Estimating Particulate Matter Concentrations in Central Thailand Using Satellite Data

Article Sidebar

PDF
Published: Dec 23, 2021
Keywords:
PM10 PM2.5 MODIS-AODs Regression model Boundary layer height

Main Article Content

Pichawan Phuengsamran
Pichnaree Lalitaporn

Abstract

In this study, regression models were developed for estimating ground level particulates matter with aerodynamic diameter less than 10 micrometres (PM10) and particulate matter with diameter of less than 2.5 micrometres (PM2.5) concentrations by using satellite aerosol optical depths (AODs) data over central Thailand. Satellite data in this study were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on Terra platform with 3×3 km² and 10×10 km² resolution products. Daily satellite AODs and ground particulate matter data in Thailand were collected over 10 years from January 2008 to December 2018. The results of the study showed that the highest level of PM10 and PM2.5 concentrations generally presented from November to April. The study also showed that meteorological parameters including temperature (T), relative humidity (RH), wind speed (WS) and boundary layer height (BLH) affected particulate matter concentrations. The comparison between satellite products presented that 10×10 km² product gave better correlations with PM10 and PM2.5 compared to 3×3 km² product. Multiple linear regression models were developed to estimate particulate matter concentrations using AODs and meteorological data. The models gave coefficient of determination (R²) of 0.26-0.65 with root mean square error (RMSE) of 15.90-45.90 µg/m³ for PM10 and R² of 0.16-0.50 of with RMSE of 14.00-51.26 µg/m³ for PM2.5.

Article Details

Issue
Vol. 35 No. 3 (2021): September-December
Section
Research Articles

References

Samuel. 1973. Fundamentals of air pollution. Addison-Wesley Publishing Comp. (New York University): 107-111.

Xinpeng, W., Wenbin S., Kangning Z., Xiangyu R., Peiwen H. 2019. Estimating hourly PM2.5 concentrations using MODIS 3km AOD and an improved spatiotemporal model over Beijing-Tianjin-Hebei, China. Atmos. Environ. (2019).

Kloog, I., Chudnovsky, A. A., Just, A. C., Nordio, F., Koutrakis, P., Coull, B. A., Lyapustin, A., Wang, Y. and Schwartz, J. 2014. A new hybrid spatio-temporal model for estimating daily multi-year PM2.5 concentrations across northeastern USA using high resolution aerosol optical depth data. Atmos. Environ. 95: 581-590.

Crouse, D. L., Peters, P. A., Van, D. A., Goldberg, M. S., Villeneuve, P. J., Brion, O., Khan, S., Atari, D. O., Jerrett, M. and Pope, C. A. 2012. Risk of nonaccidental and cardiovascular mortality in relation to long-term exposure to low concentrations of fine particulate matter: a Canadian national-level cohort study. Environ Health Perspect 120: 708-714.

Xie, Y., Wang, Y., Zhang, K., Dong, W., Lv, B. and Bai, Y. 2015. Daily estimation of ground-level PM2.5 concentrations over Beijing using 3 km resolution MODIS AOD. Environ. Sci. Technol. 49: 12280-12288.

Chen, G., Li, S., Knibbs, L. D., Hamm, N. A. S., Cao, W., Li, T., Guo, J., Ren, H., Abramson, M. J. and Guo, Y. 2018. A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information. Sci. Total Environ. 636: 52-60.

Hu, X., Waller, L. A., Al-Hamdan, M. Z., Crosson, W. L., Estes Jr, M. G., Estes, S.M., Quattrochi, D. A., Sarnat, J. A. and Liu, Y. 2013. Estimating ground-level PM2. 5 concentrations in the southeastern US using geographically weighted regression. Environ. Res. 121: 1-10.

Ma, Z., Hu, X., Huang, L., Bi, J. and Liu, Y. 2014. Estimating ground-level PM2.5 in China using satellite remote sensing. Environ. Sci. Technol. 48: 7436-7444.

Wei, Y., Li, Z., Zhang, Y., Chen, C., Xie, Y., Lv, Y., & Dubovik, O. 2021. Derivation of PM10 mass concentration from advanced satellite retrieval products based on a semi-empirical physical approach. Remote Sensing of Environment, 256, 112319.

Kumar, N., Chu, A., Foster, A. 2007. An empirical relationship between PM2.5 and aerosol optical depth in Delhi Metropolitan. Atmospheric Environment. 41(21): 4492-4503.

Adelaide D., Maria R.P., Pasquale B., Application of MODIS Products for Air Quality Studies Over Southeastern Italy. Remote Sensing (2010): 1767-1796.

Koelemeijer, R.B.A.; Homan, C.D.; Matthijsen, J. Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter in Europe. Atmos. Environ. 2006, 40: 5304-5315.

Shtein, A., Karnieli, A., Katra, I., et.al. Estimating daily and intra-daily PM10 and PM2.5 in Israel using a spatiotemporal hybrid modeling approach. Atmospheric Environment. 191(2018): 142-152.

The Bureau of Registration Administration. 2019. Population data. Retrived Feb 10, 2021 from https://bps.moph.go.th/new_bps.

Office of the National Economic and Social Development Council 2019 Central and Bangkok Area Development Plan 2017 to 2022. Retrived Feb 10, 2021 from https://www.nesdc.go.th/

Papadimas, C.D., Hatzianastassiou, N., Mihalopoulos, N., Kanakidou, M., Katsoulis, B.D., Vardavas, I., 2009. Assessment of the MODIS Collections C005 and C004 aerosol optical depth products over the Mediterranean basin. Atmos. Chem. Phys. 9: 2987-2999.

You, W., Zang, Z., Zhang, L., Zhang, M., Pan, X., Li, Y. 2016. A nonlinear model for estimating ground-level PM10 concentration in Xi’an using MODIS aerosol optical depth retrieval. Atmos. Res. 168: 169-179.

Zeeshan, M., & Kim Oanh, N. T. 2014. Assessment of the relationship between satellite AOD and ground PM10 measurement data considering synoptic meteorological patterns and Lidar data. Science of the Total Environment, 473-474: 609-618.

Lalitaporn, P., Mekaumnuaychai, T., 2020. Satellite measurements of aerosol optical depth and carbon monoxide and comparison with ground data. Environ Monit Assess. (2020): 192-369.

Anzel, de, Langea., Rebecca, M. Garland., Liesl, L. Dyson., Estimating particulate matter (PM) concentrations from a meteorological index for data-scarce regions: A pilot study. Atmospheric Pollution Research. 10(2019): 1553-1564.

Wongkaewsakul, P., Lalitaporn, P., 2017. Satellite observed data of aerosol optical depths and fire hotpots over Thailand. M.E. Thesis, Kasetsart University.

Mongkolphew, S., Lalitaporn, P., 2019. Assessment of the Relationship of Particulate Matter between Satellite and Ground based Measurements in Thailand. M.E. Thesis, Kasetsart University.

Kwanma, P., Pukngam, S., Arunpraparu, W., 2019. Meteorological factors affecting concentration of pm10 at naphra lan sub-district, chaloem phra kiat district, Saraburi province. PSRU Journal of Science and Technology. 4(2): 85-94.

He, Q., Zhang, M., Huang, B., Tong, X., MODIS 3 km and 10 km aerosol optical depth for China: Evaluation and comparison, Atmospheric Environment (2017).

Zhang, H., Hoff, R. M., Engel-Cox, J. A. 2009. The relation between Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth and PM2.5 over the United States: a geographical comparison by U.S. Environmental Protection Agency regions. Journal of the Air & Waste Management Association. 59(11): 1359-1369.