Importance of transboundary transport of biomass burning emissions to regional air quality in Southeast Asia during a high fire event
Importance of transboundary transport of biomass burning emissions to regional air quality in Southeast Asia during a high fire event
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DOI：
10.5194/acp-15-363-2015
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发表时间：
2015-01-01
影响因子：
6.3
通讯作者：
Betha, R.
中科院分区：
文献类型：
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作者：
Aouizerats, B.;van der Werf, G. R.;Betha, R.
生物质和泥炭燃烧产生的烟雾对东南亚地区的环境空气质量和气候有显著影响。我们使用天气研究和预报模型结合化学（WRF-Chem）对2006年主要来自印度尼西亚的大规模火灾引发的雾霾事件进行了建模。我们专注于火羽流的组成和它的相互作用与城市国家的新加坡的城市化地区的演变，并对模拟和测量的气溶胶和一氧化碳（CO）浓度的比较。两个模拟运行WRF-Chem使用复杂的挥发性基集（VBS）计划再现初级和次级气溶胶的演变和浓度。第一个模拟称为WRF-FIRE，包括来自全球火灾排放数据库（GFED 3）的人为、生物源和生物质燃烧排放，而第二个模拟称为WRF-NOFIRE，没有生物质燃烧排放。为了测试模型的性能，我们使用了三个独立的数据集进行比较，包括空气中测量的颗粒物（PM）的直径为10 μ m或更小（PM10）在新加坡，CO测量在苏门答腊岛，气溶胶光学厚度（AOD）柱观测从四个卫星传感器。我们发现合理的协议之间的模型运行和基于地面的测量CO和PM10。与AOD的比较是不太有利的，并表明该模型低估了AOD，虽然不同的卫星数据集之间的不匹配程度不同。在我们的研究期间，苏门答腊的森林和泥炭火灾是新加坡区域输送气溶胶浓度增强的主要原因。生物质燃烧羽流的分析表明，高浓度的初级有机气溶胶（POA）的值高达600 μ g m（-3）以上的火灾地点。POA的浓度保持相当稳定的主要燃烧区和新加坡之间的羽流，而二次有机气溶胶（SOA）的浓度略有增加。然而，SOA的绝对浓度（高达20 μ g m（-3））远低于POA，表明SOA在这些生物质燃烧羽流中的作用很小。我们的研究结果表明，在新加坡的7月至10月的研究期间，环境PM10的总质量负荷的约21%是由于生物质和泥炭燃烧在苏门答腊岛，但这种贡献在高燃烧期间增加。总的来说，我们的模型结果表明，在35天的气溶胶浓度在新加坡高于50 μ g m（-3）天1的阈值，表明空气质量差。在17天内，这是由于火灾，根据有和没有火灾的模拟之间的差异。当地污染加上空气质量的再循环可能是其他18天空气质量差的主要原因，尽管苏门答腊岛和加里曼丹（婆罗洲岛的印度尼西亚部分）的火灾可能也增加了PM10浓度。模型与测量结果的比较突出表明，在我们的研究期间和区域，GFED 3生物质燃烧气溶胶排放量比其他研究中发现的更符合观测结果。这表明在使用AOD来限制排放或估计地面空气质量时应小心。这项研究还表明，需要相对高分辨率的建模，以准确地再现必要的空气质量的平流量化的影响和反馈区域空气质量。
Smoke from biomass and peat burning has a notable impact on ambient air quality and climate in the Southeast Asia (SEA) region. We modeled a large fire-induced haze episode in 2006 stemming mostly from Indonesia using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem). We focused on the evolution of the fire plume composition and its interaction with the urbanized area of the city state of Singapore, and on comparisons of modeled and measured aerosol and carbon monoxide (CO) concentrations. Two simulations were run with WRF-Chem using the complex volatility basis set (VBS) scheme to reproduce primary and secondary aerosol evolution and concentration. The first simulation referred to as WRF-FIRE included anthropogenic, biogenic and biomass burning emissions from the Global Fire Emissions Database (GFED3) while the second simulation referred to as WRF-NOFIRE was run without emissions from biomass burning. To test model performance, we used three independent data sets for comparison including airborne measurements of particulate matter (PM) with a diameter of 10 mu m or less (PM10) in Singapore, CO measurements in Sumatra, and aerosol optical depth (AOD) column observations from four satellite-based sensors. We found reasonable agreement between the model runs and both ground-based measurements of CO and PM10. The comparison with AOD was less favorable and indicated the model underestimated AOD, although the degree of mismatch varied between different satellite data sets. During our study period, forest and peat fires in Sumatra were the main cause of enhanced aerosol concentrations from regional transport over Singapore. Analysis of the biomass burning plume showed high concentrations of primary organic aerosols (POA) with values up to 600 mu g m(-3) over the fire locations. The concentration of POA remained quite stable within the plume between the main burning region and Singapore while the secondary organic aerosol (SOA) concentration slightly increased. However, the absolute concentrations of SOA (up to 20 mu g m(-3)) were much lower than those from POA, indicating a minor role of SOA in these biomass burning plumes. Our results show that about 21% of the total mass loading of ambient PM10 during the July-October study period in Singapore was due to biomass and peat burning in Sumatra, but this contribution increased during high burning periods. In total, our model results indicated that during 35 days aerosol concentrations in Singapore were above the threshold of 50 mu g m(-3) day 1 indicating poor air quality. During 17 days this was due to fires, based on the difference between the simulations with and without fires. Local pollution in combination with recirculation of air masses was probably the main cause of poor air quality during the other 18 days, although fires from Sumatra and probably also from Kalimantan (Indonesian part of the island of Borneo) added to the enhanced PM10 concentrations. The model versus measurement comparisons highlighted that for our study period and region the GFED3 biomass burning aerosol emissions were more in line with observations than found in other studies. This indicates that care should be taken when using AOD to constrain emissions or estimate ground-level air quality. This study also shows the need for relatively high resolution modeling to accurately reproduce the advection of air masses necessary to quantify the impacts and feedbacks on regional air quality.