%0 Journal Article %1 MUELLER2016171 %A Mueller, M.D. %A Hasenfratz, David %A Saukh, Olga %A Fierz, Martin %A Hueglin, Christoph %D 2016 %J Atmospheric Environment %K p2map2:antrag %P 171 - 181 %R https://doi.org/10.1016/j.atmosenv.2015.11.033 %T Statistical modelling of particle number concentration in Zurich at high spatio-temporal resolution utilizing data from a mobile sensor network %U http://www.sciencedirect.com/science/article/pii/S1352231015305409 %V 126 %X Highly resolved pollution maps are a valuable resource for many issues related to air quality including exposure modelling and urban planning. We present an approach for their generation based on data from a mobile sensor network and statistical modelling. An extensive record of particle number concentrations (PNCs) spanning more than 1.5 years was compiled by the tram-based OpenSense mobile sensor network in the City of Zurich. The sensor network consists of 10 sensor nodes installed on the roof of trams operating on different services according to their regular operation schedules. We developed a statistical modelling approach based on Generalized Additive models (GAMs) utilizing the PNC data obtained along the tram tracks as well as georeferenced information as predictor variables. Our approach includes a variable selection algorithm to ensure that individual models rely on the optimal set of predictor variables. Our models have high temporal and spatial resolutions of 30 min and 10 m by 10 m, respectively, and allow the spatial prediction of PNC in the municipal area of Zurich. We applied our approach to PNC data from two dedicated time periods: July–Sept. 2013 and Dec. 2013–Feb. 2014. The models strongly rely on traffic related predictor variables (vehicle counts) and, due to the hilly topography of Zurich, on elevation. We assessed the model performance by leave-one-out cross-validation and by comparing PNC predictions to measurements at fixed reference sites and to PNC measurements obtained by pedestrians. Model predictions reproduce well the main features of the PNC field in environment types similar to those passed by individual trams. Model performance is worse at elevated background locations probably due to the weak coverage of similar spots by the tram network. We end the paper by outlining a route finding algorithm which utilizes the highly resolved PNC maps providing the exposure minimal route for cyclists.

@article{MUELLER2016171, abstract = {Highly resolved pollution maps are a valuable resource for many issues related to air quality including exposure modelling and urban planning. We present an approach for their generation based on data from a mobile sensor network and statistical modelling. An extensive record of particle number concentrations (PNCs) spanning more than 1.5 years was compiled by the tram-based OpenSense mobile sensor network in the City of Zurich. The sensor network consists of 10 sensor nodes installed on the roof of trams operating on different services according to their regular operation schedules. We developed a statistical modelling approach based on Generalized Additive models (GAMs) utilizing the PNC data obtained along the tram tracks as well as georeferenced information as predictor variables. Our approach includes a variable selection algorithm to ensure that individual models rely on the optimal set of predictor variables. Our models have high temporal and spatial resolutions of 30 min and 10 m by 10 m, respectively, and allow the spatial prediction of PNC in the municipal area of Zurich. We applied our approach to PNC data from two dedicated time periods: July–Sept. 2013 and Dec. 2013–Feb. 2014. The models strongly rely on traffic related predictor variables (vehicle counts) and, due to the hilly topography of Zurich, on elevation. We assessed the model performance by leave-one-out cross-validation and by comparing PNC predictions to measurements at fixed reference sites and to PNC measurements obtained by pedestrians. Model predictions reproduce well the main features of the PNC field in environment types similar to those passed by individual trams. Model performance is worse at elevated background locations probably due to the weak coverage of similar spots by the tram network. We end the paper by outlining a route finding algorithm which utilizes the highly resolved PNC maps providing the exposure minimal route for cyclists.}, added-at = {2018-11-15T16:59:59.000+0100}, author = {Mueller, M.D. and Hasenfratz, David and Saukh, Olga and Fierz, Martin and Hueglin, Christoph}, biburl = {https://www.bibsonomy.org/bibtex/2c2f7a36c7b27b409307381a31a093d13/lautenschlager}, description = {Statistical modelling of particle number concentration in Zurich at high spatio-temporal resolution utilizing data from a mobile sensor network - ScienceDirect}, doi = {https://doi.org/10.1016/j.atmosenv.2015.11.033}, interhash = {e7db30d2f678d1e5757879479fd7bebd}, intrahash = {c2f7a36c7b27b409307381a31a093d13}, issn = {1352-2310}, journal = {Atmospheric Environment}, keywords = {p2map2:antrag}, pages = {171 - 181}, timestamp = {2018-11-15T16:59:59.000+0100}, title = {Statistical modelling of particle number concentration in Zurich at high spatio-temporal resolution utilizing data from a mobile sensor network}, url = {http://www.sciencedirect.com/science/article/pii/S1352231015305409}, volume = 126, year = 2016 }