对泰国不同人为和生物质燃烧排放的 WRF-Chem PM2.5 模拟的评估

IF 3.8 Q2 ENVIRONMENTAL SCIENCES Atmospheric Environment: X Pub Date : 2024-08-01 DOI:10.1016/j.aeaoa.2024.100282
Worapop Thongsame , Daven K. Henze , Rajesh Kumar , Mary Barth , Gabriele Pfister
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引用次数: 0

摘要

泰国面临严重的空气质量问题,主要原因是 PM2.5 污染超过了世界卫生组织的标准。主要来源是能源生产、工业活动、车辆排放、农业焚烧和污染物的跨境运输。要解决空气质量问题,就必须了解这些污染物的迁移和转化。结合化学的天气研究和预测模型(WRF-Chem)提供了有关气象、化学反应以及痕量气体和气溶胶迁移的信息。WRF-Chem 模拟的准确性在很大程度上取决于人为和生物质燃烧排放清单的选择。本研究对这些清单进行了详细评估,以模拟 2019 年雾霾和非雾霾季节泰国的 PM2.5 浓度。我们使用四种人为排放清单--CAMS-GLOB-ANT、ECLIPSE、HTAP 和 REAS,以及四种生物质燃烧排放清单--FINN1.5、FINN2.5 MOD、FINN2.5 MODVAR 和 QFED,利用地面空气质量站数据、MODIS AOD 和 MOPITT CO 卫星数据,对 WRF-Chem 进行了评估。我们的研究结果表明,CAMS-GLOB-ANT 在泰国北部表现最佳,而 HTAP 和 REAS 在泰国东部更为有效。在生物质燃烧方面,FINN1.5 显示出卓越的性能。研究还强调了捕捉 PM2.5 日变化的挑战,特别是由于在复杂地形中模拟夜间行星边界层高度的不准确性。此外,我们的分析表明,在使用泰国的全球和区域人为排放物时,模型在非雾霾季节的表现一般,这强调了改进人为排放物清单以进行可靠的空气质量预测的必要性。对于生物质燃烧排放,建议更新排放因子以反映泰国的特定植被类型,从而改善 WRF-Chem 对 PM2.5 水平的表现。
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Evaluation of WRF-Chem PM2.5 simulations in Thailand with different anthropogenic and biomass-burning emissions

Thailand experiences severe air quality issues, predominantly due to PM2.5 pollution that surpasses WHO guidelines. The main sources are attributed to energy production, industrial activities, vehicular emissions, agricultural burning, and transboundary transport of pollutants. Understanding the transport and transformation of these pollutants is necessary for addressing air quality issues. The Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) provides information about meteorology, chemical reactions, and transport of trace gases and aerosols. The accuracy of WRF-Chem simulations greatly depends on the choice of anthropogenic and biomass burning emissions inventories. This study provides a detailed evaluation of these inventories to model PM2.5 concentrations in Thailand during both haze and off-haze seasons in 2019. We evaluated WRF-Chem using four anthropogenic emission inventories—CAMS-GLOB-ANT, ECLIPSE, HTAP, and REAS—and four biomass burning emissions inventories—FINN1.5, FINN2.5 MOD, FINN2.5 MODVAR, and QFED—using data from ground-based air quality stations, MODIS AOD, and MOPITT CO satellite data. Our findings suggest CAMS-GLOB-ANT performs optimally for North Thailand, while HTAP and REAS are more effective in Eastern Thailand. For biomass burning, FINN1.5 shows superior performance. The study also highlights the challenge in capturing PM2.5 diurnal variability, particularly due to inaccuracies in simulating the planetary boundary layer height during nighttime in complex terrains. Moreover, our analysis exhibits moderate model performances during the off-haze season while using global and regional anthropogenic emissions in Thailand, emphasizing the need for improving anthropogenic inventories for reliable air quality prediction. For biomass burning emissions, updating emission factors to reflect Thailand's specific vegetation types is recommended to improve WRF-Chem's representation of PM2.5 levels.

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来源期刊
Atmospheric Environment: X
Atmospheric Environment: X Environmental Science-Environmental Science (all)
CiteScore
8.00
自引率
0.00%
发文量
47
审稿时长
12 weeks
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