Centre of Atmospheric Environment Research, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China.
Centre of Atmospheric Environment Research, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China.
Environ Pollut. 2018 Oct;241:810-820. doi: 10.1016/j.envpol.2018.06.028. Epub 2018 Jun 14.
Six different approaches are applied in the present study to apportion the sources of precipitation nitrogen making use of precipitation data of dissolved inorganic nitrogen (DIN, including NO and NH), dissolved organic nitrogen (DON) and δN signatures of DIN collected at six sampling sites in the mountain region of Southwest China. These approaches include one quantitative approach running a Bayesian isotope mixing model (SIAR model) and five qualitative approaches based on in-situ survey (ISS), ratio of NH/NO (R), principal component analysis (PCA), canonical-correlation analysis (CCA) and stable isotope approach (SIA). Biomass burning, coal combustion and mobile exhausts in the mountain region are identified as major sources for precipitation DIN while biomass burning and volatilization sources such as animal husbandries are major ones for DON. SIAR model results suggest that mobile exhausts, biomass burning and coal combustion contributed 25.1 ± 14.0%, 26.0 ± 14.1% and 27.0 ± 12.6%, respectively, to NO on the regional scale. Higher contributions of both biomass burning and coal combustion appeared at rural and urban sites with a significant difference between Houba (rural) and the wetland site (p < 0.05). The R method fails to properly identify sources of DIN, the ISS and SIA approach only respectively identifies DON and DIN sources, the PCA only tracks source types for precipitation N, while the CCA identify sources of both DIN and DON in precipitation. SIAR quantified the contributions of major sources to precipitation NO but failed for precipitation NH and DON. It is recommended to use ISS and SIAR in combination with one or more approaches from PCA, CCA and SIA to apportion precipitation NO sources. As for apportioning precipitation NH sources, more knowledge is needed for local N databases of NH and DON and N fractional mechanisms among gaseous, liquid and particulate surfaces in this mountain region and similar environments.
本研究利用中国西南山区六个采样点采集的降水无机氮(DIN,包括 NO 和 NH)、溶解有机氮(DON)和 DINδN 特征值的降水数据,采用六种不同的方法来分配降水氮的来源。这些方法包括一种定量方法,即贝叶斯同位素混合模型(SIAR 模型),以及五种基于原位调查(ISS)、NH/NO 比(R)、主成分分析(PCA)、典范对应分析(CCA)和稳定同位素分析(SIA)的定性方法。山区的生物质燃烧、煤炭燃烧和移动尾气被确定为降水 DIN 的主要来源,而生物质燃烧和畜牧业等挥发源则是 DON 的主要来源。SIAR 模型结果表明,移动尾气、生物质燃烧和煤炭燃烧分别在区域尺度上对 NO 的贡献为 25.1±14.0%、26.0±14.1%和 27.0±12.6%。在农村和城市站点,生物质燃烧和煤炭燃烧的贡献更高,侯巴(农村)和湿地站点之间存在显著差异(p<0.05)。R 方法无法正确识别 DIN 的来源,ISS 和 SIA 方法分别只能识别 DON 和 DIN 的来源,PCA 只能跟踪降水 N 的源类型,而 CCA 可以识别降水 DIN 和 DON 的来源。SIAR 量化了主要来源对降水 NO 的贡献,但对降水 NH 和 DON 则不行。建议将 ISS 和 SIAR 与 PCA、CCA 和 SIA 中的一种或多种方法结合使用,以分配降水 NO 的来源。至于分配降水 NH 的来源,需要更多了解该山区和类似环境中 NH 和 DON 的本地 N 数据库以及气态、液态和颗粒表面之间的 N 分数机制方面的知识。