Abstract
Abstract. Investigating the human health effects of atmospheric particulate matter (PM) using satellite data are gaining more attention due to their wide spatial coverage and temporal advantages. Such epidemiological studies are, however, susceptible to bias errors and resulted in poor predictive output in some locations. Current methods calibrate aerosol optical depth (AOD) retrieved from MODIS to further predict PM. The recent satellite-based AOD calibration uses a mixed effects model to predict location-specific PM on a daily basis. The shortcomings of this daily AOD calibration are for areas of high probability of persistent cloud cover throughout the year such as in the humid tropical region along the equatorial belt. Contaminated pixels due to clouds causes radiometric errors in the MODIS AOD, thus causes poor predictive power on air quality. In contrary, a periodic assessment is more practical and robust especially in minimizing these cloud-related contaminations. In this paper, a simple yet robust calibration approach based on monthly AOD period is presented. We adopted the statistical fitting method with the adjustment technique to improve the predictive power of MODIS AOD. The adjustment was made based on the long-term observation (2001–2006) of PM10-AOD residual error characteristic. Furthermore, we also incorporated the ground PM measurement into the model as a weighting to reduce the bias of the MODIS-derived AOD value. Results indicated that this robust approach with monthly AOD calibration reported an improved average accuracy of PM10 retrieval from MODIS data by 50% compared to widely used calibration methods based on linear regression models, in addition to enabling further spatial patterns of periodic PM exposure to be undertaken.
Highlights
SFarahnaknliineteat la.l,.,220M0101o7; ;dBGeeelllnetDtetaela.vl,.,2e20l00o073p;,Dm2o0me09inn; itSccihewt aarlt.z, 2006; et al., M a periodic assessment is more practical and robust espe- 1996; Slama et al, 2007; Hu, 2009)
If a 3 × 3 window is used, we found that there are many voids left in the imagery that resulted in the poor retrieval of the overall MODIS AOD in Peninsular Malaysia
Bukit Rambai, Melaka, has the highest mean (SE) PM10 concentration at 74.9 (1.81) μg m−3 followed by Klang at 72.3 (3.04) μg m−3
Summary
SFarahnaknliineteat la.l,.,220M0101o7; ;dBGeeelllnetDtetaela.vl,.,2e20l00o073p; ,Dm2o0me09inn; itSccihewt aarlt.z, 2006; et al., M a periodic assessment is more practical and robust espe- 1996; Slama et al, 2007; Hu, 2009). A linear AOD–PM relationship in a long-term daily monitoring is rather limited (Yap et al, 2011), and the time-varying assumption by Lee et al (2011) that varies minimally spatially on a given day over a specific spatial scale is rarely valid for humid tropical weather over the equatorial regions, where a high probability of cloud-cover exists and is dependent on the surroundings maritime environment It is more practical and efficient for the calibration of the satellite data to be based on a monthly basis
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
