Deriving high-resolution urban air pollution maps using mobile sensor nodes

Hasenfratz, David; Saukh, Olga; Walser, Christoph; Hueglin, Christoph; Fierz, Martin; Arn, Tabita; Beutel, Jan; Thiele, Lothar

doi:10.1016/j.pmcj.2014.11.008

Cited by 241 publications

(178 citation statements)

References 33 publications

(60 reference statements)

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“…In Hatzopoulou's work, the number of locations and the frequency of each location being visited were used as variables in the LUR models, except the commonly used predictor variables such as land use and road length [120]. Hasenfratz et al used nonlinear LUR models to develop temporal air pollution map with a high spatial resolution of 100 × 100 m 2 for Zurich, Switzerland [41,121]. Other LUR model applications in mobile monitoring can be found in [122][123][124][125].…”

Section: Approaches For Mobile Monitoringmentioning

confidence: 99%

A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods

Xie

Šemanjski

Gautama

et al. 2017

IJGI

142

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Abstract:The impact of urban air pollution on the environments and human health has drawn increasing concerns from researchers, policymakers and citizens. To reduce the negative health impact, it is of great importance to measure the air pollution at high spatial resolution in a timely manner. Traditionally, air pollution is measured using dedicated instruments at fixed monitoring stations, which are placed sparsely in urban areas. With the development of low-cost micro-scale sensing technology in the last decade, portable sensing devices installed on mobile campaigns have been increasingly used for air pollution monitoring, especially for traffic-related pollution monitoring. In the past, some reviews have been done about air pollution exposure models using monitoring data obtained from fixed stations, but no review about mobile sensing for air pollution has been undertaken. This article is a comprehensive review of the recent development in air pollution monitoring, including both the pollution data acquisition and the pollution assessment methods. Unlike the existing reviews on air pollution assessment, this paper not only introduces the models that researchers applied on the data collected from stationary stations, but also presents the efforts of applying these models on the mobile sensing data and discusses the future research of fusing the stationary and mobile sensing data.

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Section: Approaches For Mobile Monitoringmentioning

confidence: 99%

A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods

Xie

Šemanjski

Gautama

et al. 2017

IJGI

142

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“…The studies of Hasenfratz et al (2015) and Hagemann et al (2014) are examples of opportunistic data collection with a fixed structure, as they performed mobile measurements with sensors installed on the roof of public transport vehicles (trams). In these cases, the measurements are restricted to the route of the bus or the tram tracks.…”

Section: Opportunistic Mobile Monitoringmentioning

confidence: 99%

“…The case study described in this paper with city wardens carrying measurement devices during their daily surveillance tours results in unstructured measurements without distinct patterns in space or time. The studies of Hasenfratz et al (2015) and Hagemann et al (2014) require limited human interaction as the sensor nodes are supplied with power from the vehicles and data are transmitted automatically (Hasenfratz et al, 2015). When using humans as the mobile carrier (Weichenthal et al, 2008, this paper), more human interaction is needed.…”

Section: Opportunistic Mobile Monitoringmentioning

confidence: 99%

Opportunistic mobile air pollution monitoring: A case study with city wardens in Antwerp

Bossche

Theunis

Elen

et al. 2016

Atmospheric Environment

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The goal of this paper is to explore the potential of opportunistic mobile monitoring to map the exposure to air pollution in the urban environment at a high spatial resolution. Opportunistic mobile monitoring makes use of existing mobile infrastructure or people's common daily routines to move measurement devices around. Opportunistic mobile monitoring can also play a crucial role in participatory monitoring campaigns as a typical way to gather data.A case study to measure black carbon was set up in Antwerp, Belgium, with the collaboration of city employees (city wardens). The Antwerp city wardens are outdoors for a large part of the day on surveillance tours by bicycle or on foot, and gathered a total of 393 hours of measurements. The data collection is unstructured both in space and time, leading to sampling bias. A temporal adjustment can only partly counteract this bias. Although a high spatial coverage was obtained, there is still a rather large uncertainty on the average concentration levels at a spatial resolution of 50 m due to a limited number of measurements and sampling bias. Despite of this uncertainty, large spatial patterns within the city are clearly captured.This study illustrates the potential of campaigns with unstructured opportunistic mobile monitoring, including participatory monitoring campaigns. The results demonstrate that such an approach can indeed be used to identify broad spatial trends over a wider area, enabling applications including hotspot identification, personal exposure studies, regression mapping, etc. But, they also emphasize the need for repeated measurements and careful processing and interpretation of the data.

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“…One way to overcome this issue is to generate different models for every target time period. Hasenfratz et al [25] and Li et al [24] (in two separate works) built up two sets of a thousand models, each targeting one time period (e.g., one model per day) for one city. These models cannot be used for time periods other than the training ones.…”

Section: B Air Pollution Modelingmentioning

confidence: 99%

High Resolution Air Pollution Maps in Urban Environments Using Mobile Sensor Networks

Marjovi

Arfire

Martinoli

2015

2015 International Conference on Distributed Computing in Sensor Systems

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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 ultrafine 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., NO2 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 R 2 , RMSE and FAC metrics compared to a baseline KNearest Neighbor method.

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Deriving high-resolution urban air pollution maps using mobile sensor nodes

Cited by 241 publications

References 33 publications

A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods

A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods

Opportunistic mobile air pollution monitoring: A case study with city wardens in Antwerp

High Resolution Air Pollution Maps in Urban Environments Using Mobile Sensor Networks

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