ArticleEnvironmental monitoring and assessment2026
Particulate Matter concentration measurement in ambient air: analysis of filter material and recovery time variables for uncertainty assessment.
Article in Environmental monitoring and assessment, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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5 authors.
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Abstract
Accurate determination of PM concentrations is essential to ensure compliance with regulatory thresholds and to support effective strategies for air-quality management and health protection. In Europe, gravimetric analysis according to EN12341:2023 is the reference method for quantifying PM10 and PM2.5 concentrations in ambient air. While the standard acknowledges the presence of measurement uncertainty, it does not explicitly address the specific contributing variables. This study presents and discusses the systematic evaluation of the contribution of non-standard variables to measurement uncertainty in PM10 and PM2.5 determinations. Three major categories of interference (environmental, operational, and instrumental) were identified, together with related uncertainty indicators. Experimental campaigns were conducted to assess the influence of different filter materials and filter recovery times under real in-situ conditions. Results revealed that filter recovery time contributes to an uncertainty of 18.09% in PM2.5 concentration measurements, whereas filter material accounts for an uncertainty of 4.85% in PM10 determinations. Overall, the analyses reveal a consistently higher sensitivity of the finer particulate fraction to the uncertainty factors investigated. Within the broader framework of environmental monitoring and assessment, this study strengthens the scientific basis for the design and evaluation of air-quality monitoring systems supporting exposure assessment and pollution risk analysis. These findings contribute to improving data quality, comparability and robustness in monitoring networks, with direct implications for the design and development of advanced monitoring systems, sampling strategies, network optimization and quality assurance procedures.
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