{"id":115158,"date":"2026-09-11T15:09:28","date_gmt":"2026-09-11T13:09:28","guid":{"rendered":"https:\/\/nilu.gnist.dev\/publikasjoner\/citizen-operated-mobile-low-cost-sensors-for-urban-pm2-5-monitoring-field-calibration-uncertainty-estimation-and-application\/"},"modified":"2026-09-11T15:09:28","modified_gmt":"2026-09-11T13:09:28","slug":"citizen-operated-mobile-low-cost-sensors-for-urban-pm2-5-monitoring-field-calibration-uncertainty-estimation-and-application","status":"publish","type":"nva_publication","link":"https:\/\/nilu.gnist.dev\/en\/publications\/citizen-operated-mobile-low-cost-sensors-for-urban-pm2-5-monitoring-field-calibration-uncertainty-estimation-and-application\/","title":{"rendered":"Citizen-operated mobile low-cost sensors for urban PM2.5 monitoring: field calibration, uncertainty estimation, and application"},"content":{"rendered":"<p class=\"wp-block-paragraph\">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 &lt; 2.5 \u03bcm). 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 \u03bcg m\u22123. We approximate that one km h\u22121 increase in the speed of the bikes will add 0.03 &#8211; 0.04 \u03bcg m\u22123 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.<\/p>\n","protected":false},"template":"","meta":{"_acf_changed":false,"_searchwp_excluded":"","_id":"","_status":"PUBLISHED","_sync_date":"2026-09-11 14:08:02","footnotes":""},"nva_tax_category":[1099],"class_list":["post-115158","nva_publication","type-nva_publication","status-publish","hentry","nva_tax_category-scientific-journal-publication"],"acf":[],"_links":{"self":[{"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/nva_publication\/115158","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/nva_publication"}],"about":[{"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/types\/nva_publication"}],"version-history":[{"count":0,"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/nva_publication\/115158\/revisions"}],"wp:attachment":[{"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/media?parent=115158"}],"wp:term":[{"taxonomy":"nva_tax_category","embeddable":true,"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/nva_tax_category?post=115158"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}