湖泊生态系统中溶解性有机物(dissolved organic matter,DOM)来源复杂,不同污染源输入差异显著,并深刻影响着湖泊物质循环与生态功能。以洞庭湖为研究对象,利用傅里叶变换离子回旋共振质谱(Fourier transform ion cyclotron resonance m...湖泊生态系统中溶解性有机物(dissolved organic matter,DOM)来源复杂,不同污染源输入差异显著,并深刻影响着湖泊物质循环与生态功能。以洞庭湖为研究对象,利用傅里叶变换离子回旋共振质谱(Fourier transform ion cyclotron resonance mass spectrometry,FT-ICR MS)分子表征技术,结合主成分分析(principal component analysis,PCA)-绝对主成分分数(absolute principal component scores,APCS)-多元线性回归(multiple linear regression,MLR)受体模型,定量解析湖区外源DOM的分子特征及贡献。结果表明:湖水DOM以CHO化合物为主,枯水期富含含硫化合物,丰水期含氮化合物比例较高;2个季节的DOM均以高度不饱和类化合物为主,且丰水期DOM的芳香性和稳定性更强;污染源DOM整体不饱和度和芳香性较高,难以降解;受体模型定量结果显示,外源对DOM的贡献顺序为农田水(37.7%)>污水(26.7%)>鱼塘水(18.3%)>未知来源(17.3%)。研究揭示了农业面源污染和生活污水是洞庭湖DOM的主要输入来源,可为湖泊DOM迁移转化机制解析和流域污染治理提供科学依据。展开更多
Source apportionment of particulate matter （PM10） measurements taken in Delhi, India between January 2013 and June 2014 was carried out using two receptor models, principal component analysis with absolute principal...Source apportionment of particulate matter （PM10） measurements taken in Delhi, India between January 2013 and June 2014 was carried out using two receptor models, principal component analysis with absolute principal component scores （PCA/APCS） and UNMIX. The results were compared with previous estimates generated using the positive matrix factorization （PMF） receptor model to investigate each model＇s source-apportioning capability. All models used the PM10 chemical composition （organic carbon （OC）, elemental carbon （EC）, water soluble inorganic ions （WSIC）, and trace elements） for source apportionment. The average PM10 concentration during the study period was 249.7±103.9 μg/m3 （range： 61.4-584.8 μg/m3）. The UNMIX model resolved five sources （soil dust （SD）, vehicular emissions （VE）, secondary aerosols （SA）, a mixed source of biomass burning （BB） and sea salt （SS）, and industrial emissions （IE））. The PCA/APCS model also resolved five sources, two of which also included mixed sources （SD, VE, SD＋SS, （SA＋BB＋SS） and 1E）. The PMF analysis differentiated seven individual sources （SD, VE, SA, BB, SS, IE, and fossil fuel combustion （FFC））. All models identified the main sources contributing to PM10 emissions and reconfirmed that VE, SA, BB, and SD were the dominant contributors in Delhi.展开更多
文摘湖泊生态系统中溶解性有机物(dissolved organic matter,DOM)来源复杂,不同污染源输入差异显著,并深刻影响着湖泊物质循环与生态功能。以洞庭湖为研究对象,利用傅里叶变换离子回旋共振质谱(Fourier transform ion cyclotron resonance mass spectrometry,FT-ICR MS)分子表征技术,结合主成分分析(principal component analysis,PCA)-绝对主成分分数(absolute principal component scores,APCS)-多元线性回归(multiple linear regression,MLR)受体模型,定量解析湖区外源DOM的分子特征及贡献。结果表明:湖水DOM以CHO化合物为主,枯水期富含含硫化合物,丰水期含氮化合物比例较高;2个季节的DOM均以高度不饱和类化合物为主,且丰水期DOM的芳香性和稳定性更强;污染源DOM整体不饱和度和芳香性较高,难以降解;受体模型定量结果显示,外源对DOM的贡献顺序为农田水(37.7%)>污水(26.7%)>鱼塘水(18.3%)>未知来源(17.3%)。研究揭示了农业面源污染和生活污水是洞庭湖DOM的主要输入来源,可为湖泊DOM迁移转化机制解析和流域污染治理提供科学依据。
文摘Source apportionment of particulate matter （PM10） measurements taken in Delhi, India between January 2013 and June 2014 was carried out using two receptor models, principal component analysis with absolute principal component scores （PCA/APCS） and UNMIX. The results were compared with previous estimates generated using the positive matrix factorization （PMF） receptor model to investigate each model＇s source-apportioning capability. All models used the PM10 chemical composition （organic carbon （OC）, elemental carbon （EC）, water soluble inorganic ions （WSIC）, and trace elements） for source apportionment. The average PM10 concentration during the study period was 249.7±103.9 μg/m3 （range： 61.4-584.8 μg/m3）. The UNMIX model resolved five sources （soil dust （SD）, vehicular emissions （VE）, secondary aerosols （SA）, a mixed source of biomass burning （BB） and sea salt （SS）, and industrial emissions （IE））. The PCA/APCS model also resolved five sources, two of which also included mixed sources （SD, VE, SD＋SS, （SA＋BB＋SS） and 1E）. The PMF analysis differentiated seven individual sources （SD, VE, SA, BB, SS, IE, and fossil fuel combustion （FFC））. All models identified the main sources contributing to PM10 emissions and reconfirmed that VE, SA, BB, and SD were the dominant contributors in Delhi.
