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

• Type of Material: Article
• Authors: Jianzhao Bi, Emory University; Jennifer Stowell, Emory University; Edmund Y.W. Seto, University of Washington; Paul B. English, California Department of Public Health; Mohammad Z. Al-Hamdan, NASA Marshall Space Flight Center; Patrick L. Kinney, Boston University; Frank R. Freedman, San Jose State University; Yang Liu, Emory University
• Language: English
• Date: 2020-01-01
• Publisher: ACADEMIC PRESS INC ELSEVIER SCIENCE
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: 2019
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.envres.2019.108810
• Title of Journal or Parent Work: ENVIRONMENTAL RESEARCH
• Volume: 180
• Start Page: 108810
• End Page: 108810
• Grant/Funding Information: The work of J. Bi, J. Stowell, and Y. Liu was supported by the National Aeronautics and Space Administration (NASA) Applied Sciences Program (Grant # NNX16AQ28Q and 80NSSC19K0191). The work of F. Freedman was supported by the NASA Applied Sciences Program (Grant # NNX16AQ91G). The work of P. English was supported by the National Institute of Environmental Health Sciences of the National Institutes of Health (NIH) (Grant # R01ES022722). The content is solely the responsibility of the authors and does not necessarily represent the official views of NASA and NIH.
• Keywords: AMBIENT; Life Sciences & Biomedicine; VARIABILITY; Satellite AOD; POLLUTION; AIR-QUALITY; Random forest; RETRIEVALS; DUST; Low-cost sensor; LABORATORY EVALUATION; Measurement uncertainty; Public, Environmental & Occupational Health; AEROSOL OPTICAL DEPTH; Environmental Sciences & Ecology; Science & Technology; Environmental Sciences; EXPOSURE
• Research Categories: Health Sciences, Public Health; Environmental Sciences

Abstract

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.