Meteorology and emission sources are the two main factors determining concentrations of air pollutants, including fine particulate matter. A regional air quality modeling system was used to analyze the sources of fine-particulate air pollution in Hanoi, Vietnam, in December 2010. The impacts of precipitation and winds on PM_2.5 concentrations was investigated. Precipitation was negatively correlated with PM_2.5 concentrations. However, winds showed both positive and negative correlations with PM_2.5 concentrations, depending on wind direction (WD) and the level of upwind concentrations. Sensitivity simulations were conducted to investigate the contribution of local and non-local emissions sources on total PM_2.5 by perturbing the emission inputs of the model. Overall, local and non-local sources contributed equally to the total PM_2.5 in Hanoi. Local emission sources comprised 57% of the total PM_2.5 concentrations for the high PM_2.5 pollution levels, while only comprising 42% of the total PM_2.5 for low levels of PM_2.5 concentrations. In Hanoi’s urban areas, local sources contributed more to the total PM_2.5 than non-local sources. In contrast, non-local sources were the main contributors to the PM_2.5 in Hanoi’s rural areas. Additional sensitivity simulations were conducted to identify the main local emission sources of PM_2.5 concentrations in December 2010. The industrial and residential sectors collectively comprised 79% of the total PM_2.5 concentrations while the transport and power sectors comprised only 2% and 3%, respectively. This is the first case study which used a regional air quality modeling system to provide new and informative insights into PM_2.5 air pollution in Hanoi by estimating the contributions of local and non-local emissions sources, as well as the contribution of local emission sectors to PM_2.5 concentrations in Hanoi.

中文翻译：

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河内细颗粒物（PM2.5）控制因素的空气质量建模研究：以2010年12月为例

气象和排放源是确定包括细颗粒物在内的空气污染物浓度的两个主要因素。2010年12月，使用区域空气质量建模系统分析了越南河内的细颗粒空气污染源。研究了降水和风对PM _2.5浓度的影响。降水与PM _2.5浓度呈负相关。然而，取决于风向（WD）和上风浓度水平，风与PM _2.5浓度呈正相关和负相关。进行了敏感性模拟以调查本地和非本地排放源对总PM _2.5的贡献通过扰动模型的排放输入。总体而言，当地和非本地来源对河内PM _2.5的贡献均相等。对于高PM _2.5污染水平，本地排放源占总PM _2.5浓度的57％，而对于低PM _2.5浓度仅占总PM _2.5的42％。在河内市区，本地来源对PM _2.5的总贡献比非本地来源更多。相反，河内农村地区PM _2.5的主要来源是非本地来源。进行了额外的灵敏度模拟，以确定PM _2.5的主要局部排放源在2010年12月，工业和住宅部门的总浓度为PM _2.5的79％。而交通和电力部门分别仅占2％和3％。这是第一个案例研究，它使用区域空气质量模型系统通过估算本地和非本地排放源的贡献以及本地排放部门对河内PM _2.5空气污染的影响，提供了新的和有益的见解。河内的PM _2.5浓度。