Abstract
In northern Thailand, biomass burning is a major source of high concentrations of particulate matter with a diameter < 10 μm (PM10) during the burning season (January to May), leading to health concerns related to air pollution. Given the limited staffing and budget available to local agencies, identifying priority areas for management and mitigation is important. We herein developed an empirical model using Landsat 8 imagery and PM10 data from ground stations to estimate PM10 concentrations in Nan Province, achieving an error of < 20% between the predicted and measured PM10 values. The satellite-derived values were then classified into five air quality levels based on criteria defined by the Thai Ministry of Natural Resources and Environment. These levels were correlated with land use/land cover maps and fire hotspots with high confidence (> 80%) acquired by the Terra and Aqua satellites from January to May 2015–2019. Fire hotspots and problematic PM10 concentrations were most often correlated with agricultural land, followed by disturbed forests and dense forests. These results enabled us to identify critical areas where repeat burning and high PM10 levels should by prioritized for mitigation, such as the upland agricultural and forest areas of Wiang Sa District. Our methodology could benefit air pollution management in other developing countries with similar limitations.
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References
Abdullah, S., Ismail, M., Ahmed, A. N., & Abdullah, A. M. (2019). Forecasting particulate matter concentration using linear and non-linear approaches for air quality decision support. Atmosphere, 10(11), 667. https://doi.org/10.3390/atmos10110667.
Air Quality and Noise Management Bureau, Pollution Control Department, Ministry of Natural Resources and Environment of Thailand. (2013). Thailand’s air quality information. http://air4thai.pcd.go.th/webV2/aqi_info.php. Accessed November 20, 2017
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. https://doi.org/10.1016/j.atmosenv.2013.07.012.
Bhaskaran, S., Phillip, N., Rahman, A., & Mallick, J. (2011). Applications of satellite data for Aerosol Optical Depth (AOD) retrievals and validation with AERONET data. Atmospheric and Climate Sciences, 1, 61–67. https://doi.org/10.4236/acs.2011.12007.
Chu, Y., Liu, Y., Li, X., Liu, Z., Lu, H., Lu, Y., et al. (2016). A review on predicting ground PM2.5 concentration using satellite aerosol optical depth. Atmosphere, 7(10), 129. https://doi.org/10.3390/atmos7100129.
Diao, M., Holloway, T., Choi, S., O’Neill, S. M., Al-Hamdan, M. Z., van Donkelaar, A., et al. (2019). Methods, availability, and applications of PM2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models. Journal of the Air & Waste Management, 69(12), 1391–1414. https://doi.org/10.1080/10962247.2019.1668498.
Dumitrache, R. C., Iriza, A., Maco, B. A., Barbu, C. D., Hirtl, M., Mantovani, S., et al. (2016). Study on the influence of ground and satellite observations on the numerical air-quality for PM10 over Romanian territory. Atmospheric Environment, 143, 278–289. https://doi.org/10.1016/j.atmosenv.2016.08.063.
ExelisVisual Information Solutions, Inc. (2013). ENVI Classic Tutorial: Atmospherically Correcting Hyperspectral Data Using FLAASH®. Available online: http://www.harrisgeospatial.com/portals/0/pdfs/envi/FLAASH_Hyperspectral.pdf. Accessed May 13, 2017.
Fernández-Pacheco, V. M., López-Sánchez, C. A., Álvarez-Álvarez, E., Suárez López, M. J., García-Expósito, L., Antuña Yudego, E., et al. (2018). Estimation of PM10 distribution using Landsat5 and Landsat8 remote sensing. Proceedings, 2(23), 1430. https://doi.org/10.3390/proceedings2231430.
Glantz, P., & Tesche, M. (2012). Assessment of two aerosol optical thickness retrieval algorithms applied to MODIS Aqua and Terra measurements in Europe. Atmospheric Measurement Techniques, 5, 1727–1740. https://doi.org/10.5194/amt-5-1727-2012.
Guo, Y., Tang, Q., Gong, D. Y., & Zhang, Z. (2017). Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model. Remote Sensing of Environment, 198, 140–149. https://doi.org/10.1016/j.rse.2017.06.001.
Hadjimitsis, D. G. (2009). Aerosol optical thickness (AOT) retrieval over land using satellite image based algorithm. Air Quality, Atmosphere and Health, 2, 89–97. https://doi.org/10.1007/s11869-009-0036-0.
Hagolle, O., Huc, M., Villa Pascual, D., & Dedieu, G. (2015). A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images. Remote Sensing, 7, 2668–2691. https://doi.org/10.3390/rs70302668.
Jeensorn, T., Apichartwiwat, P., & Jinsart, W. (2018). PM10 and PM2.5 from haze smog and visibility effect in Chiang Mai Province Thailand. Applied Environmental Research, 40(3), 1–10.
Jiang, M., Sun, W., Yang, G., & Zhang, D. (2017). Modelling seasonal GWR of daily PM25 with proper auxiliary variables for the Yangtze River Delta. Remote Sensing, 9(4), 346. https://doi.org/10.3390/rs9040346.
Junpen, A., Garivait, S., & Bonnet, S. (2013). Estimating emissions from forest fires in Thailand using MODIS active fire product and country specific data. Asia-Pacific Journal of Atmospheric Sciences, 49, 389. https://doi.org/10.1007/s13143-013-0036-8.
Kamarul Zaman, N. A. F., Kanniah, K. D., & Kaskaoutis, D. G. (2017). Estimating particulate matter using satellite based aerosol optical depth and meteorological variables in Malaysia. Atmospheric Research, 193, 142–162. https://doi.org/10.1016/j.atmosres.2017.04.019.
Kanniah, K. D., Kaskaoutis, D. G., San Lim, H., Latif, M. T., Kamarul Zaman, N. A. F., & Liew, J. N. (2016). Overview of atmospheric aerosol studies in Malaysia: Known and unknown. Atmospheric Research, 182, 302–318. https://doi.org/10.1016/j.atmosres.2016.08.002.
Kliengchuay, W., Cooper Meeyai, A., Worakhunpiset, S., & Tantrakarnapa, K. (2018). Relationships between meteorological parameters and particulate matter in Mae Hong Son Province, Thailand. International Journal of Environmental Research and Public Health, 15(12), 2801. https://doi.org/10.3390/ijerph15122801.
Li, L., Chen, B., Zhang, Y., Zhao, Y., Xian, Y., Xu, G., et al. (2018). Retrieval of daily PM2.5 concentrations using nonlinear methods: A case study of the Beijing–Tianjin–Hebei Region, China. Remote Sensing, 10(12), 2006. https://doi.org/10.3390/rs10122006.
Meng, X., Fu, Q., Ma, Z., Chen, L., Zou, B., Zhang, Y., et al. (2016). Estimating ground-level PM10 in a Chinese city by combining satellite data, meteorological information and a land use regression model. Environmental Pollution, 208, 177–184. https://doi.org/10.1016/j.envpol.2015.09.042.
Mishra, R. K., Pandey, J., Chaudhary, S. K., Khalkho, A., & Singh, V. K. (2012). Estimation of air pollution concentration over Jharia coalfield based on satellite imagery of atmospheric aerosol. International Journal of Geomatics and Geosciences, 2(3), 723–729.
Nan Statistical Office. Annual Report 2015–2017. Available online: http://nan.nso.go.th/index.php?option=com_content&view=article&id=314:2017-10-12-03-58-03&catid=102&Itemid=507 (accessed on 19 July, 2019)
NASA. (2018) Aerosol and flux networks. Available online: https://aeronet.gsfc.nasa.gov/new_web/networks.html. Accessed January 26, 2019
Nguyen, T. N. T., Bui, H. Q., Pham, H. V., Luu, H. V., Man, C. D., Pham, H. N., et al. (2015). Particulate matter concentration mapping from MODIS satellite data: A Vietnamese case study. Environmental Research Letters, 10(9), 1–13. https://doi.org/10.1088/1748-9326/10/9/095016.
Nguyen, T. N. T., Ta, V. C., Le, T. H., & Mantovani, S. (2014). Particulate matter concentration estimation from satellite aerosol and meteorological parameters: Data-driven approaches. In V. Huynh, D. Tran, A. Le, & S. Pham (Eds.), Knowledge and systems engineering. Advances in intelligent systems and computing (244th ed., pp. 351–362). Cham: Springer. https://doi.org/10.1007/978-3-319-02741-8_30.
Othman, N., Jafri, M. Z. M., & San, L. H. (2010). Estimating particulate matter concentration over arid region using satellite remote sensing: A case study in Makkah, Saudi Arabia. Modern Applied Science, 11(4), 131–142. https://doi.org/10.5539/mas.v4n11p131.
Park, S., Shin, M., Im, J., Song, C. K., Choi, M., Kim, J., et al. (2019). Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea. Atmospheric Chemistry and Physics, 19, 1097–1113. https://doi.org/10.5194/acp-19-1097-2019.
Qiao, Z., Wu, F., Xu, X., Yang, J., & Liu, L. (2019). Mechanism of spatiotemporal air quality response to meteorological parameters: A national-scale analysis in China. Sustainability, 11(14), 3957.
Remer, L. A., Kaufman, Y. J., Tanré, D., Matoo, S., Chu, D. A., Martins, J. V., et al. (2005). The MODIS aerosol algorithm, products, and validation. Journal of the Atmospheric Sciences. https://doi.org/10.1175/JAS3385.1.
Roy, A., Jivani, A., & Parekh, B. (2017). Estimation of PM10 distribution using Landsat 7 ETM + remote sensing data. International Journal of Advanced Remote Sensing & GIS, 6(1), 2246–2252. https://doi.org/10.23953/cloud.ijarsg.284.
Ryan, W. A., Gombojav, E., Barkhasragchaa, B., Byambaa, T., Lkhasuren, O., Amram, O., et al. (2013). An assessment of air pollution and its attributable mortality in Ulaanbaatar, Mongolia. Air Quality, Atmosphere and Health, 6(1), 137–150. https://doi.org/10.1007/s11869-011-0154-3.
Saleh, S., & Hasan, G. (2014). Estimation of PM10 concentration using ground measurements and Landsat 8 OLI satellite image. Journal of Geophysics & Remote Sensing, 3, 1–6. https://doi.org/10.4172/2169-0049.1000120.
Saraswat, I., Mishra, R. K., & Kumar, A. (2017). Estimation of PM10 concentration from Landsat 8 OLI satellite imagery over Delhi, India. Remote Sensing Applications: Society and Environment, 8, 251–257. https://doi.org/10.1016/j.rsase.2017.10.006.
Shaheen, A., Kidwai, A. A., Ain, N. U., Aldabash, M., & Zeeshan, A. (2017). Estimating air particulate matter 10 using Landsat multi-temporal data and analyzing its annual temporal pattern over Gaza Strip, Palestine. Journal of Asian Scientific Research, 7(2), 22–38. https://doi.org/10.18488/journal.2/2017.7.2/2.2.22.37.
Shtein, A., Kloog, I., Schwartz, J., Silibello, C., Michelozzi, P., Gariazzo, C., et al. (2020). Estimating daily PM25 and PM10 over Italy using an ensemble model. Environmental Science and Technology, 54(1), 120–128. https://doi.org/10.1021/acs.est.9b04279.
Silva, R. A., West, J. J., Lamarque, J. F., Shindell, D. T., Collins, W. J., Dalsoren, S., et al. (2016). The effect of future ambient air pollution on human premature mortality to 2100 using output from the ACCMIP model ensemble. Atmospheric Chemistry and Physics, 16, 9847–9862. https://doi.org/10.5194/acp-16-9847-2016.
Sun, L., Wei, J., Bilal, M., Tian, X., Jia, C., Guo, Y., et al. (2016). Aerosol optical depth retrieval over bright areas using Landsat 8 OLI images. Remote Sensing, 8(1), 1–14. https://doi.org/10.3390/rs8010023.
Thai Meteorological Department. (2015) The Climate of Thailand. Available online: https://www.tmd.go.th/en/archive/thailand_climate.pdf (Accessed 3 March 2017).
Themistocleous, K., Hadjimitsisa, D. G., Retalisb, A., Chrysoulakisc, N. (2012) The development of air quality indices through image-retrieved AOT and PM10 measurements in Limassol Cyprus. In Remote Sensing of Clouds and the Atmosphere XVII; and Lidar Technologies, Techniques, and Measurements for Atmospheric Remote Sensing VIII. Proceedings of SPIE Vol. 8534 85340B-1, Edinburgh, United Kingdom, 1 November 2012. https://doi.org/10.1117/12.974701
United States Environmental Protection Agency, July 1999, Guideline for Reporting of Daily Air Quality - Air Quality Index (AQI), 40 CFR Part 58, Appendix G.
Vadrevu, K., Ohara, T., & Justice, C. (2018). Land cover, land use changes and air pollution in Asia: A synthesis. Environmental Research Letters, 12(12), 1–17. https://doi.org/10.1088/1748-9326/aa9c5d.
Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment, 185, 46–56. https://doi.org/10.1016/j.rse.2016.04.008.
von Hoyningen-Huene, W., Freitag, W. M., & Burrows, J. B. (2003). Retrieval of aerosol optical thickness over land surfaces from top-of-atmosphere radiance. Journal Geophysical Research, 108, 1–20. https://doi.org/10.1029/2001JD002018.
Wu, S., Mickley, L. J., Kaplan, J. O., & Jacob, D. J. (2012). Impacts of changes in land use and land cover on atmospheric chemistry and air quality over the 21st century. Atmospheric Chemistry and Physics, 12, 1597–1609. https://doi.org/10.5194/acp-12-1597-2012.
Yin, Q., Wang, J., Hu, M., & Wong, H. (2016). Estimation of daily PM2.5 concentration and its relationship with meteorological conditions in Beijing. Journal of Environmental Science, 48, 161–168. https://doi.org/10.1016/j.jes.2016.03.024.
Yoram, J. K. (1993). Aerosol optical thickness and atmospheric path radiance. Journal Geophysical Research, 98(D2), 2677–2692. https://doi.org/10.1029/92JD02427.
Zahari, M. A. Z., Majid, M. R., Ho, C. S., Kurata, G., Nordin, N., & Irina, S. Z. (2016). An investigation on the relationship between land use composition and PM10 pollution in Iskandar Malaysia. Clean Technologies and Environmental, 18, 2429–2439. https://doi.org/10.1007/s10098-016-1263-3.
Zhang, B., Zhang, M., Kang, J., Hong, D., Xu, J., & Zhu, X. (2019). Estimation of PMx concentrations from Landsat 8 OLI images based on a multilayer perceptron neural network. Remote Sensing, 11(6), 646. https://doi.org/10.3390/rs11060646.
Zou, B., Chen, J., Zhai, L., Fang, X., & Zheng, Z. (2017). Satellite based mapping of ground PM2.5 concentration using generalized additive modeling. Remote Sensing, 9(1), 1. https://doi.org/10.3390/rs9010001.
Zou, B., Pu, Q., Bilal, M., Weng, Q., Zhai, L., & Nichol, J. E. (2016a). High-resolution satellite mapping of fine particulates based on geographically weighted regression. IEEE Geoscience and Remote Sensing Letters, 13(4), 495–499. https://doi.org/10.1109/LGRS.2016.2520480.
Zou, B., Xu, S., Sternberg, T., & Fang, X. (2016b). Effect of land use and cover change on air quality in urban sprawl. Sustainability, 8(7), 677. https://doi.org/10.3390/su8070677.
Acknowledgements
This research was funded by the Faculty of Liberal Arts, Thammasat University, Thailand, grant number 3/2559. We are grateful for the data provided by the US Geological Survey (USGS); the Fire Information for Resource Management System (FIRMS), NASA; the Pollution Control Department and the Department of National Parks, Wildlife and Plant Conservation, Ministry of Natural Resources and Environment, and the Ministry of Interior, Thailand.
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Kamthonkiat, D., Thanyapraneedkul, J., Nuengjumnong, N. et al. Identifying priority air pollution management areas during the burning season in Nan Province, Northern Thailand. Environ Dev Sustain 23, 5865–5884 (2021). https://doi.org/10.1007/s10668-020-00850-7
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DOI: https://doi.org/10.1007/s10668-020-00850-7