Send message to Authors

Short Abstract

We introduce FILTER, a collection of algorithms and recommendations for quality control and correction of citizen-collected environmental data. It increases the reliability of measurements obtained from stationary and mobile low-cost sensors operating under different environmental conditions.

Abstract

The growing adoption of low-cost sensors (LCSs) has opened new opportunities for participatory air quality and environmental noise monitoring. However, the resulting data streams often vary in quality due to diversities in device performance, environmental conditions and deployment settings.

To address these challenges, we present FILTER (Framework for Improving Low-cost Technology Effectiveness and Reliability) – a collection of algorithms for quality control and correction of citizen-collected environmental data using advanced statistical and machine learning methods. FILTER statistically evaluates these datasets, and assigns both overall and individual quality flags, providing an additional measure of data reliability and trustworthiness. Originally designed for stationary PM2.5 measurements (Hassani et al., 2025), FILTER has been expanded to include modules for mobile and wearable LCSs (m-FILTER) as well as environmental noise measurements (n-FILTER).

All FILTER versions follow a processing pipeline. The initial steps involve basic statistical tests to identify the physical consistency of the collected measurements, their temporal stability, and the identification of potential outliers based on both historical trends and spatial comparison with neighboring LCSs. More advanced steps evaluate the relative and absolute performance of LCSs against higher quality instruments and reference stations. All algorithms are designed to be user- and case- specific, allowing easy adaptation to diverse monitoring contexts and study objectives.