEEE Conference on Cybernetics and Intelligent Systems, pp. 1055–1060. IEEE (2008)Google Scholar
  14. 14.
    Widmer, G., Kubat, M.: Effective learning in dynamic environments by explicit context tracking. In: Brazdil, P.B. (ed.) ECML 1993. LNCS, vol. 667, pp. 227–243. Springer, Heidelberg (1993).  https://doi.org/10.1007/3-540-56602-3_139CrossRefGoogle Scholar
  15. 15.
    Zheng, Y., et al.: Forecasting fine-grained air quality based on big data. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2267–2276. ACM (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Hamish Huggard
    • 1
  • Yun Sing Koh
    • 1
    Email author
  • Patricia Riddle
    • 1
  • Gustavo Olivares
    • 2
  1. 1.The University of AucklandAucklandNew Zealand
  2. 2.National Institute of Water and Atmospheric ResearchAucklandNew Zealand

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