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一种用于识别地中海港口基于风险的管理中重金属和多环芳烃污染源的方法。

A Method for Identifying Pollution Sources of Heavy Metals and PAH for a Risk-Based Management of a Mediterranean Harbour.

作者信息

Paladino Ombretta, Moranda Arianna, Seyedsalehi Mahdi

机构信息

Department of Civil, Chemical and Environmental Engineering, Università di Genova, Via Opera Pia 15, 16145 Genova, Italy.

Environmental Chemical Processes Lab, Università di Genova, Savona Campus, Via Magliotto 2, 17100 Savona, Italy.

出版信息

Scientifica (Cairo). 2017;2017:4690715. doi: 10.1155/2017/4690715. Epub 2017 Nov 14.

DOI:10.1155/2017/4690715
PMID:29270328
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5706084/
Abstract

A procedure for assessing harbour pollution by heavy metals and PAH and the possible sources of contamination is proposed. The procedure is based on a ratio-matching method applied to the results of principal component analysis (PCA), and it allows discrimination between point and nonpoint sources. The approach can be adopted when many sources of pollution can contribute in a very narrow coastal ecosystem, both internal and outside but close to the harbour, and was used to identify the possible point sources of contamination in a Mediterranean Harbour (Port of Vado, Savona, Italy). 235 sediment samples were collected in 81 sampling points during four monitoring campaigns and 28 chemicals were searched for within the collected samples. PCA of total samples allowed the assessment of 8 main possible point sources, while the refining ratio-matching identified 1 sampling point as a possible PAH source, 2 sampling points as Cd point sources, and 3 sampling points as C > 12 point sources. By a map analysis it was possible to assess two internal sources of pollution directly related to terminals activity. The study is the prosecution of a previous work aimed at assessing Savona-Vado Harbour pollution levels and suggested strategies to regulate the harbour activities.

摘要

本文提出了一种评估港口重金属和多环芳烃污染及其可能污染源的方法。该方法基于主成分分析(PCA)结果的比值匹配法,能够区分点源和面源。当多种污染源可能对非常狭窄的沿海生态系统(包括港口内部以及靠近港口的外部区域)产生影响时,可采用此方法。本文利用该方法确定了地中海港口(意大利萨沃纳的瓦多港)可能的点污染源。在四次监测活动中,于81个采样点采集了235份沉积物样本,并对采集样本中的28种化学物质进行了检测。对所有样本进行主成分分析,确定了8个主要可能点源,而通过精细比值匹配法,确定1个采样点可能是多环芳烃污染源,2个采样点可能是镉污染源,3个采样点可能是碳含量大于12的污染源。通过地图分析,评估出两个与码头活动直接相关的内部污染源。本研究是之前一项旨在评估萨沃纳 - 瓦多港污染水平及提出港口活动监管策略的工作的延续。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/5706084/f6c9511ee791/SCIENTIFICA2017-4690715.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/5706084/2b416fa6bb57/SCIENTIFICA2017-4690715.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/5706084/0cb6c512b7b3/SCIENTIFICA2017-4690715.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/5706084/253f6b97a578/SCIENTIFICA2017-4690715.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/5706084/6f50fbaa25d0/SCIENTIFICA2017-4690715.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/5706084/df78e3720cbd/SCIENTIFICA2017-4690715.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/5706084/1c6a5ed43ef5/SCIENTIFICA2017-4690715.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/5706084/ce0b95ceef65/SCIENTIFICA2017-4690715.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/5706084/1d09efa2673a/SCIENTIFICA2017-4690715.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/5706084/f6c9511ee791/SCIENTIFICA2017-4690715.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/5706084/2b416fa6bb57/SCIENTIFICA2017-4690715.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/5706084/0cb6c512b7b3/SCIENTIFICA2017-4690715.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/5706084/253f6b97a578/SCIENTIFICA2017-4690715.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/5706084/6f50fbaa25d0/SCIENTIFICA2017-4690715.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/5706084/df78e3720cbd/SCIENTIFICA2017-4690715.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/5706084/1c6a5ed43ef5/SCIENTIFICA2017-4690715.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/5706084/ce0b95ceef65/SCIENTIFICA2017-4690715.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/5706084/1d09efa2673a/SCIENTIFICA2017-4690715.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/5706084/f6c9511ee791/SCIENTIFICA2017-4690715.009.jpg

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