• 关键词：昆山市  PM_2.5  水溶性无机离子  主成分分析(PCA)  源解析

• 基金项目：江苏省青年基金（No.BK20150909）；南京土壤研究所开放基金（No.Y20160038）；中国博士后科学基金（No.2016M591884）

• 吕青
• 1. 南京信息工程大学气象灾害预报和评估协同创新中心, 南京 210044;2. 南京信息工程大学江苏省农业气象重点实验室, 南京 210044;3. 中国气象局交通气象重点开放实验室, 南京 210009

• 包云轩
• 1. 南京信息工程大学气象灾害预报和评估协同创新中心, 南京 210044;2. 南京信息工程大学江苏省农业气象重点实验室, 南京 210044;3. 中国气象局交通气象重点开放实验室, 南京 210009

• 陈粲
• 1. 南京信息工程大学气象灾害预报和评估协同创新中心, 南京 210044;2. 南京信息工程大学江苏省农业气象重点实验室, 南京 210044;3. 中国气象局交通气象重点开放实验室, 南京 210009

• 汪婷
• 昆山市气象局, 昆山 215300

• 吴俊梅
• 昆山市气象局, 昆山 215300

• 唐倩
• 1. 南京信息工程大学气象灾害预报和评估协同创新中心, 南京 210044;2. 南京信息工程大学江苏省农业气象重点实验室, 南京 210044;3. 中国气象局交通气象重点开放实验室, 南京 210009

• 季群
• 1. 南京信息工程大学气象灾害预报和评估协同创新中心, 南京 210044;2. 南京信息工程大学江苏省农业气象重点实验室, 南京 210044;3. 中国气象局交通气象重点开放实验室, 南京 210009

• 摘要：为了探明昆山市不同污染条件下PM_2.5中水溶性无机离子的污染特征以及本地源排放占主导时对污染过程的贡献，本研究使用昆山市2017年3月—2018年2月期间PM_2.5、水溶性无机离子及其气态前体物数据，分别探讨了水溶性无机离子及其气态前体物在污染天气和清洁天气情况下的变化特征，揭示了它们在污染天气和清洁天气下的变化机制.同时结合周围城市PM_2.5浓度筛选出昆山市秋、冬季局地污染事件，利用主成分分析（principle component analysis，PCA）方法对筛选出的局地污染事件中的水溶性无机离子数据进行了来源解析，定量评估了本地源排放占主导时不同水溶性无机离子对灰霾污染事件过程中PM_2.5浓度的贡献.结果表明：①SO₄^2-、NO₃^-、NH₄⁺（合称SNA）是PM_2.5的重要组分，且其相对贡献随着大气污染加重而变化.3种离子在清洁和污染条件下对PM_2.5的相对贡献分别是49.4%~62.3%和52.7%~65.9%.在3种主要的水溶性无机离子中，NO₃^-浓度最高，其次是SO₄^2-和NH₄⁺.随着污染加重，SO₄^2-的贡献率下降，而NO₃^-的贡献率上升.②污染天气下3种离子日变化规律不同，且存在明显季节差异.其中秋冬季SO₄^2-和NH₄⁺与各自气态前体物变化趋势一致且为单峰型；NO₃^-为单峰型而其前体物则为双峰型.另外，NO₃^-与NH₄⁺日变化趋势较为一致，表明昆山地区SNA多以NH₄NO₃形式存在.③2017—2018年秋冬季由本地源排放占主导的污染天气下，PM_2.5的主要来源是二次气粒转化、建筑扬尘、生物质燃烧和燃煤；除了Mg²⁺和Ca²⁺，其他水溶性离子浓度均低于非本地源排放占主导的污染天气下的浓度.

• Abstract：One-year observational data of PM_2.5, water-soluble inorganic ions, gaseous precursors observed at Kunshan from March 2017 to February 2018 were presented to characterize daily and seasonal variation patterns of water-soluble inorganic ions of PM_2.5 under different pollution conditions. The principle component analysis (PCA) was then used to identify individual sources that were responsible for water-soluble inorganic ions under the local-source dominated air pollution events during fall and winter of 2017—2018. Three major findings were identified from this study. First, SO₄^2-,NO₃^-,NH₄⁺(SNA) were the three major contributors to PM_2.5, with the percentage contributions of 49.4%~62.3% and 52.7%~65.9% for the clean and pollution conditions, respectively. Among the three major water-soluble inorganic ions, NO₃^- had the highest concentrations, followed by SO₄^2- and NH₄⁺. Percentage contribution of SO₄^2- decreased whereas that of NO₃^- increased as haze pollution became heavier. Second, the three water-soluble inorganic ions of PM_2.5 showed different diurnal variation patterns with a large seasonal discrepancy under polluted conditions. Both SO₄^2- and NH₄⁺ showed unimodal diurnal variation patterns which were similar to their respective gaseous precursors in autumn and winter. NO₃^- showed an unimodal diurnal variation pattern whereas its precursor displayed bimodal pattern. Similar diurnal variation trends of NO₃^- and NH₄⁺ indicated that NH₄NO₃ accounted for a large portion of SNA in Kunshan and surrounding areas. Third, gas-to-particle conversion, construction dust, biomass burning, and coal consumption were the three major contributors to the local sources dominated PM_2.5 pollution events in fall and winter from 2017 to 2018. It is noticed that the local sources dominated PM_2.5 pollution events had lower water-soluble inorganic ions than non-local sources dominated events except for Mg²⁺ and Ca²⁺ in Kunshan and surrounding areas.

• Key words：Kunshan  PM_2.5  water-soluble inorganic ions  principal component analysis (PCA)  source analysis

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