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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
N. A. S. Hamm (2016)
Publisher: Copernicus Publications
Journal: The International Archives of the Photogrammetry
Languages: English
Types: Article
Subjects: TA1-2040, T, TA1501-1820, Applied optics. Photonics, Engineering (General). Civil engineering (General), Technology
Epidemiological studies of the health effects of air pollution require estimation of individual exposure. It is not possible to obtain measurements at all relevant locations so it is necessary to predict at these space-time locations, either on the basis of dispersion from emission sources or by interpolating observations. This study used data obtained from a low-cost sensor network of 32 air quality monitoring stations in the Dutch city of Eindhoven, which make up the ILM (innovative air (quality) measurement system). These stations currently provide PM10 and PM2.5 (particulate matter less than 10 and 2.5 m in diameter), aggregated to hourly means. The data provide an unprecedented level of spatial and temporal detail for a city of this size. Despite these benefits the time series of measurements is characterized by missing values and noisy values. In this paper a space-time analysis is presented that is based on a dynamic model for the temporal component and a Gaussian process geostatistical for the spatial component. Spatial-temporal variability was dominated by the temporal component, although the spatial variability was also substantial. The model delivered accurate predictions for both isolated missing values and 24-hour periods of missing values (RMSE = 1.4 μg m−3 and 1.8 μg m−3 respectively). Outliers could be detected by comparison to the 95% prediction interval. The model shows promise for predicting missing values, outlier detection and for mapping to support health impact studies.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Akita, Y., Baldasano, J. M., Beelen, R., Cirach, M., de Hoogh, K., Hoek, G., Nieuwenhuijsen, M., Serre, M. L. and de Nazelle, A., 2014. Large scale air pollution estimation method combining land use regression and chemical transport modeling in a geostatistical framework. Environmental Science & Technology 48(8), pp. 4452-4459.
    • Close, J.-P. (ed.), 2016. AiREAS: Sustainocracy for a Healthy City. Springer. DOI: 10.1007/978-3-319-26940-5, Dordrecht.
    • Dash, I., 2016. Space-time observations for city level air quality modelling and mapping. Master's thesis, University of Twente, The Netherlands.
    • Finley, A., Banerjee, S. and Carlin, B., 2007. spBayes: An R package for univariate and multivariate hierarchical pointreferenced spatial models. Journal of Statistical Software 19(1), pp. doi: 10.18637/jss.v019.i04.
    • Finley, A. O., Banerjee, S. and Gelfand, A. E., 2012. Bayesian dynamic modeling for large space-time datasets using Gaussian predictive processes. Journal of Geographical Systems 14(1), pp. 29-47.
    • Gelfand, A. E., Banerjee, S. and Gamerman, D., 2005. Spatial process modelling for univariate and multivariate dynamic spatial data. Environmetrics 16(5), pp. 465-479.
    • Hamm, N. A. S., Finley, A. O., Schaap, M. and Stein, A., 2015. A spatially varying coefficient model for mapping air quality at the European scale. Atmospheric Environment 102, pp. 393- 405.
    • Hamm, N. A. S., van Lochem, M., Hoek, G., Otjes, R., van der Sterren, S. and Verhoeven, H., 2016. The invisible made visible: Science and technology. In: J.-P. Close (ed.), AiREAS: Sustainocracy for a Healthy City: The Invisible made Visible Phase 1, Springer, Dordrecht, pp. 51-77.
    • R Core Team, 2016. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
    • Zhang, Y., Hamm, N. A. S., Meratnia, N., Stein, A., van de Voort, M. and Havinga, P. J. M., 2012. Statistics-based outlier detection for wireless sensor networks. International Journal of Geographical Information Science 26(8), pp. 1373-1392.
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