Sewer sediments contain various hazardous compounds, leading to significant pollution risks when combined sewer overflows (CSOs) occur without appropriate controls. This paper presents a comprehensive review of the issues associated with particles in sewers, specifically focusing on the non-negligible contribution of particulate matter to CSOs, which leads to pollution in urban rivers. Therefore, the sources of particulate matter in sewers, their contributions to the overflow particles, and the specific areas of concern when it comes to managing particulate matter during particle transportation are outlined. Overall, carefully considering the goal of avoiding sedimentation during the drainage system design is the most effective prevention and control method for pipeline sediment, where minimum velocity and minimum shear stress are the core parameters. The establishment of a flexible and adaptive particle simulation method in drainage pipelines requires reliable simulation of particle sedimentation and erosion, the development of sediment prevention facilities with strong adaptability, and a comprehensive evaluation of economic and environmental benefits. With the ongoing enhancement of urbanization in developing countries, such studies will have more practical significance.

  • Understanding the sedimentation and scouring process is beneficial to discovering core measures.

  • The accumulation of particles causes various problems in drainage systems.

  • The differential analysis for particles of different types cannot be ignored.

  • Source analysis is the basis of targeted control for sediment particles.

  • Drainage pipeline design needs to consider shear stress.

WWTP

wastewater treatment plant

ADWP

antecedent dry-weather period

BF

upper biofilm

BMPs

best management practices

BOD5

5-day biochemical oxygen demand

Cd

cadmium

COD

chemical oxygen demand

Cr

chromium

CSO

combined sewer overflow

Cu

copper

CVAI

Climate Variability Adaptation Index

D50

median particle size

E. coli

Escherichia coli

EC

end-of-pipe control

EPS

extracellular polymeric substance

GBS

bottom coarse bed sediment

Gram + ves

Gram-positive bacteria

HM

heavy metal

IPCC

the Intergovernmental Panel on Climate Change

IRUDA

the Index for Reliability of Urban Drainage Adaptation

LID

low impact development

Me

sediment erosion rate

n

Manning's roughness coefficient

NH3-N

ammonia nitrogen

OL

middle organic layer

PAH

polycyclic aromatic hydrocarbon

Pb

lead

PC

process control

PCBs

polychlorinated biphenyls

PSD

particle size distribution

PZC

the lower point of zero charge

Rh

hydraulic radius

SC

source control

SUDS

sustainable urban drainage systems

SWMM5

Storm Water Management Model, the 5th version

SP

settlement performance

TN

total nitrogen

TP

Total phosphorus

TSS

total suspended particles

V50

median settling velocities

VICAS

Vitesse de chute en assainissement

VOC

volatile organic compound

WWFs

wet-weather flows

τb

bed shear stress

τc

critical shear stress

τcs

critical shear stress of the bed surface

τcu

critical shear stress at the bottom of the wake layer

the balance between friction velocity and settling velocity

friction velocity

settling velocity

Urban river pollution is a common problem in developed and developing countries. The imperfection of the urban drainage system is causing a series of problems, such as the insufficient collection rate of sewage and pollutants, poor biodegradability of wastewater treatment plant (WWTP) influent, high frequency of combined sewer overflows (CSOs), a considerable groundwater infiltration, and mixed- and crossed-connection of drainage pipes (Xu et al. 2019). With the continuous improvement of urbanization, the adverse effects of CSOs has become increasingly serious (Peng et al. 2015; Wu et al. 2021). In recent years, the researchers have conducted extensive studies on this topic both in developed and developing countries (Roseboro et al. 2021; Owolabi et al. 2022; Sojobi & Zayed 2022; Li et al. 2023; Muttil et al. 2023; Saddiqi et al. 2023).

The discharge of the CSO is enormous (Botturi et al. 2020), which could reach up to 1 million m3 per event for the Seine River (Gasperi et al. 2012), and 50 million m3 for the whole year in Shanghai. The IPCC reported that if greenhouse gas emissions persist at or exceed the present rate, there is a high probability that the rate of global warming would accelerate in the 21st century, posing a significant stress on the drainage system (Li et al. 2023). Through the simulation of a combined sewer system, Abdellatif et al. (2015) predicted that with climate change, by 2080, the maximum annual projected increases are 37% increase in CSO volume, 32% increase in the overflow duration and 12% increase in the number of CSO events. Therefore, various topics, such as the prediction of CSO events (Sørup et al. 2018; Saddiqi et al. 2023), the optimization of the combined system (Daniel et al. 2019), the optimal combination of grey/green facilities (Roseboro et al. 2021; Xiong et al. 2022; Muttil et al. 2023), and the comparison between the combined and separated drainage systems (Mannina & Viviani 2009; Saddiqi et al. 2023) have attracted the great attention in recent years.

The peak concentrations in CSOs for chemical oxygen demand (COD), NH3-N, TSS and other regular indices are higher than stormwater effluents, domestic sewage and influents of WWTPs (Table 1). According to Table 2, particulate COD, total nitrogen (TN), and total phosphorus (TP) account for >58.5, >44.2, and >72.3% of the total contents in the CSOs, respectively; additionally, the overflow particles are easy to enrich highly proportion of pathogenic microorganisms and persistent pollutants, such as Escherichia coli, heavy metals (HMs), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and other pathogenic microorganisms. The above persistent pollutants with particles cause greater harm to the urban river than that in the dissolved state, which is not only because of the high concentration, but also because of the long stabilization time. Gram positives show a higher frequency in HM-polluted sediments than in the control sediment (Xu et al. 2018). Wu et al. (2019) have found that the proportion of Gram positives in the river sediments affected by the CSO discharge were significantly higher than that in the adjacent unaffected areas mainly due to the enrichment of particulate pollutants. In CSOs, the content of particulate HMs could account for more than 72%, and the finer the particle is, the higher the HM concentration is (Xu et al. 2018). Furthermore, about 20% of these particulate HMs are bioavailable, which are easy to accumulate in the urban rivers, also showing negative impacts on the microbial composition in the riparian sediment, and leading to the variation of some sensitive gene function (Xu et al. 2018). Except for naphthalene, fluorene, and acenaphthalene, the average proportion of particulate PAHs are more than 70% (Table 2). In addition, trichlorobenzene, ethylbenzene, toluene, xylenes, tetrachloroethylene, six kinds of PCBs, and Deca-BDE chloroalkanes in CSOs are also mainly in the granular state (Table 2). In recent years, microplastics have been widely studied by scholars in marine, environmental, medical, and other fields. Microplastics are a kind of granular pollutants, distinct from other pollutants that are attached to particles like gravel, being able to adsorb other pollutants. In the Vorno estuary of the southwest Baltic Sea, the direct contribution of CSOs to microplastics was 6.1% (Piehl et al. 2021), the diameter of which covered 20–500 μm (Schernewski et al. 2020). Generally, the PSD of overflow particles is mainly between 1 and 4,750 μm, which is beneficial to the enrichment of various pollutants. Therefore, reducing particulate matters entering the urban rivers through the source, process and end-of-pipe control measures becomes a key step for the effectively reduction of CSOs' negative impacts.

Table 1

Concentration of various pollutants in CSOs, wet-weather flow and WWTP

SiteCODNH3-NTNTKNTPTSS∑16PAHCrPbE. coliLog10E.coil CFU/100 mL
mg/Lmg/Lmg/Lmg/Lmg/Lmg/Lμg/Lμg/Lμg/L
Combined sewer overflows General characteristics in the USA (Metcalf & Eddy, 1991260–480    1.2–2.8 270–550     
Cosenza, Italy (Piro et al., 201246–2,607     32–4,487     
Paris, France (Gasperi et al. 2012281–737   13–39  144–495 0.805–5.2 –20 34–337  
Germany (Brombach et al. 2005141 1.94 12.6  1.25 174.5     
North-Rhine Westphalia, Germany (Tondera et al. 201915–918    0.1–16.1 1–1,123     
Santiago de Compostela, Spain (Diaz-Fierros T et al. 2002134–540 5.2–12.8   0.5–4.6 160–411     
Slovakia (Sztruhár et al. 2002445 6.21 16.8  2.63 430     
Bogota, Colombia (Caradot et al. 2015192–850          
Korea (Lee & Bang, 2000119–224     99–215.7     
China (Xu et al. 201798–881 9.68–26.54   1.54–6.78      
Shanghai, China (Li et al. 2010243–484 7.6–17.2    91–529     
Stormwater effluents Stuttgart, Germany (David et al. 201139–767  3–28.4  <0.06–5.83 58–965   6.4–62.4  
Paris, France (Zgheib et al. 2012      0.677–6.477 <10–45 <10–129  
Sydney, Australia (Carleton, 1990180     186     
Shanghai, China (Yin et al. 201747–714 1.4–33.6 5.9–42.2  0.6–8.16 80–336     
Toronto, Canada (Staley et al. 2016         1.91–5.16 
Raw wastewater from households Verona, Italy (Botturi et al. 2020464 ± 171 65 ± 18   7.2 ± 1.8 270 ± 105    7.26 ± 6.86 
China (Wang et al. 2012; Li et al. 2009182–427 20–69.45 24–33  4.11–8.0 186–320     
Influent of WWTP GLWA WRRF, the US (Yang et al. 2019280 ± 85          
Castiglione Torinese, Itlia (Borzooei et al. 2019395 ± 111.1 22.24 ± 5.98    185 ± 61.5     
Stuttgart, Germany (Launay et al. 2016      0.283–1.808    
China (nWWTPs ≥ 2,794) (Sun et al. 201662.5–750 4.5–76.7 6.5–85  ∼15 25–495     
Effluent of WWTP China (nWWTPs ≥ 2,794) (Sun et al., 201633.8 4.1 10.9  0.7 12.1     
SiteCODNH3-NTNTKNTPTSS∑16PAHCrPbE. coliLog10E.coil CFU/100 mL
mg/Lmg/Lmg/Lmg/Lmg/Lmg/Lμg/Lμg/Lμg/L
Combined sewer overflows General characteristics in the USA (Metcalf & Eddy, 1991260–480    1.2–2.8 270–550     
Cosenza, Italy (Piro et al., 201246–2,607     32–4,487     
Paris, France (Gasperi et al. 2012281–737   13–39  144–495 0.805–5.2 –20 34–337  
Germany (Brombach et al. 2005141 1.94 12.6  1.25 174.5     
North-Rhine Westphalia, Germany (Tondera et al. 201915–918    0.1–16.1 1–1,123     
Santiago de Compostela, Spain (Diaz-Fierros T et al. 2002134–540 5.2–12.8   0.5–4.6 160–411     
Slovakia (Sztruhár et al. 2002445 6.21 16.8  2.63 430     
Bogota, Colombia (Caradot et al. 2015192–850          
Korea (Lee & Bang, 2000119–224     99–215.7     
China (Xu et al. 201798–881 9.68–26.54   1.54–6.78      
Shanghai, China (Li et al. 2010243–484 7.6–17.2    91–529     
Stormwater effluents Stuttgart, Germany (David et al. 201139–767  3–28.4  <0.06–5.83 58–965   6.4–62.4  
Paris, France (Zgheib et al. 2012      0.677–6.477 <10–45 <10–129  
Sydney, Australia (Carleton, 1990180     186     
Shanghai, China (Yin et al. 201747–714 1.4–33.6 5.9–42.2  0.6–8.16 80–336     
Toronto, Canada (Staley et al. 2016         1.91–5.16 
Raw wastewater from households Verona, Italy (Botturi et al. 2020464 ± 171 65 ± 18   7.2 ± 1.8 270 ± 105    7.26 ± 6.86 
China (Wang et al. 2012; Li et al. 2009182–427 20–69.45 24–33  4.11–8.0 186–320     
Influent of WWTP GLWA WRRF, the US (Yang et al. 2019280 ± 85          
Castiglione Torinese, Itlia (Borzooei et al. 2019395 ± 111.1 22.24 ± 5.98    185 ± 61.5     
Stuttgart, Germany (Launay et al. 2016      0.283–1.808    
China (nWWTPs ≥ 2,794) (Sun et al. 201662.5–750 4.5–76.7 6.5–85  ∼15 25–495     
Effluent of WWTP China (nWWTPs ≥ 2,794) (Sun et al., 201633.8 4.1 10.9  0.7 12.1     
Table 2

The proportion of particulate matter concentration in the different types of sewage

Combined sewer overflows
Stormwater effluents/stormwater storage pond
Raw wastewaterInfluent of WWTPEffluent of WWTP
TypesPollutantsCosenza, Italy (Piro et al. 2007)Paris, France (Paijens et al. 2020)Paris, France (Passerat et al. 2011)Clichy, Paris, France (mean ± SE) (Gasperi et al. 2012)Ecully, the Greater Lyon area, France (Becouze-Lareure et al. 2019)Shanghai, China (mean ± SE) (Zhang & Li, 2015)Staßfurt, Germany (David et al. 2011)Paris, France (Paijens et al. 2020)Chassieu, the Greater Lyon area, France (Becouze-Lareure et al. 2019)Grand Nancy, Fance (Houhou et al. 2009)Paris, France (Paijens et al. 2020)Paris, France (Paijens et al. 2020)
Conventional indices CODcr 58.5%–69.5% – – – – 88.0% ± 2.3% 54.4–70.5% – – – – – 
TN – – – – – 48.5% ± 4.3% > 66.3% – – – – – 
TP – – – – – 75.2% ± 2.9% > 79.7% – – – – – 
Heavy metal Cr – – – 100% 72.0% – – – 86.4% 30% – – 
Pb – – – 100% 84.0% – > 94.1% – 89.8% 62% – – 
Cu – – – 100% 73.3% – > 95.0% – 68.8% 35% – – 
Zn – – – 89.5% ± 2.9% 73.5% – 75.7–94.1% – 47.8% 49% – – 
Cd – – – – 77.8% – 100% – 57.1% 34% – – 
Pest Isoproturon – 30% – 0% 0% – – 16.3% 0% – 6.5% 2% 
Methylisothiazolinone – 41.3% – – – – – 11.3% – – 0% 0% 
Benzisothiazolinone – 73.7% – – – – – 77% – – 6.3% 1.7% 
Terbuthylazine – 39.7% – – – – – 2.3% – – 3% 1.5% 
Thiabendazole – 61.7% – – – – – 11% – – 7% 0% 
Atrazine – – – 0% 0% – – – 0% – – – 
Chlorfenvinphos – – – – 0% – – – 0% – – – 
Diuron – – – – 0.3% – – – 0.5% – – – 
Simazine – – – – 0% – – – 0.5% – – – 
Dieldrin – – – 100% – – – – – – – – 
Aldrin – – – 100% – – – – – – – – 
Tributyltin – – – 100% – – – – – – – – 
Dibutyltin – – – 100% – – – – – – – – 
Monobutyltin – – – 95.1% ± 4.1% – – – – – – – – 
PAH Benzo(a)pyrene – – – 97.1% ± 1.8% – – – – – – – – 
Benzo(b)fluoranthene – – – 100.0% 100.0% – – – 100.0% – – – 
Benzo(ghi)perylene – – – 97.7% ± 2.3% 100.0% – – – 100.0% – – – 
Benzo(k)fluoranthene – – – 100.0% 100.0% – – – 100.0% – – – 
Indeno(cd)pyrene – – – 97.1% ± 2.9% 100.0% – – – 100.0% – – – 
Anthracene – – – 89.1% ± 6.3% 100.0% – – – 100.0% – – – 
Naphthalene – – – 48.5% ± 17.9% – – – – – – – – 
Fluoranthene – – – 92.5% ± 2.9% 97.3% – – – 99.6% – – – 
Acenaphtylene – – – 100.0% – – – – – – – – 
Fluorene – – – 48.2% ± 20.4% – – – – – – – – 
Phenanthrene – – – 70.2% ± 6.2% – – – – – – – – 
Pyrene – – – 93.5% ± 2.3% – – – – – – – – 
Benzo(a)anthracene – – – 100.0% – – – – – – – – 
Chrysene – – – 100.0% – – – – – – – – 
Dibenz(ah)anthracene – – – 100.0% – – – – – – – – 
Acenaphthalene – – – 15.2% ± 8.8% – – – – – – – – 
Others Nonylphenols – – – 69.6% ± 6.2% – – – – – – – – 
Octylphenol – – – 100% – – – – – – – – 
Butylphenol – – – 30.9% – – – – – – – – 
E. coli – – 77% – – – – – – – – – 
Trichlorobenzene – – – 100% 100.0% – – – 100.0% – – – 
Ethylbenzene – – – 100% – – – – – – – – 
Toluene – – – 100% – – – – – – – – 
Xylenes – – – 100% – – – – – – – – 
Tetrachloroethylene – – – 100% – – – – – – – – 
PCB 28, PCB 101, PCB 118, PCB 138, PCB 153, PCB 180 – – – 100% – – – – – – – – 
Deca-BDE – – – 100% – – – – – – – – 
Chloroalkanes – – – 0% – – – – – – – – 
Combined sewer overflows
Stormwater effluents/stormwater storage pond
Raw wastewaterInfluent of WWTPEffluent of WWTP
TypesPollutantsCosenza, Italy (Piro et al. 2007)Paris, France (Paijens et al. 2020)Paris, France (Passerat et al. 2011)Clichy, Paris, France (mean ± SE) (Gasperi et al. 2012)Ecully, the Greater Lyon area, France (Becouze-Lareure et al. 2019)Shanghai, China (mean ± SE) (Zhang & Li, 2015)Staßfurt, Germany (David et al. 2011)Paris, France (Paijens et al. 2020)Chassieu, the Greater Lyon area, France (Becouze-Lareure et al. 2019)Grand Nancy, Fance (Houhou et al. 2009)Paris, France (Paijens et al. 2020)Paris, France (Paijens et al. 2020)
Conventional indices CODcr 58.5%–69.5% – – – – 88.0% ± 2.3% 54.4–70.5% – – – – – 
TN – – – – – 48.5% ± 4.3% > 66.3% – – – – – 
TP – – – – – 75.2% ± 2.9% > 79.7% – – – – – 
Heavy metal Cr – – – 100% 72.0% – – – 86.4% 30% – – 
Pb – – – 100% 84.0% – > 94.1% – 89.8% 62% – – 
Cu – – – 100% 73.3% – > 95.0% – 68.8% 35% – – 
Zn – – – 89.5% ± 2.9% 73.5% – 75.7–94.1% – 47.8% 49% – – 
Cd – – – – 77.8% – 100% – 57.1% 34% – – 
Pest Isoproturon – 30% – 0% 0% – – 16.3% 0% – 6.5% 2% 
Methylisothiazolinone – 41.3% – – – – – 11.3% – – 0% 0% 
Benzisothiazolinone – 73.7% – – – – – 77% – – 6.3% 1.7% 
Terbuthylazine – 39.7% – – – – – 2.3% – – 3% 1.5% 
Thiabendazole – 61.7% – – – – – 11% – – 7% 0% 
Atrazine – – – 0% 0% – – – 0% – – – 
Chlorfenvinphos – – – – 0% – – – 0% – – – 
Diuron – – – – 0.3% – – – 0.5% – – – 
Simazine – – – – 0% – – – 0.5% – – – 
Dieldrin – – – 100% – – – – – – – – 
Aldrin – – – 100% – – – – – – – – 
Tributyltin – – – 100% – – – – – – – – 
Dibutyltin – – – 100% – – – – – – – – 
Monobutyltin – – – 95.1% ± 4.1% – – – – – – – – 
PAH Benzo(a)pyrene – – – 97.1% ± 1.8% – – – – – – – – 
Benzo(b)fluoranthene – – – 100.0% 100.0% – – – 100.0% – – – 
Benzo(ghi)perylene – – – 97.7% ± 2.3% 100.0% – – – 100.0% – – – 
Benzo(k)fluoranthene – – – 100.0% 100.0% – – – 100.0% – – – 
Indeno(cd)pyrene – – – 97.1% ± 2.9% 100.0% – – – 100.0% – – – 
Anthracene – – – 89.1% ± 6.3% 100.0% – – – 100.0% – – – 
Naphthalene – – – 48.5% ± 17.9% – – – – – – – – 
Fluoranthene – – – 92.5% ± 2.9% 97.3% – – – 99.6% – – – 
Acenaphtylene – – – 100.0% – – – – – – – – 
Fluorene – – – 48.2% ± 20.4% – – – – – – – – 
Phenanthrene – – – 70.2% ± 6.2% – – – – – – – – 
Pyrene – – – 93.5% ± 2.3% – – – – – – – – 
Benzo(a)anthracene – – – 100.0% – – – – – – – – 
Chrysene – – – 100.0% – – – – – – – – 
Dibenz(ah)anthracene – – – 100.0% – – – – – – – – 
Acenaphthalene – – – 15.2% ± 8.8% – – – – – – – – 
Others Nonylphenols – – – 69.6% ± 6.2% – – – – – – – – 
Octylphenol – – – 100% – – – – – – – – 
Butylphenol – – – 30.9% – – – – – – – – 
E. coli – – 77% – – – – – – – – – 
Trichlorobenzene – – – 100% 100.0% – – – 100.0% – – – 
Ethylbenzene – – – 100% – – – – – – – – 
Toluene – – – 100% – – – – – – – – 
Xylenes – – – 100% – – – – – – – – 
Tetrachloroethylene – – – 100% – – – – – – – – 
PCB 28, PCB 101, PCB 118, PCB 138, PCB 153, PCB 180 – – – 100% – – – – – – – – 
Deca-BDE – – – 100% – – – – – – – – 
Chloroalkanes – – – 0% – – – – – – – – 

Consequently, the objectives of this review are to (i) identify the most influential keywords, scientists, journals, and countries active in sewer sediment research; (ii) summarize the source of particulate matters in sewers; (iii) analyze their sedimentation and erosion characteristics in sewers to clarify the sewer process; and (iv) propose the control points for various stages of particulate matter transmission.

In order to conduct a proper review process, a comprehensive review approach is developed, which contains scientometric analysis, in-depth discussion, and concluding remarks. Scientometric analysis was applied using the phrase ‘(particle or particle * or sedimentation * or deposit *) and (‘ separate * sewage system * ‘or’ separate system * ‘or’ separate * drainage system ‘or’ separate * flow system * ‘or’ drainage system ‘or’ sewer * or ‘drainage pipeline’ or CSO * or ‘combined sewer’)’ in the Web of Science core database. The filters include: (1) Literature type: Research paper, (2) Language: English; (3) Research direction: Environmental Sciences Ecology or Engineering or Water Resources. Finally, 477 relevant papers were selected and imported into VOSviewer software for scientometric analysis. The analysis methods are detailed in some references (Owolabi et al. 2022; Sojobi & Zayed 2022).

An in-depth discussion was undertaken after the scientometric analysis. This article reviews the theoretical approaches, the experimental results, and the effects of field application of relevant papers with a focus on the sources, the settlement, the erosion and the control measures of particulate matters in sewers. Research gaps and future research directions are provided after the discussion. The concluding remarks are covered in the final section.

Findings from scientometric analysis

The results of the scientometric analysis show that the top 10 keywords with the highest frequency of occurrence are ‘model’, ‘source’, ‘water’, ‘condition’, ‘sample’, ‘area’, ‘position’, ‘parameter’, ‘velocity’, and ‘size’, which are divided into two clusters, namely cluster I (model, condition, position, parameter, velocity, size) and cluster II (source, water, sample, and area) (Figure 1(a)). This result indicates that (1) ‘model’ and ‘source’ appear more frequently, which shows that these fields are the trending topics attracting a great attention in recent years; (2) models are usually appiled with the critical considerition of the flow velocity and other design parameters in the research of sewer sediments; and (3) the source analysis is commonly stem from in situ sampling methods.
Figure 1

Results of scientometric analysis: (a) keywords, (b) journals' contribution, (c) countries' contribution, and (d) co-citation.

Figure 1

Results of scientometric analysis: (a) keywords, (b) journals' contribution, (c) countries' contribution, and (d) co-citation.

Close modal

‘Water science and technology’, ‘Science of the total environment’, ‘Water research’, ‘Water air&soil pollution’, and ‘Environmental science and technology’ present their higher impact on this field of research than other journals (Figure 1(b)). Developing countries, such as China, Türkiye, Brazil, and India, have seen a significant increase in the number of research focusing on overflow pollutants (Figure 1(c)). This trend can be attributed to the emergence of these pollutants as a result of urbanization improvements in these countries.

Co-citation analysis reveals that the key research issues in this field are mainly divided into six aspects, the representative researchers are Chebbo, G., Bonakdari, H., Liu C.Y., Viklander M., Saul Aj., and Carbone M. (Figure 1(d)). In each aspect, researchers have more co-citations than those across the different aspects. The scholars in each field dominate this area and have major roles in their developments.

Source of particulate pollutants

Stormwater runoff, dry-weather flow, road washing water, and sewer sediments are the four main sources of particulate matters in the overflow pollution. Sewer sediments mainly come from the sedimentation of the particles in the first three sources.

Stormwater runoff

The contribution of stormwater runoff to particulate matters in CSOs was about 10–20% (Gasperi et al. 2010), which was mainly from the eroded road deposits (Zhang et al. 2017), and the pollutants which enrich mainly include peptide, metal, PAH, phosphate, PCB, VOC, chlorophenol, alkylphenol, and organotin (Zgheib et al. 2011). The proportion of HMs in granular state is more prominent, e.g., Cr, Pb, Cu, and Cd (David et al. 2011; Passerat et al. 2011; Becouze-Lareure et al. 2019) (Table 2). For some pesticides in CSOs and stormwater runoff (Table 2), there are great differences in concentration and species among different areas (Gasperi et al. 2012; Becouze-Lareure et al. 2019; Paijens et al. 2020). Therefore, the impact of particulate pollutants in stormwater runoff on the micro-ecological environment of urban rivers cannot be ignored (Xu et al. 2018; Wu et al. 2021). Numerous studies (Barco et al. 2008; Bach et al. 2010; Hathaway & Hunt 2011) suggest that during the occurrence of ‘first flush’, the rain and sewage mixed water with higher particle concentration could be discharged into the urban rivers. Wanielista & Yousef (1993) believed that ‘first flush’ is that 25–30% of the runoff volume at the initial stage carries at least 70% of the pollution load. The intensity and period of the stormwater runoff, the ADWP, and the amount of sediment are the key factors affecting the characteristics of ‘first flush’ or mid-late effects, which determine the scouring process of ground deposits, and affect the transfer of particles in the pipeline.

Sewage

The contribution of dry-weather flow to particulate matters in overflow pollution could reach 50% (Gasperi et al. 2010), which mainly includes domestic sewage, pretreated industrial sewage and commercial sewage in combined sewers and storm sewers with the mixed connection. The contents of particulate matters in domestic sewage are usually the highest among them. In order to improve the influent concentration and the treatment efficiency of WWTP, septic tanks in certain metropolitan regions have been progressively phased out (Liao et al. 2015). However, this has resulted in an overall rise in the quantity of residues and particles in drainage systems. In addition, with the rising application of kitchen waste shredders, more and more residues of the high viscosity flow into the pipeline directly. Therefore, the current design standards for drainage systems have challenges in effectively accommodating the heightened volume of particles.

Road washing water

While people generally acknowledge the logic behind road cleaning, they tend to overlook the associated issues. Street flushers usually operate on sunny days. As a result of the forceful flush of the washing water, a large number of road deposits are rapidly eroded to the roadside, and the majority of which enter the rainwater inlet. However, the cleaning duration at a certain point is exceedingly short, making it difficult to sustain the flow for an extended length. Consequently, the particles that enter the inlet may accumulate either in it or in the connecting pipe from the inlet to the main pipe. This accumulation may decrease the capacity of the sewer and greatly raise the probability of siltation in the main pipe. However, the road sweeping is generally more effective for coarse particles (Walker & Wong 1999; Pitt et al. 2005), and Sartor & Boyd (1972) found that the removal rate of the road sweeping particles (>2 mm) could reach 80%.

Sewer sediment

At the water–sediment interface, particles in motion were observed (Chebbo et al. 2003), and Crabtree (1989) determined that its organic content can reach 50%, with particle sizes not exceeding 2 mm. However, in the bottom granular layer, Crabtree (1989) determined that its inorganic content can reach 93%, with a high specificity in particle size distribution, for instance, particles ranging from 0.063 to 2.0 mm could reach up to 87%, but particles ranging from 2.0 to 5.0 mm could also reach 90%.

Sewer sediment is the main source of particulate pollutants in CSOs, and the contribution is easily affected by the rainfall intensity, the physical and chemical properties of sediments, the duration of deposit existence, and other internal and external factors (Gunkel & Pawlowsky-Reusing 2017). Xu et al. (2017) indicated that the erosion characteristics of sewer sediments had a significant influence on the total amount of emission particulate pollutants in CSOs. In the Clichy catchment area in Paris, Gasperi et al. (2010) indicated that between 47 and 69% of suspended particles in CSOs were from the eroded deposits. This means that the release law of eroded particles could influence the change in pollutant concentration during an overflow event. Passerat et al. (2011) conducted a study in the same area and found that the contribution of sediment to the suspended particles in CSOs could reach about 75%, of which 32% particles were eroded in the early 30 min of the rainfall events. Through the calculation of the above data in the Clichy area, it can be seen that 35.3–51.8% of the particles in the overflow pollution come from the eroded sediments, of which nearly 1/3 come from the initial 30 min of the rainfall. Xu et al. (2018) revealed that the initial effects, which are influenced by several factors such as peak rainfall periods, total rainfalls, and water volume, have the potential to modify the contribution of eroded sediments in rainy days (Xu et al. 2017). Overall, the removal of sewer sediments is of great significance to mitigating the chronic and acute harm of overflow pollution.

Briefly, the sedimentation of the particles from the above sources is mainly result of lacking necessary measures and maintenance (Figure 2(a) and 2(b)). However, sediments that are hard to erode are also washed up during heavy rainfall events (Figure 2(c)), which has become the most important reason for the heavy polluted CSOs and the rapid deterioration of the urban rivers. Section 3.3 will summarize and analyze the accumulation and erosion processes of particulate matters in pipelines.
Figure 2

Transport characteristics of particulate matters in the drainage system on dry and wet weather.

Figure 2

Transport characteristics of particulate matters in the drainage system on dry and wet weather.

Close modal

Sewer processes

Sources, particle properties, pipeline parameters, hydraulic conditions, the operation mode, and other relevant factors affect the erosion and settlement characteristics of particles in sewers. The following summarizes the fundamental concepts of sewer processes and analyzes the effects of different influences on these processes.

Settlement performance of pollutants in pipelines

Principle of settlement
Ahyerre et al. (2000) found that 30% of the particles present in sewage ultimately deposit and become part of sediments. The characteristics of particles and water flow are the main factors influencing their settlement, which can be explained by formula (1),
formula
(1)
where denotes the balance between friction velocity (, m/s) and settling velocity (, m/s). Breusers & Raudkivi (1991) specified the state of regular spherical particles in turbulent fluid, when > 6, the particles have a higher tendency of sedimentation; when 6 > > 2, the particles are easier to transport at the bottom; when < 2, the particles are basically suspended. When the low-density organic matter is attached to the particulate matter or itself is organic matter, ω will be diversified (Lecvine et al. 1985; Ashley et al. 1996).

ω can be measured under static and turbulent conditions. Static settlement methods include U.S. EPA Column (Dalrymple et al. 1975), Aston Column (Tyack et al. 1996), Brombach column (Michelbach & Wöhrle 1993), Cergrene apparatus (Aiguier et al. 1996) and Vitesse de chute en assainissement (VICAS) (Torres & Bertrand-Krajewski 2008). A significant drawback of the above measures is the large disparity between these detection processes and the sedimentation process of particles in the fields (Krishnappan et al. 2004). For this purpose, the Water Elutriation Apparatus (Krishnappan et al. 2004; Exall et al. 2009) has been developed and applied, which can be used to determine the mass proportion of particles with different ω in the water flow or sediments and isolate particles with different ω for subsequent analysis (Krishnappan et al. 2004; Exall et al. 2009).

Influencing factors of settlement

Dry and wet weather. The dry-weather flow presents periodic characteristics. The flow experiences a low intensity for a considerable portion of the time, resulting in occasional reverse flow or a drop in flow rate (Kirchheim 2005). It was found that 238 kg TSS sediments were accumulated at a rate of 8.5 km per day in a combined sewer in Berlin with an average diameter of DN1250, though the flow rate can reach the sediment incipient motion condition at certain times (Gunkel & Pawlowsky-Reusing 2017).

The particulate pollutants in stormwater runoff mainly come from the underlying surfaces such as roofs, pavements, green spaces and parks. According to numerous case studies (Torres & Bertrand-Krajewski 2008; Exall et al. 2009; Piro et al. 2011), the V50 of these particles were ranging from 0.2 to 11.0 m/h, depending on storm events, land use, urbanization and soil characteristics. On average, 50% w/w of particles smaller than 50 μm in diameter fall at a speed less than 2.98 m/h on average, and 50% of particles between 50 and 100 μm in diameter fall at a speed less than 9.8 m/h on average (Michelbach & Wöhrle 1993). Exall et al. (2009) revealed that the ω distributions in dry-weather samples followed the similar patterns as wet weather, though the constituents were in general less settleable than in wet-weather flows (WWFs). On the contrary, some studies believed that the ω in dry-weather samples (i.e., sanitary sewage) were greater than in WWFs (Piro et al. 2011). However, affected by the eroded sediments, the ω of particles from CSOs is expected to be consistently greater on global scale compared to the measurements taken from both runoff and dry-weather flow.

Particle properties. The size and density of particles, as well as the characteristics of interparticle connections, are intrinsic factors that affect their SP. The proportions of inorganic and organic matter in the particles have a certain effect on their density, whereas the mucus material in organics and microbial secretions, and the microbial flora have a more prominent effect on the density and SP. The type and the magnitude of interparticle force, which in turn affect their SP, are primarily influenced by microbial secretion, pH, the components, and the concentration (Ghashoghchi et al. 2017). In the bio-flocculation process, extracellular polymeric substance (EPS) molecules are attached to mineral surfaces, inter-bridge the divides between particles and creates steady three-dimensional network of flocks which improves the ω of mineral particles (Devi & Natarajan 2015). Moreover, when pH < 7, there is an intensified electrostatic force between microbial secretions and particles, hence enhancing particle adhesion. Conversely, as the pH value increases, the electrostatic force decreases, leading to a repulsion force in alkaline conditions (Ghashoghchi et al. 2017). In the bioflocculant solution, the lower PZC value of kaolin makes its lower sedimentation performance than the quartz (Ghashoghchi et al. 2017). The microbial activity is prominent on the surface of sewer sediments, leading a much higher volume of the microbial secretion contrasted to the middle and lower layers. The surface microorganisms are more likely to adhere to the particles in the aqueous phase as the flow velocity decreases, accelerating their settlement. The effect of the polysaccharide in the sediment is higher than that of proteins (Meng et al. 2019). Ghashoghchi et al. (2017) confirmed that the adhesion of polysaccharide to quartz and kaolin is greater than that of protein. The larger proportion of fine particles results in the relatively poor ω of particles in stormwater runoffs. However, viscous substances are non-selective to particles, and the long distance transportation can increase these bonding time, therefore these fine particles still have the potential to settle. When designing drainage systems, various countries adopt slightly different standards, primarily based on non-silting conditions (such as minimum velocity and minimum shear stress, as shown in Tables 3 and 4). However, these standards rarely take into account their variations caused by the changes in sediment components and properties.

Table 3

Minimum velocity of design criteria

CountrySewer typeMinimum velocity (m/s)FullnessReference
USA Sanitary 0.6 Design fullness American Society Of Civil Engineers (ASCE) (1970)  
Storm 0.9 Design fullness 
Colorado, USA Sanitary 0.6096 – Department Of Wastewater (2010)  
North Carolina, USA Gravity sewers 0.6096 Half full North Carolina Department of Environment & Natural Resources (2008)  
Europe Gravity sewers 0.7 (D < 300 mm) – CEN (European Committee for Standardization) (2008)  
≥ 0.7 (900 mm > D ≥ 300 mm) – 
1.0 (D ≥ 900 mm) Full 
Germany Gravity sewers 0.48 (D = 150 mm)-2.03 (D = 3,000 mm) ≥ 0.3 (0.1–0.3, velocity plus 10%) ATV-DVWK-Regelwerk (2001)  
France Sanitary 0.3 Mean daily (0.2, at least) Minister of Interior (1977)  
Combined 1.0 Full 
Storm 0.6 (Return period = 10 years) 0.1 
UK Gravity sewers 1.0 Full British Standard Institution (1987)  
Malaysia Storm (Lined channel) 0.9 – Department of Irrigation & Drainage (1975)  
Storm (Open lined sewer) 0.6 – Department of Irrigation & Drainage (2012)  
Kuwait Storm (Rectangular open channel) 0.75 – Bong (2016)  
Toronto, Canada Sanitary/Combined 0.6 Full Engineering & Construction Services – Business Improvement & Standards (2021)  
Storm 0.8 Full 
China Sanitary 0.6 Design fullness Ministry of Housing & Urban-Rural Development (2021)  
Combined/Storm 0.75 Full 
Open channel 0.4 Superelevation > 0.2 mm 
CountrySewer typeMinimum velocity (m/s)FullnessReference
USA Sanitary 0.6 Design fullness American Society Of Civil Engineers (ASCE) (1970)  
Storm 0.9 Design fullness 
Colorado, USA Sanitary 0.6096 – Department Of Wastewater (2010)  
North Carolina, USA Gravity sewers 0.6096 Half full North Carolina Department of Environment & Natural Resources (2008)  
Europe Gravity sewers 0.7 (D < 300 mm) – CEN (European Committee for Standardization) (2008)  
≥ 0.7 (900 mm > D ≥ 300 mm) – 
1.0 (D ≥ 900 mm) Full 
Germany Gravity sewers 0.48 (D = 150 mm)-2.03 (D = 3,000 mm) ≥ 0.3 (0.1–0.3, velocity plus 10%) ATV-DVWK-Regelwerk (2001)  
France Sanitary 0.3 Mean daily (0.2, at least) Minister of Interior (1977)  
Combined 1.0 Full 
Storm 0.6 (Return period = 10 years) 0.1 
UK Gravity sewers 1.0 Full British Standard Institution (1987)  
Malaysia Storm (Lined channel) 0.9 – Department of Irrigation & Drainage (1975)  
Storm (Open lined sewer) 0.6 – Department of Irrigation & Drainage (2012)  
Kuwait Storm (Rectangular open channel) 0.75 – Bong (2016)  
Toronto, Canada Sanitary/Combined 0.6 Full Engineering & Construction Services – Business Improvement & Standards (2021)  
Storm 0.8 Full 
China Sanitary 0.6 Design fullness Ministry of Housing & Urban-Rural Development (2021)  
Combined/Storm 0.75 Full 
Open channel 0.4 Superelevation > 0.2 mm 
Table 4

Minimum shear stress of design criteria

CountrySewer typeMinimum shear stress (N/m2)Reference
USA – 2.0–4.0 Lysne (1969)  
– 1.3–12.6 American Society Of Civil Engineers (ASCE) (1970)  
Sanitary 1.0–2.0 Yao (1974)  
Storm 3.0–4.0 
Germany Sanitary Depends on transport capacity and concentration Macke (1982)  
Combined 
Storm 
Combined 1.6 (transport 90% of all sediments) Brombach et al. (1992)  
– 1.0 ATV-DVWK Arbeitsblatt A 110 (2006)  
UK – 6.2 CIRIA (1986)  
Norway Combined 3.0–4.0 Lindholm (1984)  
Storm 2.0 
Sweden Gravity sewers 1.0–1.5 (if sediments are present) Scandiaconsult (1974)  
Dundee/Hanover Gravity sewers 1.5–2.0 Ashley et al. (1992); Ashley et al. (1993)  
Sweden Gravity sewers 2.0–4.0 Stotz & Krauth (1986)  
Laboratory study – 2.5 (a weaker sediment) Nalluri & Alvarez (1992)  
6.0–7.0 (the sewer sediment analogues) 
Laboratory study – 2.0 (Cohesive sediment) Ackers et al. (2001)  
CountrySewer typeMinimum shear stress (N/m2)Reference
USA – 2.0–4.0 Lysne (1969)  
– 1.3–12.6 American Society Of Civil Engineers (ASCE) (1970)  
Sanitary 1.0–2.0 Yao (1974)  
Storm 3.0–4.0 
Germany Sanitary Depends on transport capacity and concentration Macke (1982)  
Combined 
Storm 
Combined 1.6 (transport 90% of all sediments) Brombach et al. (1992)  
– 1.0 ATV-DVWK Arbeitsblatt A 110 (2006)  
UK – 6.2 CIRIA (1986)  
Norway Combined 3.0–4.0 Lindholm (1984)  
Storm 2.0 
Sweden Gravity sewers 1.0–1.5 (if sediments are present) Scandiaconsult (1974)  
Dundee/Hanover Gravity sewers 1.5–2.0 Ashley et al. (1992); Ashley et al. (1993)  
Sweden Gravity sewers 2.0–4.0 Stotz & Krauth (1986)  
Laboratory study – 2.5 (a weaker sediment) Nalluri & Alvarez (1992)  
6.0–7.0 (the sewer sediment analogues) 
Laboratory study – 2.0 (Cohesive sediment) Ackers et al. (2001)  

Type of pollutant. The types of the attached pollutants affect particles' density, shape and size, and then change their SP. Food residues and domestic garbage with higher uncertainty are transported from domestic sources, resulting in significant variations in the ω of particles in dry flow (0.36–313.2 m/h, (Michelbach & Wöhrle 1992)). Legge et al. (2021) analyzed the ω of 18 kinds of food residues and found that all of them were higher than 10 m/h, which is the maximum surface loading recommended by the German ATV A128 guideline (ATV 1992) for the design of stormwater tanks. Nevertheless, as a consequence of the low influent concentrations of WWTPs, certain countries began to progressively eliminate septic tanks in urban areas (Liao et al. 2015). Accordingly, a large quantity of domestic residues are now directly entering drainage systems (Liao et al. 2015; Wei et al. 2020). However, there has not been a simultaneous alteration in the design and operational approach of drainage systems. Therefore, these residues are bound to aggravate the sedimentation.

Existing sediment bed deposits. Sewer sediments occupy the space inside the pipe and cause the inner surface to become rougher. Ackers et al. (2001) found that if there is no sediment initially, transporting particles would reduce the transportation capacity of the pipeline by 1%; if the thickness of the sediment reaches 5% of the pipe diameter, the reduced conveying capacity is about 3% merely on account of the reduced volume, while the reduced capacity caused by the increase of bottom roughness coefficient can reach about 20%. Therefore, the prompt removal of sewer sediments is highly important in enhancing the efficiency of pipeline operations.

Operation mode. The flow pattern affected by the operation mode of pump stations shows great influence on the SP. If the pump station stops running, the flow velocity in the pipe upstream would be lower than the non-silting velocity. In some urban areas with high groundwater level, in order to reduce groundwater infiltration and the risk of pipeline collapse, the high liquid level in the pipeline is set up all year round (Yu & Chen 2021).

The high fullness and low flow velocity inevitably intensify the sedimentation of particles from the stormwater runoff and the sewage, especially some domestic residues with strong anti-scouring capacity (Legge et al. 2021).

Sediment erosion

Principle of erosion
The sediment would be eroded and transported, when the bed shear stress is greater than the critical shear stress. Ariathurai & Krone (1976) established the erosion equation of the sewer sediment and calculated the sediment erosion rate Me.
formula
(2)
where τb (N/m2) denotes the bed shear stress at time t (s), τc denotes the critical shear stress at the point of erosion, Mc denotes a model parameter. Equation (2) could better reflect and predict the sediment release in combined sewers under various hydraulic conditions, therefore it could be applied in a sediment transport model coupling with SWMM5 (Storm Water Management Model, the fifth version) (Seco et al. 2017).
Influence of erosion

According to Equation (2), it can be found that, the larger τb(t) or the smaller τc is, the more sediment eroded. τc is mainly affected by the properties of particles and the stratification of sediments (Seco et al. 2017). Camp (1942) first explained the minimum scouring velocity, where the particle properties, such as the particle size and the density was positive to the scouring velocity, (m/s).

MOHTAR et al. (2022) established a relationship between the flow velocity and the critical shear stress, (N/m2) in the form:
formula
(3)
where ρs represent the density of sediment (kg/m3). MOHTAR et al. (2022) evaluated the incipient motion on various bed thicknesses based on Equation (3), and concluded that the effect of the thickness is rather evident on the design parameters of the drainage systems.

However, through the scouring experiment in the laboratory, Liu et al. (2021) found that the proportion of eroded particles increases gradually with the increase in D50 from 0.33 to 0.83 mm, where some unknown factors may change the anti-scouring ability of particles. Additionally, the imperfect reflection of PSD by D50 can conceal the correlation between PSD and the eroded particles. Thus, when quantitatively calculating the eroded particles, it is necessary to consider the range of PSD, so as to approach the real situation, although more complex measurement and calculation are required to meet the above requirements.

It is viscous substances in organic matter that directly affect the anti-scouring characteristics of particulate matter, which mainly include EPS, polysaccharide, protein, etc (Meng et al. 2019). Meng et al. (2019) found that the biofilm on the surface was significantly higher than that in the middle or on the bottom of the sediment. With the passage of time, under the anaerobic environment in the pipe, the filamentous and multilayer structure formed by the biofilm on the sediment surface gradually expands and thickens, improving their anti-scouring performance (Black et al. 2002).

Sediment stratification is mainly affected by the thickness and physicochemical properties of each layer in sediments (Meng et al. 2019). The properties are quite different at different depths (Meng et al. 2019). Bottom coarse bed sediment (GBS), middle organic layer (OL) and upper biofilm (BF) are the three categories of sewer sediment (Ahyerre 2001), where the inorganic components of GBS accounted for a high proportion, their particle size could reach millimeter level, and the mass accounted for the largest proportion in the sediments; OL is composed of fine particles with a higher content of attached pollutants and a weaker τc (<0.1 N/m2) than that of GBS; BF is mainly distributed on the pipe wall near the water and sediment surface. Previous studies (Ahyerre & Chebbo 2002) believed that the characteristics of pollutants in OL were similar to CSOs. However, Rammal et al. (2017) recalculated and found that only up to 36% of the pollutants in the CSOs came from OL and the rest came from unknown components. This difference will also be biased by the selection of CSO events. Parchure & Mehta (1985) further defined τc,
formula
(4)
where τcs (N/m2) denotes the critical shear stress of the bed surface, τcu (N/m2) denotes the critical shear stress at the bottom of the wake layer (Oms et al. 2005) having a depth d′ (m), d represents the erosion depth (m), b is a dimensionless parameter. Equation (4) combined with the bed sediment structure hypothesized by Peter et al. (1999) were used to estimate the sediment erosion rate (Mannina et al. 2017) and their contribution in CSOs (Seco et al. 2017). The variation of d′ affects τcs theoretically (Meng et al. 2019), but when the upper scourable component thickens, the change rule of τcs is not clear; τcs is also mainly related to the properties of the bottom particles. Therefore, these parameters of Equation (4) are difficult to calibrate. Kanso et al. (2005) tried to explore the relationship between τc and their attenuation gradient under different thickness. But the results showed that the attenuation gradient was uncertain, which brings great complexity to the analysis of sediment erosion characteristics. In addition, Regueiro-Picallo et al. (2020) believed that the equation obtained from non-cohesive materials differs from the actual situation in the pipeline.
Eroded particles

Eroded particles are the main source of pollutants discharged in rainy days. With the increase in rainfall, their contribution can reach up to 80% of particles in overflow pollution (Li et al. 2022). According to Table 5, the contribution of their attached COD, BOD5, TN, TP, E. coli, Internal entercocci, PAHs and Cu, respectively representing oxygen consuming pollutants, biodegradable pollutants, nutrients, common flora, and persistent pollutants, can reach more than 50% of the total pollution loads in overflow pollution.

Table 5

Contribution of eroded particles to wet-weather flow

SiteKunmingBeijingParisParisthe USShanghaiSwitzerland
SS 15–60% 31.9% 40–60% 40–81% 75% 23.8% 35–70% 
COD 13–66% 33.8% 25–50% 26–72%    
BOD5 10–55%  25–65% 32–80%    
TN 1–60% 23.1%      
TP 1–60% 30%      
E. coli     10–70%   
Intestinal enterococci     40–80%   
PAHs   40–80%     
Cu   30–65%     
Method Entry-exit mass balance model Stochastic modeling of TSS 
Reference Zhao et al. (2008)  Li et al. (2013)  Gasperi et al. (2010)  Gromaire et al. (2001)  Passerat et al. (2011)  Dai et al. (2013)  Rossi et al. (2005)  
SiteKunmingBeijingParisParisthe USShanghaiSwitzerland
SS 15–60% 31.9% 40–60% 40–81% 75% 23.8% 35–70% 
COD 13–66% 33.8% 25–50% 26–72%    
BOD5 10–55%  25–65% 32–80%    
TN 1–60% 23.1%      
TP 1–60% 30%      
E. coli     10–70%   
Intestinal enterococci     40–80%   
PAHs   40–80%     
Cu   30–65%     
Method Entry-exit mass balance model Stochastic modeling of TSS 
Reference Zhao et al. (2008)  Li et al. (2013)  Gasperi et al. (2010)  Gromaire et al. (2001)  Passerat et al. (2011)  Dai et al. (2013)  Rossi et al. (2005)  

Gasperi et al. (2010) analyzed the organic matter and PAHs in eroded particles and found that the contents are similar in OL (Table 6), but they did not find the OL within the scope of the investigation. On the one hand, the survey scope may not cover the whole service area (Gasperi et al. 2010); on the other hand, it may be the unknown source mentioned above. To master this source, it is necessary to systematically analyze the particle size distribution and the proportion of attached pollutants. Xu et al. (2018) made some explorations in a similar area by analyzing the concentration and mass proportion of HMs in different particle sizes and chemical species among the sewer sediment, the stormwater runoff, the dry-weather flow and CSOs, and finally identified the possible sources of pollutants, where the characteristics of Cu in CSOs are relatively similar to those in the sewer sediment. However, this research (Xu et al. 2018) failed to compare the pollutants with different particle sizes and chemical species in the different layers of sediments and the eroded particles. El et al. (2004) also qualitatively expounded that the eroded particles were the main source of HMs in CSOs by analyzing the changes in chemical forms of HMs. In conclusion, to further explore the release rule of eroded particles, more detailed research is still needed.

Table 6

Properties of eroded particles and sewer deposits

VSS (%)COD (g O2/g)BOD (g O2/g)PAHs (μg/g)Cu (μg/g)Zn (μg/g)Reference
EP 62–70 0.95–1.35 0.32–0.48 4–15 – – Gasperi et al. (2010)  
OL 56–72 0.65–1.26 – – 416–1,420 1,441–2,836 Kafi (2006)  
63–80 0.8–1.8 0.25–0.55 2.7–14.8 – – Gasperi (2006); Oms (2003)  
63–76 1.02–1.37 0.14–0.34 2.1–31.7 – – Ahyerre et al. (2001); Rocher et al. (2004)  
BF 49–87 1.4–2.1 0.26–0.54 0.1–5.5 – – Ahyerre et al. (2001); Rocher et al. (2004)  
GBS 6.2–11 0.18 0.019 9.3–54 – – Ahyerre et al. (2001); Rocher et al. (2004)  
– – – – 426–1,279 1,145–4,622 Kafi (2006)  
WP 85–91 1.47–1.79 0.58–0.76 3.8–7.1 – – Gasperi (2006); Kafi (2006)  
VSS (%)COD (g O2/g)BOD (g O2/g)PAHs (μg/g)Cu (μg/g)Zn (μg/g)Reference
EP 62–70 0.95–1.35 0.32–0.48 4–15 – – Gasperi et al. (2010)  
OL 56–72 0.65–1.26 – – 416–1,420 1,441–2,836 Kafi (2006)  
63–80 0.8–1.8 0.25–0.55 2.7–14.8 – – Gasperi (2006); Oms (2003)  
63–76 1.02–1.37 0.14–0.34 2.1–31.7 – – Ahyerre et al. (2001); Rocher et al. (2004)  
BF 49–87 1.4–2.1 0.26–0.54 0.1–5.5 – – Ahyerre et al. (2001); Rocher et al. (2004)  
GBS 6.2–11 0.18 0.019 9.3–54 – – Ahyerre et al. (2001); Rocher et al. (2004)  
– – – – 426–1,279 1,145–4,622 Kafi (2006)  
WP 85–91 1.47–1.79 0.58–0.76 3.8–7.1 – – Gasperi (2006); Kafi (2006)  

EP, eroded particle; OL, organic layer; BF, biofilm; GBS, gross bed sediment; WP, wastewater particle.

Control technologies for particulate matters in drainage system

This chapter primarily emphasizes the control mode of overflow particles (Figure 3). The actions implemented to prevent pollutants from entering the drainage system are referred to as SC. To reduce particle deposition and enhance scouring capacity, PC can be implemented. For managing dry- and wet- weather flow as well as eroded sediments, EC can be adopted. Some control measures are discussed as follows.
Figure 3

Particle transport paths and potential control measures.

Figure 3

Particle transport paths and potential control measures.

Close modal

Source control

SC is aimed at reducing particulate matter from sewage, surface runoff, and road washing water at the source of generation.

Dry-weather flow

Dry-weather flow mainly includes domestic sewage, pretreated industrial wastewater, and groundwater. The septic tank not only removes the particles in domestic sewage, but also reduces some biodegradable organic matter, thus reducing the influent COD concentration of WWTP (Liao et al. 2015). In addition, fecal particles could be mixed with viruses into the septic tank, which increases the risk of virus transmission such as SARS-COV-2 (Zhang et al. 2020). The septic tank is still the most suitable measure to preliminarily remove particles from the pollution source. With the advantages of the simple structure, the low investment and the less maintenance, its average removal efficiency of TSS reaches 61–63%, and the reduced TSS concentration is up to 700 mg/L (Sellami et al. 2009; Sharma & Kazmi 2021). Therefore, whether to use septic tanks or other pretreatment facilities should be considered based on the whole process of pollutant treatment. For industrial wastewater, all countries have relatively strict treatment standards. In addition to the centralized treatment of industrial wastewater, separate factories also need to meet pretreatment emission standards. For example, the limit value of pretreated discharge in the US is set below 20 mg/L TSS (Unite States Environmental Protection Agency 2014); the standard in Shanghai is lower than 70 mg/L (Shanghai Municipal Bureau of Environment 2009). Considering the relatively low emission concentrations, the pretreated industrial wastewater usually does not contribute much to TSS in dry-weather flow. Similarly, even in the mining area, the average TSS concentration of groundwater is only 73 mg/L (Saalidong et al. 2022), thus their contribution is also insufficient.

Stormwater runoff

SUDS, BMPs, and LID are the main SC means for particulate matters in stormwater runoff, specifically including Green Roof (Rowe 2011), Rain Barrel (Putri et al. 2023), Planter Trench (Lucas & Sample 2015), Porous Pavement (Newman et al. 2013), Bioretention (Bratieres et al. 2008; Read et al. 2008; Hurley & Forman 2011), Pervious Concrete Infiltration Basin (Horst et al. 2011), and combination of multiple technologies (Scholes et al. 2008). The treatment efficiency of the above measures for particulate matters is mainly subject to the designed hydraulic- and pollution- load (Lucas & Sample 2015). Exploring the suitability, the rationality and the economics of each technology is the key to their optimizations in practice. In the process of the application, the impact of the climate change (Salerno et al. 2018), hydrological and topographic conditions (Fan et al. 2022), the parameters of each facility, and the process layout (Fan et al. 2022) are the key factors affecting the efficiency of SC measures. The parameters, including surface roughness, surface slope, berm height, soil thickness, soil porosity, storage volume ratio, and vegetation volume fraction, also have non-negligible impacts on the first two factors. Fan et al. (2022) compared the parameters of the bioretention, the rain garden, and the vegetative swale in high-density urban areas through the sensitive analysis. The results showed that ‘berm height’ had the most significant effect on the peak concentration and the reduction rate of TSS (Fan et al. 2022). Kourtis et al. (2021) recommended two indexes to integrate various parameters for the optimization of the selection process, IRUDA and CVAI.

In high-density urban areas, the capacity of SC measures to resist high hydraulic load is insufficient, but they could effectively reduce TSS in the systems with low hydraulic load (Fan et al. 2022). Xiong et al. (2022) recommended LID-gray measures and found that the optimal LID-gray measure increased TSS removal rate by 37–74% compared with only LID patches under 9.1–21.8 mm rainfalls. Improving the permeability of the whole area is more efficient than constructing gray facilities in areas characterized by low population densities and high rainfalls (Kourtis et al. 2021). Therefore, various green measures can be applied as SC measures, but their local adaptabilities require additional consideration (Vijayaraghavan et al. 2021).

Road washing water

The road washing in rainy days and the particle interception in the rain comb (Sommer et al. 2007) are both feasible ways to reduce the deposits in the connecting pipe. In order to reduce the particles entering the pipeline, gully pots are usually installed. Rietveld (2021) reported that the size of particles which precipitated in gully pots was significantly higher than that entering the pipeline. This result indicates that the coarse particles with a higher organic fraction are more likely to settle in gully pots, while the fine particles are more likely to enter the drainage pipeline. Rietveld (2021) also found that when the deposition amount in the gully pot rises, its efficiency can diminish to zero. Therefore, the substantial deposition of sediments in the gully pot resulting from road washing on sunny days could conversely reduce its efficiency. As a result, the SP and the organic fraction of particles entering the drainage pipe could significantly increase, thereby aggravating the deposition within the pipeline. Therefore, it is imperative to choose an appropriate time for cleaning gully pots and optimize the schedule of the road washing in order to minimize particles entering from roads. According to the analysis provided, the cleaning frequency of gully pots is both related to the frequency of the road washing process and the characteristics of stormwater runoffs. It is crucial to clarify the impact of these factors and the relationship between them.

Since the hydraulic conditions are fixed, it is not feasible to reduce the deposition of particles in the pipeline by improving the washing parameters. Therefore, it is considered imperative to carry out the flushing during heavy rainfalls to ensure the efficient transportation of particles to the main pipe. The utilization of vacuum suction has the potential to fundamentally reduce these deposits at a reasonable frequency.

Process control

The main purpose of PC is to prevent sedimentation from various sources and remove the sediments in situ (Figure 3). Both patterns can be implemented by increasing τb in the pipe, and the latter one can be realized by mechanical or hydraulic cleaning.

The ways to increase τb mainly include improving the design standard of the non-silting velocity (Vanegas et al. 2022), adding hydraulic scouring device (Laplace et al. 2003) and making full use of the dispatching process of the pump station (Khudair & Jbbar 2018).

Improvement of design standards

Under the conservative design slope, the accumulation of sediment in the drainage system is inevitable in pipes. Therefore, engineers and researchers need to consider how to achieve the dynamic balance by not only controlling the buried depth of the pipeline, but also ensuring its operation healthily.

For more than 50 years, many departments around the world have stipulated the shear velocity or minimum velocity of gravity sewers (American Society of Civil Engineers (ASCE) 1970; Department of Irrigation & Drainage 1975, 2012; Minister of Interior 1977; British Standard Institution 1987; ATV-DVWK-Regelwerk 2001; CEN (European Committee for Standardization) 2008; North Carolina Department of Environment & Natural Resources 2008; Department Of Wastewater 2010; Bong 2016; Engineering & Construction Services – Business Improvement & Standards 2021; Ministry of Housing & Urban-Rural Development 2021) (Table 3). By comparing the data of the storm- and the combined- sewer, and the sewage pipes in the same area (Table 3), it can be seen that the design values of τ or minimum velocity of the first two kinds of pipes are higher than the last one; the larger the pipe diameter, the greater the value required; and the value required in the pipeline is higher than that in the open channel. At present, the adopted drainage pipe design standards are all applicable to the ideal situation. In the fields, some food residues with strong SP are easy to accumulate in the sewers, thus the formation of sediments is inevitable after a period of time (Vanegas et al. 2022). Even under the non-silting standard, sediments could still occur in some points of the pipeline (Vanegas et al. 2022). Accompanying existing sediments, the rise of the wall roughness coefficient could show significant negative effects on τb (Ackers et al. 2001). Therefore, the shear stress design criteria are also specified in some countries (Lysne 1969; American Society Of Civil Engineers (ASCE) 1970; Yao 1974; Macke 1982; Lindholm 1984; CIRIA 1986; Stotz & Krauth 1986; Ashley et al. 1992; Brombach et al. 1992; Nalluri & Alvarez 1992; Ashley et al. 1993; Ackers et al. 2001) (Table 4). These τ values are distributed between 1.0 and 12.6 N/m2. The difference of more than 10 times may imply that the sources, directly affecting the density and the degree of polymerization of sediments, vary greatly in different regions. According to the above discussion, when taking τ as the design standard, both internal factors, such as sediment properties and fluid viscosity, and external factors, such as the contact characteristics between fluid and sedimentary layer, are considered (Bouteligier et al. 2002; Lange 2013).

Consequently, when designing the non-silting velocity, we could first quantitatively analyze the settlement characteristics of particles from the sewage and the stormwater runoff in the service area, evaluate the possible accumulation of particles from various sources under the periodic discharge, and analyze the anti-scouring performance of the accumulated particles. Then, the non-silting velocity or τ can be obtained by calculating whether the daily flow meets the scouring requirement. Arthur et al. (1999) proposed to optimize the design parameters according to the non-silting conditions of different types of sediments. Combined with the previous discussion, this kind of design method can be further optimized. Firstly, on the premise of reasonably controlling the pipeline slope based on geological and economic conditions, the characteristics of easily sedimentary components need to be analyzed. Secondly, the potential sedimentation should be considered in the design process by modifying the parameters such as the water cross section and the roughness coefficient, so as to prevent sedimentation. The specific design process is seen in Figure 4:
Figure 4

Schematic application of the modified design procedure. The source of 4.2 is the research promoted by Arthur et al. (1999).

Figure 4

Schematic application of the modified design procedure. The source of 4.2 is the research promoted by Arthur et al. (1999).

Close modal

(1) Consider the local design standards and the management regulations on the sewer sediment thickness; (2) Collect the relevant sediment and hydraulic data; (3) Data analysis; (4) Set specific design objectives, including meeting the volume requirements, preventing sedimentation and reducing groundwater infiltration. Among them, three aspects need to be considered to prevent sedimentation (Arthur et al. 1999): suspended sediment transport, bed load transport, and sediment erosion; (5) Modify design parameters according to the above analysis; (6) Result output.

Hydraulic scouring measures

Hydraulic scouring measures could make up for the problem generated by insufficient slope of the pipeline, so as to avoid the excessive buried depth of the pipeline. Hydrass gate (Bertrand-Krajewski et al. 2003), Flush gate (Bong et al. 2016), Hydroself, and Biogest all contain two operating processes: water storage and drainage scouring. Laplace et al. (2003) believe that the pollutants enriched in the mud-water binding layer of the sediments can account for 40–70% of the total amount of overflow pollutants in rainy days, which can be effectively removed by the flush gate before heavy rainfall events. The lifting pump station may receive a large amount of particles, which could greatly reduce the service life of the pump. Therefore, while using hydraulic scouring measures, ‘sumps’ (Howard et al. 2011) and sediment traps (Chebbo et al. 1996) can be used to collect the eroded particles downstream of hydraulic scouring measures, and then these deposits can be collected by suction.

Optimization of pump station

Under the periodic flow in dry days, the conventional operation mode of the pump station is bound to cause the accumulation of sewer sediments (Gunkel & Pawlowsky-Reusing 2017). The long hydralic residence time and duration of low velocity period in the pipeline are both the main reasons for it. Since the peak flow is limited by the pipe diameter and the operation capacity of the pump station, increasing the span of peak flow seems to be the only way to improve the hydraulic conditions. According to the research results of Gunkel & Pawlowsky-Reusing (2017), the TSS concentration in the nighttime was lower than the daytime. Therefore, it seems to be one of the feasible methods to carry out the intermittent operation of the pump station during non-peak period, so as to increase the duration under the peak τ.

Sewer sediment clearing

Sewer sediment clearing methods include high-pressure water jet, negative pressure suction, physical scraping, etc. Different methods can be applied separately or in combination under different conditions. For example, the physical scraping method can be used as a supplementary measure for the high-pressure water jet method when dealing with large diameter pipes. The latter method can also be utilized for the routine maintenance to improve the lifespan of hydraulic facilities, as these facilities are prone to the accumulation of particles in their mechanical slides and the blind corners. In addition, the eroded particles are intermittently transported to the downstream sedimentation well with the application of the hydraulic facilities, which require regular cleaning using negative pressure suction.

‘End-of-pipe’ control

According to the author's limited knowledge, the technologies that can effectively reduce the discharged particulate matters mainly include constructed wetlands, soil treatment systems, coagulation and sedimentation treatment systems, cyclone separation measures and their combined systems. According to Supplementary material, Table S2, when the hydraulic load exceeded 1.0 m/h, the treatment efficiencies of TSS by different facilities were basically lower than those of light hydraulic loads. However, the lower hydraulic load is, the larger area is required for the layout of treatment facilities. According to Supplementary material, Table S2, the treatment efficiency of TSS by various technologies has reached more than 93% in the pilot and laboratory studies (except for a pilot study in Portugal). The field-scale application is essential to find and modify the shortcomings of each measure, so simply taking treatment efficiency as the evaluation standard of technical quality is not reasonable. Therefore, it is necessary to design the control measures in line with adequate territorial characteristics in practices ( Supplementary material, Table S1).

Implications of in-depth review

Due to complex factors such as the mixing ratio, pollutant concentrations, uneven rainfalls, endogenous pollutions and so on, end-of-pipe control measures do not work effectively and economically to some extent. Therefore, the local economy, the population, the urbanization level, and the management ability are all important prerequisites for the selection of the adaptable grey, green, and blue measures.

Pollutants in sewage are the main source of sewer sediments. Without changing the operation mode of the existing drainage system, the impact of large-scale elimination of septic tanks on the accumulation of pollutants in the pipeline still needs to be demonstrated. Despite the outstanding carbon emissions from septic tanks (Willis 2017), is the carbon emissions from sewer sediments caused by the cancelation of septic tanks still serious? In addition, due to the existence of sewer sediments, the increased operation investment of drainage networks has to be considered.

Because of the various characteristics of sewage, sediments, pipelines and operation modes in different places, there is great uncertainty in the quantitative prediction of sewer sediment through the erosion and settlement characteristics of particles. If the pipeline is taken as a black box, and the inflow and the outflow water quantity and quality are taken as input conditions, is it more practical to quantitatively and real-time evaluate the sewer sediments?

Most importantly, carefully considering the goal of avoiding sedimentation during the drainage system design is the most effective prevention and control method for pipeline sediment. In the built area, sewer sediments, overflow pollution, operating costs, and treatment facilities need to be considered simultaneously in order to make control measures more sustainable and comprehensive.

Because the release patterns of particulate pollutants in drainage systems are complex and diverse, adapting the control measures to the patterns has become the key to improving the reduction of overflow pollution. Though the preliminary research (Venditto et al. 2022) has been achieved in this field, the current technology is difficult to achieve universal applicability due to the factors such as cost investments, vocational skills, and maintenance capabilities.

In the analysis of sewer processes, particle size, density, structural morphology, and sediment stratification characteristics are all key factors in the sedimentation and erosion simulation. How to find a balance among the calculation amount, the accuracy of results, and the above factors is the key to applying models in the practice of controlling sediments.

Though the design specifications consider the minimum velocity and the minimum shear stress as the core parameters for preventing sedimentation, sewer sediments are still widely distributed in pipes, which is mainly due to the inadequate operating mode, the heavily polluted influent, and incomplete pipelines. Unfortunately, due to the complexity and the concealment of the pipelines, researchers often overlook this study area.

The results of the scientometric analysis show that ‘model’ and ‘source’ are the hot topics attracting great attention and the numbers of relevant studies conducted in developing countries are at the forefront in recent years. This trend can be attributed to the gradual emergence of overflow pollutants in these developing countries.

The sources of sewer sediments are evident, however, a dependable method for quantifying or evaluating their overall quantities is still lacking. Therefore, the primary objective of future research is to establish a set of flexible and adaptive particle simulation method in drainage pipelines, so as to provide a solid and reliable theoretical support for optimizing the design of drainage systems, improving their operation and management level, and mitigating environmental pollution.

The comprehensive evaluation of economic and environmental benefits is essential to the establishment of the analysis, along with the accurate simulation of sedimentation and erosion. In the process, systematic analysis of the physical and chemical properties of particles from different sources is crucial for developing particle accumulation and erosion simulation.

It may be the only way to fundamentally demonstrate the benefits of sewer sediment control to establish an economic and environmental benefit evaluation system for the drainage system design from the comprehensive dimensions of sewer sediment control, sewage collection efficiency, overflow frequency, environmental impact on receiving water, benefits of terminal sewage treatment facilities, and carbon emissions. Therefore, in the built-up area, a more comprehensive consideration of various control measures could be beneficial for utilizing the maximum functionality of drainage systems. For developing regions, it is more meaningful to comprehensively consider the impact of various factors at the beginning of the drainage system design.

This work is supported by ‘National Key R&D Program of China’ (Grant number: 2022YFC3801000), ‘Shanghai Science and Technology Innovation Action Plan’ (Grant number: 21DZ1209901), ‘the National Natural Science Foundation of China (NSFC)’ (Grant number: 51979168), ‘Shanghai Chengtou Water Group Co., Ltd Scientific Research Projects’ (Grant number: KY.JT.23.004) and, ‘Shanghai Chengtou Group Corporation Scientific Research Project’ (Grant number: CTKY-PTZX-2019-007, CTKY-PTZX-2021-006, CTKY-PTZX-2022-030).

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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