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Journals / Atmospheric Pollution Research / 2017 / Cilt: 8 - Sayı: 5

Assessment of fire emission inventories for simulating particulate matter in Upper Southeast Asia using WRF-CMAQ

Patipat Vongruang
Prungchan Wongwises
Sittichai Pimonsree
Pages
921–929
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Fire emission estimations require improved specific calculation parameters (i.e. burned area and fuel loading factor for FINN, conversion factor to estimate dry matter burned from fire radiative power for GFAS, emission factors and plume height) to accurately estimate biomass burning emissions for the U-SEA region.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Biomass burning is a major source of particulate matter pollution in Upper Southeast Asia (U-SEA), and accurate fire emissions estimates are of great importance in predicting air quality.
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Key Findings & Data:This study investigates a biomass burning episode that occurred in March 2012.
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Conclusion & Impact:Fire emission estimations require improved specific calculation parameters (i.e. burned area and fuel loading factor for FINN, conversion factor to estimate dry matter burned from fire radiative power for GFAS, emission factors and plume height) to accurately estimate biomass burning emissions for the U-SEA region.
🔬Design:Modeling & Simulation
📍Scope:2012 Verileri
⚙️Variables:Domain Parameters

Özet

Biomass burning is a major source of particulate matter pollution in Upper Southeast Asia (U-SEA), and accurate fire emissions estimates are of great importance in predicting air quality. In this work, we assess two global fire emission inventories, the Fire INventory from NCAR (FINN) and the Global Fire Assimi- lation System (GFAS), using the WRF-CMAQ modeling system to simulate aerosol in U-SEA. Simulated particulate matter is compared to observational data from satellites and ground measurements. This study investigates a biomass burning episode that occurred in March 2012. Both emission inventories show similar temporal and spatial variations in PM; however, the quantities of total PM10 emissions differ greatly between FINN and GFAS, with an approximate fivefold ratio of FINN/GFAS. In the source region, simulated PM10 using FINN (PM10FINN) is greatly overestimated, whereas simulated PM10 using GFAS (PM10GFAS) is considerably underestimated. The monthly PM10 averages of PM10FINN, PM10GFAS and observed PM10 in Northern Thailand are 312, 79 and 143 mg/m3, respectively. This study found considerable uncertainties in both FINN and GFAS emission inventories in U-SEA, and these uncertainties were analyzed in this paper. Fire emission estimations require improved specific calculation parameters (i.e. burned area and fuel loading factor for FINN, conversion factor to estimate dry matter burned from fire radiative power for GFAS, emission factors and plume height) to accurately estimate biomass burning emissions for the U-SEA region.

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Cite This Article

Vongruang, P., Wongwises, P., Pimonsree, S. (2017). Assessment of fire emission inventories for simulating particulate matter in Upper Southeast Asia using WRF-CMAQ. Atmospheric Pollution Research, Cilt: 8 - Sayı: 5, 921–929. https://ajindex.com/articles/216970

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References (1)

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