Identification of PM sources by principal component analysis (PCA) coupled with wind direction data.
Identification of PM sources by principal component analysis (PCA) coupled with wind direction data.
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DOI：
10.1016/j.chemosphere.2006.04.060
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发表时间：
2006-12
期刊：
影响因子：
8.8
通讯作者：
M. Viana;X. Querol;A. Alastuey;J. Gil;M. Menéndez
中科院分区：
文献类型：
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作者：
M. Viana;X. Querol;A. Alastuey;J. Gil;M. Menéndez
本研究验证了主成分分析（PCA）与多元线性回归（MLRA）及风向资料相结合的有效性。PM数据从三个粒度级在西班牙北方高度工业化的地区进行了分析。通过主成分分析确定了7个独立的PM源：钢铁（Pb、Zn、Cd、Mn）和颜料（Cr、Mo、Ni）制造业、道路灰尘（Fe、Ba、Cd）、交通尾气（P、OC+EC）、区域尺度输送（NH 4+、SO 42-、V）、地壳贡献（Al 2 O3、Sr、K）和海水喷淋（Na、Cl）。通过将主成分分析与风向数据相结合，获得了这些来源的空间分布，这有助于确定区域排水流是地壳物质的主要来源。同样的分析表明，高速公路交通对PM10水平的贡献比当地交通高4-5μgm− 3。PCA-MLRA与风向数据的耦合被证明是有用的，从而提取进一步的信息源的贡献和位置。PM源的正确识别和表征对于有效减排策略的设计和应用至关重要。
The effectiveness of combining principal component analysis (PCA) with multi-linear regression (MLRA) and wind direction data was demonstrated in this study. PM data from three grain-size fractions from a highly industrialised area in Northern Spain were analysed. Seven independent PM sources were identified by PCA: steel (Pb, Zn, Cd, Mn) and pigment (Cr, Mo, Ni) manufacture, road dust (Fe, Ba, Cd), traffic exhaust (P, OC+EC), regional-scale transport (NH4+, SO42-, V), crustal contributions (Al2O3, Sr, K) and sea spray (Na, Cl). The spatial distribution of the sources was obtained by coupling PCA with wind direction data, which helped identify regional drainage flows as the main source of crustal material. The same analysis showed that the contribution of motorway traffic to PM10 levels is 4–5μgm−3higher than that of local traffic. The coupling of PCA-MLRA with wind direction data proved thus to be useful in extracting further information on source contributions and locations. Correct identification and characterisation of PM sources is essential for the design and application of effective abatement strategies.