Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA
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. In this study, the contribution of low-cost sensor measurements to high-resolution PM2.5 prediction was evaluated in an exemplary region – Imperial County in California – with a community-operated low-cost network entitled Identifying Violations Affecting Neighborhoods (IVAN). This study shows that the integration of low-cost sensor measurements is an effective way to significantly improve the quality of PM2.5 modeling, while the remaining uncertainty in calibrated sensor measurements still caused apparent outliers in the prediction results. This study highlights the need for more effective calibration or integration methods to relieve the negative impact of remaining sensor uncertainty.
Bi, J., Stowell, J., Seto, E.Y., English, P.B., Al-Hamdan, M.Z., Kinney, P.L., Freedman, F.R. & Liu, Y. (2020). Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA. Environmental Research, p.108810. 10.1016/j.envres.2019.108810
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