Particle size-based evaluation of stormwater control measures in reducing solids, polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs)

IF 12.4 1区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research Pub Date : 2025-02-14 DOI:10.1016/j.watres.2025.123299

César Gómez-Ávila , Balaji Rao , Tariq Hussain , Huayun Zhou , Robert Pitt , Molly Colvin , Nicholas Hayman , Mathew DeMyers , Danny Reible

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Abstract

This study evaluates the effectiveness of various stormwater control measures (SCMs) in removing polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs), both in dissolved forms and associated with different-sized solids. The SCMs evaluated include biofilters, a hybrid biofilter + media filter, a retention pond, and treatment trains with hydrodynamic separators and cartridge filters. The targeted particle size fractions were clay (0.7–2.7 µm), fine silt (2.7–20 µm), coarse silt (20–63 µm), and sand (>63 µm), along with their associated PAHs and PCBs. Samples were collected from multiple storm events at the inlets and outlets of these SCMs on current and former military bases in the Southwestern and Northwestern US. The study found that coarse particles (>20 µm) contained significantly higher fractions of organic carbon (f_oc), which correlated with higher concentrations of contaminants in these particulates. All SCMs effectively reduced particulate-bound contaminants, especially within the coarse particle fractions, but the removal of aqueous phase contaminants was generally minimal. Despite the overall effectiveness of the SCMs, maintenance challenges—such as biofilter erosion and insufficient cleanout of cartridge filters—can hinder their performance. The study highlights the importance of considering particle size and its relationship to contaminant distribution to comprehensively assess the performance of stormwater control measures and the potential for sediment recontamination.

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基于粒径的雨水控制措施在减少固体、多环芳烃和多氯联苯方面的评价

本研究评估了各种雨水控制措施（SCMs）去除多环芳烃（PAHs）和多氯联苯（PCBs）的有效性，包括溶解形式和与不同大小固体相关的多环芳烃（PAHs）。评估的SCMs包括生物过滤器、混合生物过滤器 + 介质过滤器、保留池以及带有水动力分离器和滤筒过滤器的处理系统。目标粒径为粘土（0.7-2.7µm）、细粉土（2.7-20µm）、粗粉土（20-63µm）和砂（63µm）及其相关的多环芳烃和多氯联苯。样本是从美国西南部和西北部现有和以前的军事基地的这些scm的入口和出口的多次风暴事件中收集的。研究发现，粗颗粒（>20 μ m）中有机碳（foc）的含量明显较高，这与这些颗粒中污染物的浓度较高有关。所有SCMs都能有效地减少颗粒结合的污染物，特别是在粗颗粒馏分中，但对水相污染物的去除通常是最小的。尽管SCMs的整体效率很高，但维护方面的挑战，如生物过滤器侵蚀和滤筒过滤器清洗不足，可能会阻碍其性能。该研究强调了考虑颗粒大小及其与污染物分布的关系对于综合评估雨水控制措施的效果和沉积物再污染的可能性的重要性。

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来源期刊

Water Research 环境科学-工程：环境

CiteScore

20.80

自引率

9.40%

发文量

1307

审稿时长

38 days

期刊介绍： Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.