Towards high spatial resolution air quality mapping : a methodology to assess street-level exposure based on mobile monitoring

Exposure to air pollution has a severe impact on human health. Especially in urban areas, where most of the European population lives and which are typically hot-spots of air pollution, a lot of people are exposed to air pollution. However, the urban environment shows a high variability in air pollutant concentrations and available data are often lacking to accurately estimate the actual concentration levels citizens are exposed to. The emergence of lower-cost and portable sensors makes it possible to perform mobile measurements and to collect additional data at locations where stationary measurements are lacking. Further, this also makes it possible to engage citizens in participatory monitoring techniques. However, several issues on spatial and temporal representativeness can interfere with the real-life applicability of mobile monitoring. This thesis presents the possibilities and challenges of the use of mobile data to map the urban air quality. Based on an extensive targeted campaign, it is shown that mobile monitoring is a suitable approach to map the urban air quality at a high spatial resolution when using a carefully developed methodology. However, a large number of repeated measurements are still required to obtain representative results. A possible way to gather a large number of measurements is to make use of people’s common daily routines to move measurement devices around, which is defined as opportunistic measurements. An example case study with the collaboration of the city wardens of Antwerp is presented in this thesis. Mobile monitoring typically does not yet result in city-wide pollution maps. Based on the data, regression models can be built to predict the concentration levels at other locations. The results highlighted the potential to construct near-real-time pollution maps that can be used for providing personalized information about air quality to citizens

Development of a land use regression model for black carbon using mobile monitoring data and its application to pollution-avoiding routing

Black carbon is often used as an indicator for combustion-related air pollution. In urban environments, on-road black carbon concentrations have a large spatial variability, suggesting that the personal exposure of a cyclist to black carbon can heavily depend on the route that is chosen to reach a destination. In this paper, we describe the development of a cyclist routing procedure that minimizes personal exposure to black carbon. Firstly, a land use regression model for predicting black carbon concentrations in an urban environment is developed using mobile monitoring data, collected by cyclists. The optimal model is selected and validated using a spatially stratified cross-validation scheme. The resulting model is integrated in a dedicated routing procedure that minimizes personal exposure to black carbon during cycling. The best model obtains a coefficient of multiple correlation of R = 0.520. Simulations with the black carbon exposure minimizing routing procedure indicate that the inhaled amount of black carbon is reduced by 1.58% on average as compared to the shortest-path route, with extreme cases where a reduction of up to 13.35% is obtained. Moreover, we observed that the average exposure to black carbon and the exposure to local peak concentrations on a route are competing objectives, and propose a parametrized cost function for the routing problem that allows for a gradual transition from routes that minimize average exposure to routes that minimize peak exposure

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

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. (C) 2017 Elsevier Ltd. All rights reserved

M & L Jaargang 22/4

Herman Van den Bossche, Roger Deneef en Jo Wijnant De Alpentuin Het Bloemdal in het Provinciaal Domein van Huizingen. Apotheose van een tuinenbeweging en beschermd monument. [The Alpine Garden Het Bloemdal at Huizingen, Apotheosis of a garden design movement and protected monument.]Marcel M. Celis, Joris Snaet en Lode De Clercq Antoine en Emile Beernaert, steenhouwers (ca. 1850-1924). [Antoine and Emile Beernaert, stonecutters (1850-1924).]Lode De Clercq De assimilatie van een aantal zachte Franse kalksteensoorten in het midden van de 19de eeuw in België en hun conservatieproblematiek. [The nineteenth century assimilation of French soft limestones in Belgium and their conservation.]Jacques Vereecke en Bénédicte Reynders De restauratie van het stenen grafmonument van de architecten Suys op het kerkfhof van Laken. [The restoration of the funerary monument to the architects Suys on the Laken cemetery.]Summar

Participatory Patterns in an International Air Quality Monitoring Initiative

The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.Comment: 17 pages, 6 figures, 1 supplementary fil

Participatory Patterns in an International Air Quality Monitoring Initiative

The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution