A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth

Abstract

This study reviewed the prediction of fine particulate matter (PM 2.5 ) from satellite aerosol optical depth (AOD) and summarized the advantages and limitations of these predicting models. A total of 116 articles were included from 1436 records retrieved. The number of such studies has been increasing since 2003. Among these studies, four predicting models were widely used: Multiple Linear Regression (MLR) (25 articles), Mixed-Effect Model (MEM) (23 articles), Chemical Transport Model (CTM) (16 articles) and Geographically Weighted Regression (GWR) (10 articles). We found that there is no so-called best model among them and each has both advantages and limitations. Regarding the prediction accuracy, MEM performs the best, while MLR performs worst. CTM predicts PM 2.5 better on a global scale, while GWR tends to perform well on a regional level. Moreover, prediction performance can be significantly improved by combining meteorological variables with land use factors of each region, instead of only considering meteorological variables. In addition, MEM has advantages in dealing with the AOD data with missing values. We recommend that with the help of higher resolution AOD data, future works could be focused on developing satellite-based predicting models for the prediction of historical PM 2.5 and other air pollutants.

Contribution of Satellite-Derived Aerosol Optical Depth PM2.5 Bayesian Concentration Surfaces to Respiratory-Cardiovascular Chronic Disease Hospitalizations in Baltimore, Maryland

“…Once AOD unitless measurements have been calibrated with actual PM 2.5 readings from on-the-ground ambient air monitors, it is then possible to utilize the derived AOD-PM 2.5 concentration readings to estimate actual ambient PM 2.5 concentration in areas where there are no on-the-ground air monitors. The relationship between AOD measurements and on-the-ground measurements of PM 2.5 concentration readings has been confirmed in available publications [16,[27][28][29][39][40][41][42][43][44].By incorporating AOD-PM 2.5 concentration values into the currently utilized PMB, we hypothesized there would be a further improvement in the fused AOD-PM 2.5 -PMB surface [16]. Our intention, in this preliminary work, was to test this hypothesis by using these four experimental AOD-PM 2.5 and PMB fused concentration surfaces with linked health outcome data from Baltimore, Maryland, and New York City, New York, in a case-crossover epidemiologic study design data files analyzed by using conditional logistic regression [16].…”

“…CDC subsequently incorporated PMB into its Environmental Public Health Tracking (EPHT) network of state and New York City partners [16,18,22,26]. To date, PMB has been used by federal and state epidemiologists completing EPHT projects in different parts of the US [16,18,22,26].Within this decade, the availability and use of satellite AOD data have become more routine [6,16,[27][28][29][30][31]. Newer generation satellite instruments measure AOD with increased temporal accuracy and finer spatial resolution [27,[32][33][34][35][36][37].…”

“…To date, PMB has been used by federal and state epidemiologists completing EPHT projects in different parts of the US [16,18,22,26].Within this decade, the availability and use of satellite AOD data have become more routine [6,16,[27][28][29][30][31]. Newer generation satellite instruments measure AOD with increased temporal accuracy and finer spatial resolution [27,[32][33][34][35][36][37]. AOD is a unitless measure of the scattering and absorption of visible light by aerosols (particles) in the atmosphere [38][39][40].…”

Spatial cluster detection of regression coefficients in a mixed‐effects model

“…Another proxy measurement of particle air pollution data is the AOD obtained from satellites. Previous studies on predicting PM 2.5 concentration from AOD were reviewed by Chu et al (2016). In addition, those regression-based models showed that PM 2.5 concentrations have positive relationships with AOD (Chang, Hu, & Liu, 2013;Grantham, Reich, Liu, & Chang, 2018;Kloog, Koutrakis, Coull, Lee, & Schwartz, 2011;Kloog, Nordio, Coull, & Schwartz, 2012;Lee, Liu, Coull, Schwartz, & Koutrakis, 2011;Liu, Sarnat, Kilaru, Jacob, & Koutrakis, 2005;Ma et al, 2016;Paciorek, Liu, Moreno-Macias, & Kondragunta, 2008;Yu, Liu, Ma, & Bi, 2017) because AOD measures light extinction due to particles (e.g., dust, smoke, and pollution) in the atmospheric column.…”

Real‐time PM2.5 mapping and anomaly detection from AirBoxes in Taiwan

“…For example, Liu, Sarnat, Kilaru, Jacob, and Koutrakis (2005) and Lee, Liu, Coull, Schwartz, and Koutrakis (2011) incorporated aerosol optical depth data from satellites in a linear regression model and a linear mixed model, respectively, to obtain daily ground-level PM 2.5 concentrations. A comprehensive review on estimating ground-level PM 2.5 concentrations based on aerosol optical depth can be found in the work of Chu et al (2016). Many spatial and spatio-temporal kriging models have also been developed for mapping PM 2.5 .…”