Comparison of Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer (MISR) Aerosol Optical Depth Correlation with Particulate Matter (PM¬2.5) Concentrations at the Manila Observatory (14.635 N, 121.078 E)

HANZ VLADIMIR CABANES^1*, IRWIN ANGELO AMAGO¹, and NOFEL LAGROSAS^1,2

¹Physics Department, Ateneo de Manila University, Loyola Heights, Quezon City, Philippines

²Manila Observatory, Ateneo de Manila University, Loyola Heights, Quezon City, Philippines

hanzcabanes@yahoo.com

In this paper, a correlation analysis for both Moderate Resolution Imaging Spectroradiometer aerosol optical depth and Multiangle Imaging Spectroradiometer aerosol optical depth with ground-based PM_2.5 concentration measurements at the Manila Observatory for January to December 2011. The resulting analysis showed that MISR AOD had a better correlation (R²=0.7360) than MODIS AOD (R²=0.6793). Using this analysis, it was observed that the MISR AOD readings have a tendency to overestimate the ground-measured PM_2.5 concentration values while the MODIS AOD underestimates the PM_2.5 ground-measured data. Moreover, given the higher R² value, the results affirm those of previous studies, which say that MISR AOD is more accurate in determining PM_2.5 concentrations than MODIS AOD [1]. However, MODIS is known to have a better temporal resolution because it is onboard both TERRA and AQUA satellites whereas MISR is only onboard the TERRA satellite. To couple the individual advantages of these two data sets, the multivariate regression approach was used with the MODIS and MISR AOD values as predictors to the ground-measured PM_2.5 values. The resulting equation from the analysis was PM_2.5c=52.94MISR+37.10MODIS+15.33 where the PM­_2.5c is the computed PM_2.5 value, and MISR and MODIS represent the corresponding MISR AOD and MODIS AOD values, respectively. The computed PM_2.5 values from this equation yielded an absolute error of 21.85% and an R² of 0.7249 compared to the actual values. The resulting error value is lower than the 30-40% range often reported in literature, which affirms the utility of the multivariate regression method in relating satellite-derived data with ground-measured data [1, 2]. The analysis presented in this paper foregrounds further work geared towards the utilization of satellite data as a surrogate for ground-measured data.

References

[1]     Engel-Cox, J.A.; Hoff, R.M.; Haymet, A.D.J., “Recommendations on the Use of Satellite Remote-Sensing Data for Urban Air Quality,” Journal of Air & Waste Management Association. 54, 1360-1371, 2004.

[2]     Gupta, P., et al, “Satellite remote sensing of particulate matter and air quality assessment over global cities.” Atmospheric Environment 40, 5880-5892, 2006.

Presentation

Open publication - Free publishing - More aerosol

Open publication - Free publishing - More physics

Multivariate Regression Analysis on Multiangle Imaging Spectroradiometer (MISR) Fractional Aerosol Optical Depth for the Identification of Primary Aerosol Components in the Manila Observatory (14.635 N, 121.078 E)

IRWIN ANGELO AMAGO^1*, HANZ VLADIMIR CABANES¹ and NOFEL LAGROSAS^1,2

¹Physics Department, Ateneo de Manila University, Loyola Heights, Quezon City, Philippines

²Manila Observatory, Ateneo de Manila University, Loyola Heights, Quezon City, Philippines

irwinamago320@gmail.com

MISR fractional aerosol optical depth data were explored for the purpose of identifying aerosol components. MISR data over Manila Observatory were downloaded from January to December of 2011. The goal of the paper is to show the utility of the MISR data set and the possibility of using satellite remote-sensed data as surrogates for ground based data. A multivariate regression approach with the fractional data as input data yields an error of 17.4%, which is lower than literature, when computing the corresponding ground-measured PM_2.5 data. In this method, the relationship between ground measured to computed (from the multivariate regression) PM_2.5 concentration is governed by the equation PM_2.5c=0.9676PM_2.5m, where PM_2.5c and PM_2.5m are the computed and ground measured PM_2.5 concentrations. [2]. Moreover, using the MISR fractional AOD data, one can identify the primary aerosol constituents and their percentage contribution [2]. Using this method, the highest contributor to the particulate matter concentration was carbon-based aerosols that compose 65.65% of the total aerosol optical depth. The other two contributors were sulfates and nitrates (13.64%) and dust (20.71%). These numbers are consistent with the results of urban air quality measurements in several Asian cities [1] and at the Manila Observatory. Previous source apportionment studies yield more than 50% of PM_2.5 comes from traffic, which is characteristic of black carbon while around 30% comes from sulfates. In terms of coarse mode aerosols, around 70% come from soil, which would account for the dust contribution for the west season. For the dry season, around 40% comes from traffic and sulfates while around 70% of coarse mode aerosols are still contributed by the soil. (http://www.observatory.ph/downloads/381b698b8ec5c2d0/MO_poster_052.jpg)

References

[1]     Kim Oanh, N.T., et al., “Particulate air pollution in six Asian cities: Spatial and temporal distributions and associated sources,” Atmospheric Environment, 40. Pp3367-3380.

[2]     Yang L., et al., “Estimating Fine Particulate Matter Component Concentrations and Size Distributions Using Satellite-Retrieved Fractional Aerosol Optical Depth: Part 1—Method Development,” Air and Waste Management, 57. Pp1351, 1360.