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Heavy metal pollution has been a global concern and key points of environmental pollution prevention and control due to the growing problems of urbanization and industrialization. Rapidly and correctly apportioning sources of heavy metal is still a great challenge because of the stability of source fingerprint and complex interaction of multiple contaminants and sources. In this study, we perform a combination of optimization of pollution source fingerprint and source apportionment through jointly utilizing two machine classification and screening methods for characterizing the pollution sources of heavy metal in the sediments of an urban river and its surrounding soils. Dominance-based rough set model (DRS), content optimization tools, and multivariate curve resolution-alternating least squares model (MCR-WALS) were employed to screen representative pollution source samples, optimize pollution source fingerprint, and apportion the potential sources of heavy metals, respectively. Further, Support vector machine (SVM) was adopted to correspondence analysis results and pollution fingerprint based on the factor characteristics for achieving source apportionment accurately. Results showed that the pollution source pollution source fingerprints optimized by DRS and optimization tools are more representative and stable, and the results obtained by SVM and MCR-WALS are more accurate comparing with traditional methods. As whole, source apportionment suggested that printing and dyeing, chemical, electroplating, metal processing were the main origins of heavy metals in this area and the proportions of them in sediment and soil pollution sources were 67.05% and 28.43%, respectively. Besides, coal combustion was also the main sources of heavy metal pollution in soils, accounting about 34.16%. Results of the study can advance our knowledge to better understand the characterization of heavy metal pollution in the peri-urban ecosystem and to design effective targeted strategies for reducing heavy metal pollution diffusion. [Display omitted] • A combination of fingerprint screening and source apportionment was performed. • Characteristics and source-sink relationship of heavy metals in the river-soil system was studied. • Source apportionment was executed using MLR-WALS combined with SVM. • Multiple complex industrial activities were the dominant same type contributor of heavy metals in the river sediments. [ABSTRACT FROM AUTHOR]

Here, a new integrated methodology framework has been proposed for source apportionment and source-oriented risk evaluation, and applied to identify the characteristics, sources and health risks of PAHs in the soils of a typical cold region in Northeastern China. To this end, a large-scale data set containing 1780 soil samples and 16 priority PAHs has been collected from the study area. Two advanced receptor models, positive matrix factorization (PMF) and multivariate curve resolution-weighted alternating least-squares (MCR-WALS), have been comparatively employed to apportion the pollution sources of soil PAHs, with the help of a set of modified literature PAH source fingerprints. Further, the apportionment results have been incorporated into a probabilistic incremental lifetime cancer risk model for assessing the source-specific health risk of soil PAHs. Notably, the PMF and MCR-WALS models have apportioned essentially same results. The coal combustion and gasoline engine are identified as the main contributors of soil PAHs, with contributions of 57.9–58.1% and 25.2–22.2%, respectively. The health risks posed by PAHs in the soils are negligible for both adult and children; relatively, source-oriented risk assessment shows coal combustions make the largest contribution to the total risk of PAHs (56.1%), followed by gasoline engine (22.5%) and coke oven (21.4%). [Display omitted] • A new integrated framework has been proposed for characterizing sources and risks of PAHs. • Apportionment results have been used for assessing source-specific health risk of PAHs. • The MCR-WALS model has apportioned essentially same results with PMF. • Coal combustion, gasoline engine and coke oven are the main pollution sources of PAHs. • Coal combustion has made the largest contribution to the total risk of PAHs (56.1%). [ABSTRACT FROM AUTHOR]