Contribution of low-cost sensor measurements to the prediction of PM_2.5 levels: A case study in Imperial County, California, USA.

ABSTRACT: Regulatory monitoring networks are often too sparse to support community-scale PM_2.5 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 PM_2.5 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 PM_2.5 prediction. We adopted the Random Forest algorithm to estimate daily PM_2.5 concentrations at a 1

SUBMITTER: Bi J 

PROVIDER: S-EPMC6899193 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature