Water Research X最新文献_第3页

In-sewer iron dosing enhances bioenergy recovery in downstream sewage sludge anaerobic digestion: The impact of iron salt types and thermal hydrolysis pretreatment 污水管内加铁可提高下游污水污泥厌氧消化的生物能回收率：铁盐类型和热水解预处理的影响

IF 7.2 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research X

Pub Date : 2024-10-29 DOI: 10.1016/j.wroa.2024.100273

Jingya Xu , Yizhen Wang , Yanzhao Wang , Lai Peng , Yifeng Xu , Hailong Yin , Bin Dong , Xiaohu Dai , Jing Sun

Dosing iron salts is a widely adopted strategy for sewer odor and corrosion management, and it can affect bioenergy recovery during anaerobic digestion (AD) of sludge in downstream wastewater treatment plants. However, the different impacts of in-sewer iron salt dosing on AD, depending on the types of iron and digestion conditions, remain unclear. Therefore, this study investigated the impact of in-sewer ferrous (Fe(II)) and ferrate (Fe(VI)) dosing on bioenergy recovery in both conventional AD and AD with thermal hydrolysis pretreatment (THP). The results showed that in-sewer Fe(VI) dosing notably enhanced methane production in AD more than in-sewer Fe(II) dosing, with cumulative methane yields of 197.1±1.9 mLCH₄∙gVSadded⁻¹ for Fe(VI) and 186.5±10.4 mLCH₄∙gVSadded⁻¹ for Fe(II), respectively. Microbial analyses and iron particle characterizations suggested that the superior promotion with Fe(VI) dosing may be attributed to the smaller particle sizes and higher iron oxide content of Fe(VI) resultant products. This led to a greater enhancement in direct interspecies electron transfer (DIET) between syntrophic bacteria and methanogens, as indicated by the upregulation of Methanosaeta and key functional genes involved in CO₂-utilizing methanogenesis. Additionally, in THP-AD, the methane production enhancement caused by in-sewer iron dosing (35.5 mLCH₄∙gVSadded⁻¹) exceeded that in conventional AD (26.9 mLCH₄∙gVSadded⁻¹), although organic degradation during THP was unaffected. As THP-AD gains popularity for improved bioenergy recovery from sludge, our findings suggest that in-sewer iron dosing supports this advancement. Furthermore, in-sewer Fe(VI) dosing appears more promising within integrated wastewater management strategies, facilitating energy- and carbon-neutralization of urban water systems.

投加铁盐是下水道臭味和腐蚀管理中广泛采用的一种策略，而且会影响下游污水处理厂污泥厌氧消化（AD）过程中的生物能回收。然而，根据铁的类型和消化条件的不同，下水道内投加铁盐对厌氧消化的不同影响仍不清楚。因此，本研究调查了污水中加入亚铁（Fe(II)）和铁酸盐（Fe(VI)）对传统厌氧消化（AD）和热水解预处理（THP）厌氧消化（AD）中生物能源回收的影响。结果表明，污水中添加铁（VI）比添加铁（II）更显著地提高了厌氧消化（AD）中的甲烷产量，铁（VI）和铁（II）的累计甲烷产量分别为 197.1±1.9 mLCH4∙gVSadded-1 和 186.5±10.4 mLCH4∙gVSadded-1。微生物分析和铁颗粒特征表明，加入 Fe（VI）后，促进作用更强，这可能是由于加入 Fe（VI）后的产物颗粒更小，氧化铁含量更高。这导致合成细菌和甲烷菌之间的直接种间电子传递（DIET）得到了更大的增强，Methanosaeta 和参与二氧化碳利用甲烷生成的关键功能基因的上调表明了这一点。此外，在 THP-AD 中，尽管 THP 过程中的有机物降解未受影响，但污水中加入铁元素（35.5 mLCH4∙gVSadded-1 ）可提高甲烷产量，超过传统 AD（26.9 mLCH4∙gVSadded-1 ）。随着 THP-AD 在提高污泥生物能回收率方面的普及，我们的研究结果表明，污水中铁的投加有助于这一进步。此外，在废水综合管理战略中，污水中添加铁（VI）似乎更有前景，可促进城市水系统的能源和碳中和。

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引用次数: 0

Efficient and sustainable removal of linear alkylbenzene sulfonate in a membrane biofilm: Oxygen supply dosage impacts mineralization pathway 在膜生物膜中高效、可持续地去除线性烷基苯磺酸盐：供氧量对矿化途径的影响

IF 7.2 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research X

Pub Date : 2024-10-24 DOI: 10.1016/j.wroa.2024.100268

Ting Wei, Ting Ran, Weikang Rong, Yun Zhou

Linear alkylbenzene sulfonate (LAS) can be thoroughly mineralized within sufficient oxygen (O₂), but which is energy intensive and may causes serious foaming problem. Although cometabolism can achieve efficient LAS removal within a wide range of O₂ dosages, how O₂ dosage systematically affects LAS metabolic pathway is still unclear. Here, membrane aerated biofilm reactor (MABR) enabled accurate O₂ delivery and bulk dissolved oxygen (DO) control. MABR achieved efficient removal of LAS (>96.4 %), nitrate (>97.8 %) and total nitrogen (>96.2 %) at the three target DO conditions. At high DO condition (0.6 mg/L), LAS was efficiently removed by aerobic mineralization (predominant) coupled with aerobic denitrification biodegradation with the related functional enzymes. Pseudomonas, Flavobacterium, Hydrogenophaga, and Pseudoxanthomonas were dominant genus contributing to four possible LAS aerobic metabolic pathways. As O₂ dosage reduced to only 29.7 % of the demand for LAS mineralization, O₂ facilitated LAS activation, benzene-ring cleavage and a portion of respiration. NO₃^--N respiration-induced anaerobic denitrification also contributed to ring-opening and organics mineralization. Desulfomicrobium and Desulfonema related two possible anaerobic metabolic pathways also contributed to LAS removal. The findings provide a promising strategy for achieving low-cost high LAS-containing greywater treatment.

线性烷基苯磺酸盐（LAS）可以在充足的氧气（O2）条件下被彻底矿化，但这需要消耗大量能量，而且可能导致严重的泡沫问题。虽然在氧气用量较宽的范围内，彗星代谢可以实现高效去除 LAS，但氧气用量如何系统地影响 LAS 的代谢途径仍不清楚。在这里，膜充气生物膜反应器（MABR）实现了精确的氧气输送和大量溶解氧（DO）控制。在三个目标溶解氧条件下，膜充气生物膜反应器都能高效去除 LAS（96.4%）、硝酸盐（97.8%）和总氮（96.2%）。在高溶解氧条件下（0.6 毫克/升），LAS 通过好氧矿化（占主导地位）和好氧反硝化生物降解以及相关功能酶的作用被有效去除。假单胞菌、黄杆菌、嗜氢单胞菌和假黄单胞菌是主要的菌属，构成了四种可能的 LAS 好氧代谢途径。由于氧气用量仅占 LAS 矿化所需量的 29.7%，氧气促进了 LAS 的活化、苯环裂解和部分呼吸作用。NO3--N呼吸诱导的厌氧反硝化作用也有助于开环和有机物矿化。脱硫微生物（Desulfomicrobium）和脱硫水藻（Desulfonema）这两种可能的厌氧代谢途径也有助于去除 LAS。这些研究结果为实现低成本高含 LAS 中水处理提供了一种前景广阔的策略。

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引用次数: 0

Water demand forecasting in multiple district metered areas based on a multi-scale correction module neural network architecture 基于多尺度校正模块神经网络架构的多区计量区域用水需求预测

IF 7.2 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research X

Pub Date : 2024-10-22 DOI: 10.1016/j.wroa.2024.100269

Qidong Que , Jinliang Gao , Yizhou Qian

Short-term water demand forecasting (STWDF) for multiple spatially and temporally correlated District Metering Areas (DMAs) is an essential foundation for achieving more refined management of urban water supply networks. However, due to the greater uncertainty associated with specific DMA demand compared to overall water usage, accurately predicting STWDF poses significant challenges. This study introduces an innovative network architecture—the multi-scale correction module neural network, built upon Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN) enhanced with Attention mechanisms—for simultaneous STWDF with a temporal resolution of one hour over a week for 10 DMAs located in a single city in northern Italy. This framework utilizes multivariate corrections to refine and enhance the output accuracy. The results reveal that, in comparison to traditional Gated Recurrent Unit or LSTM models, the proposed model with integrated correction modules, particularly those that leverage inter-DMA correlations, improves performance across all evaluation metrics by an average of 5 %-20 % per DMA. Additionally, it consistently delivers superior accuracy across three scenarios: single DMA forecasting, total water demand, and extreme conditions, while maintaining stable performance throughout. Furthermore, the interpretability analysis underscores the feasibility of this innovative structure and highlights the contribution of meteorological features to the predictive model in some DMA-level STWDF. The unified input-output framework elegantly simplifies the STWDF process across multiple DMAs, providing new insights and methodologies for future research in this domain.

对多个空间和时间上相关的地区计量区域（DMA）进行短期需水预测（STWDF）是实现城市供水管网精细化管理的重要基础。然而，由于与总体用水量相比，特定地区计量区域需求具有更大的不确定性，因此准确预测 STWDF 面临着巨大挑战。本研究引入了一种创新的网络架构--多尺度校正模块神经网络，该架构建立在长短期记忆（LSTM）网络和卷积神经网络（CNN）基础上，并增强了注意机制，可同时预测意大利北部一个城市中 10 个 DMA 一周内一小时的 STWDF 时间分辨率。该框架利用多变量修正来完善和提高输出精度。研究结果表明，与传统的门控递归单元或 LSTM 模型相比，所提出的模型集成了校正模块，特别是那些利用 DMA 间相关性的模块，在所有评估指标中平均每个 DMA 的性能提高了 5%-20% 。此外，该模型在单个 DMA 预测、总需水量和极端条件这三种情况下都能持续提供卓越的准确性，并在整个过程中保持稳定的性能。此外，可解释性分析强调了这一创新结构的可行性，并突出了气象特征对某些 DMA 级 STWDF 预测模型的贡献。统一的输入输出框架优雅地简化了多个 DMA 的 STWDF 流程，为该领域的未来研究提供了新的见解和方法。

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引用次数: 0

Near-Complete Phosphorus Recovery from Challenging Water Matrices Using Multiuse Ceramsite Made from Water Treatment Residual (WTR) 利用水处理剩余物 (WTR) 制成的多用途铈镧石从具有挑战性的水基质中近乎完全地回收磷

IF 7.2 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research X

Pub Date : 2024-10-21 DOI: 10.1016/j.wroa.2024.100267

Jianfei Chen , Jinkai Xue , Jinyong Liu , Seyed Hesam-Aldin Samaei , Leslie J. Robbins

Water treatment residual (WTR) is a burden for many water treatment plants due to the large volumes and associated management costs. In this study, we transform aluminum-salt WTR (Al-WTR) into ceramsite (ASC) to recover phosphate from challenging waters. ASC showed remarkably higher specific surface area (SSA, 70.53 m²/g) and phosphate adsorption capacity (calculated 47.2 mg P/g) compared to previously reported ceramsite materials (< 40 m²/g SSA and < 20 mg P/g). ASC recovered over 94.9% of phosphate across a wide pH range (3 – 11) and generally sustained > 90% of its phosphate recovery at high concentrations of competing anions (i.e., Cl^-, F^-, SO₄^2-, or HCO₃^-) or humic acid (HA). We challenged the material with real municipal wastewater at 10°C and achieved simultaneous phosphate (>97.1%) and COD removal (71.2%). Once saturated with phosphate, ASC can be repurposed for landscaping or soil amendment. The economic analysis indicates that ASC can be a competitive alternative to natural clay-based ceramsite, biochar, or other useful materials. Therefore, ASC is an eco-friendly, cost-effective adsorbent for phosphate recovery from complex waters, shedding light upon a circular economy in the water sector.

由于水处理残渣（WTR）量大且管理成本高，因此是许多水处理厂的负担。在这项研究中，我们将铝盐水处理残渣（Al-WTR）转化为陶瓷石（ASC），以从具有挑战性的水体中回收磷酸盐。与之前报道的陶瓷石材料（40 m2/g SSA 和 20 mg P/g）相比，ASC 显现出更高的比表面积（70.53 m2/g）和磷酸盐吸附能力（计算结果为 47.2 mg P/g）。在较宽的 pH 值范围（3 - 11）内，ASC 可回收 94.9% 以上的磷酸盐，而且在高浓度的竞争阴离子（即 Cl-、F-、SO42- 或 HCO3-）或腐殖酸（HA）条件下，其磷酸盐回收率一般可维持 90%。我们在 10°C 的温度下用真实的城市污水对该材料进行了测试，结果表明它能同时去除磷酸盐（97.1%）和化学需氧量（71.2%）。一旦磷酸盐达到饱和，ASC 可重新用于景观美化或土壤改良。经济分析表明，ASC 可以替代天然粘土陶土、生物炭或其他有用材料。因此，ASC 是一种生态友好、经济高效的吸附剂，可用于从复杂水体中回收磷酸盐，为水务领域的循环经济带来曙光。

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引用次数: 0

Modeling transient mixed flows in sewer systems with data fusion via physics-informed machine learning 通过物理信息机器学习进行数据融合，为下水道系统中的瞬态混合流建模

IF 7.2 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research X

Pub Date : 2024-10-15 DOI: 10.1016/j.wroa.2024.100266

Shixun Li , Wenchong Tian , Hexiang Yan , Wei Zeng , Tao Tao , Kunlun Xin

Transitions between free-surface and pressurized flows, known as transient mixed flows, have posed significant challenges in urban drainage systems (UDS), e.g., pipe bursts, road collapses, and geysers. However, traditional mechanistic modeling for mixed flows is challenged by the difficult integration of multi-source data, complex equation forms for the discovery of dynamic processes, and high computational demands. In response, we proposed a data-driven model, TMF-PINN, which utilizes a Physics-Informed Neural Network (PINN) to simulate and invert Transient Mixed Flow (TMF) in sewer networks. This model integrates experimental data, simulation results and Partial Differential Equations (PDEs) into its loss function, leveraging the extensive data available in smart urban water systems. A status factor (α) has been introduced to seamlessly link open channel and pressurized flow dynamics, facilitating rapid adjustments in wave speed. On this basis, Fourier feature extraction and quadratic neural networks have been employed to capture complex dynamic processes featuring high-frequency. Validation through three classical cases using the Storm Water Management Model (SWMM) and comparisons with finite volume Harten-Lax-van Leer (HLL) solver reveal that the proposed model circumvents the constraints of spatiotemporal resolution, yielding accurate flow field predictions.

自由表面流与加压流之间的过渡，即所谓的瞬态混合流，给城市排水系统（UDS）带来了巨大挑战，例如管道爆裂、道路塌方和喷泉。然而，由于难以整合多源数据、发现动态过程的方程形式复杂以及计算要求高，传统的混合流机理建模面临挑战。为此，我们提出了一个数据驱动模型 TMF-PINN，它利用物理信息神经网络（PINN）来模拟和反演下水道网络中的瞬态混合流（TMF）。该模型将实验数据、模拟结果和偏微分方程（PDE）整合到其损失函数中，充分利用了智能城市供水系统中的大量数据。模型引入了状态因子（α），将明渠和加压水流动态无缝连接，便于快速调整波速。在此基础上，采用了傅立叶特征提取和二次元神经网络来捕捉具有高频特征的复杂动态过程。通过使用暴雨管理模型（SWMM）对三个经典案例进行验证，并与有限体积哈顿-拉克斯-范里尔（HLL）求解器进行比较，结果表明所提出的模型规避了时空分辨率的限制，能够准确预测流场。

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引用次数: 0

Rapid and selective quantitative colourimetric analysis of nitrite in water using a S-Nitrosothiol based method 使用基于 S-亚硝基硫醇的方法快速、选择性地定量比色分析水中的亚硝酸盐

IF 7.2 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research X

Pub Date : 2024-10-11 DOI: 10.1016/j.wroa.2024.100265

E. Latvyte , A. Greenwood , A. Bogush , J.E. Graves

This study introduces a novel S-nitrosothiol based method for the rapid and highly selective detection of nitrite in complex water matrices. Sodium 3-mercapto-1-propanesulfonate forms a distinctive pink S-nitrosothiol compound upon interaction with nitrite in acidic media, allowing both visual and quantitative detection. Various factors affecting the absorbance of the final product were investigated, including pH, reaction time, acid type, and sodium 3-mercapto-1-propanesulfonate concentration. UV–Vis spectrophotometric analysis demonstrated an excellent linear correlation (R² = 0.99) across a broad detection range (0.05 to 80 mmol l^-1), while showing no interference from common ions such as nitrate or dissolved organic matter, a limitation frequently observed in conventional UV-based nitrite detection methods. The assay was further adapted into a pellet form to simplify field use, operating effectively at room temperature with a low detection limit (1.4 ppm). The S-nitrosothiol based method represents a safer and more environmentally friendly option for nitrite detection and shows a promising potential as a valuable addition to both field and laboratory water testing kits for nitrite analysis.

本研究介绍了一种基于 S-亚硝硫醇的新型方法，用于快速、高选择性地检测复杂水基质中的亚硝酸盐。在酸性介质中，3-巯基-1-丙磺酸钠与亚硝酸盐作用后形成一种独特的粉红色 S-亚硝基硫醇化合物，可进行目测和定量检测。研究了影响最终产物吸光度的各种因素，包括 pH 值、反应时间、酸类型和 3-巯基-1-丙磺酸钠浓度。紫外-可见分光光度分析在很宽的检测范围（0.05 至 80 mmol l-1）内显示出极好的线性相关性（R2 = 0.99），同时没有显示出常见离子（如硝酸盐或溶解有机物）的干扰，而这正是基于紫外的传统亚硝酸盐检测方法经常出现的局限性。为了简化现场使用，该检测方法被进一步改装成颗粒状，可在室温下有效工作，检测限低（1.4 ppm）。这种基于 S-亚硝硫醇的方法是一种更安全、更环保的亚硝酸盐检测方法，有望成为现场和实验室亚硝酸盐分析水检测试剂盒的重要补充。

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引用次数: 0

Three-dimensional convolutional neural network for leak detection and localization in smart water distribution systems 用于智能配水系统泄漏检测和定位的三维卷积神经网络

IF 7.2 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research X

Pub Date : 2024-10-09 DOI: 10.1016/j.wroa.2024.100264

Sanghoon Jun , Donghwi Jung , Kevin Lansey

Smart meters such as advanced metering infrastructure (AMI) can significantly improve identifying realistic sized leaks in water distribution networks (WDNs). However, to date, detection/localization methods for AMI systems are extremely limited. In this study, to examine the benefits of using AMIs for leak detection within distribution network, a three-dimensional (3D) convolutional neural network (CNN) deep learning (DL) model is proposed that can account for temporally and spatially distributed information of pressures. The 3D CNN is tested for a real WDN in Austin using the realistic sized leaks (e.g., 3 L/s for 150-mm pipes) that are generated from hydraulic simulations. The model's performance is evaluated using detection probability, false alarm rate, and localization pipe distance metrics. In addition, the strength of using DL for leak identification is examined by comparing the CNN results with those from an optimization-based model. The 3D CNN performed better than the optimization model indicating that DL has advantages over conventional tools such as optimization methods. However, its adaptability may limit its use in some cases. Since DL can be significantly impacted by hydraulic simulation model, a way to handle modelling error must be determined. In addition, as network changes occur, retraining is required that may be time consuming and have difficulty with the number of failure conditions.

先进计量基础设施（AMI）等智能仪表可以大大提高识别配水管网（WDN）中实际大小漏水点的能力。然而，迄今为止，AMI 系统的检测/定位方法极为有限。在本研究中，为了检验使用 AMI 对配水管网进行泄漏检测的益处，提出了一种三维（3D）卷积神经网络（CNN）深度学习（DL）模型，该模型可以考虑压力的时间和空间分布信息。该三维卷积神经网络在奥斯汀的一个真实 WDN 中进行了测试，使用的是水力模拟生成的真实泄漏量（例如，150 毫米管道的泄漏量为 3 升/秒）。使用检测概率、误报率和定位管道距离指标对模型的性能进行了评估。此外，通过比较 CNN 与基于优化的模型的结果，还考察了使用 DL 进行泄漏识别的优势。3D CNN 的表现优于优化模型，这表明 DL 比优化方法等传统工具更具优势。然而，其适应性可能会限制其在某些情况下的使用。由于 DL 可能会受到液压模拟模型的重大影响，因此必须确定处理建模误差的方法。此外，当网络发生变化时，需要进行重新训练，这可能会耗费大量时间，并且难以应对故障条件的数量。

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引用次数: 0

Coarse bubble mixing in anoxic zone greatly stimulates nitrous oxide emissions from biological nitrogen removal process 缺氧区的粗大气泡混合大大刺激了生物脱氮过程中的氧化亚氮排放

IF 7.2 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research X

Pub Date : 2024-10-06 DOI: 10.1016/j.wroa.2024.100263

Haoran Duan , Shane Watt , Dirk Erler , Huijuan Li , Zhiyao Wang , Min Zheng , Shihu Hu , Liu Ye , Zhiguo Yuan

The biological nitrogen removal process in wastewater treatment inevitably produces nitrous oxide (N₂O), a potent greenhouse gas. Coarse bubble mixing is widely employed in wastewater treatment processes to mix anoxic tanks; however, its impacts on N₂O emissions are rarely reported. This study investigates the effects of coarse bubble mixing on N₂O emissions in a pilot-scale mainstream nitrite shunt reactor over a 50-day steady-state period. Online and offline N₂O monitoring campaigns show that coarse bubble mixing in the anoxic zones significantly elevates N₂O emissions, yielding an extremely high emission factor of 15.5 ± 3.5 %. Intensive sampling and isotopic analyses suggest that the elevated emissions are primarily due to the inhibition of the N₂O denitrification process by oxygen in the anoxic phase introduced by coarse bubbling. Substituting coarse bubble mixing with submersible pump mixing resulted in a substantial reduction of N₂O emissions, decreasing the emission factor by an order of magnitude to 1.2 ± 0.8 %. The findings reveal that a previously overlooked factor, coarse bubble mixing, can significantly stimulate N₂O emissions. The use of coarse bubble mixing in anoxic tanks of biological nitrogen removal warrants caution.

废水处理中的生物脱氮过程不可避免地会产生一氧化二氮（N2O），这是一种强烈的温室气体。废水处理过程中广泛采用粗泡混合来混合缺氧池，但其对氧化亚氮排放的影响却鲜有报道。本研究调查了在一个中试规模的主流亚硝酸盐分流反应器中，粗泡混合在 50 天稳态期间对 N2O 排放的影响。在线和离线 N2O 监测活动表明，缺氧区的粗大气泡混合显著提高了 N2O 排放量，排放系数高达 15.5 ± 3.5 %。大量取样和同位素分析表明，排放量增加的主要原因是粗大气泡引入的缺氧相中的氧气抑制了一氧化二氮的脱硝过程。用潜水泵混合代替粗气泡混合后，N2O 排放量大幅减少，排放系数降低了一个数量级，为 1.2 ± 0.8 %。研究结果表明，粗气泡混合这一以前被忽视的因素会大大刺激一氧化二氮的排放。在生物脱氮的缺氧池中使用粗气泡混合值得警惕。

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引用次数: 0

The impact of blue-green infrastructure on trace contaminants: A catchment-wide assessment 蓝绿基础设施对痕量污染物的影响：全流域评估

IF 7.2 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research X

Pub Date : 2024-09-27 DOI: 10.1016/j.wroa.2024.100261

Marisa Poggioli , Giovan Battista Cavadini , Zhaozhi Zheng , Mayra Rodriguez , Lena Mutzner

Blue-green infrastructure (BGI) reduce urban combined sewer overflows (CSOs) and stormwater outlets (SWOs). However, most conventional BGI are not designed to remove trace organic contaminants. Little is known about the potential of conventional BGI to improve surface water quality by reducing the discharge of trace organic contaminants. We derived wash-off loads for street runoff (6PPD-q, DPG, and HMMM), construction materials (diuron), and wastewater-derived contaminants (diclofenac) based on measurements in the combined sewer system. Subsequently, the performance of four BGI types (bioretention cells, green roofs, porous pavements, and urban wetlands) to reduce the discharge of trace organic contaminants via SWOs and CSOs was quantified with a hydrodynamic SWMM model. Moreover, the catchment-wide impact of SWOs and CSOs on surface water was assessed using risk quotients. We found that the annually discharged load can be considerably reduced by implementing BGI. Among the studied BGI types, bioretention cells are the most effective, with a load reduction of up to 80% to surface waters, mainly due to a larger suitable implementation area and a substantial stormwater infiltration. BGI implemented in the separate sewer system are more effective in reducing stormwater contaminant loads than BGI in the combined system. The assessment of the risk quotient in the surface water showed that the concentrations during SWO and CSO discharges exceed the acute environmental threshold in the surface water for 6PPD-q, DPG, diuron, and diclofenac during several events. The implementation of BGI reduced the hours of exceeded risk quotient in the surface water by 93% for bioretention cells. These findings underscore the need for a catchment-wide assessment of future BGI implementations to quantify, manage, and mitigate the impacts of urban pollution.

蓝绿基础设施（BGI）可减少城市污水合流溢流（CSO）和雨水排放口（SWO）。然而，大多数传统蓝绿基础设施的设计并不能去除痕量有机污染物。人们对传统 BGI 通过减少痕量有机污染物排放来改善地表水水质的潜力知之甚少。我们根据联合污水处理系统的测量结果，得出了街道径流（6PPD-q、DPG 和 HMMM）、建筑材料（利脲）和废水衍生污染物（双氯芬酸）的冲刷负荷。随后，利用水动力 SWMM 模型量化了四种 BGI 类型（生物滞留池、绿色屋顶、多孔路面和城市湿地）的性能，以减少通过 SWO 和 CSO 排放的痕量有机污染物。此外，我们还利用风险商数评估了整个集水区的 SWOs 和 CSOs 对地表水的影响。我们发现，通过实施 BGI，每年排放的负荷可以大大减少。在所研究的 BGI 类型中，生物滞留池最为有效，可减少地表水 80% 的负荷，这主要归功于较大的合适实施区域和大量的雨水渗透。在单独下水道系统中实施的生物蓄渗池比在联合下水道系统中实施的生物蓄渗池更能有效减少雨水污染物负荷。地表水中的风险商数评估显示，在几次事件中，SWO 和 CSO 排放过程中地表水中 6PPD-q、DPG、利脲和双氯芬酸的浓度超过了急性环境阈值。实施生物蓄渗池后，地表水中风险商数超标的小时数减少了 93%。这些研究结果突出表明，有必要对未来 BGI 的实施情况进行全流域评估，以量化、管理和减轻城市污染的影响。

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引用次数: 0

An overlooked nanofluids effect from Fe3O4 nanoparticles enhances mass transfer in anammox granular sludge 由 Fe3O4 纳米粒子产生的被忽视的纳米流体效应可增强厌氧颗粒污泥中的传质效果

IF 7.2 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research X

Pub Date : 2024-09-26 DOI: 10.1016/j.wroa.2024.100260

Dongdong Xu , Aqiang Ding , Yang Yu , Ping Zheng , Meng Zhang , Zhetai Hu

Magnetite (Fe₃O₄) particles have been widely reported to enhance the anammox's activity in anammox granular sludge (AnGS), yet the underlying mechanisms remain unclear. This study demonstrates that both Fe₃O₄ microparticles (MPs) and nanoparticles (NPs) at a dosage of 200 mg Fe₃O₄/L significantly increased the specific anammox activity (SAA) of AnGS. Additionally, the transcriptional activities of the hzs and hdh genes involved in the anammox process, as well as the heme c content in AnGS, were also notably enhanced. Notably, Fe₃O₄ NPs were more effective than MPs in boosting anammox activity within AnGS. Mechanistically, Fe₃O₄ MPs released free iron, which anammox bacteria utilized to promote the synthesis of key enzymes, thereby enhancing their activity. Compared to MPs, Fe₃O₄ NPs not only elevated the synthesis of these key enzymes to a higher level but also induced a nanofluids effect on the surface of AnGS, improving substrate permeability and accessibility to intragranular anammox bacteria. Moreover, the nanofluids effect was identified as the primary mechanism through which Fe₃O₄ NPs enhanced anammox activity within AnGS. These findings provide new insights into the effects of nanoparticles on granular sludge systems, extending beyond AnGS.

磁铁矿（Fe3O4）颗粒被广泛报道用于提高厌氧颗粒污泥（AnGS）中的厌氧活性，但其基本机制仍不清楚。本研究表明，200 毫克 Fe3O4/L 剂量的 Fe3O4 微颗粒（MPs）和纳米颗粒（NPs）都能显著提高 AnGS 的特定厌氧活性（SAA）。此外，参与厌氧过程的 hzs 和 hdh 基因的转录活性以及 AnGS 中的血红素 c 含量也明显提高。值得注意的是，Fe3O4 NPs 比 MPs 更有效地提高了 AnGS 中的anammox 活性。从机理上讲，Fe3O4 MPs 释放出游离铁，anammox 细菌利用游离铁促进关键酶的合成，从而提高了其活性。与 MPs 相比，Fe3O4 NPs 不仅能将这些关键酶的合成提高到一个更高的水平，还能在 AnGS 表面产生纳米流体效应，改善底物的渗透性和粒内 Anammox 细菌的可及性。此外，纳米流体效应被确定为 Fe3O4 NPs 增强 AnGS 内 Anammox 活性的主要机制。这些发现为纳米粒子对颗粒污泥系统的影响提供了新的见解，其影响范围超出了 AnGS。

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