{"id":72542,"date":"2026-10-27T22:58:23","date_gmt":"2026-10-27T21:58:23","guid":{"rendered":"https:\/\/nilu.gnist.dev\/publikasjoner\/improving-data-reliability-in-air-quality-monitoring-from-static-and-mobile-sensor-platforms-and-networks-using-the-filter-framework\/"},"modified":"2026-10-27T22:58:23","modified_gmt":"2026-10-27T21:58:23","slug":"improving-data-reliability-in-air-quality-monitoring-from-static-and-mobile-sensor-platforms-and-networks-using-the-filter-framework","status":"publish","type":"nva_publication","link":"https:\/\/nilu.gnist.dev\/en\/publications\/improving-data-reliability-in-air-quality-monitoring-from-static-and-mobile-sensor-platforms-and-networks-using-the-filter-framework\/","title":{"rendered":"Improving data reliability in air quality monitoring from static and mobile sensor platforms and networks using the FILTER framework"},"content":{"rendered":"<p class=\"wp-block-paragraph\">The growing adoption of low-cost sensors (LCSs) has significantly enhanced environmental monitoring by enabling widespread, community-driven data collection, particularly in regions\u00a0requiring dense monitoring,\u00a0and in regions with\u00a0limited or\u00a0no\u00a0reference\u00a0instrumentation.\u00a0Increased public awareness and demand for dense environmental monitoring have resulted in extensive air quality and meteorological datasets from diverse sources. However, the integration of such datasets into regulatory frameworks and large-scale environmental monitoring\u00a0remains\u00a0challenging due to persistent issues related to data quality, standardization, and interoperability.\u00a0To address these challenges, the FILTER (Framework for Improving Low-cost Technology Effectiveness and Reliability)\u00a0approach\u00a0developed\u00a0by\u00a0Hassani et al.\u00a0(2025)\u00a0provides a suite of algorithms to harmonize, quality-check, flag, and perform\u00a0in-situ\u00a0corrections\u00a0on crowdsourced PM2.5\u00a0LCS datasets. While FILTER was initially designed and\u00a0validated\u00a0for static PM2.5\u00a0sensors, it has since been extended to address data quality challenges associated with\u00a0the dynamics of mobile and wearable sensing.\u00a0Across both static and mobile LCS platforms, FILTER employs a unified processing pipeline that generates measurement-level quality flags based on multiple statistical tests,\u00a0to quantify the\u00a0reliability of each observation. Quality control (QC) includes statistical tests to: (a) assess physical measurement consistency (range validity test), (b) detect flatline behavior (constant value test), and (c)\u00a0identify\u00a0abnormal patterns (spatiotemporal outlier detection test) using both historical trends and spatial comparisons with neighboring LCSs. Beyond these\u00a0mandatory\u00a0QC steps, more advanced\u00a0statistical\u00a0procedures incorporate relative (spatial correlation test) and absolute (spatial similarity test) comparisons with nearby LCSs, higher-quality instruments, and reference monitoring stations.\u00a0For mobile and wearable sensing, FILTER has been specifically adapted to support pairwise comparisons between mobile sensors and comparisons with higher-accuracy nodes, accounting for operation under dynamic environmental and operational conditions. In this context, statistical comparisons are evaluated during rendezvous events, that is, periods in which the mobile sensor and a higher-accuracy node provide temporally coincident measurements. The modified framework\u00a0retains\u00a0the core principles of transparency, scalability, and sensor independence, while introducing\u00a0additional\u00a0steps to address motion-related artifacts, intermittent time series, and location-specific uncertainties.\u00a0FILTER\u00a0is\u00a0developed\u00a0in the open-source R environment. Its modular and hierarchical design allows flexible adaptation of quality control and correction workflows based on data availability, the spatiotemporal characteristics of LCS networks, and application-specific requirements.\u00a0By improving data reliability and usability, FILTER enables crowdsourced LCS datasets to serve as a reliable complement to official monitoring networks for\u00a0air quality management, urban- and regional-scale modeling, and policymaking.\u00a0References\u202f\u00a0Hassani, A., Salamalikis, V., Schneider, P., Stebel, K., and Castell, N.: A scalable framework for harmonizing, standardization, and correcting crowd-sourced low-cost sensor PM2. 5 data across Europe, J. Environ.\u00a0Manage., 380, 125100, 2025.\u00a0<\/p>\n","protected":false},"template":"","meta":{"_acf_changed":false,"_searchwp_excluded":"","_id":"","_status":"PUBLISHED","_sync_date":"2026-10-27 22:54:33","footnotes":""},"nva_tax_category":[913],"class_list":["post-72542","nva_publication","type-nva_publication","status-publish","hentry","nva_tax_category-lecture"],"acf":[],"_links":{"self":[{"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/nva_publication\/72542","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\/72542\/revisions"}],"wp:attachment":[{"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/media?parent=72542"}],"wp:term":[{"taxonomy":"nva_tax_category","embeddable":true,"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/nva_tax_category?post=72542"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}