Estimation of Respiratory Disease Burden Attributed to Particulate Matter from Biomass Burning in Northern Thailand Using 1-km Resolution MAIAC-AOD
Main Article Content
Abstract
The upper northern Thailand suffers from air pollution due to open burning, which has been known for a long time. It was also found that different respiratory diseases were attributed to air pollution, especially particulate matter. This study estimated the health impacts attributed to PM10 between 2014 and 2016 using the burden of disease in terms of the disability adjusted life year (DALYs). The spatial correlation was evaluated based on applicable remote sensing data using the geographically weighted regression (GWR) model. The average measured PM10 concentrations for the summer and annual periods between 2014 and 2016 were 73 and 89 µg m-3, respectively, exceeded the national standard (50 µg m-3). In the months of March and April, when PM10 concentrations were at their highest, the maximum values of the Multi-Angle Implementation of Atmospheric Correction (MAIAC-AOD), 2.70 and 3.48, were recorded. There was a strong correlation between the MAIAC-AOD and the ground-based AOD measurements (AERONET stations), with R of 0.8468, 0.8396, and 0.8334 between 2014–2016. The correlation coefficients for the 3,208 co-located gridded of PM10 emissions vs. measured PM10, measured PM10 vs. MAIAC-AOD, and MAIAC-AOD vs. PM10 emissions were 0.6656, 0.6446, and 0.5580, respectively. The spatial correlation between the interpolated measured PM10 and 1-km MAIAC-AOD was 0.5979, 0.3741, and 0.7584 as an outcome of GWR. The total DALYs of chronic obstructive pulmonary disease (COPD) attributable to PM10 in 2014–2016 were 115,930 years per 100,000 population, with the relative risk of COPD related to PM10 at a 95% confidence interval of 1.2045–1.2107.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Published articles are under the copyright of the Applied Environmental Research effective when the article is accepted for publication thus granting Applied Environmental Research all rights for the work so that both parties may be protected from the consequences of unauthorized use. Partially or totally publication of an article elsewhere is possible only after the consent from the editors.
References
Lyapustin, A., Wang, Y., Laszlo, I., Laszlo, R., Korkin, S., Remer, L., ..., Reid, J.S. Multiangle implementation of atmospheric correction (MAIAC): 2. Aerosol algorithm. Journal of Geophysical Research, 2011, 116:D03211.
Huang, K., Xiao, Q., Meng, X., Geng, G., Wang, Y., Lyapustin, A., …, Liu, Y. Predicting monthly high-resolution PM2.5 concentrations with random forest model in the North China Plain. Environmental Pollution, 2018, 242(Pt A), 675–683.
Han, W., Tong, L., Chen, Y., Li, R., Yan, B., Liu, X. Estimation of high-resolution daily ground-level PM2.5 concentration in Beijing 2013–2017 using 1 km MAIAC AOT data. Applied Sciences, 2018, 8, 2624.
Li, Y., Xue, Y., Guang, J., She, L., Fan, C., Chen, G. Ground-Level PM2.5 Concentration Estimation from Satellite Data in the Beijing Area Using a Specific Particle Swarm Extinction Mass Conversion Algorithm. Remote Sensing, 2018, 10, 1906.
Stafoggia, M., Schwartz, J., Badaloni, C., Bellander, T., Alessandrini, E., Cattani, G., …, Kloog, I. Estimation of daily PM10 concentrations in Italy (2006–2012) using finely resolved satellite data, land use variables and meteorology. Environment International, 2017, 99, 234–244.
Tang, C.H., Coull, B.A., Schwartz, J., Lyapustin, A.I., Di, Q., Koutrakis, P. Developing Particle Emission Inventories Using Remote Sensing (PEIRS). Journal of the Air & Waste Management Association 2017, 67(1), 53–63.
Li, L., Franklin, M., Girguis, M., Lurmann, F., Wu, J., Pavlovic, N, …, Habre, R. Spatiotemporal Imputation of MAIAC AOD Using Deep Learning with Downscaling. Remote Sensing of Environment, 2020, 237.
Chen, N., Yang, M., Du, W., Huang, M. P. PM2.5 Estimation and Spatial-Temporal Pattern Analysis Based on the Modified Support Vector Regression Model and the 1 km Resolution MAIAC AOD in Hubei, China. International Journal of Geo-Information, 2021, 10, 31.
Kanabkaew, T. Prediction of hourly particulate matter concentrations in Chiangmai, Thailand using MODIS aerosol optical depth and ground-based meteorological data. Environmentasia, 20136(2), 65-70.
Lalitaporn, P., Mekaumnuaychai, T. Satellite measurements of aerosol optical depth and carbon monoxide and comparison with ground data. Environmental Monitoring and Assessment, 2020, 192, 369.
Zeeshan, M., Kim Oanh, N.T. Assessment of the relationship between satellite AOD and ground PM10 measurement data considering synoptic meteorological patterns and Lidar data. Science of The Total Environment, 2014, 473–474, 609–618.
HITAP. Burden of Disease Thailand. International Health Policy Program, Thailand, 2014. [Online] Available from: http://bodthai.net
Hongthong, A., Nanthapong, K., Kanabkaew, T. Biomass burning emission inventory of multi-year PM10 and PM2.5 with high temporal and spatial resolution for Northern Thailand. ScienceAsia, 2022, 48, 302-309.
Emili, E., Lyapustin, A., Wang, Y., Popp, C., Korkin, S., Zebisch, M., …, Petitta, M. High spatial resolution aerosol retrieval with MAIAC: Application to mountain regions, Journal of Geophysical Research, 2011, 116, D23211.
Liang, F., Xiao, Q., Wang, Y., Lyapustin, A., Li, G., Gu, D., …, Liu, Y. MAIAC-based long-term spatiotemporal trends of PM2.5 in Beijing, China. Science of the Total Environment, 2018, 616–617, 1589–1598.
Lyapustin, A., Wang, Y., Laszlo, U., Hilker, T., Hall, F.G., Sellers, P,J., …, Korkin, S.V. Multi-angle implementation of atmospheric correction for MODIS (MAIAC): 3. Atmospheric correction. Remote Sensing of Environment, 2012, 127, 385–393.
Xiao, Q., Wang, Y., Chang, H.H., Meng, X., Geng, G., Lyapustin, A., Liu, Y. Full-coverage high-resolution daily PM2.5 estimation using MAIAC AOD in the Yangtze River Delta of China. Remote Sensing of Environment, 2017, 199, 437–446.
Zhang, G., Rui, X., Fan, Y. Critical Review of Methods to Estimate PM2.5 Concentrations within Specified Research Region. ISPRS International Journal of Geo-Information, 2018, 7(9), 368.
Guo, Y., Tang, Q., Gong, D.Y., Zhang, Z. Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model. Remote Sensing of Environment, 2017, 198, 140-149.
He, Q., Huang, B. Satellite-based high-resolution PM2.5 estimation over the Beijing-Tianjin-Hebei region of China using an improved geographically and temporally weighted regression mode. Environmental Pollution, 2018, 236, 1027-1037.
Pothirat, C., Tosukhowong, A., Chaiwong, W., Liwsrisakun, C., Inchai, J. Effects of seasonal smog on asthma and COPD exacerbations requiring emergency visits in Chiang Mai, Thailand. Asian Pacific Journal of Allergy and Immunology, 2016, 34, 284-289.
Zeng, Q., Wu, Z., Jiang, G., Li, P., Ni, Y., Li, G., Pan, X. The association between inhalable particulate matter and YLL caused by COPD in a typical city in northern China. Atmospheric Environment, 2018, 172, 26–31.
Maji, K.J., Arora, M., Dikshit, A.K. Burden of disease attributed to ambient PM2.5 and PM10 exposure in 190 cities in China. Environmental Science and Pollution Research, 2017.
Ostro, B. Outdoor air pollution: Assessing the environmental burden of disease at national and local levels. Geneva, World Health Organization, 2004. (WHO Environmental Burden of Disease Sires, No. 5).
Haagsma, J.A., De Maertens Noordhout, C., Polinder, S., Vos, T., Havelaar, A.H., Cassini, A., …, Salomon, J.A. Assessing disability weights based on the responses of 30,660 people from four European countries. Population Health Metrics, 2015, 13, 10.
Nakapan, S., Hongthong, A. Applying surface reflectance to investigate the spatial and temporal distribution of PM2.5 in Northern Thailand. ScienceAsia, 2022, 48, 75-81.
Ferrada, G.A, Zhou, M., Wang, J., Lyapustin, A., Wang, Y., Freitas, S.R., Carmichael, G.R. Introducing a VIIRS-based Fire Emission Inventory version 0 (VFEIv0). Geoscientific Model Development 2022, Discuss. [preprint]. [Online] Available from: https://doi.org/10.5194/gmd-2022-54
Vadrevu, K., Lasko, K. Intercomparison of MODIS AQUA and VIIRS I-Band Fires and Emissions in an Agricultural Landscape-Implications for Air Pollution Research. Remote Sensing (Basel), 2018, 10(7).
Upadhyay, A., Dey, S., Chowdhury, S., Goyal, P. Expected health benefits from mitigation of emissions from major anthropogenic PM2.5 sources in India: Statistics at state level. Environmental Pollution, 2018, 242, 1817-1826.
McCarty J.L. Remote Sensing-Based Estimates of Annual and Seasonal Emissions from Crop Residue Burning in the Contiguous United States. Journal of the Air & Waste Management Association, 2011, 61, 1, 22-34.
Sirithian, D., Thepanondh, S., Sattler, M.L., Laowagul, W. Emissions of volatile organic compounds from maize residue open burning in the northern region of Thailand. Atmospheric Environment, 2018, 176, 179–187.
HPAP. Thailand Health And Pollution Assessment and Prioritization Program: Accelerating Actions to Advance the Environmental Health Action Plan 2017-2021. The Thailand Health and Pollution Assessment and Prioritization 2019, Program. [Online] Available from: https://gahp.net/hpap-thailand/
Vajanapoom, N., Kooncumchoo, P., Thach, T.Q. Acute effects of air pollution on all-cause mortality: a natural experiment from haze control measures in Chiang Mai Province, Thailand. PeerJ, 2020, 8, e9207.