Patterns of relationship between PM10 from air monitoring quality station and AOT data from MODIS sensor onboard of Terra satellite

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Winai Suriya
Poramate Chunpang
Teerawong Laosuwan

Keywords : remote sensing, MODIS sensor, PM10, aerosol optical thickness, AOT, air quality index, AQI
Thailand, especially in the northern region, often encounters the problem of having PM10 exceeding the normal standard level, which could do harm to people’s health. Mostly, such problem is caused by the burning of forest area and open area; this is clearly seen during January–April of every year. Also, the problem as mentioned is caused by the meteorological conditions and the terrains in the northern region that make it easy for PM10 to be accumulated. The aim of this study was to analyze the patterns of relationship between PM10 measured from the ground monitoring station and AOT data received from MODIS sensor onboard of Terra satellite in Phrae Province located in the northern region of Thailand. The method performed was by analyzing the correlation between PM10 data obtained from the ground monitoring station and the AOT data received from the MODIS sensor onboard of Terra satellite during January–April 2018. It was found from the study that the change of the intensity of PM10 and AOT in the climate was highly related; it appeared that the correlation coefficient (r) in January–April was 0.92, 0.91, 0.91 and 0.92, respectively. This research pointed out that during February– –April, the areas of Phrae Province had the level of PM10 that affected health. Besides, from the method in this research, it revealed AOT data received from MODIS sensor onboard of Terra satellite could be applied in order to follow up, monitor, and notify the spatial changes of PM10 efficiently.

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Suriya, W., Chunpang, P., & Laosuwan, T. (2021). Patterns of relationship between PM10 from air monitoring quality station and AOT data from MODIS sensor onboard of Terra satellite. Scientific Review Engineering and Environmental Sciences (SREES), 30(2), 236–249.

Adams, K., Greenbaum, D.S., Shaikh, R., Erp, A.M. van & Russell, A.G. (2015). Particulate matter components, sources, and health: Systematic approaches to testing effects. Journal of the Air & Waste Management Association, 65(5), 544-558.

Amphanthong, P. & Busababodhin, P. (2015). Forecasting PM10 in the Upper Northern Area of Thailand with Grey System Theory. Burapha Science Journal, 20(1), 15-24.

Benas, N., Beloconi, A. & Chrysoulakis, N. (2013). Estimation of urban PM10 concentration, based on MODIS and MERIS/ /AATSR synergistic observations. Atmospheric Environment, 79, 448-454.

Emetere, M.E., Sanni, S.E., Okoro, E.E. & Adeyemi, G.A. (2018). Aerosol loading and its effect on respiratory dysfunction disorder over Dapaong-Togo. Scientific Review Engineering and Environmental Sciences, 27(4), 410-424.

GreenFacts (2018). Air pollution particulate matter. Retrieved from: https://www.greenfacts. org/en/particulate-matter-pm/level-2/01presentation.htm [access 15.08.2020].

He, Q. & Huang, B. (2018). Satellite-based mapping of daily high-resolution ground PM2.5 in China via space-time regression modeling. Remote Sensing of Environment, 206, 72-83.

Kloog, I., Koutrakis, P., Coull, B.A., Lee, H.J. & Schwartz, J. (2011). Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements. Atmospheric Environment, 45(35), 6267-6275. https://doi. org/10.1016/j.atmosenv.2011.08.066

Lalitaporn, P. & Mekaumnuaychai, T. (2020). Satellite measurements of aerosol optical depth and carbon monoxide and comparison with ground data. Environmental Monitoring and Assessment, 192, 369. https://doi. org/10.1007/s10661-020-08346-7

Liu, Y., Sarnat, J.A., Kilaru, V., Jacob, D.J. & Koutrakis, P. (2005). Estimating ground-level PM2.5 in the eastern United States using satellite remote sensing. Environmental Science & Technology, 39(9), 3269-3278. https://doi. org/10.1021/es049352m

Meng, X., Wu, Y., Pan, Z., Wang, H., Yin, G. & Zhao, H. (2019). Seasonal Characteristics and Particle-size Distributions of Particulate Air Pollutants in Urumqi. International Journal of Environmental Research and Public Health, 16(3), 396.

Nathapindhu, G., Sttheetham, D. & Ketkowit, K. (2011). Public Participation in Open Burning Control. KKU Research Journal, 16(4), 408-415.

Nguyen, H., Cressie, N. & Braverman, A. (2012). Spatial statistical data fusion for remote sensing applications. Journal of the American Statistical Association, 107(499), 1004-1018.

Outapa, P. & Ivanovitch, K. (2019). The effect of seasonal variation and meteorological data on PM10 concentrations in Northern Thailand. International Journal of GEOMATE, 16(56), 46-53.

Phayungwiwatthanakoon, C., Suwanwaree, P., Dasananda, S. (2014). Application of new MODIS-based Aerosol Index for Air Pollution Severity Assessment and Mapping in Upper Northern Thailand. Environment Asia, 7(2), 133-141. https://doi. org/10.14456/ea.2014.32

Pollution Control Department [PCD] (2004). Air pollution. Retrieved from: http://www. [access 04.05.2020].

Porter, J.N. & Clarke, A.D. (1997). Aerosol size distribution models based on in situ measurements. Journal of Geophysical Research Atmospheres, 102(D5), 6035-6045. https://doi. org/10.1029/96JD03403

Rotjanakusol, T. & Laosuwan, T. (2018). Estimation of land surface temperature using Landsat satellite data: a case study of Mueang Maha Sarakham District, Maha Sarakham

Province, Thailand for the years 2006 and 2015. Scientific Review Engineering and Environmental Sciences, 27(4), 401-409.

Rotjanakusol, T. & Laosuwan, T. (2019). Drought Evaluation with NDVI-Based Standardized Vegetation Index in Lower Northeastern Region of Thailand. Geographia Technica, 14(1), 118-130.

Sukitpaneenit, M. & Oanh, N.T.K. (2014). Satellite monitoring for carbon monoxide and particulate matter during forest fire episodes in Northern Thailand. Environmental Monitoring and Assessment, 186(4), 2495-2504.

Suwanprasit, C., Charoenpanyanet, A., Pardthaisong, L. & Sin-ampol, P. (2018). Spatial and temporal variations of satellite-derived PM10 of Chiang Mai: an exploratory analysis. Procedia Engineering, 212, 141-148. https://doi. org/10.1016/j.proeng.2018.01.019

Supasri, T., Intra, P., Jomjunyong, S. & Sampattagul, S. (2018). Evaluation of Particulate Matter Concentration by Using a Wireless Sensor System for Continuous Monitoring of Particulate Air Pollution in Northern of Thailand. Journal of Innovative Technology United States Environmental Protection Agency [USEPA] (2018). Particulate Matter (PM) Pollution. Retrieved from: https://www.epa. gov/pm-pollution/particulate-matter-pm-basics [access 20.01.2020].Research, 2(1), 69-83.

Uttaruk, Y. & Laosuwan, T. (2019). Drought Analysis Using Satellite-Based Data and Spectral Index in Upper Northeastern Thailand. Polish Journal of Environmental Studies, 28(6), 4447-4454.

Vienneau, D., Hoogh, K. de, Bechle, M.J., Beelen, R., Donkelaar, A. van, Martin, R.V., Millet, D.B., Hoek, G. & Marshall, J.D. (2013). Western European land use regression incorporating satellite- and ground-based measurements of NO2 and PM10. Environmental Science & Technology, 47(23), 13555-13564.

World Health Organization [WHO] (2017). Air pollution. Retrieved from: https://www.who. int/docs/default-source/thailand/air-pollution/briefing-on-air-pollution-th-thai.pdf? sfvrsn=408572d4_2 [access 02.10.2020].



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