Hassani Amirhossein, Salamalikis Vasileios, Schneider Philipp, Stebel Kerstin, Castell Núria
The Climate and Environmental Research Institute NILU, P.O. Box 100, Kjeller, 2027, Norway.
J Environ Manage. 2025 Apr;380:125100. doi: 10.1016/j.jenvman.2025.125100. Epub 2025 Mar 29.
Citizen-operated low-cost air quality sensors (LCSs) have expanded air quality monitoring through community engagement. However, still challenges related to lack of semantic standards, data quality, and interoperability hinder their integration into official air quality assessments, management, and research. Here, we introduce FILTER, a geospatially scalable framework designed to unify, correct, and enhance the reliability of crowd-sourced PM data across various LCS networks. FILTER assesses data quality through five steps: range check, constant value detection, outlier detection, spatial correlation, and spatial similarity. Using official data, we modeled PM spatial correlation and similarity (Euclidean distance) as functions of geographic distance as benchmarks for evaluating whether LCS measurements are sufficiently correlated/consistent with neighbors. Our study suggests a -10 to 10 Median Absolute Deviation threshold for outlier flagging (360 h). We find higher PM spatial correlation in DJF compared to JJA across Europe while lower PM similarity in DJF compared to JJA. We observe seasonal variability in the maximum possible distance between sensors and reference stations for in-situ (remote) PM data correction, with optimal thresholds of ∼11.5 km (DJF), ∼12.7 km (MAM), ∼20 km (JJA), and ∼17 km (SON). The values implicitly reflect the spatial representativeness of stations. ±15 km relaxation for each season remains feasible when data loss is a concern. We demonstrate and validate FILTER's effectiveness using European-scale data originating from the two community-based monitoring networks, sensor.community and PurpleAir with QC-ed/corrected output including 37,085 locations and 521,115,762 hourly timestamps. Results facilitate uptake and adoption of crowd-sourced LCS data in regulatory applications.
公民操作的低成本空气质量传感器（LCS）通过社区参与扩大了空气质量监测。然而，与缺乏语义标准、数据质量和互操作性相关的挑战仍然阻碍着它们融入官方空气质量评估、管理和研究。在此，我们介绍了FILTER，这是一个地理空间可扩展框架，旨在统一、校正并提高来自各种LCS网络的众包PM数据的可靠性。FILTER通过五个步骤评估数据质量：范围检查、常量值检测、异常值检测、空间相关性和空间相似性。我们使用官方数据，将PM空间相关性和相似性（欧几里得距离）建模为地理距离的函数，作为评估LCS测量值与相邻测量值是否具有足够相关性/一致性的基准。我们的研究表明，异常值标记（360小时）的中位数绝对偏差阈值为-10至10。我们发现，与欧洲的JJA相比，DJF期间的PM空间相关性更高，而DJF期间的PM相似性低于JJA。我们观察到，用于现场（远程）PM数据校正的传感器与参考站之间的最大可能距离存在季节变化，最佳阈值约为11.5公里（DJF）、约12.7公里（MAM）、约20公里（JJA）和约17公里（SON）。这些值隐含地反映了站点的空间代表性。当担心数据丢失时，每个季节±15公里的放宽仍然可行。我们使用来自两个基于社区的监测网络sensor.community和PurpleAir的欧洲规模数据演示并验证了FILTER的有效性，其质量控制/校正后的输出包括37,085个位置和521,115,762个每小时时间戳。结果有助于在监管应用中采用和使用众包LCS数据。
