Improving satellite‐driven PM_2.5 models with Moderate Resolution Imaging Spectroradiometer fire counts in the southeastern U.S.

Abstract

Multiple studies have developed surface PM 2.5 (particle size less than 2.5 μm in aerodynamic diameter) prediction models using satellite-derived aerosol optical depth as the primary predictor and meteorological and land use variables as secondary variables. To our knowledge, satellite-retrieved fire information has not been used for PM 2.5 concentration prediction in statistical models. Fire data could be a useful predictor since fires are significant contributors of PM 2.5 . In this paper, we examined whether remotely sensed fire count data could improve PM 2.5 prediction accuracy in the southeastern U.S. in a spatial statistical model setting. A sensitivity analysis showed that when the radius of the buffer zone centered at each PM 2.5 monitoring site reached 75 km, fire count data generally have the greatest predictive power of PM 2.5 across the models considered. Cross validation (CV) generated an R 2 of 0.69, a mean prediction error of 2.75 μg/m 3 , and root-mean-square prediction errors (RMSPEs) of 4.29 μg/m 3 , indicating a good fit between the dependent and predictor variables. A comparison showed that the prediction accuracy was improved more substantially from the nonfire model to the fire model at sites with higher fire counts. With increasing fire counts, CV RMSPE decreased by values up to 1.5 μg/m 3 , exhibiting a maximum improvement of 13.4% in prediction accuracy. Fire count data were shown to have better performance in southern Georgia and in the spring season due to higher fire occurrence. Our findings indicate that fire count data provide a measurable improvement in PM 2.5 concentration estimation, especially in areas and seasons prone to fire events.

Satellite‐Based Daily PM2.5 Estimates During Fire Seasons in Colorado

“…Liu et al, 2007;Wang & Christopher, 2003). Many studies have proposed statistical models using AOD to predict PM 2.5 concentrations (X. F. Hu et al, 2014b;X. F. Hu, Waller, Lyapustin, Wang, Al-Hamdan, et al, 2014;Kloog et al, 2012).…”

“…Statistical models have obtained good performance in PM 2.5 exposure estimation in the eastern part of the United States (X. F. Hu et al, 2014b;X. F. Hu, Waller, Lyapustin, Wang, Al-Hamdan, et al, 2014;Kloog et al, 2014).…”

National-Scale Estimates of Ground-Level PM2.5 Concentration in China Using Geographically Weighted Regression Based on 3 km Resolution MODIS AOD

“…To improve accuracy, we considered fire emissions in the GWR model development process as previous studies have reported that biomass burning can affect AOD-PM2.5 relations [23,26]. This paper is organized as follows.…”

National PM2.5 and NO2 Exposure Models for China Based on Land Use Regression, Satellite Measurements, and Universal Kriging

“…Previous research suggested that remotely sensed fire count data could improve PM2.5 prediction accuracy, and will have good prediction power when the buffer zone reach 50km. [30] To capture fire emissions, we used number of fire spots within 10 buffer lengths from 5km to 100km using Moderate-resolution Imaging Spectroradiometer (MODIS) Global Monthly Fire Location Product (MCD14ML) [31].…”