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Contains fulltext : 207854pub.pdf (Publisher’s version ) (Open Access) Highlights: • Citizens are highly motivated to contribute to air quality measure- ments that complement existing measurement networks because of their high spatio-temporal resolution; • Data needs to be assimilated, for example, using models; • Low-cost sensors need to be developed further, and their applica- tion calibrated and validated; • Easily accessible expert information and feedback is needed to sup- port participants; • Environment protection agencies (EPAs) can both support and benefit from citizen science using small sensor networks.

Contains fulltext : 164809pre.pdf (Author’s version preprint ) (Open Access) In this chapter, we describe a number of the citizen science projects RIVM took part in. The Smart Emission project in Nijmegen is one of the pilot projects. In this book chapter, we give examples of the lessons learned and the experiences gained. We provide a brief description of the planned innovations of the environmental monitoring, and how these innovations will help to ensure the continuity and effectiveness of citizen science measurements. The initial focus will be on air quality, but we expect that application is possible with other environmental parameters such as noise and water quality, light, vibration, radiation and meteorological parameters. These applications will follow in a later phase of the program. The final goal is to have a hybrid, flexible (air quality) network using different types of sensors, consisting of reference instruments, sensors of intermediate cost and quality, low cost sensors, and satellite observations. The data of this network may be provided by different parties, including citizens groups, cities, NGOs and official measurement institutes. In short, our ambition is to make citizen science data an integral part of standard procedures and models for determining air quality. Book after the First European Citizen Science Conference, Berlin, 19-21 May 2016

The performance of a Land Use Regression (LUR) model and a dispersion model (URBIS - URBis Information System) was compared in a Dutch urban area. For the Rijnmond area, i.e. Rotterdam and surroundings, nitrogen dioxide (NO2) concentrations for 2001 were estimated for nearly 70 000 centroids of a regular grid of 100 × 100 m.A LUR model based upon measurements carried out on 44 sites from the Dutch national monitoring network and upon Geographic Information System (GIS) predictor variables including traffic intensity, industry, population and residential land use was developed. Interpolation of regional background concentration measurements was used to obtain the regional background. The URBIS system was used to estimate NO2 concentrations using dispersion modelling. URBIS includes the CAR model (Calculation of Air pollution from Road traffic) to calculate concentrations of air pollutants near urban roads and Gaussian plume models to calculate air pollution levels near motorways and industrial sources. Background concentrations were accounted for using 1 × 1 km maps derived from monitoring and model calculations.Moderate agreement was found between the URBIS and LUR in calculating NO2 concentrations (R = 0.55). The predictions agreed well for the central part of the concentration distribution but differed substantially for the highest and lowest concentrations. The URBIS dispersion model performed better than the LUR model (R = 0.77 versus R = 0.47 respectively) in the comparison between measured and calculated concentrations on 18 validation sites. Differences can be understood because of the use of different regional background concentrations, inclusion of rather coarse land use category industry as a predictor variable in the LUR model and different treatment of conversion of NO to NO2.Moderate agreement was found between a dispersion model and a land use regression model in calculating annual average NO2 concentrations in an area with multiple sources. The dispersion model explained concentrations at validation sites better. © 2010 Elsevier Ltd.