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CITI-SENSE. Final report on methodology. Deliverable 6.4, Work Package 6. NILU report
Dette dokumentet er en teknisk beskrivelse av CITI-SENSE sine hovedprodukter som er beskrevet i CITI-SENSE portefølje av produkter. Disse produktene ble utviklet, endret eller videreutviklet i dette prosjektet for å innfri og opprettholde den totale kjeden «sensor-plattform-produkter-bruker» med hovedfokus på grensesnittet og koblingen mellom produktene og sluttbrukerne.
Produktene er beskrevet fra en systemutviklers ståsted og inneholder informasjon om eksempelstudier og deres krav, teknisk beskrivelse av arkitektur, kodestrukturer og database og lagringsplattformer. Informasjon om gjeldende status per 30. juni 2016 med forslag til forbedringer og erfaringer i prosjektprosessen, er inkludert for hvert produkt.
2016
2020
Citizen participation approaches in environmental health. Molecular and integrative toxicology
2016
2017
Citizen science and its role in sustainable development: status, trends, issues, and opportunities. Advances in knowledge acquisition, transfer, and management
2017
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
2014
2017
2015
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
2007