Abstract
Among all air pollutants, PM\(_{2.5}\), which can be inhaled into lungs, is most harmful for peoples’ health. However, the number of fixed air quality measurement stations is insufficient. In order to make people be more aware of the air quality around them, this paper have proposed a method to infer fine-grained PM\(_{2.5}\) concentration. We leverage different type of collected by crowdsourcing for data mining. Then, features which have strong correlation with PM\(_{2.5}\) concentration are extracted. Furthermore, we train the proposed model using integrated radial basis function (rbf) kernel based ridge regression. The performance of the proposed method is evaluated thoroughly by real dataset collected by crowdsourcing. The results show that, our method can accurate infer the PM\(_{2.5}\) concentration.
W. Wang—This work was supported in part by the National High Technology Research and Development Program (863 Program) of China (Grant No. 2015AA016101, 2015AA015601), National Natural Science Foundation of China (Grant No. 61370197, 61402045).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lim, S.S., Vos, T., Flaxman, A.D., Danaei, G., Shibuya, K., Adair-Rohani, M., Amann, M., Anderson, H.R., Andrews, K.G., Aryee, M.: A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the global burden of disease study 2010. Lancet 380(9859), 2224–2260 (2012)
Zheng, Y., Liu, F., Hsieh, H.P.: U-air: when urban air quality inference meets big data. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Chicago, Illinois, USA, pp. 1436–1444, 11th–14th August 2013
Van Donkelaar, A., Martin, R.V., Brauer, M., Boys, B.L.: Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter. Environ. Health Perspect. 123(2), 135 (2015)
Martin, R.V.: Satellite remote sensing of surface air quality. Atmos. Environ. 42(34), 7823–7843 (2008)
Van Donkelaar, A., Martin, R.V., Levy, R.C., da Silva, A.M., Krzyzanowski, M., Chubarova, N.E., Semutnikova, E., Cohen, A.J.: Satellite-based estimates of ground-level fine particulate matter during extreme events: a case study of the moscow fires in 2010. Atmos. Environ. 45(34), 6225–6232 (2011)
Scaar, H., Teodorov, T., Ziegler, T., Mellmann, J.: Computational fluid dynamics (CFD) analysis of air flow uniformity in a fixed-bed dryer for medicinal plants. In: Ist International Symposium on CFD Applications in Agriculture, vol. 1008, pp. 119–126 (2012)
Rakowska, A., Wong, K.C., Townsend, T., Chan, K.L., Westerdahl, D., Ng, S., Močnik, G., Drinovec, L., Ning, Z.: Impact of traffic volume and composition on the air quality and pedestrian exposure in urban street canyon. Atmos. Environ. 98, 260–270 (2014)
Chen, L., Cai, Y., Ding, Y., Lv, M., Yuan, C., Chen, G.: Spatially fine-grained urban air quality estimation using ensemble semi-supervised learning and pruning. In: ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 1076–1087 (2016)
Raanan, F.: Single image dehazing. ACM (2008)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). doi:10.1007/11744023_32
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Li, S., Xi, T., Que, X., Wang, W. (2017). Fine-Grained Infer \(PM_{2.5}\) Using Images from Crowdsourcing. In: Ibrahim, S., Choo, KK., Yan, Z., Pedrycz, W. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2017. Lecture Notes in Computer Science(), vol 10393. Springer, Cham. https://doi.org/10.1007/978-3-319-65482-9_53
Download citation
DOI: https://doi.org/10.1007/978-3-319-65482-9_53
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-65481-2
Online ISBN: 978-3-319-65482-9
eBook Packages: Computer ScienceComputer Science (R0)