DocumentCode :
725371
Title :
High Resolution Air Pollution Maps in Urban Environments Using Mobile Sensor Networks
Author :
Marjovi, Ali ; Arfire, Adrian ; Martinoli, Alcherio
Author_Institution :
Distrib. Intell. Syst. & Algorithms Lab., Ecole Polytech. Fed. de Lausanne, Lausanne, Switzerland
fYear :
2015
fDate :
10-12 June 2015
Firstpage :
11
Lastpage :
20
Abstract :
We propose three modeling methods using a mobile sensor network to generate high spatio-temporal resolution air pollution maps for urban environments. In our deployment in Lausanne (Switzerland), dedicated sensing nodes are anchored to the public buses and measure multiple air quality parameters including the Lung Deposited Surface Area (LDSA), a state of the art metric for quantifying human exposure to ultra fine particles. In this paper, our focus is on generating LDSA maps. In particular, since the sensor network coverage is spatially and temporally dynamic, we leverage models to estimate the values for the locations and times where the data are not available. We first discretize the area topologically based on the street segments in the city and we then propose the following three prediction models: i) a log-linear regression model based on nine meteorological (e.g., Temperature and precipitations) and gaseous (e.g., NO 2 and CO) explanatory variables measured at two static stations in the city, ii) a novel network-based log-linear regression model that takes into account the LDSA values of the most correlated streets and also the nine explanatory variables mentioned above, iii) a novel Probabilistic Graphical Model (PGM) in which each street segment is considered as one node of the graph, and inference on conditional joint probability distributions of the nodes results in estimating the values in the nodes of interest. More than 44 millions of geo- and time-stamped LDSA measurements (i.e., More than 14 months of real data) are used in this paper to evaluate the proposed modeling approaches in various time resolutions (hourly, daily, weekly and monthly). The results show that the three approaches bring significant improvements in R2, RMSE and FAC metrics compared to a baseline K-Nearest Neighbor method.
Keywords :
air pollution measurement; sensor placement; wireless sensor networks; air quality parameters; high resolution air pollution maps; log linear regression model; lung deposited surface area; meteorological data; mobile sensor networks; precipitation data; probabilistic graphical model; public buses; sensor network coverage; street segments; temperature data; ultra fine particle exposure; urban environments; Air pollution; Atmospheric measurements; Atmospheric modeling; Cities and towns; Mobile communication; Monitoring; Pollution measurement; Air pollution map; Environmental monitoring; Mobile sensor network; Urban air pollution;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Distributed Computing in Sensor Systems (DCOSS), 2015 International Conference on
Conference_Location :
Fortaleza
Type :
conf
DOI :
10.1109/DCOSS.2015.32
Filename :
7165019
Link To Document :
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