Utilisation of WRF-HYSPLIT modelling approach and GEMS to identify PM2.5 sources in Central Kalimantan – study case: 2023 forest fire
Amalia Nurlatifah
A * , Prawira Yudha Kombara A , Alvin Pratama B , Rizky Faristyawan C , Aulia Arip Rakhman D and Nindia Noviastuti A
A
B
C
D
Abstract
Biomass burning is a significant particulate matter (PM) source, substantially contributing to elevated PM2.5 levels. Exposure to PM2.5 has been associated with various severe chronic illnesses. Therefore, it is crucial to address biomass burning occurrences, mitigate their impacts, and manage their consequences effectively. A key strategy for managing biomass burning haze involves identifying its sources, which facilitates the implementation of fire prevention and suppression measures. This study explores the sources and impacts of PM2.5 emissions from forest fires in Central Kalimantan in October 2023 using an integrated approach. We employed the WRF-HYSPLIT (Weather Research and Forecasting model coupled with the Hybrid Single Particle Lagrangian Integrated Trajectory model) and satellite instruments, including the GEMS (Geostationary Environment Monitoring Spectrometer), MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite), to identify PM2.5 sources and analyse their spatial distribution. Palangka Raya experienced substantial impacts from multiple hotspot occurrences on 4 October 2023, particularly from the south-eastern and eastern regions of Central Kalimantan and South Kalimantan. Conversely, Pangkalan Bun showed relatively lower PM2.5 concentrations on 2 October 2024 owing to prevailing sea winds. Most PM2.5 in Palangka Raya originated from the south-east. Geopotential height and topography analyses with wind plots suggested stable atmospheric conditions in Palangka Raya, whereas GEMS satellite data revealed high aerosol optical depth values, indicating elevated PM2.5 concentrations. These findings underscore the importance of understanding local meteorological conditions and hotspot distributions for effective management and mitigation of forest fire impacts on air quality in Central Kalimantan.
Keywords: air pollution, biomass burning, Central Kalimantan, forest fire, GEMS, haze, hotspot, MODIS, PM2.5, satellite remote sensing, VIIRS, WRF-HYSPLIT.
References
Arisanty D, Feindhi Ramadhan M, Angriani P, Muhaimin M, Nur Saputra A, Puji Hastuti K, Rosadi D (2022) Utilizing Sentinel-2 data for mapping burned areas in Banjarbaru wetlands, South Kalimantan province. International Journal of Forestry Research 2022, 1-12.
| Crossref | Google Scholar |
Ballesteros-González K, Sullivan AP, Morales-Betancourt R (2020) Estimating the air quality and health impacts of biomass burning in northern South America using a chemical transport model. The Science of The Total Environment 739, 139755.
| Crossref | Google Scholar | PubMed |
Basith S, Manavalan B, Shin TH, Park CB, Lee WS, Kim J, Lee G (2022) The impact of fine particulate matter 2.5 on the cardiovascular system: a review of the invisible killer. Nanomaterials 12(15), 2656.
| Crossref | Google Scholar | PubMed |
Cattau ME, Harrison ME, Shinyo I, Tungau S, Uriarte M, DeFries R (2016) Sources of anthropogenic fire ignitions on the peat-swamp landscape in Kalimantan, Indonesia. Global Environmental Change 39, 205-219.
| Crossref | Google Scholar |
Chen S-H, Sun W-Y (2002) A one-dimensional time dependent cloud model. Journal of the Meteorological Society of Japan – Series II 80(1), 99-118.
| Crossref | Google Scholar |
Cheng Z, Wang S, Fu X, Watson JG, Jiang J, Fu Q, et al. (2014) Impact of biomass burning on haze pollution in the Yangtze River delta, China: a case study in summer 2011. Atmospheric Chemistry and Physics 14(9), 4573-4585.
| Crossref | Google Scholar |
Cho Y, Kim J, Go S, Kim M, Lee S, Kim M, et al. (2024) First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia. Atmospheric Measurement Techniques 17(14), 4369-4390.
| Crossref | Google Scholar |
Crutzen PJ, Heidt LE, Krasnec JP, Pollock WH, Seiler W (1979) Biomass burning as a source of atmospheric gases CO, H2, N2O, NO, CH3Cl and COS. Nature 282(5736), 253-256.
| Crossref | Google Scholar |
Dreher JG (2009) Configuring the HYSPLIT model for National Weather Service Forecast Office and Spaceflight Meteorology Group applications. NASA Contractor Report NASA/CR-2009-214764. (NASA Center for AeroSpace Information: Hanover, MD, USA) Available at https://ntrs.nasa.gov/api/citations/20090023414/downloads/20090023414.pdf
Dudhia J, Moncrieff MW, So DWK (1987) The two‐dimensional dynamics of West African squall lines. Quarterly Journal of the Royal Meteorological Society 113(475), 121-146.
| Crossref | Google Scholar |
Engling G, He J, Betha R, Balasubramanian R (2014) Assessing the regional impact of Indonesian biomass burning emissions based on organic molecular tracers and chemical mass balance modeling. Atmospheric Chemistry and Physics 14(15), 8043-8054.
| Crossref | Google Scholar |
Grell GA (1993) Prognostic evaluation of assumptions used by cumulus parameterizations. Monthly Weather Review 121(3), 764-787.
| Crossref | Google Scholar |
Grell GA, Dévényi D (2002) A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophysical Research Letters 29(14), 38-1-38-4.
| Crossref | Google Scholar |
Hong S-Y, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Monthly Weather Review 134(9), 2318-2341.
| Crossref | Google Scholar |
Jamil B, Akhtar N (2017) Comparative analysis of diffuse solar radiation models based on sky-clearness index and sunshine period for humid-subtropical climatic region of India: a case study. Renewable and Sustainable Energy Reviews 78, 329-355.
| Crossref | Google Scholar |
Kaskaoutis DG, Kambezidis HD, Hatzianastassiou N, Kosmopoulos PG, Badarinath KVS (2007) Aerosol climatology: on the discrimination of aerosol types over four AERONET sites. Atmospheric Chemistry and Physics 7, 6357-6411.
| Crossref | Google Scholar |
Kim B-R, Kim G, Cho M, Choi Y-S, Kim J (2024) The first results of cloud retrieval from geostationary environmental monitoring spectrometer. Atmospheric Measurement Techniques 17, 453-470.
| Crossref | Google Scholar |
Kim J, Jeong U, Ahn M, Kim JH, Park RJ, Lee H, Song CH, Choi Y, Lee K, Yoo J, Jeong M, Park SK, Lee K, Song C, Kim S, Kim YJ, Kim S, Kim M, Go S, Liu X, Chance K, Chan Miller C, Al-Saadi J, Veihelmann B, Bhartia PK, Torres O, Abad GG, Haffner DP, Ko DH, Lee SH, Woo J, Chong H, Park SS, Nicks D, Choi WJ, Moon K, Cho A, Yoon J, Kim S, Hong H, Lee K, Lee H, Lee S, Choi M, Veefkind P, Levelt PF, Edwards DP, Kang M, Eo M, Bak J, Baek K, Kwon H, Yang J, Park J, Han KM, Kim B, Shin H, Choi H, Lee E, Chong J, Cha Y, Koo J, Irie H, Hayashida S, Kasai Y, Kanaya Y, Liu C, Lin J, Crawford JH, Carmichael GR, Newchurch MJ, Lefer BL, Herman JR, Swap RJ, Lau AKH, Kurosu TP, Jaross G, Ahlers B, Dobber M, McElroy CT, Choi Y (2020) New era of air quality monitoring from space: Geostationary Environment Monitoring Spectrometer (GEMS). Bulletin of the American Meteorological Society 101(1), E1-E22.
| Crossref | Google Scholar |
Kotroni V, Kallos G, Lagouvardos K, Varinou M, Walko R (1999) Numerical simulations of the meteorological and dispersion conditions during an air pollution episode over Athens, Greece. Journal of Applied Meteorology and Climatology 38, 432-447.
| Crossref | Google Scholar |
Levine JS (2000) Global biomass burning: a case study of the gaseous and particulate emissions released to the atmosphere during the 1997 Fires in Kalimantan and Sumatra, Indonesia. In ‘Biomass Burning and Its Inter-Relationships with the Climate System. Advances in Global Change Research. Vol. 3’. (Eds JL Innes, M Beniston, MM Verstraete) pp. 15–31. (Springer: Dordrecht, Netherlands) 10.1007/0-306-47959-1_2
Manisalidis I, Stavropoulou E, Stavropoulos A, Bezirtzoglou E (2020) Environmental and health impacts of air pollution: a review. Frontiers in Public Health 8, 14.
| Crossref | Google Scholar | PubMed |
Mehmood T, Zhu T, Ahmad I, Li X (2020) Ambient PM2.5 and PM10 bound PAHs in Islamabad, Pakistan: concentration, source and health risk assessment. Chemosphere 257, 127187.
| Crossref | Google Scholar | PubMed |
Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated‐k model for the longwave. Journal of Geophysical Research: Atmospheres 102(D14), 16663-16682.
| Crossref | Google Scholar |
Morakinyo OM, Mokgobu MI, Mukhola MS, Hunter RP (2016) Health outcomes of exposure to biological and chemical components of inhalable and respirable particulate matter. International Journal of Environmental Research and Public Health 13(6), 592.
| Crossref | Google Scholar | PubMed |
Ngan F, Cohen M, Luke W, Ren X, Draxler R (2015) Meteorological modeling using the WRF-ARW Model for Grand Bay intensive studies of atmospheric mercury. Atmosphere 6(3), 209-233.
| Crossref | Google Scholar |
Nurdiati S, Bukhari F, Julianto MT, Sopaheluwakan A, Aprilia M, Fajar I, et al. (2022) The impact of El Niño–Southern Oscillation and Indian Ocean Dipole on the burned area in Indonesia. Terrestrial, Atmospheric and Oceanic Sciences 33(1), 16.
| Crossref | Google Scholar |
Nurlatifah A, Adetya E, Indrawati A, Risyanto, Sumaryati, Komala N, et al. (2023) GEMS satellite identification on volcanic ash distribution of Mount Dukono Eruption in April 2022. In ‘Proceedings of the International Conference on Radioscience, Equatorial Atmospheric Science and Environment and Humanosphere Science’, 22–23 November 2022, held virtually. (Eds A Basit, E Yulihastin, SY Cahyarini, H Santoso, WS Pranowo, L Slamet S, HA Belgaman) Springer Proceedings in Physics, vol. 290, pp. 173–184. (Springer) 10.1007/978-981-19-9768-6_17
Park SS, Kim S-W, Song C-K, Park J-U, Bae K-H (2020) Spatio-temporal variability of aerosol optical depth, total ozone and NO2 over East Asia: strategy for the validation to the GEMS scientific products. Remote Sensing 12(14), 2256.
| Crossref | Google Scholar |
Physick WL, Abbs DJ (1991) Modelling summertime dispersion in the coastal terrain of southeastern Australia. Monthly Weather Review 119, 1014-1030.
| Google Scholar |
Pimonsree S, Vongruang P (2018) Impact of biomass burning and its control on particulate matter over a city in mainland southeast Asia during a smog episode. Atmospheric Environment 195, 196-209.
| Crossref | Google Scholar |
Putra EI (2011) The effect of the precipitation pattern of the dry season on peat fire occurrence in the Mega Rice Project area, Central Kalimantan, Indonesia. Tropics 19, 145-156.
| Crossref | Google Scholar |
Rahman MS, Azad MAK, Hasanuzzaman M, Salam R, Islam ARMT, Rahman MM, Hoque MMM (2021) How air quality and COVID-19 transmission change under different lockdown scenarios? A case from Dhaka city, Bangladesh. The Science of The Total Environment 762, 143161.
| Crossref | Google Scholar | PubMed |
Reid JS, Koppmann R, Eck TF, Eleuterio DP (2005) A review of biomass burning emissions. Part II: intensive physical properties of biomass burning particles. Atmospheric Chemistry and Physics 5(3), 799-825.
| Crossref | Google Scholar |
Rodgers WC, Nicewander WA (1988) Pearson’s product-moment correlation coefficient. Statistical Science 3(3), 290-301.
| Google Scholar |
Rojano R, Arregocés H, Gámez Frías E (2021) Changes in ambient particulate matter during the COVID-19 and associations with biomass burning and Sahara dust in northern Colombia. Heliyon 7(12), e08595.
| Crossref | Google Scholar | PubMed |
Seiler W, Crutzen PJ (1980) Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning. Climatic Change 2, 207-247.
| Crossref | Google Scholar |
Stein AF, Draxler RR, Rolph GD, Stunder BJB, Cohen MD, Ngan F (2015) NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bulletin of the American Meteorological Society 96(12), 2059-2077.
| Crossref | Google Scholar |
Tao L, Harley RA (2014) Changes in fine particulate matter measurement methods and ambient concentrations in California. Atmospheric Environment 98, 676-684.
| Crossref | Google Scholar |
Taravat A, Los H (2021) Sentinel-2 based service for identify and map wildfire events. In ‘2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS’, 11–16 July 2021, Brussels, Belgium. pp. 6728–6730. (IEEE) 10.1109/igarss47720.2021.9555057
Thangavel P, Park D, Lee YC (2022) Recent insights into particulate matter (PM2.5)-mediated toxicity in humans: an overview. International Journal of Environmental Research and Public Health 19(12), 7511.
| Crossref | Google Scholar | PubMed |
Usup A, Hayasaka H (2023) Peatland fire weather conditions in Central Kalimantan, Indonesia. Fire 6(5), 182.
| Crossref | Google Scholar |
van der Werf GR, Randerson JT, Giglio L, van Leeuwen TT, Chen Y, Rogers BM, et al. (2017) Global fire emissions estimates during 1997–2016. Earth System Science Data 9(2), 697-720.
| Crossref | Google Scholar |
Wang G, Ostoja-Starzewski M (2004) Influence of topography on the Phoenix CO2 dome: a computational study. Atmospheric Science Letters 5, 103-107.
| Crossref | Google Scholar |
Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research 30(1)), 81-89.
| Crossref | Google Scholar |
Yadav IC, Devi NL (2019) Biomass burning, regional air quality, and climate change. In ‘Encyclopedia of Environmental Health’, 2nd edn. (Ed. J Nriagu) pp. 386–391. (Elsevier) 10.1016/B978-0-12-409548-9.11022-X
Yulianti N, Hayasaka H (2013) Recent active fires under El Niño conditions in Kalimantan, Indonesia. American Journal of Plant Sciences 4(3), 685-696.
| Crossref | Google Scholar |
Zhang R, Liu C, Zhou G, Sun J, Liu N, Hsu P-C, et al. (2017) Morphology and property investigation of primary particulate matter particles from different sources. Nano Research 11, 3182-3192.
| Crossref | Google Scholar |
Zhao H (2022) Chapter 18 - Biomass burning emission and impacts on air pollution in China. In ‘Asian Atmospheric Pollution’. (Ed. RP Singh) pp. 335–347. (Elsevier) 10.1016/B978-0-12-816693-2.00024-X
Zong Z, Wang X, Tian C, Chen Y, Qu L, Ji L, et al. (2016) Source apportionment of PM2.5 at a regional background site in North China using PMF linked with radiocarbon analysis: insight into the contribution of biomass burning. Atmospheric Chemistry and Physics 16(17), 11249-11265.
| Crossref | Google Scholar |
