A New Approach for Estimation of Fine Particulate Concentrations Using Satellite Aerosol Optical Depth and Binning of Meteorological Variables
Author(s) -
Muhammad Bilal,
Janet E. Nichol,
Scott N. Spak
Publication year - 2016
Publication title -
aerosol and air quality research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.866
H-Index - 55
eISSN - 2071-1409
pISSN - 1680-8584
DOI - 10.4209/aaqr.2016.03.0097
Subject(s) - aerosol , environmental science , particulates , moderate resolution imaging spectroradiometer , satellite , meteorology , remote sensing , spatial distribution , deep blue , asian dust , atmospheric sciences , geography , geology , chemistry , ecology , photochemistry , aerospace engineering , engineering , biology
9th Asian Aerosol Conference (AAC), Kanazawa, Japan, Jun 24-27, 2015Fine particulate matter (PM2.5) has recently gained attention worldwide as being responsible for severe respiratory and cardiovascular diseases, but point based ground monitoring stations are inadequate for understanding the spatial distribution of PM2.5 over complex urban surfaces. In this study, a new approach is introduced for prediction of PM2.5 which uses satellite aerosol optical depth (AOD) and binning of meteorological variables. AOD from the MODerate resolution Imaging Spectroradiometer (MODIS) Collection 6 (C006) aerosol products, MOD04_3k Dark-Target (DT) at 3 km, MOD04 DT at 10 km, and MOD04 Deep-Blue (DB) at 10 km spatial resolution, and the Simplified Aerosol Retrieval Algorithm (SARA) at 500 m resolution were obtained for Hong Kong and the industrialized Pearl River Delta (PRD) region. The SARA AOD at 500 m alone achieved a higher correlation (R = 0.72) with PM2.5 concentrations than the MODIS C6 DT AOD at 3 km (R = 0.60), the DT AOD at 10 km (R = 0.61), and the DB AOD at 10 km (R = 0.51). The SARA binning model ([PM2.5] = 110.5 [AOD] + 12.56) was developed using SARA AOD and binning of surface pressure (996-1010 hPa). This model exhibits good correlation, accurate slope, low intercept, low errors, and accurately represents the spatial distribution of PM2.5 at 500 m resolution over urban areas. Overall, the prediction power of the SARA binning model is much better than for previous models reported for Hong Kong and East Asia, and indicates the potential value of applying meteorologicallyspecific empirical models and incorporating boundary layer height in operational PM2.5 forecasting from satellite AOD retrievals.Department of Land Surveying and Geo-Informatic
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom