# Methods to monitor the defects of the drainage pipe network: a review

Boyuan Xue, Eric Lichtfouse, Xiaohong Zhou

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## To cite this version:

Boyuan Xue, Eric Lichtfouse, Xiaohong Zhou. Methods to monitor the defects of the drainage pipe network: a review. Environmental Chemistry Letters, In press, 10.1007/s10311-025-01874-z. http://05289840

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# Methods to monitor the defects of the drainage pipe network:
a review

Boyuan Xue<fnref n="1" /> · Eric Lichtfouse<fnref n="2" /> ID · Xiaohong Zhou<fnref n="1" /> ID

## Abstract

Wastewater transport and recycling are major issues in the context of global pollution and climate change. In particular, drainage pipe networks are critical urban infrastructures for sewage transportation and flood drainage, yet these pipes are susceptible to structural and functional defects that compromise their integrity and efficiency. Failure in drainage pipelines can lead to catastrophic consequences, including urban flooding, soil contamination, sinkholes, and significant economic losses. Here, we review advanced methods to monitor structural and functional defects of drainage pipe networks. We present common defects of drainage pipe networks. Defect detection methods include software-based methods, acoustic methods, infrared thermography, smart ball systems, ground-penetrating radar, and distributed fiber-optic sensing. We found that integrating multi-sensor fusion, software-hardware synergy, and artificial intelligence significantly improves defect detection accuracy and predictive maintenance. We provide insights for selecting optimal monitoring strategies by comparing the principles, applications, and performance of the detection methods.

**Keywords** Drainage pipe network · Defect monitoring · Software-based methods · Acoustic methods · Emerging methods

## Introduction

The drainage pipe network is a critical component of urban infrastructure, specifically designed to collect and transport domestic sewage, industrial wastewater, and rainwater [1, 2]. Unlike water supply systems, which deliver potable water to users, drainage networks handle used water, directing it to wastewater treatment plants or, in some cases, discharging it into rivers or other water bodies after preliminary treatment. The pipe networks play a vital role in maintaining public health, environmental sustainability, and economic stability [3, 4]. For example in China, drainage pipe length increased from 19,556 km in 1978 to 802,721 km in 2020, with an average annual growth rate of 9.3% [5]. However, over time,

these networks are susceptible to various structural and func-
tional defects, such as corrosion, cracks, sediment buildup,
and root intrusion, which can compromise their integrity
and operational efficiency (Fig. 1) [6–10]. These issues are
further exacerbated by environmental factors like soil prop-
erties, climate conditions, and human activities, leading to
accelerated deterioration and increased maintenance costs
[11]. Failure in drainage pipelines can lead to catastrophic
consequences, including urban flooding, soil contamina-
tion, sinkholes, and significant economic losses [12, 13]. For
instance, pipe collapses can disrupt transportation, damage
adjacent utilities, and pose public health risks due to sew-
age leaks [14]. Given the increasing frequency of extreme
weather events and urbanization pressures, ensuring the
structural integrity of drainage systems has become a critical
challenge for municipal authorities and civil engineers [15].

Defect monitoring allows to assess pipeline conditions,
detect early-stage defects, and prioritize maintenance inter-
ventions [16]. Effective monitoring systems must address
key challenges, including the detection of internal cracks,
joint displacements, leaks, and external soil erosion, all
while operating in harsh, buried environments [17]. As cities
worldwide invest in smart infrastructure and resilient water
management systems, advanced monitoring technologies are
becoming essential for sustainable urban development [18].

<footnote>☑ Eric Lichtfouse
eric.lichtfouse@icloud.com</footnote>

<footnote>☒ Xiaohong Zhou
xhzhou@mail.tsinghua.edu.cn</footnote>

<footnote n="1">¹ State Key Laboratory of Regional Environment and Sustainability, Center for Sensor Technology of Environment and Health, School of Environment, Tsinghua University, Beijing 100084, China</footnote>

<footnote n="2">² State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xian 710049, China</footnote>
<figure><img src="image_3.png" /><figcaption>**Fig. 1** Typical defects in drainage pipe networks. Structural defects lead to leakage, and functional defects lead to waterlogging. These failures can compromise the integrity and operational efficiency of drainage pipe networks</figcaption></figure>

Traditional methods for monitoring drainage pipe networks, such as visual inspections and manual assessments, are often labor-intensive, time-consuming, and limited in their ability to detect early-stage defects. In recent years, advancements in sensing technologies and data analysis techniques have introduced more efficient and accurate monitoring solutions [19, 20]. These include software-based methods and hardware-based methods, each offering unique advantages and limitations in detecting and locating anomalies [21–23].

While existing studies have explored pipeline monitoring technologies (Table 1), critical gaps remain, such as lack of drainage-specific reviews. Most reviews focus on oil and gas pipelines or general leakage detection, neglecting summary

of specific methods for addressing the particular defect types of the drainage network. This review screened 1,250 papers in Web of Science database by title and key words, selected 287 papers for full-text review, analyzed the common types of defects in drainage pipe networks, categorize them into structural and functional defects, and evaluated the effectiveness of current monitoring technologies. By comparing the principles, applications, and performance of these methods, this study seeks to provide insights into selecting the most suitable monitoring strategies for different scenarios. Furthermore, the paper explores emerging trends, such as multi-sensor fusion, software-hardware integration, and artificial intelligence, which hold promise for revolutionizing pipeline

<table><caption>**Table 1** Comparison of recent reviews on pipeline monitoring. The reviews mainly focused on oil, gas, and water distribution pipelines, or did not focus on specific types of pipelines. Most reviews focus on investigating the leakage of pipelines</caption><thead><tr><th>Review study</th><th>Year</th><th>Focus area</th><th>Defect types</th><th>Monitoring methods</th><th>Ref</th></tr></thead><tbody><tr><td>Datta and Sarkar</td><td>2016</td><td>All pipelines</td><td>Blockage and Leakage</td><td>Software-based and Hardware-based methods</td><td>[24]</td></tr><tr><td>Sekhavati et al</td><td>2022</td><td>All pipelines</td><td>Leakage</td><td>Software-based methods</td><td>[19]</td></tr><tr><td>Ge et al</td><td>2021</td><td>Buried non-metallic pipelines</td><td>-</td><td>Acoustic methods, electromagnetic methods, and emerging methods</td><td>[25]</td></tr><tr><td>Latif et al</td><td>2022</td><td>Water distribution network</td><td>Leakage</td><td>Acoustic methods, visual methods, electromagnetic methods, ultrasonic methods, and emerging methods</td><td>[26]</td></tr><tr><td>Tean et al</td><td>2022</td><td>Water distribution network</td><td>Structural defects</td><td>Acoustic methods, fiber optic sensing method, and hydraulic monitoring methods</td><td>[27]</td></tr><tr><td>Islam et al</td><td>2022</td><td>Water distribution network</td><td>Leakage</td><td>Software-based methods, acoustic methods and emerging methods</td><td>[28]</td></tr><tr><td>Fan et al</td><td>2022</td><td>Water distribution network</td><td>Leakage</td><td>Acoustic methods</td><td>[29]</td></tr><tr><td>Yuan et al</td><td>2023</td><td>Oil and gas pipelines</td><td>Leakage</td><td>Software-based and hardware-based methods</td><td>[30]</td></tr><tr><td>Joseph et al</td><td>2023</td><td>Water distribution network</td><td>Leakage and burst</td><td>Software-based and hardware-based methods</td><td>[31]</td></tr><tr><td>Rajasekaran and Kothandaraman</td><td>2024</td><td>All pipelines</td><td>Leakage</td><td>Software-based methods</td><td>[20]</td></tr></tbody></table>
defect monitoring in the future. The findings of this review will contribute to a deeper understanding of drainage pipe network monitoring, offering valuable guidance for researchers, engineers, and policymakers to enhance the resilience and longevity of urban drainage systems.

## Common defect types in drainage pipe networks

There are a wide variety of defects caused by different problems in drainage pipe networks, which seriously threaten the integrity and safety of drainage networks. The defects of drainage pipe networks can be roughly divided into two major categories, structural defects and functional defects, and influenced by environmental factors (Fig. 2) [32]. Clarifying the types of common defect types is the prerequisite for choosing reasonable monitoring methods. The main characteristics and causes of different defects were systematically sorted out as follows:

### Structural defects

Structural defects refer to physical damage or deformation occurring in the drainage pipe network, directly compromising the mechanical performance, sealing integrity, and service life of pipelines. These defects typically exhibit progressive development characteristics, initially manifesting as micro-cracks or minor deformations. Without timely intervention, they may ultimately lead to functional failure or even structural collapse of the pipeline (leakage) [33]. Although there is uncertainty regarding the leakage situation

of the entire network, the conclusions of different studies indicate that the leakage rate varies between 0.01 and 0.2 L/(s • km), which is quite significant in terms of network scale [34, 35]. Based on the mechanisms and manifestations of damage, structural defects can be further categorized into three types: pipe corrosion and abrasion, joint misalignment and disconnection, and pipe fracture and collapse (Fig. 2).

Pipe corrosion and abrasion, defined as the deterioration of pipeline materials caused by chemical, electrochemical, or biological processes, manifests as cracks, perforations, reinforcement corrosion, and structural failure [36]. Based on the underlying mechanisms, corrosion can be classified into three categories: microbiologically influenced corrosion, chemical corrosion, and physical abrasion. Microbiologically influenced corrosion primarily results from the metabolic activity of sulfate-reducing bacteria, which generate hydrogen sulfide that reacts with dissolved oxygen in wastewater to form sulfuric acid, thereby corroding concrete and inducing steel reinforcement rust [36]. Chemical corrosion involves direct reactions with acidic substances present in wastewater [37]. Physical abrasion is typically caused by hydrodynamic scouring or sediment friction [38]. Research indicates that periodic wastewater flow exacerbates microbial erosion of concrete, significantly accelerating corrosion rates [36]. Additional contributing factors include material aging, construction defects, e.g., inadequate joint sealing, and environmental conditions, e.g., soil pH [39]. Collectively, these factors degrade the structural integrity of pipelines, ultimately leading to secondary failures such as leakage and collapse.

<figure><img src="image_4.png" /><figcaption>**Fig. 2** Defect types and their specific classification in drainage pipe networks. Structural defects include pipe corrosion and abrasion, joint misalignment and disconnection, and pipe fracture and collapse. Structural defects include sediment deposition, root intrusion, and</figcaption></figure>

foreign object blockage. Environmental factors include soil properties, climatic conditions, and human activities. Structural defects and functional defects and interact and are influenced by environmental factors
Joint misalignment and disconnection are characterized by geometric displacement or connection failure at pipeline joints due to installation deviations, material deformation, or external loading [33]. Misalignment typically presents as pipeline axis deviation, flange offset, or abnormal socket clearance, while disconnection refers to complete separation at joints resulting from sealing failure or structural damage [40]. These defects can be categorized into three primary failure mechanisms: (1) Construction-induced misalignment, often caused by improper foundation treatment, joint alignment errors, or insufficient bolt fastening; (2) Material creep-induced disconnection, commonly observed in plastic pipes due to long-term viscoelastic deformation or thermal expansion/contraction; and (3) Subsidence-induced disconnection, primarily associated with differential settlement stemming from inadequate backfill compaction, groundwater fluctuation, or adjacent construction disturbances [41]. The progressive nature of these defects necessitates early detection, as continued deterioration may lead to complete joint failure, compromising the entire pipeline system's structural integrity and hydraulic performance [42].

Pipeline fracture and collapse represent the most severe manifestations of structural defects in drainage systems, characterized by sudden structural failure resulting from material degradation, excessive external loads, or soil instability. Fractures typically manifest as longitudinal or circumferential ruptures in pipe walls, while collapse refers to localized or complete structural failure caused by loss of surrounding soil support [43]. These catastrophic failures can be classified into three primary mechanisms as follows. Material fatigue fracture is caused by cyclic stresses from aging materials, internal pressure fluctuations, or thermal expansion/contraction [40]. Overload-induced fracture results from extreme loading conditions such as heavy vehicle traffic or seismic activity [44]. Geotechnical instability collapse is associated with loss of soil bearing capacity due to inadequate compaction, groundwater variation, or freeze-thaw cycles [45]. The risk of fracture and collapse is significantly exacerbated by pre-existing structural weaknesses, i.e., pipe corrosion and abrasion, Joint misalignment and disconnection, etc. [43].

## Functional defects

Functional defects refer to operational issues that impair the drainage capacity of pipe networks without directly compromising structural integrity, primarily including sediment deposition, root intrusion, and foreign object blockage (Fig. 2). Although such defects do not directly threaten pipeline safety like structural defects, they can significantly reduce drainage efficiency, increase operation and maintenance costs, and may induce more serious secondary problems. Their development is influenced by design parameters,

operational conditions, maintenance practices, and environmental factors, exhibiting progressive, seasonal, and regionally variable characteristics. Early detection is crucial to prevent further deterioration and ensure optimal system performance.

Sediment deposition refers to the gradual accumulation of particulate matter leading to reduced hydraulic cross section, increased wall roughness, and elevated flow resistance [46]. This phenomenon can be systematically classified into three distinct mechanisms based on deposition composition and formation processes. Inorganic scaling is predominantly observed in hard water regions, resulting from precipitation of calcium/magnesium-based compounds, e.g., $CaCO_3$ and $Mg(OH)_2$, under alkaline pH conditions. Organic deposition is primarily caused by polymerization of lipid/protein/cellulose contaminants, frequently encountered in pipelines receiving food service wastewater [40, 47]. Biofilm-mediated deposition is formed through microbial metabolic byproducts and cellular detritus, whose adhesive matrix facilitates particulate entrapment [48, 49]. Notably, pre-existing structural defects (e.g., cracks or joint failures) may exacerbate deposition by permitting soil infiltration, thereby accelerating the accumulation rate.

Root intrusion means that vegetation root systems penetrate pipeline interiors through existing cracks or defective joints, progressively obstructing flow paths and exacerbating structural damage [50, 51]. This phenomenon demonstrates distinct seasonal patterns, with accelerated progression during active plant growth periods. The mechanical expansion forces exerted by proliferating root networks frequently lead to progressive enlargement of initial defects, structural deformation at penetration points and complete pipe section blockage in advanced cases [52]. This phenomenon is particularly common in old residential areas because the early pipeline construction often lacked effective root-proofing measures [53].

Foreign object blockage refers to the obstruction of pipeline flow caused by either external material intrusion or internal accumulation, leading to partial or complete flow restriction [54]. This defect is primarily categorized into domestic waste blockage, e.g., plastic bags and food waste, and construction debris blockage, e.g., concrete fragments and sediment [55]. Domestic waste blockage is often caused by untimely maintenance of the pipeline network or users' illegal discharge, and construction debris blockage is often due to the lack of protective measures during the construction period [55]. Studies show that insufficient pipeline slope and too low flow rate will accelerate the sedimentation of suspended solids, while the design of the pipeline network without effective interception facilities (e.g., grilles) will significantly increase the risk of blockage [44]. In addition, local depressions formed by joint misalignment or pipe corrosion are prone to create sediment deposition areas, further inducing blockages [56].
## Environmental factors

Environmental factors represent critical external contribu-
tors to the degradation of drainage pipe networks, exerting
physical, chemical, and biological influences that continu-
ously affect the structural safety and usage functions of the
pipelines. Unlike the aging of the pipeline networks, the
influence of environmental factors often exhibits distinct
regional and seasonal variations, and its intensity of action
is closely related to local geological conditions, climatic fea-
tures, and urbanization degree, primarily categorized into
soil properties, climatic conditions, and human activities
(Fig. 2).

Physical and chemical properties of soils directly influ-
ence stress distribution and corrosion processes in pipelines.
[57]. In terms of soil type, clay soils with high expansiveness
can easily cause foundation settlement, while sandy soils
with low bearing capacity may lead to pipe joint discon-
nection [58, 59]. Regarding pH levels, acidic soils acceler-
ate concrete carbonation and steel reinforcement corrosion,
whereas alkaline soils may promote mineral deposits that
clog pipelines [60]. Mouallif [61] revealed that different
sewer pipes exhibit distinct diffusion behaviors under acid/
sulfate exposure at 25 °C/40 °C, demonstrating a clear sol-
vent diffusion-temperature-time-mechanical property cor-
relation. For water content and pore water pressure, high
groundwater levels result in accumulated pore water pres-
sure, reducing friction between pipes and surrounding soil
and potentially inducing longitudinal displacement [38, 62].

Climate conditions directly impact the material properties and mechanical stability of pipelines through temperature fluctuations, precipitation patterns, and extreme weather events [63]. Seasonal thermal expansion and contraction generate thermal stress in concrete pipes, accelerating the propagation rate of circumferential cracks. High temperatures increase the rate of material degradation, while low temperatures reduce the ductility of pipe walls [64]. The sudden overload caused by heavy rainstorms not only tests the drainage capacity of pipelines but also intensifies wall abrasion due to debris carried by high-velocity flows [65]. The high-speed flow during a storm event can erode the sediment layer, which increase the leakage rate of the entire network by up to 20 times [3].

Human activities have become increasingly prominent
as a major external factor inducing structural defects in
drainage pipe networks during urbanization. Third-party
intrusions (e.g., construction damage, unauthorized exca-
vation, and dumping of corrosive substances) directly
lead to pipe collapse, joint misalignment, and accelerated
corrosion [66]. The ground vibrations generated by heavy
vehicle traffic transmit to underground pipe networks, sig-
nificantly increasing the risk of fatigue damage to pipe-
lines. Construction activities like foundation pit excavation

can disturb the soil balance around pipes [27]. Unauthor-
ized discharge of industrial wastewater containing chemi-
cal pollutants may cause severe material deterioration in
pipelines within a short period.

## Comparison of different defect types

According to our analysis of different defect types, we
can conclude that defects in drainage pipe networks can
be broadly classified into structural defects, functional
defects, and those influenced by environmental factors,
each with distinct characteristics (Table 2) yet often inter-
related (Fig. 2). Structural defects, such as pipe corrosion
and joint misalignment, directly compromise the pipeline’s
mechanical integrity, leading to leaks or collapses if left
unaddressed. These defects typically develop progres-
sively, with early-stage structural defects escalating into
functional defects over time. Functional defects primarily
hinder drainage efficiency but can also exacerbate struc-
tural issues. For instance, root intrusion widens existing
cracks, while sediment buildup increases hydraulic pres-
sure on weakened pipe walls. Environmental factors, such
as soil properties, climate conditions, and human activi-
ties, further accelerate both structural and functional
deterioration—acidic soils corrode pipes, heavy rainfall
overloads systems, and construction vibrations destabilize
joints. The interconnected nature of these defects further
underscores the importance of targeted monitoring strate-
gies. Proactive detection not only prevents catastrophic
failures but also reduces maintenance costs and extends
pipeline lifespan.

## Defect monitoring methods for drainage pipe networks

Currently, several technologies are available for defect
monitoring in drainage pipe networks, particularly leakage
monitoring. With ongoing advancements in interdiscipli-
nary research, pipeline anomaly monitoring technologies
have diversified significantly, with each method exhibiting
distinct advantages and limitations. Common monitoring
techniques for urban drainage networks primarily include:
software-based methods, acoustic methods, infrared ther-
mography, smart ball system, ground penetrating radar,
and distributed fiber optic sensing [6, 19].

## Software-based methods

Software-based methods, also known as data-driven detec-
tion approaches, analyze sensor data of pipeline internal
<table><caption>**Table 2** Main types of defects in drainage networks. Different defects have different contributing factors and different expected periods of failure occurrence. The interconnected nature of these defects emphasizes the importance of targeted monitoring strategies</caption><thead><tr><th>Defect categories</th><th>Impact on failure</th><th>Defects</th><th>Contributing factors</th><th>Inspected period</th><th>Ref</th></tr></thead><tbody><tr><td rowspan="4">Structural defects</td><td rowspan="4">Leads to leakage</td><td>Pipe corrosion and abrasion</td><td>Microbiologically influenced corrosion<br>Chemical corrosion<br>Physical abrasion</td><td>Continuous</td><td>[36–39]</td></tr><tr><td>Joint misalignment and disconnection</td><td>Construction-induced misalignment<br>Material creep-induced disconnection<br>Subsidence-induced disconnection</td><td>Continuous</td><td>[33, 40–42]</td></tr><tr><td>Pipe fracture and collapse</td><td>Material fatigue fracture<br>Overload-induced fracture<br>Geotechnical instability collapse<br>Pipe corrosion and abrasion<br>Joint misalignment and disconnection</td><td>Accidental</td><td>[40, 43–45]</td></tr><tr><td>Sediment deposition</td><td>Inorganic scaling<br>Organic deposition<br>Biofilm-mediated deposition<br>Entrance of soil particles through structural defects of the pipeline</td><td>Continuous</td><td>[40, 46–49]</td></tr><tr><td rowspan="2">Functional defects</td><td rowspan="2">Leads to waterlogging</td><td>Root intrusion</td><td>Structural defects<br>Sewer maintenance practices (improper root removal)</td><td>Continuous</td><td>[50–53]</td></tr><tr><td>Foreign object blockage</td><td>Domestic waste blockage<br>Construction debris blockage<br>Sediment deposition areas caused by joint misalignment or pipe corrosion</td><td>Accidental</td><td>[44, 54–56]</td></tr></tbody></table>

parameters, e.g., pressure, temperature, and flow rate, using various computational algorithms to infer the occurrence of pipeline anomalies. Notably, these methods leverage existing infrastructure, e.g., liquid level gages and flow meters, and computational models rather than installing new hardware. Defects are inferred from hydraulic abnormalities, not direct measurements. Typical software-based methods include mass-volume balance, pressure point analysis, negative pressure wave, and statistical methods (Fig. 3). These methods are relatively easy to implement since they primarily rely on analyzing readily available fluid flow parameter data from pipeline systems [67]. However, they have limitations in transient condition monitoring and often struggle with precise localization. Furthermore, they are generally suitable only for leak detection.

The mass-volume balance method operates on the principle of mass/volume conservation, where fluid entering a pipe segment must either remain within the segment or exit it (Fig. 3). Typically, any discrepancy between the mass flow rates at the inlet and outlet must be balanced against changes in the mass within the pipe segment [68]. While this method is straightforward and advantageous for steady-state pipeline

operations, it is overly sensitive to disturbances and prone to false alarms during transient conditions [69]. Additionally, it cannot pinpoint the exact location of leaks.

Pressure-based leakage detection operates on the principle that leaks cause sudden changes in pressure and flow rates at pipeline inlets and outlets. These leakage detection approaches can be categorized into two main types: pressure point analysis and negative pressure wave. Pressure point analysis requires continuous pressure measurements at multiple points along the pipeline length (Fig. 3). By statistically analyzing these pressure readings, the system triggers a leakage alarm when the average measured pressure falls below a predefined threshold [70]. Pressure point analysis can operate in various environments (underwater, high temperature and low temperature) and detect leakage rates below 0.1% flow [71]. However, its major limitations include unreliable performance during transient pipeline conditions and inability to precisely localize leakage points [19].

For the negative pressure wave method, when a leak occurs and causes a pressure drop in the pipeline fluid, negative pressure wave signals propagate outward from the leakage point both upstream and downstream (Fig. 3). These
<figure><img src="image_5.png" /><figcaption>**Fig. 3** Software-based monitoring methods for drainage pipe networks. Mass-volume balance, pressure point analysis, negative pressure wave, and statistical methods are included. These methods</figcaption></figure>

analyze internal parameters of pipe networks such as pressure, temperature, and flow rate to infer defects

signals are detected by pressure sensors installed at pipeline terminals [72, 73]. The leakage location can be estimated by applying cross-correlation analysis to the time difference of negative wave signals received by two pressure sensors [74]. While this method is particularly effective for liquid pipelines, it is not suitable for long-distance pipelines. Moreover, during transient operations like valve openings/closures, significant pressure drops often occur, which the method may misinterpret as leakage events, resulting in false alarms [75].

Statistical methods primarily analyze historical pressure and flow measurement data to detect pipeline leaks (Fig. 3). When the system identifies significant deviations in pressure and flow patterns through statistical analysis, a leakage alarm is triggered [76]. This technique is straightforward to implement as it does not require complex modeling to estimate leak locations along pipelines [77]. Wang et al. [78] presented a model based on two pressure sensors for pipeline condition monitoring, leveraging pressure matrix features across stations and achieving 100% accuracy in detecting operational anomalies. However, sufficient data from various operating conditions are essential for accurate anomaly detection and localization in pipe networks [79]. Consequently, data collection and analysis present significant challenges for statistical methods.

## Acoustic methods

Acoustic instruments are primarily used for detecting leaks in drainage pipe networks, where detection accuracy depends not only on instrument precision but also on analysis software and operator experience. Although these methods are time-consuming and labor-intensive, and mainly applicable to metal pipelines, e.g., cast iron or gray cast iron pipes, their strong operability and portability make them one of the predominant leak detection approaches currently in use. Specifically, widely employed acoustic methods include listening sticks, correlators, and noise loggers (Fig. 4) [80]. These techniques primarily rely on sound characteristics or principles for detection.

The fundamental acoustic instrument is the listening stick, which is placed against valves, fire hydrants or other access points to detect acoustic signals propagating from pipeline leaks (Fig. 4) [26]. It is most suitable for metal pipes between 75 and 250 mm with a pressure higher than 15 psi [28]. As one of the oldest detection methods, listening sticks offer simple operation and low equipment costs, though their accuracy largely depends on the operator's experience. More advanced electronic versions (electronic listening sticks and listening devices) operate on the same principle but achieve

<figure><img src="image_6.png" /><figcaption>**Fig. 4** Acoustic monitoring methods for drainage pipe networks. Listening sticks, correlators, and noise loggers are included. These methods primarily rely on sound characteristics of pipeline for detection</figcaption></figure>
significantly improved detection accuracy through major advancements in electronic zoom functionality [28]. Xu et al. [81] presented a compact microelectromechanical system hydrophone-based leak detection system with – 180 dB sensitivity and 60 dB noise resolution, successfully identifying 30–180 L/min leaks in a 3.2 bar pressurized 30-m pipe through acoustic analysis.

The correlator system consists of a main unit, two sensors, and accompanying wireless transmitters (Fig. 4). By capturing the time difference of acoustic wave propagation from leakage points and transmitting the signal data wirelessly to the main unit, the system determines the precise leak location [82]. Unlike listening sticks that rely on noise levels, this method calculates leak positions based on distance between two sensors, time difference of sound arrival at each sensor, and acoustic propagation velocity in the pipeline [83]. As the most widely used and accurate method in current application of drainage pipelines, correlation detectors perform particularly well in noisy environments, deeply buried pipelines, and areas unsuitable for surface listening methods [84]. Almeida et al. [85] pointed out the correlator demonstrated outstanding performance in detecting weak leaks and had a wider sensing frequency range (≤ 104 Hz). Due to its sensor characteristics, this technique is especially effective for long-distance pipeline inspection and requires relatively lower operator skill levels compared to other acoustic methods.

Noise loggers typically consist of multiple data recorders and acoustic receiver controllers installed at exposed pipeline locations such as pipe connections, valves, and other accessible sections (Fig. 4) [86]. The recorders automatically activate according to preset schedules to capture acoustic signals throughout the pipeline network. The collected data are then processed through specialized computer software, which identifies leaks based on acoustic intensity

and frequency characteristics, and automatically generates 2D/3D maps of leakage locations [87]. Compared to listening sticks, the system offers simplified deployment and requires minimal manual intervention while maintaining high detection accuracy [29]. El-Zahab et al. [88] developed a noise logger-based leak detection model using frequency domain analysis, Shannon's entropy, and k-means clustering to classify acoustic signals and identify leaks with 100% accuracy in a Montréal pilot project.

## Infrared thermography

Infrared thermography is a non-contact temperature measurement technology that captures infrared radiation emitted by objects and converts it into electrical signals to generate temperature distribution images (Fig. 5) [89]. In drainage pipe networks, pipeline anomalies such as leaks, blockages, or ruptures typically induce characteristic temperature variations [90]. For example, leaking fluids carrying away heat create localized cooling around the defect, while blockages may cause temperature increases from water evaporation or decreases due to winter freezing [91]. These temperature changes can be captured by infrared thermal imaging cameras, thereby helping inspectors locate the problem areas.

In drainage pipeline anomaly monitoring, infrared thermography has emerged as a crucial detection technique due to its non-destructive testing capability, rapid imaging performance, and remote operation advantages [90]. However, practical applications present certain limitations: environmental factors (e.g., wind speed, humidity, and solar radiation) may compromise infrared image quality, while the thermal insulation properties of soil can significantly reduce detection effectiveness for deeply buried pipelines [25]. Artificial intelligence can be effective in improving image quality and detection efficiency. Rezayiye et al. [92] enhanced

<figure><img src="image_7.png" /><figcaption>**Fig. 5** Emerging monitoring methods for drainage pipe networks. Infrared thermography, smart ball system, ground penetrating radar, and distributed fiber optic sensing are included. These methods moni-</figcaption></figure>

tor defects in drainage pipe networks through different principles, demonstrating broad application prospects
moisture-induced defect detection in insulated steel pipes by combining infrared thermography with machine learning models trained on hybrid datasets, significantly improving accuracy and generalization in identifying corrosion under insulation. Massaro et al. [93] combined an artificial neural network approach with infrared thermography for real-time water pipeline leak detection, achieving 100% accuracy rate within 20 training epochs.

## Smart ball system

The smart ball, also known as a pipeline inspection robot or detection sphere, is an automated device specifically designed for internal pipeline inspection that has gained increasing importance in pipeline monitoring due to its flexibility, efficiency, and precision (Fig. 5) [94]. Typically spherical in shape for enhanced mobility, these devices integrate multiple sensors and cameras to collect comprehensive pipeline condition data through technologies including eddy current testing, ultrasonics, and video recording while navigating autonomously through pipes [95]. Equipped with global position system or other positioning systems, smart balls can precisely locate defects within pipelines and synchronize data with external control systems to facilitate subsequent maintenance and repair operations [96]. Upon detecting abnormalities, the smart ball can instantly alert operators, enabling rapid response to minimize potential incidents. The current studies focus on computer vision and intelligent balls equipped with closed circuit television. Hasan et al. [97] proposed a smart ball based automated defect classification system for sewer inspection videos, achieving 96.3% accuracy on 47,072 labeled images. Li et al. [51] proposed a novel smart ball method of sewer defects using convolutional block attention module for data augmentation, achieving high accuracy and faster recognition speeds. Wang et al. [98] introduced a lightweight and high-performance convolutional neural network automatically designed using neural architecture search for multi-label sewer pipe defect classification, addressing challenges like imbalanced datasets and computational efficiency.

The smart ball system offers significant advantages through its autonomous operation in complex pipeline environments, eliminating the need for direct human intervention while substantially improving inspection efficiency and operational safety [99]. However, this method is limited to intermittent detection and faces challenges in extremely small-diameter pipelines or harsh conditions (e.g., high temperature/pressure environments), where it may potentially cause pipe blockages or operational interruptions [94, 100]. Furthermore, the relatively high development and maintenance costs of smart ball system restrict their applicability in certain scenarios.

## Ground penetrating radar

Ground penetrating radar is a non-destructive geophysical detection technology that identifies subsurface medium variations by emitting high-frequency electromagnetic waves and analyzing the reflected signals (Fig. 5) [16]. Its operational principle relies on antennas emitting specific-frequency electromagnetic waves (between 10 MHz and 2.6 GHz) that undergo reflection, refraction, and attenuation when propagating through different underground media [101–103]. When encountering drainage pipelines or other subsurface discontinuities, these electromagnetic waves generate characteristic reflection signals at the interfaces [104]. In the field of drainage pipeline monitoring, ground penetrating radar is primarily employed to detect pipeline location, alignment, burial depth, and variations in soil compaction density around pipes [105]. Through analysis of radar echo signals, the technology enables identification of pipeline anomalies such as structural damage, leaks, and blockages, while also detecting potential hazards including subsurface voids and settlement zones that may lead to ground collapse risks.

Due to its high resolution, operational simplicity, and rapid imaging capabilities, ground penetrating radar method has been widely adopted for anomaly monitoring in drainage networks [106]. Zhang et al. [107] proposed a novel leakage diagnostic method for water pipelines using ground penetrating radar and image correlation algorithms, validated through laboratory and field tests to improve accuracy and reduce subjectivity in leakage detection. In practical applications, this method is typically integrated with complementary inspection methods such as closed-circuit television and infrared thermography to establish a comprehensive pipeline monitoring system. Atef et al. [108] achieved 97% accuracy in leak detection by combining infrared thermography imaging and ground penetrating radar, using a seeded region-growing algorithm and Green's theorem to identify and locate leaks in both simulated and real scenarios. This multi-technology integration approach significantly enhances detection accuracy and reliability while providing more complete data support for drainage network maintenance and management [25]. However, despite its advantages, the method demonstrates certain limitations: (1) Reduced adaptability for metallic pipelines and complex geological conditions; (2) Potential interference from groundwater levels and variable soil environments in specific scenarios [109].

## Distributed fiber-optic sensing

Distributed fiber-optic sensing is a novel sensing technology that emerged in the 1980s, where optical fibers serve dual functions as both transmission media and sensing elements,
enabling real-time acquisition of spatiotemporal variations
in vibration, strain, temperature and other environmental
parameters along the fiber deployment area (Fig. 5) [110,
111]. Light propagation in optical fibers produces back-
scattering effects: Rayleigh scattering maintains the inci-
dent light frequency, while Brillouin scattering and Raman
scattering exhibit frequency shifts (Fig. 6) [112]. When
measured parameters alter at specific fiber segments, they
modify the fiber core's dimensions and refractive index, con-
sequently changing the phase and intensity of optical sig-
nals. By monitoring variations in polarization state, phase,
and wavelength of these optical signals, the system obtains
precise information about local measured parameters [113,
114]. Distributed fiber-optic sensing technologies are cat-
egorized into three main types: distributed acoustic sens-
ing, distributed strain sensing, and distributed temperature
sensing.

These sensing technologies exhibit distinct character-
istics in underground pipeline monitoring due to differ-
ences in their operating principles and sensing methodolo-
gies (Table 3). Rayleigh scattering is an elastic scattering
phenomenon caused by random fluctuations in the optical
fiber's refractive index. While strain and temperature meas-
urements based on Rayleigh-scattered light waves typically
require extensive optical frequency scanning and high-
performance light sources for static parameter detection,
these characteristics make Rayleigh scattering particularly
suitable for vibration monitoring. The phase-sensitive opti-
cal time-domain reflectometry technique can effectively
utilize Rayleigh scattering to measure dynamic vibration

parameters, enabling sensitive detection of various types of
abnormal events [115]. Brillouin scattering is an inelastic
scattering phenomenon resulting from interactions between
incident light waves and optical phonons. Since these inter-
actions exhibit dependencies on both strain and temperature,
Brillouin optical time-domain reflectometry and Brillouin
optical time-domain analysis can be employed to simultane-
ously measure these two parameters. This capability makes
them particularly effective for detecting stress variations
caused by pipeline deformation, corrosion, or joint damage
[116, 117]. However, the time-consuming frequency scan-
ning process inherent in these methods renders them unsuit-
able for vibration measurement applications. Raman scatter-
ing represents another form of inelastic scattering generated
through interactions between incident light waves and opti-
cal phonons. Characterized by its exclusive temperature sen-
sitivity, this phenomenon enables distributed temperature
measurement via Raman optical time-domain reflectometry
without strain-induced interference. In pipeline applications,
it is particularly valuable for detecting infiltration and inflow
phenomena [118, 119].

Distributed fiber-optic sensing technology offers sig-
nificant advantages including simple installation, rapid
response, high spatial resolution, superior sensitivity, and
strong environmental resistance, driving its rapid adoption
in pipeline monitoring applications in recent years. As a
hybrid monitoring approach combining hardware and soft-
ware methodologies, this method integrates the strengths
of both while overcoming their individual limitations. This
technique effectively mitigates signal attenuation over long

<figure><img src="image_8.png" /><figcaption>**Fig. 6** Three backscattering spectra in optical fibers. Rayleigh scattering occurs at the same wavelength as the incident light. Brillouin scattering and Raman scattering form peaks on both sides of the incident light wavelength</figcaption></figure>
<table><caption>**Table 3** Characteristics of different distributed fiber-optic sensing systems used for drainage pipeline monitoring. Each technology demonstrates unique measurement capabilities for vibration, strain, and temperature parameters with varying spatial strengths and weak-</caption><thead><tr><th>Categories</th><th>Methods</th><th>Measurement parameters</th><th>Strengths</th><th>Weaknesses</th><th>Ref</th></tr></thead><tbody><tr><td>Rayleigh</td><td>Phase-sensitive optical time-domain reflectometry</td><td>Vibration</td><td>High spatial resolution (m)<br>High sensitivity<br>Quick dynamic response (up to kHz)</td><td>High cost<br>High system complexity<br>Weak detection signals<br>Requirement for narrow linewidth lasers</td><td>[115, 120]</td></tr><tr><td>Brillouin</td><td>Brillouin optical time-domain reflectometry/analysis</td><td>Temperature and strain</td><td>Long sensing distance<br>High spatial resolution (cm to m)<br>High precision</td><td>High cost<br>Weak detection signals<br>Requirement for narrow linewidth lasers<br>Cross interference of temperature and strain</td><td>[116, 117]</td></tr><tr><td>Raman</td><td>Raman optical time-domain reflectometry</td><td>Temperature</td><td>High spatial resolution (m)<br>High temperature resolution (1 °C)<br>High measurement accuracy</td><td>Weak return signals<br>Require for high-power light sources</td><td>[118, 119]</td></tr></tbody></table>

nesses. In practical applications, it is necessary to select appropriate sensing technologies based on specific pipeline monitoring requirements and environmental conditions

distances and demonstrates excellent corrosion resistance, electromagnetic interference immunity, and long-range transmission capabilities [6, 121]. Ong et al. [122] developed a bend-coated fiber optic sensor that detects leaks through bending-loss-induced optical power loss (22.44 dB at ≤ 8 mm bend radius), demonstrating high sensitivity to small deformations and robustness in harsh environments. Ren et al. [123] proposed a distributed fiber optic sensing method for pipeline corrosion and leakage monitoring, achieving high-precision real-time detection by measuring hoop strain distribution on pipe surfaces. The combination of distributed fiber-optic sensing and artificial intelligence is currently a research trend. Wu et al. [124] present a novel method for detecting water pipe leaks by integrating optical fiber networks with water pipelines, using distributed acoustic sensing and three-dimensional convolutional neural network algorithms to accurately identify, locate, and quantify leaks, even at very low flow rates. Compared to conventional methods, distributed fiber-optic sensing provides comprehensive monitoring beyond just leak detection, enabling identification of various structural defects, functional abnormalities, and anomalous inflow events [115, 123, 125–128]. However, challenges such as weak backscattered signals, system complexity, and stringent requirements for instrument precision, laser sources, and data processing have limited its current applications primarily to oil/gas pipelines, with relatively fewer implementations in drainage networks to date [30].

## Comparison of monitoring methods

The effectiveness of defect monitoring methods for drainage pipe networks varies significantly depending on the type of

defect, pipeline material, and environmental conditions. The characteristics of different methods are shown in Table 4. Software-based methods are cost-effective and straightforward but lack precision in leak localization and struggle with transient conditions. Acoustic methods excel in leak detection for metallic pipes but are less effective for non-metallic materials and require skilled operators. Infrared thermography offers rapid, non-contact detection of temperature anomalies caused by leaks or blockages, though its accuracy diminishes with soil depth and environmental interference. Smart ball system provides high-resolution internal inspections but are limited by pipe diameter and operational costs. Ground penetration radar is versatile for locating pipes and detecting soil voids but performs poorly in conductive or clay-rich soils. Distributed fiber-optic sensing stands out for its ability to monitor strain, temperature, and vibration continuously over long distances with high sensitivity, making it suitable for detecting a wide range of defects, though its high cost and system complexity remain challenges.

For routine and cost-effective monitoring, software-based methods and acoustic techniques are the most commonly used. For critical infrastructure requiring high precision, distributed fiber-optic sensing and smart ball systems are superior, albeit less common due to their cost and complexity. Although advanced methods offer unparalleled accuracy, municipalities should balance innovation and budget constraints. The choice of monitoring methods has significant implications for urban infrastructure management, where factors like population density, climate resilience, and maintenance costs are critical considerations. For instance, distributed fiber-optic sensing is particularly suitable for high-density cities due to its continuous monitoring capability, while software-based methods offer cost-effective solutions
<caption>**Table 4** Comparison of commonly used monitoring methods for defects in drainage networks. Different positioning accuracy, sensitivity, detection range, strengths, and weaknesses highlight each method's suitability for different pipeline materials, defect types, and</caption>

operational environments. The findings emphasize how method selection should consider specific application scenarios while acknowledging technological limitations and potential integration opportunities

<table><thead><tr><th>Methods</th><th>Positioning accuracy</th><th>Sensitivity</th><th>Detection range</th><th>Initial Cost</th><th>Setup Cost</th><th>Operational Cost</th><th>Strengths</th><th>Weaknesses</th><th>Ref</th></tr></thead><tbody><tr><td>Mass-volume balance</td><td>-</td><td>Relatively high</td><td>Short</td><td>Low</td><td>Low</td><td>Low</td><td>Easy operation Fast response</td><td>Unable to locate accurately<br>Unable to accurately monitor transient conditions</td><td>[68, 69]</td></tr><tr><td>Pressure point analysis</td><td>-</td><td>Relatively high</td><td>Medium</td><td>Low</td><td>Low</td><td>Low</td><td>Easy operation</td><td>Many pressure sensors needed<br>Unable to locate accurately<br>Unable to accurately monitor transient conditions</td><td>[19, 70, 71]</td></tr><tr><td>Negative pressure wave</td><td>Ordinary</td><td>Ordinary</td><td>Short</td><td>Low</td><td>Low</td><td>Low</td><td>Fast response speed<br>Satisfactory detection accuracy</td><td>Sensitive to environmental noise<br>Poor leakage positioning accuracy<br>Not suitable for long pipelines<br>Unable to detect small leaks</td><td>[72-75]</td></tr><tr><td>Statistical methods</td><td>Ordinary</td><td>Relatively high</td><td>Medium</td><td>Low</td><td>Low</td><td>Low</td><td>Using intelligent algorithm<br>Good positioning ability<br>Multiple leakage detection</td><td>Accurate performance based on large databases<br>Unable to accurately monitor transient conditions</td><td>[76-79]</td></tr><tr><td>Listening stick</td><td>Ordinary</td><td>Relatively high</td><td>Medium</td><td>Low</td><td>Medium</td><td>Medium</td><td>Easy operation</td><td>Low accuracy<br>Experience related<br>Not suitable for non-metallic pipes</td><td>[26, 28, 81]</td></tr><tr><td>Correlator</td><td>Relatively high</td><td>Relatively high</td><td>Medium</td><td>Medium</td><td>Medium</td><td>Medium</td><td>Easy deployment</td><td>Not suitable for non-metallic pipes</td><td>[82-85]</td></tr></tbody></table>
<table><caption>**Table 4** (continued)</caption><thead><tr><th>Methods</th><th>Positioning accuracy</th><th>Sensitivity</th><th>Detection range</th><th>Initial Setup Cost</th><th>Operational Cost</th><th>Strengths</th><th>Weaknesses</th><th>Ref</th></tr></thead><tbody><tr><td>Noise logger</td><td>Relatively high</td><td>Relatively high</td><td>Related to required accuracy</td><td>Relatively high</td><td>Low</td><td>Automatic detection<br>Without relying on artificial experience<br>Easy deployment</td><td>Not suitable for non-metallic pipes</td><td>[29, 86–88]</td></tr><tr><td>Infrared thermography</td><td>Relatively high</td><td>Ordinary</td><td>Related to temperature</td><td>Medium</td><td>Relatively low</td><td>High speed</td><td>Sensitivity to external environment</td><td>[25, 89–93]</td></tr><tr><td>Smart ball system</td><td>High</td><td>Relatively high</td><td>Long</td><td>Very high</td><td>High</td><td>High precision<br>High adaptability<br>Unlimited pipeline material</td><td>Difficult operation<br>Blockage possibility of the pipe</td><td>[94–100]</td></tr><tr><td>Ground penetration radar</td><td>High</td><td>High</td><td>Long</td><td>High</td><td>medium</td><td>High precision<br>High speed<br>High adaptability<br>Unlimited pipeline material</td><td>Different operation<br>Requirement for passing ability above the pipe<br>Susceptible to electromagnetic interference</td><td>[16, 25, 101–106, 108, 109]</td></tr><tr><td>Distributed fiber-optic sensing</td><td>High</td><td>High</td><td>Long</td><td>Very high</td><td>Low</td><td>High precision<br>High speed<br>High adaptability<br>Unlimited pipeline material</td><td>Difficult deployment</td><td>[110–124]</td></tr></tbody></table>

for rapidly expanding urban areas with limited budgets. Hybrid approaches—combining low-cost software-based monitoring for routine checks with targeted high-resolution inspections—can optimize resource allocation. Overall, we observed that the choice of method depends on balancing cost, accuracy, and operational constraints, with emerging trends favoring integrated systems that combine multiple technologies for robust, real-time pipeline health assessment.

## Perspective

The future of drainage pipe network health monitoring is poised for transformative advancements through the integration of cutting-edge technologies and interdisciplinary methodologies. As urban infrastructure faces increasing pressures from aging systems, climate change, and growing populations, the need for more accurate, efficient, and reliable monitoring solutions has never been greater. Emerging

trends in sensor fusion, software-hardware integration, and artificial intelligence are paving the way for next-generation monitoring systems that can detect defects earlier, reduce maintenance costs, and extend the lifespan of critical infrastructure. This section explores three key directions that will shape the evolution of drainage pipe network monitoring: multi-sensor and multi-feature fusion, the combination of software and hardware technologies, and the application of artificial intelligence and database systems.

## Multi-sensor and multi-feature fusion

The integration of multiple sensing technologies and data features is essential for achieving a comprehensive understanding of pipeline health [26]. Traditional monitoring methods often rely on single-sensor systems, which may miss subtle or complex defects. By combining acoustic emission sensors, infrared thermography, ground penetrating radar, and distributed fiber-optic sensing, a more robust
and multi-dimensional assessment of pipeline conditions
can be obtained [108]. For example, distributed fiber-optic
sensing provides continuous strain and temperature meas-
urements, while acoustic sensors detect leaks through sound
wave analysis. Ground penetrating radar, on the other hand,
identifies soil voids and structural weaknesses around pipes.
Future research should focus on data fusion algorithms that
harmonize these diverse datasets, reducing false positives
and improving defect localization. The development of
standardized fusion protocols will also be crucial for ensur-
ing compatibility across different monitoring systems and
facilitating widespread adoption in municipal infrastructure
management.

Integration of software and hardware technologies

The convergence of advanced hardware and intelligent soft-
ware is revolutionizing pipeline monitoring by enabling
real-time, automated, and high-resolution data analysis [31].
Distributed fiber-optic sensing exemplifies this synergy,
where optical fibers act as both sensors and data transmis-
sion channels, while machine learning algorithms interpret
the collected signals to detect anomalies. To maximize the
potential of these technologies, collaboration between civil
engineers, data scientists, and software developers will
be essential. Standardized open-source platforms for data
sharing and analysis could accelerate innovation and make
advanced monitoring solutions more accessible to munici-
palities with limited budgets.

Application of artificial intelligence and database
systems

Artificial intelligence and big data analytics are set to play
a pivotal role in the future of pipeline health monitoring.
Machine learning models can analyze vast datasets from
sensors to detect patterns indicative of corrosion, leaks, or
structural fatigue [20]. These models improve over time
as they process more data, leading to higher accuracy in
defect prediction. Various intelligent algorithms have been
developed to facilitate the analysis of statistical methods in
complex situations [129]. These techniques include support
vector machine [130], principal component analysis [131],
decision tree [79], convolutional neural network [132],
etc. Moreover, a critical enabler of artificial intelligence-
driven monitoring is the development of comprehensive
infrastructure databases that store historical performance
data, environmental conditions, maintenance records, and
material specifications. Such databases allow artificial intel-
ligence systems to correlate past failures with current sensor
readings, improving predictive maintenance strategies. For
instance, if a particular pipe material is prone to corrosion

in acidic soil conditions, artificial intelligence can prioritize
inspections in high-risk areas.

Conclusion

By addressing key gaps—including the lack of defect-spe-
cific reviews and fragmented technology evaluations—this
review provides a unified framework for selecting monitor-
ing strategies. Our comparative analysis reveals that while
traditional methods remain practical for routine checks,
emerging approaches like distributed fiber-optic sensing
and artificial intelligence-driven analytics offer superior
precision for critical infrastructure. The integration of multi-
sensor fusion and software-hardware synergy represents a
paradigm shift toward real-time, predictive maintenance.
Future research should prioritize scalable, cost-effective
implementations of these advanced systems to bolster urban
resilience. This work serves as a comprehensive reference
for optimizing monitoring strategies and guiding next-gen-
eration infrastructure management.

**Author contributions** All authors contributed to the conceptualization and preparation of this article. Boyuan Xue contributed to conceptualization, investigation, methodology, and writing—original draft; Eric Lichtfouse contributed to conceptualization, resources, and writing—review & editing; Xiaohong Zhou contributed to conceptualization, data curation, resources, supervision, and writing—review & editing.

**Funding** This work was supported by the National Natural Science Foundation of China (22376112), the Open Fund of National Engineering Research Center for Advanced Technology and Equipment of Water Environmental Pollution Monitoring (2024KFJJ0106), and the Special Fund of State Key Laboratory of Regional Environment and Sustainability (25Y01REST).

Declaration

EL and XZ declare that they are Editors of Environmental Chemistry Letters.

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