Contribution of low-cost sensor measurements to the prediction of PM2.5 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-km spatial resolution using three different PM_2.5 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 PM_2.5 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 PM_2.5. 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.

Keywords: Low-cost sensor; Measurement uncertainty; Random forest; Satellite AOD.

Figure 1

Study domain (latitude: [32.2°N, 33.9°N]; longitude: [116.6°W, 113.9°W]). Imperial County is part of the Southern California border region contiguous to the Mexican state of Baja California. The area surrounded by the dashed line is a buffer mainly used for better reflecting transboundary pollution.

Figure 2

10-fold CV scatter plots of three models: (a) AQS-only model, (b) IVAN-only model, and (c) AQS/IVAN model.

Figure 3

Mean PM_2.5 distributions for the period September 2016 to November 2017 generated by three models: (a) AQS-only model, (b) IVAN-only model, and (c) AQS/IVAN model. The points show mean PM_2.5 concentrations at the AQS and IVAN stations during the period.

Figure 4

The 10-fold CV scatter plot of the AQS/IVAN model. The black dashed lines (with slopes of 2 and 0.5) divide the points into normal predictions and outliers. The points in red are overestimated outliers and the points in blue are underestimated outliers.

Figure 5

The contiguous U.S. counties in blue are those with a higher (a) number (~2% of the total counties) or (b) density (~20% of the total counties) of AQS PM_2.5 stations than Imperial County as of 2017. The red areas are the potential regions in the U.S. where our proposed PM_2.5 prediction framework with low-cost sensor measurements can be applied to generate PM_2.5 spatial details.