Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA

• Published in: Environmental research Vol. 180; p. 108810
• Main Authors: Bi, Jianzhao, Stowell, Jennifer, Seto, Edmund Y.W., English, Paul B., Al-Hamdan, Mohammad Z., Kinney, Patrick L., Freedman, Frank R., Liu, Yang
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.01.2020
• Subjects: Low-cost sensor; Measurement uncertainty; Random forest; Satellite AOD; Random forest; Low-cost sensor; Satellite AOD; Measurement uncertainty
• ISSN: 0013-9351; 1096-0953
• DOI: 10.1016/j.envres.2019.108810

Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment while emerging low-cost sensors have the potential to fill in the gaps. To date, limited studies, if any, have been conducted to utilize low-cost sensor measurements to improve PM2.5 prediction with high spatiotemporal resolutions based on statistical models. Imperial County in California is an exemplary region with sparse Air Quality System (AQS) monitors and a community-operated low-cost network entitled Identifying Violations Affecting Neighborhoods (IVAN). This study aims to evaluate the contribution of IVAN measurements to the quality of PM2.5 prediction. We adopted the Random Forest algorithm to estimate daily PM2.5 concentrations at a 1-km spatial resolution using three different PM2.5 datasets (AQS-only, IVAN-only, and AQS/IVAN combined). The results show that the integration of low-cost sensor measurements is an effective way to significantly improve the quality of PM2.5 prediction with an increase of cross-validation (CV) R2 by ~0.2. The IVAN measurements also contributed to the increased importance of emission source-related covariates and more reasonable spatial patterns of PM2.5. The remaining uncertainty in the calibrated IVAN measurements could still cause apparent outliers in the prediction model, highlighting the need for more effective calibration or integration methods to relieve its negative impact. •Ground-level PM2.5 was assessed with low-cost, regulatory, and satellite data.•Low-cost sensor measurements contributed to improved modeling performance.•Reasonable PM2.5 spatial details were revealed due to abundant low-cost data.•Remaining uncertainty in calibrated low-cost data still affected modeling precision.