Mobile measurements and regression modeling of the spatial particulate matter variability in an urban area

Merbitz, Hendrik; Fritz, Sabine; Schneider, Christoph

doi:10.1016/j.scitotenv.2012.08.049

Cited by 58 publications

(25 citation statements)

References 41 publications

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“…In order to perform suitable monitoring at the urban scale, the grid size should be comparable with urban blocks (often less than 100 m) [146]. In addition, Merbitz et al [147] showed that PM 10 concentration in urban areas decreases exponentially from the emission source, and the effect of the emission source is dampened at a very short distance (about 100 m). Therefore, to suitably consider short-range PM 10 variations, the grid size for high-resolved spatial PM 10 prediction in urban areas should be less than 100 m. Figure 2 depicts the summary of this study.…”

Section: Spatio-temporal Prediction (Forecasting Of Spatial Distributmentioning

confidence: 99%

Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies

2016

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PM 10 prediction has attracted special legislative and scientific attention due to its harmful effects on human health. Statistical techniques have the potential for high-accuracy PM 10 prediction and accordingly, previous studies on statistical methods for temporal, spatial and spatio-temporal prediction of PM 10 are reviewed and discussed in this paper. A review of previous studies demonstrates that Support Vector Machines, Artificial Neural Networks and hybrid techniques show promise for suitable temporal PM 10 prediction. A review of the spatial predictions of PM 10 shows that the LUR (Land Use Regression) approach has been successfully utilized for spatial prediction of PM 10 in urban areas. Of the six introduced approaches for spatio-temporal prediction of PM 10 , only one approach is suitable for high-resolved prediction (Spatial resolution < 100 m; Temporal resolution ď 24 h). In this approach, based upon the LUR modeling method, short-term dynamic input variables are employed as explanatory variables alongside typical non-dynamic input variables in a non-linear modeling procedure.

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Section: Spatio-temporal Prediction (Forecasting Of Spatial Distributmentioning

confidence: 99%

Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies

2016

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“…Some studies use a mobile platform to perform short-term measurements at many locations (e.g. Larson et al, 2009;Merbitz et al, 2012;Ghassoun et al, 2015;Montagne et al, 2015). Only few studies use mobile measurements as a basis for LUR modelling.…”

Section: Introductionmentioning

confidence: 99%

Development and evaluation of land use regression models for black carbon based on bicycle and pedestrian measurements in the urban environment

Bossche

Baets

Verwaeren

et al. 2018

Environmental Modelling & Software

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Land use regression (LUR) modelling is increasingly used in epidemiological studies to predict air pollution exposure. The use of stationary measurements at a limited number of locations to build a LUR model, however, can lead to an overestimation of its predictive abilities. We use opportunistic mobile monitoring to gather data at a high spatial resolution to build LUR models to predict annual average concentrations of black carbon (BC). The models explain a significant part of the variance in BC concentrations. However, the overall predictive performance remains low, due to input uncertainty and lack of predictive variables that can properly capture the complex characteristics of local concentrations. We stress the importance of using an appropriate cross-validation scheme to estimate the predictive performance of the model. By using independent data for the validation and excluding those data also during variable selection in the model building procedure, overly optimistic performance estimates are avoided.

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“…Mobile measurements are used for a range of different purposes, e.g. to assess personal exposure by equipping the study object with a portable monitor (Berghmans et al, 2009;Boogaard et al, 2009;Dons et al, 2011;SpiraCohen et al, 2010), to assess the exposure in different modes of transport (Kaur et al, 2005;Int Panis et al, 2010;Kingham et al, 2013), to study spatial variation in air pollution (Weijers et al, 2004;Zwack et al, 2011c;MacNaughton et al, 2014), to investigate seasonal and regional variation (Bukowiecki et al, 2003), to study spatio-temporal correlation with noise (Weber, 2009), or to develop and validate air quality models (Zwack et al, 2011a;Merbitz et al, 2012). Other studies address the potential of using mobile measurements to construct air pollution maps at a high spatial resolution (e.g.…”

Section: Introductionmentioning

confidence: 99%

Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset

Bossche

Peters

Verwaeren

et al. 2015

Atmospheric Environment

196

132

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Mobile monitoring is increasingly used as an additional tool to acquire air quality data at a high spatial resolution. However, given the high temporal variability of urban air quality, a limited number of mobile measurements may only represent a snapshot and not be representative. In this study, the impact of this temporal variability on the representativeness is investigated and a methodology to map urban air quality using mobile monitoring is developed and evaluated.A large set of black carbon (BC) measurements was collected in Antwerp, Belgium, using a bicycle equipped with a portable BC monitor (micro-aethalometer). The campaign consisted of 256 and 96 runs along two fixed routes (2 and 5 km long). Large gradients over short distances and differences up to a factor of 10 in mean BC concentrations aggregated at a resolution of 20 m are observed. Mapping at such a high resolution is possible, but a lot of repeated measurements are required. After computing a trimmed mean and applying background normalisation, depending on the location 24 to 94 repeated measurement runs (median of 41) are required to map the BC concentrations at a 50 m resolution with an uncertainty of 25 %. When relaxing the uncertainty to 50 %, these numbers reduce to 5 to 11 (median of 8) runs. We conclude that mobile monitoring is a suitable approach for mapping the urban air quality at a high spatial resolution, and can provide insight into the spatial variability that would not be possible with stationary monitors. A careful set-up is needed with a sufficient number of repetitions in relation to the desired reliability and spatial resolution. Specific data processing methods such as background normalisation and event detection have to be applied.

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Mobile measurements and regression modeling of the spatial particulate matter variability in an urban area

Cited by 58 publications

References 41 publications

Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies

Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies

Development and evaluation of land use regression models for black carbon based on bicycle and pedestrian measurements in the urban environment

Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset

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