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Clean air and healthy lungs. Enhancing the World Bank's Approach to Air Quality Management. Environment and natural resources global practice discussion paper; 03
This report specifically deals with air pollution, which was reported, by the World Health Organization (WHO), as the single largest environmental health risk globally in 2012 (WHO, 2014a). Air pollution from outdoor and household sources jointly account for more than 7 million deaths (3.7 million from ambient air pollution and 4.3 million from household air pollution). The following sections of this chapter present the objectives of, and key aspects of the institutional context for, this report followed by an examination of some of the major drivers of deteriorating ambient air quality in developing countries; air pollution sources and impacts; and the status of air quality management in developing countries. Chapter two presents the results of a desk-based portfolio review of World Bank projects that are relevant to reduction of air pollution. This is followed, in chapter three, by an examination of case studies of World Bank projects whose objectives include addressing ambient air pollution, highlighting good practices and lessons for future work of the Bank in supporting clients. Chapter four presents possible approaches for enhancing future Bank support in helping clients to improve air quality and reduce the associated adverse health outcomes. Chapter five presents overall conclusions and recommendations.
2015
ClairCity Project
2020
ClairCity Project
2019
ClairCity Project
2020
2007
This paper examines the creation of fine resolution maps at 100 m x 100 m resolution using statistical downscaling for the area of Prague, as a case study. This Czech city was selected due to the fine resolution proxy data available for this city. The reference downscaling methodology used is the linear regression and the interpolation of its residuals by the area-to-point kriging. Next to this, several other methods of statistical downscaling have been also executed. The results of different downscaling methods have been compared mutually and against the data from the monitoring stations of Prague, separately for urban background and traffic areas.
The downscaled maps in 100 m x 100 m resolution have been constructed for the area of Prague for three pollutants, namely for NO2, PM10 and PM2.5. Several methods of the statistical downscaling have been compared mutually and against the data from the monitoring stations. In general, the best results are given by the linear regression and the interpolation of its residuals, either by the area-to-point kriging or the bilinear interpolation. In the maps, one can see overall realistic spatial patterns, the main roads in Prague are visible through higher air pollution levels. This is distinct especially for NO2, while for PM10 and PM2.5 the differences between road increments and urban background are smaller as would be expected. The results of the case study for Prague have proven the usefulness of the statistical downscaling for the air quality mapping, especially for NO2. In addition, the population exposure estimates based on the downscaled mapping results have been also calculated.
ETC/HE
2023
2015
2017
2014
Research communities, engagement campaigns, and administrative agents are increasingly valuing low-cost air-quality monitoring technologies, despite data quality concerns. Mobile low-cost sensors have already been used for delivering a spatial representation of pollutant concentrations, though less attention is given to their uncertainty quantification. Here, we perform static/on-bike inter-comparison tests to assess the performance of the Snifferbike sensor kit in measuring outdoor PM2.5 (Particulate Matter < 2.5 μm). We build a network of citizen-operated Snifferbike sensors in Kristiansand, Norway, and calibrate the measurements using Machine Learning techniques to estimate the concentrations of PM2.5 along the city roads. We also propose a method to estimate the minimum number of PM2.5 measurements required per road segment to assure data representativeness. The co-location of three Snifferbike kits (Sensirion SPS30) at the monitoring station showed a RMSD of 7.55 μg m−3. We approximate that one km h−1 increase in the speed of the bikes will add 0.03 - 0.04 μg m−3 to the Standard Deviation of the Snifferbike PM2.5 measurements. We estimate that at least 27 measurements per road segment are required (50 m here) if the data are sufficiently dispersed over time. We recommend calibrating the mobile sensors when they coincide with reference monitoring stations.
Elsevier
2023