Sistema de consulta e alerta de poluição causada pela fumaça decorrente do fogo na vegetação (VFSP-WAS) da Organização Meteorológica Mundial (WMO
conceito, capacidades atuais, desafios de pesquisa e desenvolvimento, e o caminho a seguir
DOI:
https://doi.org/10.37002/biodiversidadebrasileira.v11i2.1738Palavras-chave:
Modelagem da poluição por fumaça e fogo, previsão meteorológica numérica, observação da poluição atmosférica, sistemas de alerta precoceResumo
O fogo na vegetação, incluindo a aplicação do fogo no uso da terra e na mudança de uso da terra, assim como os incêndios florestais, afetam o funcionamento do sistema terrestre e impõem ameaças significativas à saúde e segurança públicas. Este documento apresenta o conceito de um Sistema de Avaliação e Alerta de Poluição causada por Fumaça decorrente do Fogo na Vegetação (VFSP-WAS, na sigla em inglês). Apresenta-se o fundamento científico do sistema e diretrizes para abordar as questões de fogo na vegetação e poluição por fumaça, indicando-se os principais desafios para a pesquisa. O artigo propõe o estabelecimento de centros regionais VFSP-WAS e mostra exemplos potenciais do conceito VFSP-WAS em duas regiões (sudeste da Ásia e América do Norte), onde centros regionais VFSP-WAS trabalham em parceria com Centros Regionais de Monitoramento de Fogo/Manejo de fogo.
Referências
Agastra A. Forecasting Prescribed Fires Using Weather Forecasts, Satellite Data, and Machine Learning.<http://purl.flvc.org/fsu/fd/FSU_libsubv1_scholarship_submission_1587740055_f1f7bbf0>. Accessed: 30/06/2020
Barnston AG, Tippett MK, Ranganathan M, L'Heureux ML. Deterministic skill of ENSO predictions from the North American Multimodel Ensemble. Climate Dynamics. 53: 7215-7234, 2017.
Bedia J, et al. Global patterns in the sensitivity of burned area to fire-weather: Implications for climate change. Agricultural and Forest Meteorology, 214-215: 369-379, 2015
Benedetti A, et al. Status and future of numerical atmospheric aerosol prediction with a focus on data requirements. Atmospheric Chemistry and Physics, 18: 10615-10643, 2018.
Bleck R, Benjamin S, Lee J, MacDonald AE. On the Use of an Adaptive, Hybrid-Isentropic Vertical Coordinate in Global Atmospheric Modeling. Monthly Weather Review, 138: 2188-2210, 2010.
Bocquet M, et al. Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models. Atmospheric Chemistry and Physics. 15:5325-5358, 2015.
Bowman DMJS, Johnston FH. Wildfire Smoke, Fire Management, and Human Health. EcoHealth, 2: 76-80, 2005.
Burki TK. The pressing problem of Indonesia's forest fires. The Lancet Respiratory Medicine, 5:685-686, 2017.
Chen J, et al. The FireWork v2.0 air quality forecast system with biomass burning emissions from the Canadian Forest Fire Emissions Prediction System v2.03. Geoscientific Model Development 12: 3283-3310, 2019.
Chew BN, et al. Tropical cirrus cloud contamination in sun photometer data. Atmospheric Environment, 45: 6724-6731, 2011.
Chew BN, et al. Aerosol particle vertical distributions and optical properties over Singapore. Atmospheric Environment 79: 599-613, 2013.
Coelho CAS, et al. Climate diagnostics of three major drought events in the Amazon and illustrations of their seasonal precipitation predictions. Meteorological Applications. 19: 237-255, 2012.Crippa P, et al. Population exposure to hazardous air quality due to the 2015 fires in Equatorial Asia. Scientific Reports, 6, 2016.
Dennekamp M, et al. Forest Fire Smoke Exposures and Out-of-Hospital Cardiac Arrests in Melbourne, Australia: A Case-Crossover Study. Environmental Health Perspectives, 123: 959-964, 2015.
de Groot WJ, Goldammer JG. 2013. The Global Early Warning System for Wildland Fire, p. 277-284. In: Goldammer JG (Ed.). Vegetation Fires and Global Change - Challenges for Concerted International Action. A White Paper directed to the United Nations and International Organizations. Kessel Publishing House. 400 p.de Groot WJ, Field RD, Brady MA, Roswintiarti O, Mohamad M. Development of the Indonesian and Malaysian fire danger rating systems, Mitigation and Adaptation Strategies for Global Change, 12(1): 165-180, 2007.
de Groot WJ, Flannigan MD. 2014. Climate Change and Early Warning Systems for Wildland Fire. In: Singh A., Zommers Z. (Eds). Reducing Disaster: Early Warning Systems For Climate Change. Springer, Dordrecht. 387p.
de Groot WJ, Wotton BM, Flannigan MD. 2015. Wildland Fire Danger Rating and Early Warning Systems, p. 207-228. In: Paton D, McCaffrey S, Buergelt P, Tedim F, Shroder JF. (Eds.). Wildfire Hazards, Risks and Disasters. Elsevier Inc. Amsterdam. 284p.
Di Giuseppe F, et al. The Potential Predictability of Fire Danger Provided by Numerical Weather Prediction. Journal of Applied Meteorology and Climatology, 55: 2469-2491, 2016.
Field RD. Evaluation of Global Fire Weather Database reanalysis and short-term forecast products. Natural Hazards and Earth System Sciences, 20: 1123-1147, 2020.
Field RD, et al. Indonesian fire activity and smoke pollution in 2015 show persistent nonlinear sensitivity to El Niño-induced drought. Proceedings of the National Academy of Sciences, 113: 9204-9209, 2016.
Frassoni A, et al. PREP-CHEM-SRC VERSION 1.8: improvements to better represent local urban and biomass burning emissions over South America. <http://bluebook.meteoinfo.ru/uploads/2018/docs/04_Frassoni_Ariane_PREP_CHEM_SRC_VERSION_1.8.pdf>. Accessed: 15/07/2019.
Frassoni A, et al. Biomass Burning Susceptibility Modeling for Amazon: a numerical study for application in preventive monitoring. <https://www.researchgate.net/project/Biomass-Burning-Susceptibility-Modeling-for-Amazon-a-numerical-study-for-application-in-preventive-monitoring>. Accessed: 15/07/2020
Freitas SR, et al. The Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS) - Part 1: Model description and evaluation. Atmospheric Chemistry and Physics, 9: 2843-2861, 2009.
Freitas, SR., et al. PREP-CHEM-SRC 1.0: a preprocessor of trace gas and aerosol emission fields for regional and global atmospheric chemistry models. Geosci. Model Devel., 4: 419-433, 2011.
Freitas SR, et al. The Brazilian developments on the Regional Atmospheric Modeling System (BRAMS 5.2): An integrated environmental model tuned for tropical areas. Geoscientific Model Development, 10(1): 189-222, 2017.
Gaveau DLA, et al. Major atmospheric emissions from peat fires in Southeast Asia during non-drought years: evidence from the 2013 Sumatran fires. Scientific Reports, 4, 2014.
GFEWS (Global Fire Early Warning System). Website of the Global Early Warning System for Wildland Fire online. <https://gfmc.online/fwf/EWS.html>. Accessed: 12/12/2009Goldammer JG (Ed.). 2013. Vegetation Fires and Global Change - Challenges for Concerted International Action. A White Paper directed to the United Nations and International Organizations. Kessel Publishing House. 400p.
Goldammer JG (Ed.). 2013. Vegetation Fires and Global Change - Challenges for Concerted International Action. A White Paper directed to the United Nations and International Organizations. Kessel Publishing House. 400p.
Grell GA, et al. Fully coupled "online" chemistry within the WRF model. Atmospheric Environment, 39: 6957-6975, 2005.
Guimarães BS, et al. Configuration and hindcast quality assessment of a Brazilian global subâ€seasonal prediction system. Quarterly Journal of the Royal Meteorological Society. 146: 1067-1084. 2020.
Gupta P, et al. Impact of California Fires on Local and Regional Air Quality: The Role of a Lowâ€Cost Sensor Network and Satellite Observations. GeoHealth, 2: 172-181, 2018.
Hansen MC, et al. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science. 342: 850-853, 2013.
Henderson SB, Brauer M, MacNab YC, Kennedy SM. Three Measures of Forest Fire Smoke Exposure and Their Associations with Respiratory and Cardiovascular Health Outcomes in a Population-Based Cohort. Environmental Health Perspectives, 119: 1266-1271, 2011.
Hertwig D, et al. Development and demonstration of a Lagrangian dispersion modeling system for real-time prediction of smoke haze pollution from biomass burning in Southeast Asia. Journal of Geophysical Research: Atmospheres, 120: 12605-12630, 2015.
Holben BN, et al. AERONET—A Federated Instrument Network and Data Archive for Aerosol Characterization. Remote Sensing of Environment. 66: 1-16, 1998.
Huijnen V, et al. Fire carbon emissions over maritime southeast Asia in 2015 largest since 1997. Scientific Reports, 6, 2016.
Inness A, et al. Data assimilation of satellite-retrieved ozone, carbon monoxide and nitrogen dioxide with ECMWF's Composition-IFS. Atmospheric Chemistry and Physics. 15: 5275-5303, 2015.
INPE (Instituto Nacional de Pesquisas Espaciais), 2020. INPE e Censipam discutem cooperação para aperfeiçoar o monitoramento da Amazônia. <http://www.inpe.br/noticias/noticia.php?Cod_Noticia=5425>. Accessed: 24/05/2020.
IWFC (International Wildland Fire Conference), 2015a. 6th International Wildland Fire Conference Pyeongchang Declaration "Fire Management and Sustainable Development". <https://gfmc.online/iwfc/korea-2015/IWFC-6-Conference-Declaration.pdf>. Accessed: 12/12/2015.
IWFC (International Wildland Fire Conference), 2015b. 6th International Wildland Fire Conference Conference Statement - Annex to the Conference Declaration. <https://gfmc.online/iwfc/korea-2015/IWFC-6-Conference-Statement.pdf>. Accessed: 12/12/2015.
Johnston FH, et al. Estimated Global Mortality Attributable to Smoke from Landscape Fires. Environmental Health Perspectives, 120: 695-701, 2012.
Kaiser JW, et al. Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power. Biogeosciences, 9: 527-554, 2012.
Kaiser JW and Keywood M. Preface for Atmospheric Environment Special issue on IBBI. Atmospheric Environment, 121: 1-3, 2015.
Karagulian F, et al. Review of the Performance of Low-Cost Sensors for Air Quality Monitoring. Atmosphere, 10: 506, 2019.
Koplitz SN, et al. health impacts of the severe haze in Equatorial Asia in September-October 2015: demonstration of a new framework for informing fire management strategies to reduce downwind smoke exposure. Environmental Research Letters, 11(9): 094023, 2016.
Lee S-Y, Gan C, Chew BN. Visibility deterioration and hygroscopic growth of biomass burning aerosols over a tropical coastal city: a case study over Singapore's airport. Atmospheric Science Letters. 17: 624-629, 2016.
Lelieveld J, Evans JS, Fnais M, Giannadaki D, Pozzer A. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature, 525: 367-371, 2015.
Lewis A, von Schneidemesser E, Peltier R. (Eds.) Low-cost sensors for the measurement of atmospheric composition: overview of topic and future applications valid as of May 2018. 2018. Chairperson, tions Board World Meteorological Organization (WMO). 68p.
Li S, Robertson AW. Evaluation of Submonthly Precipitation Forecast Skill from Global Ensemble Prediction Systems. Monthly Weather Review, 143: 2871-2889, 2015.
Longo KM, Freitas SR, Andreae MO, Setzer A, Prins E, Artaxo P. The Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS) - Part 2: Model sensitivity to the biomass burning inventories. Atmospheric Chemistry and Physics, 10: 5785-5795, 2010.
Marécal V, et al. A regional air quality forecasting system over Europe: the MACC-II daily ensemble production. Geoscientific Model Development. 8: 2777-2813, 2015.
Masunaga H, L'Ecuyer TS. The Southeast Pacific Warm Band and Double ITCZ. Journal of Climate, 23: 1189-1208, 2010.
Matz CJ, et al. Health impact analysis of PM2.5from wildfire smoke in Canada (2013-2015, 2017-2018). Sci Total Environ. 725: 138506, 2020.Morawska L, et al. Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone? Environment International, 116: 286-299, 2018.
Munoz-Alpizar R, et al. Multi-Year (2013-2016) PM2.5 Wildfire Pollution Exposure over North America as Determined from Operational Air Quality Forecasts. Atmosphere, 8: 179, 2017.
Pavlovic R, et al. The FireWork air quality forecast system with near-real-time biomass burning emissions: Recent developments and evaluation of performance for the 2015 North American wildfire season. Journal of the Air & Waste Management Association, 66: 819-841, 2016.
Pavlovic R, et al. Multi-model Air Quality Performance Analysis over North America for ECCC, NOAA/NWS and CAMS Operational Forecast Systems. <https://atmosphere.copernicus.eu/sites/default/files/2018-11/2_3rd_ECCC_NOAA_ECMWF_v06.pdf> .Acesso em: 16/10/2018.
Randerson JT, Chen Y, van der Werf GR, Rogers BM, Morton DC. Global burned area and biomass burning emissions from small fires. Journal of Geophysical Research: Biogeosciences, 117: G04012, 2012.
Regional Haze Action Plan. 1997. <https://cil.nus.edu.sg/wp-content/uploads/formidable/18/1997-Regional-Haze-Action-Plan.pdf>. Accessed: 15/07/2019.
Reid JS, et al. Multi-scale meteorological conceptual analysis of observed active fire hotspot activity and smoke optical depth in the Maritime Continent. Atmospheric Chemistry and Physics, 12: 2117-2147, 2012.
Reid JS, et al. Observing and understanding the Southeast Asian aerosol system by remote sensing: An initial review and analysis for the Seven Southeast Asian Studies (7SEAS) program. Atmospheric Research, 122: 403-468, 2013.
Reisen F, Duran SM, Flannigan M, Elliott C, Rideout K. Wildfire smoke and health risk. International Journal of Wildland Fire, 24: 1029, 2015.
Rienecker MM, et al. 2008. The GEOS-5 data assimilation system - Documentation of versions 5.0.1, 5.1.0, and 5.2.0. 27. Technical Report Series on Global Modeling and Data Assimilation, 118p.
Salinas SV, Chew BN, Liew SC. Retrievals of aerosol optical depth and Ångström exponent from ground-based Sun-photometer data of Singapore. Applied Optics, 48: 1473, 2009.
Schwela DH, Goldammer JG, Schwela LH, Simpson O. (eds.). 1999. Health guidelines for vegetation fire events. Institute of Environmental Epidemiology, Ministry of the Environment. Singapore. 21p.
Sessions WR, et al. Development towards a global operational aerosol consensus: basic climatological characteristics of the International Cooperative for Aerosol Prediction Multi-Model Ensemble (ICAP-MME). Atmospheric Chemistry and Physics, 15: 335-362, 2015.
Setiawan AM, Lee W-S, Rhee J. Spatio-temporal characteristics of Indonesian drought related to El Niño events and its predictability using the multi-model ensemble. International Journal of Climatology, 37: 4700-4719, 2017.
Setzer AW, Sismanoglu RA, dos Santos JGM, Método do Cálculo do Risco de Fogo do Programa do INPE - Versão 11, 2019 <http://queimadas.dgi.inpe.br/~rqueimadas/documentos/RiscoFogo_Sucinto.pdf>. Accessed: 01/10/2019
Shawki D, et al. Longâ€Lead Prediction of the 2015 Fire and Haze Episode in Indonesia. Geophysical Research Letters, 44: 9996, 2017.
Shi L, Hendon HH, Alves O, Luo J-J, Balmaseda M, Anderson D. How Predictable is the Indian Ocean Dipole? Monthly Weather Review, 140: 3867-3884, 2012.
Smirnov A, Holben BN, Eck TF, Dubovik O, Slutsker I. Cloud-Screening and Quality Control Algorithms for the AERONET Database. Remote Sensing of Environment, 73: 337-349, 2000.
Soares J, Sofiev M, Hakkarainen J. Uncertainties of wild-land fires emission in AQMEII phase 2 case study. Atmospheric Environment, 115: 361-370, 2015.
Sofiev M, et al. An operational system for the assimilation of the satellite information on wild-land fires for the needs of air quality modelling and forecasting. Atmospheric Chemistry and Physics, 9: 6833-6847, 2009.
Spessa AC, et al. Seasonal forecasting of fire over Kalimantan, Indonesia. Natural Hazards and Earth System Sciences, 15: 429-442, 2015.
Statheropoulos M, Karma S, Goldammer JG. 2013. Vegetation fire smoke emissions and human health. p. 239-249. In: Goldammer JG (Ed.). Vegetation Fires and Global Change - Challenges for Concerted International Action. A White Paper directed to the United Nations and International Organizations. Kessel Publishing House. 400 p.
Tacconi L. Preventing fires and haze in Southeast Asia. Nature Climate Change, 6: 640-643, 2016.
Tanaka TY, Orito K, Sekiyama TT, Shibata K, Chiba M, Tanaka H. MASINGAR, a global tropospheric aerosol chemical transport model coupled with MRI/JMA98 GCM: Model description. Papers in Meteorology and Geophysics, 53: 119-138, 2003.
Vitart F et al. The Subseasonal to Seasonal (S2S) Prediction Project Database. Bulletin of the American Meteorological Society, 98: 163-173, 2017.
Vitolo C, Di Giuseppe F, D'Andrea M. Caliver: An R package for CALIbration and VERification of forest fire gridded model outputs. PLoS ONE, 13(1): e0189419, 2018.
van der Werf GR, et al. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997-2009). Atmospheric Chemistry and Physics. 10: 11707-11735, 2010.
White CJ, et al. Potential applications of subseasonalâ€toâ€seasonal (S2S) predictions. Meteorological. Applications, 24: 315-325, 2017.
Wiedinmyer C, et al. The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning. Geoscientific Model Development, 4: 625-641, 2011.
WMO (World Meteorological Organization). Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS): Science and Implementation Plan 2015-2020. World Meteorological Organization. <https://library.wmo.int/doc_num.php?explnum_id=3383>. Accessed: 12/12/2015
WMO (World Meteorological Organization). Revised Manual on the Global Data-Processing and Forecasting System. <http://www.wmo.int/pages/prog/www/DPS/documents/Manual-GDPFS-Jul2017.pdf>. Accessed: 12/12/2017.
WMO (World Meteorological Organization). Vegetation Fire and Smoke Pollution Warning and Advisory System (VFSP-WAS): Concept Note and Expert recommendations (prepared by Goldammer JG et al.). <https://library.wmo.int/opac/index.php?lvl=notice_display&id=20244>. Accessed: 12/12/2018.
Wooster MJ, Roberts G, Perry GLW, Kaufman YJ. Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release. Journal of Geophysical Research, 110, 2005.
Xian P, Reid JS, et al. Current state of the global operational aerosol multiâ€model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP). Quarterly Journal of the Royal Meteorological Society, 145: 176-209, 2019.
Zhu J, Huang B, Kumar A, Kinter III JL. Seasonality in Prediction Skill and Predictable Pattern of Tropical Indian Ocean SST. Journal of Climate, 28: 7962-7984, 2015.
Zimmerman N, et al. A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring. Atmospheric Measurement Techniques, 11: 291-313, 2018.
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