源解析 – 用特征示踪物辨别PM2.5来源

准确定量判别特定时间和地点空气中的PM2.5来源是前沿性的科学研究课题。这类研究在大气环境科学领域被称作 “源解析”。源解析的英文是source apportionment,直译过来是源分担的意思。从源解析英文的意思,不难理解,所谓源解析就是通过科学手段解析出某种污染物,比如PM2.5的实 测浓度,是具体由那些种类的源排放分担贡献的,并且每种源贡献的比例是多少。

受体模型法(receptor models)是目前在大气环境科学领域的科学家用来进行PM2.5源解析的最重要的科学手段。简 单来说,受体模型法,就是在受体点位,即监测地点测量PM2.5浓度,以及PM2.5的详尽的组分浓度,即PM2.5中各无机离子(包括硫酸离子、硝酸离 子、铵离子等)、有机成分(黑炭和有机碳),以及金属和非金属元素的浓度。然后根据事先测量或获取的当地各主要源排放种类分别排放出的PM2.5的组分构 成比例(称之为PM2.5源排放廓线),建立一组以当地各主要PM2.5源排放对测量到的PM2.5浓度贡献比例为未知数的质量守恒方程组。最后科学家利用数学里的优化算法,加上各种测量误差作为限 制参数,解方程组得到这组未知数的最优解。

受 体模型法有一个很大的缺陷,那就是如果有两种源排放,它们的源排放廓线相似,那么受体模型法是无法区分这两种源的贡献比例的。一个补救办法是测量更多的PM2.5中的特征示踪物来进一步辨别PM2.5来源。例如,利用PM2.5中锌的含量可以揭示其是否来源于汽车尾气贡献,钾的含量可以示踪其是否来源于生物质燃烧。另外,还可以利用同位素碳14的分析,揭示PM2.5中有机碳成分是否来源于化石燃料(燃烧或者二次生成)还是来源于生物质燃烧或天然挥发性有机物的二次生成,而硒和锶则是燃煤污染的重要示踪物。

然而特征示踪物的测量并不是一件很容易的事情,原因是特征示踪物往往在空气中的含量很低,有的示踪物甚至在空气中并不处于稳定的状态,这些都导致特征示踪物不太容易准确测量。因此,在目前条件下,特征示踪物对受体模型用于PM2.5源解析的帮助仍然是有限的,这也限制了受体模型法的整体应用价值。

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