Tracing and Quantifying Microplastics in Bristol’s Urban Water System

Tracing and Quantifying Microplastics in Bristol’s Urban Water System | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Tracing and Quantifying Microplastics in Bristol’s Urban Water System Samuel Evans, Heike Wanke, Hazel Beaumont This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8638531/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The extent of microplastic pollution is widespread, including aquatic ecosystems in urban environments. The aim here was to build a quantified mass balance model, exploring and tracing the microplastics within Bristol’s urban water system, filling a knowledge gap, specific to this geographical region but developing a model that can be applied more generally for urban water systems. A multitude of secondary data were compiled, reviewed, analysed and quantified to inform a mass balance model. The model represents the number of microplastics passing through each section of Bristol’s water system where one of the main findings indicates domestic households as a major source of microplastics to the urban water system, while wastewater treatment plants capture ~99.8% of microplastics. Our second finding implies the transfer of microplastics from wastewater to sludge is a sink, but if the sludge is turned into fertilizer, this is to be considered as an important microplastic source for the wider environment. A source of uncertainty stems from the range of quantification methods used in the reviewed data and points out the necessity of further research regarding standardised sampling techniques and approaches to feed into sophisticated detection, quantification, and modelling systems. Microplastic pollution urban water systems mass balance model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Highlights - Domestic households are a major source of microplastics. - The mass balance model indicates that the WWPT removes 99.8% of microplastics. - Despite this the UK still produces 3.26% of microplastic entering the ocean yearly. - Removal rates are often misleading given that a large proportion of particle sizes are below the filtration and quantification thresholds. 1 Introduction With annual plastic production continually rising from 2 million tons in 1950 to over 400 million tons in 2023 (Ritchie et al., 2023 ) the infiltration of both primary and secondary microplastics into urban water systems has become a significant point of concern. A large proportion of microplastic research focuses on their presence in marine environments, though 80% of marine microplastics are sourced from terrestrial plastic mismanagement (Cole et al., 2011 ; Jambeck et al., 2015 ). Despite being the primary source, their presence in terrestrial water system is less understood and serves as a driving factor in this research. Water treatment systems in Europe have been investigated (e.g. Minteing et al., 2019) focusing on either drinking water (Adediran et al., 2024 ) or wastewater (e.g. Edo et al., 2020 ; Zhang et al., 2025 ), but not on the overall water system. This study explores the tracing and quantification of microplastics within Bristol’s urban water system as an example, identifying their sources and sinks from secondary sources while performing a mass balance and seeking to represent microplastic abundance at different stages within the water treatment system. With Bristol being the 8th largest city in the UK hosting an estimated population of 494,400 people (Bristol City Council, 2025 ), a conceptual model of this type may serve as a basis for furthering understanding of microplastics across urban water systems in the UK and as well as water systems globally. As the UK’s water systems operate under a series of regulatory frameworks, the outcome of the work aims to provide input into wider policy recommendations. The work also addresses the lack of system wide modelling of the raw quantities and concentrations of microplastics in urban water systems, the understanding of which will be vital in developing regulatory frameworks to reduce and mitigate the impacts of the plastic problem. With increasing research regarding the toxic effects of microplastics on human health (Ashrafy et al., 2022 ), the model will give an informed overview of the current situation within the geographical region, along with the current limitations and inconsistencies in the data available. Clearly highlighting the need for standardised microplastic quantification methodology to be used for further research. 2 Methodology The data used to inform this model came from academic sources, from studies of water systems primarily in England and Wales. As these systems are governed by the same or similar regulations as Bristol, the data is expected to provide a more accurate representation than that from studies conducted in other regions. No primary data was collected, and thus the model represents a synthesis of data available from previous studies. The initial consideration was to determine the volume of water specific to Bristol’s urban and natural water system. This data provided the basis for converting the microplastic concentration values observed in the studies, from microplastics per litre, into microplastic particles per day. This culminated in a dataset of both volumetric sources (data on the volume of water or material passing through each part of the system per day), and microplastic concentration values measured in each of part of the system. Concentration data was standardised in the format microplastics per litre (MP/L) and this was multiplied by the litres of water throughput per day (million litres per day - ML/day), to give a final value in the form of microplastics per day (MP/day). 2.1 Water System Framework To identify relevant focus points, the stages in Bristol’s water system were broken down into three sections: (a) inflows (rivers, reservoirs and groundwater), (b) the processes of drinking and wastewater treatment, and (c) the outflows such as sludge and processed water, forming a basic flowchart model (Fig. 1 ). This approach was informed by Mitchell et al. ( 2001 ) who presented a systematic approach to modelling the urban water cycle identifying the inflows, processes and outflows. Using this as a starting point, a database containing the subsections for each of these elements was constructed. Inflows - In a drought plan for Bristol Water, Bristol Water ( 2022 ) identified the three inflows of Bristol’s urban water system as river abstraction (from the River Severn), groundwater, and reservoirs. The report stated that around 15% of the water inflow came from groundwater, while reservoirs and river abstraction provide temporally varying proportion between 30% and 50%. For modelling efficiency, a value of 42.5% was used for both rivers and reservoirs. The sum inflow of these sources represented on average 275 ML of water per day (Bristol Water, 2022 ). Processes - For this study, the term ‘processes’ includes parts of the system where drinking water is processed, used by people and where wastewater is processed before it is released back into the environment. Bristol water operates 16 drinking water treatment plants (Bristol Water, 2019 ), which collectively process around 275 ML/day (Bristol Water, 2024 ). Once used domestically, the water is transferred to wastewater treatment plants. Water losses from the system due to stormwater discharge and subsequent releases of untreated sewage (caused by the system exceeding its water capacity) were also considered. Laville ( 2023 ) estimated that these water losses (in 2023) totalled 50 ML/year within in the Wessex Water catchment area, with Bristol accounting for 31% of this total (Wessex Water, n.d.). From this, a daily average discharge of 0.0425 ML was calculated. It was assumed that the remaining 274.96 ML/day continued through wastewater treatment works. Outflows - The primary outflow of wastewater treatment works was identified as processed water, known as effluent. The secondary outflow was the accumulation of solid particulates called sludge (Astaraie-Imani et al., 2012 ). Using data from Wessex Water (n.d.), the daily effluent used for modelling was 274.96 ML/day. The volume for sludge was not subtracted from this, as sludge is caused by additional matter added to the system. Sludge produced by Wessex Water, stood at approximately 62,000 tons/year in 2022 (Wessex Water, 2023 ). Thus, with an average sludge density of 1.035 kg/L (Jang and Schuler, 2007 ) this corresponded to a daily output of around 0.164 ML. The final outflow was the conversion of sludge into fertiliser with GENeco ( 2020 ) providing a maximum annual manufacture fertiliser mass of 70,000 tons/year. The unprocessed fertiliser was assumed to be of the same density as the sludge; thus, the daily sludge production would be 0.164 ML/day. 2.2 Database Compilation for Inflows, Wastewater Treatment, Processed Water Release, Fertilizer and Stormwater Overflow Once the water and material volumes were allocated to each node in the water system, the database was developed to organise microplastic concentrations for each node. The database (Table 1 ) was subcategorised into inflows, processes and outflows, each of which was colour coded corresponding to the flowchart in Fig. 1 . Table 1 Compilation of microplastic concentrations for water inflows, processes and outflows relevant for the study area; STD meaning Standard Deviation Node Source MP concentration reported [MP/L] Highest Lowest Average STD Rivers Johnson et al. ( 2020 ) 15 0 2.29 4.91 Rivers Gao et al. ( 2024 ) 222 0 0.608 and 0.145 Pristine Reservoirs Johnson et al. ( 2020 ) 0 0 0 0 Reservoirs (including pumped storage from lowland rivers) Johnson et al. ( 2020 ) 113 0 7.54 29.17 Groundwater Sources Lapworth and Shockley ( 2021 ) 0.17 0 0.045 0.051 Drinking water treatment influent Water UK ( 2019 ) 4.9 Tap Water Johnson et al. ( 2020 ) 0.01186 0.000654 Tap Water Al-Mansoori et al. ( 2025 ) 100 6 Tap Water Water UK. ( 2019 ) 0.00011 Wastewater Treatment UKWIR ( 2019 ) 5000 Wastewater Treatment UKWIR ( 2022 ) 1227 0 423.5 560.67 Processed Water Release UKWIR ( 2019 ) 5.1 Processed Water Release UKWIR ( 2022 ) 1.9 0 0.9225 0.9201 Sludge Johnson et al. ( 2020 ) 35,823,420 162,495 Sludge UKWIR ( 2022 ) 2708227 0 Stormwater Overflow UKWIR ( 2022 ) 365 Rivers - No research articles in which the specific microplastic concentrations of the River Severn could be found, so other UK sources for river inflows to drinking water treatment plants (DWTP’s) were utilised. Johnson et al. ( 2020 ) provided a set of concentrations from various UK river abstraction sites, taken from the raw water inflows at eight water treatment works in England and Wales. The final set of results presented by the study for river abstraction were used for statistical analysis. As standards amongst DWTP’s will be governed by the same regulations across the UK, this data can be viewed as strongly representative of that in Bristol. Johnson et al. ( 2020 ) utilised woven stainless steel 10 µm filters with a 500 cm 2 surface area for filtration. The highest value in the data is 15 MP/L and represents the extreme upper end for microplastic concentration in UK river water, thus was used to model the potential upper bound of the mass balance model. The lowest value of 0 MP/L opposingly represents the lower bound for potential concentrations. This is useful in highlighting the vast differences between sampled data (Table 1 ). The average value of 2.29 MP/L is useful in estimating long-term exposure levels but is susceptible to extreme values on both the lower and upper bound and given the relatively small sample size in this data set, a more definitive average value may be obtained through the inclusion of more data. This feature in the data is highlighted by the high standard deviation which signifies a high level of variance in the data. The review by Gao et al. ( 2024 ) presents several concentrations from rivers across Europe. The paper highlights that when extreme outliers are omitted, the mean concentrations given were 0.608 and 0.145 MP/L which correspond to the lower bound of values presented by Johnson et al. ( 2020 ; Table 1 ). However, Gao et al. ( 2024 ) also presented extreme values as high as 222 MP/L found in research conducted along the Rhine River by Faith (2019). This shows a similar variability in data as with the study by Johnson et al. ( 2020 ), however the review covers European rivers with no UK sources, its data is less geographically relevant and is not incorporated in this mass balance model. Reservoirs - Johnson et al. ( 2020 ) also studies raw, pre-treated water sourced from one pristine upland reservoir located along the England/Wales boundary. As explained previously, this UK data should provide a similarity to Bristol’s water system given it falls under the same geographical and regulatory boundaries. In this study, the pristine upland reservoir presented no quantifiable microplastics. However, the study also presents values for ‘Lowland river-pumped storage’ sites, which are described as abstracted river water stored in reservoirs. Given that this system is utilised within the Bristol area as detailed by Bristol Water ( 2024 ), these values may be more relevant to the study. Pumped storage site values presented a high value of 113 MP/L, greater than that of direct river abstraction, though this value appears to be an outlier in the data as no other concentration above 0.2 MP/L was detected, suggesting that this value may be an outlier. The data from Johnson et al. ( 2020 ) suggests that upland pristine reservoirs are less likely than rivers to be a significant source of microplastic influx, but that river-pumped storage reservoirs may contribute. However, this is difficult to present with certainty given the high standard deviation in the data provided and the likelihood of large outlying concentrations skewing the average values upwards. If the value of 113 MP/L is omitted from the river-pumped storage and pristine reservoir data, the average concentration drops significantly. This aligns with the consensus presented by Watkins et al. ( 2019 ), that reservoirs generally ‘trap’ microplastics in their sediment acting as a filter and thus have a lower concentration suspended within their body of water. Johnson et al. ( 2020 ) quantified particles of size greater than 25 µm and did not provide size distribution details specific to river or reservoir sources, though the general size distributions presented for raw water spike at the lower end of the size range. This is highlighted by the study as indicating that a significant number of sub-25 µm particles may be present in the samples but could not be quantified. Groundwater Sources – The data used to model groundwater microplastics in the mass balance model was precured by Lapworth and Shockley ( 2021 ). The study took samples from 8 sites including public and private boreholes, a private well and two Environment Agency (EA) water level monitoring wells. The average particle size detected was 80 µm with just 28% of particles below 50 µm, and thus well above the filter size of 5 µm. This can be contrasted with the data given by Johnson et al. ( 2020 ) in which no particles were detected in raw groundwater, despite using a filter size of 10 µm. Given that Johnson et al. ( 2020 ) states England and Wales as geographical locations for groundwater sampling, the geographical variations between these sources are difficult to state. Though as highlighted by Liu et al. ( 2025 ) physical aquifer properties such as material type and particle size may influence the migration of particles into groundwater. The average microplastic concentration identified in the groundwater sites is 0.045MP/L which is approximately half that of the reservoir sources, and with a lower standard deviation; indicating fewer statistical anomalies (Table 1 ). This means that in this case, the average value may be more indicative than the highest or lowest values. Wastewater Treatment – Wastewater treatment represents the particle concentration in raw used water from the urban sewage system as it enters the wastewater treatment plant. The data used from UKWIR ( 2022 ) was specifically relevant to the modelling process as it was procured from a Wessex Water WWTP in Saltford near Bath. In their study, sample water filtered through a 500 cm 2 , 5 µm stainless steel cartridge filter. Although this site mainly processes water from Bath and the surrounding area, not Bristol (Wessex Water, n.d.), the geographical relevance and the fact that it is operated by the same company gives significance to this study. The range of 1227 MP/L this data indicates a substantial variation in results obtained between sampling periods. This is also highlighted by the high standard deviation, demonstrating that the average value of 423.5 MP/L (Table 1 ) may not be truly indicative of the average value, thus a wider sample set would be required to obtain a more accurate representation. Processed Water Release – UKWIR ( 2019 ) presents an average value for the whole of UK for processed water releases of 5.1 MP/L. In their study, water was filtered through a 500 cm 2 , 5 µm stainless steel cartridge filter. However, the values presented specifically for Wessex Water’s Saltford plant may be more indicative of those for Bristol and were used to inform the model instead of the UK average. In the Saltford data from UKWIR ( 2022 ), even the highest concentration recorded is far lower than the overall average presented. This could indicate that removal by Wessex Water and thus within the Bristol water system is more effective than the UK average. However, the small sample size and relatively high variance in data means this cannot be stated as a certainty. Fertiliser – The microplastic pathway from sludge derived fertiliser to agricultural fields and subsequently groundwater is highlighted by Harley-Nyang et al. ( 2022 ), however no direct values for fertiliser are available. Assumptions could be made if considering sludge concentrations and using them to predict the particle quantities transferred into the final fertiliser products, however, given the influx of values presented at the sludge stage (which may be due to further breaking down of particles) such a result may not be accurate and thus will be omitted from the scope of this paper. Stormwater Overflow Water released in an ‘overflow’ situation is untreated and is due to the water system exceeding its volume capacity often due to heavy rain. This is to relieve pressure on the system and water commonly flows into rivers (Environment Agency, 2021). The value of 365 MP/L presented by UKWIR ( 2022 ) is an average across the year from water sampled in Exmouth. As it is untreated, its value may be representative of the Bristol region, given that the average concentrations of untreated sewage may be similar nationally. 2.3 Calculations of MP quantities for the study area for Drinking Water Treatment Influent, Tap Water and Sludge Drinking Water Treatment Influent - Based on the water sources in the study area, and their relative contribution as source water, an average microplastic concentration of 13.4 MP/L is calculated using their percentual contributions (42.5% from rivers; 42.5% from reservoirs and 15% from groundwater) and average microplastic concentrations: Calculation: (42.5% × 31.5 MP/L) + (42.5% × 0.1022 MP/L) + (15% × 0.045 MP/L) = 13.4 MP/L This calculation yields a higher concentration than the value of 4.9 MP/L provided by Water UK ( 2019 ) as a UK average, though the inclusion of potentially outlying values in the average for river water (signified by the high standard deviation) may skew the data upwards. However, Water UK ( 2019 ) does not state the methodology used for obtaining the value. This average value can be used for comparison with the inflow concentrations in the previous section. Tap Water - Given that the data from Johnson et al. ( 2020 ) contained tap water samples from river, reservoir and groundwater sources, a weighted average of the concentrations provided was produced to more accurately model Bristol’s water sources. Using the percentual contributions and upper and lower microplastic concentrations (tap water from river source: 0.001–0.0244 MP/L; from reservoir sources 0.0005–0.0028 MP/L; from groundwater sources: 0.00011–0.002 MP/L) an upper and lower bound was determined from the data set. Upper Bound = (0.0244 MP/L × 42.5%) + (0.0028 MP/L × 42.5%) + (0.002 MP/L × 15%) = 0.01186 MP/L Lower Bound = (0.001 MP/L × 42.5%) + (0.0005 MP/L × 42.5%) + (0.00011 MP/L × 15%) = 0.000654 MP/L When compared to the drinking water concentration from Water UK ( 2019 ) of 0.00011 MP/L, the lower bound determined above is 6 times greater and the upper bound over 100 times greater. Another order of magnitude is brought into consideration by the values presented by Al-Mansoori et al. ( 2025 ) in which 12 cities analysed produced an average concentration of 36 MP/L, with a lowest concentration of 6 MP/L and high of 74 MP/L. This once again highlights the great variability in data between sources. Sludge - Using data from Bristol Water ( 2022 ), the inflow proportions between river abstraction and reservoirs was taken as 42.5% each. Using this weighting, a weighted upper and lower bound for sludge was calculated. The data from Johnson et al. ( 2020 ) gave a range of values, with LR1 (Lowland River), LR3 and LRS1 being sludge from river abstraction derived wastewater, and UR (Upland Reservoir) being sludge from reservoir derived wastewater. The values are presented in Table 2 . Table 2 Microplastics per gram for sludge derived from river abstraction and reservoirs according to Johnson et al. ( 2020 ) Water Source LR2 LR3 LRS1 UR MP/g 808 1092 to 85,729 511 265 to 676 To weight the values and produce an upper and lower bound, the upper and lower values for each source were first identified Table 2 ). As the weighting in the final sludge value for Bristol’s water is 42.5% both rivers and reservoirs, both can be viewed as equally proportionate in the final sludge produced. Min = (50% × 511) + (50% × 265) = 388 MP/g Max = (50% × 676) + (50% × 85,729) = 43,202 MP/g Next, using the average sludge density of 1.035 kg/L (Jang and Schuler, 2007 ) the range has been converted into the units MP/L. Using this conversion, the lower and upper bounds for MP/L in sludge are 401,580 MP/L and 44,714,587 MP/L. Daily counts of microplastics per node have been calculated using the daily water volume multiplied with the concentration ([MP/L] × [L/Day] = [MP/Day]). The calculation has been carried out for the highest concentration for each node, along with the lowest and the average. Standard deviation and Coefficient of Variation (CV = (Standard Deviation/Average) × 100) have been calculated for the daily microplastic quantities per node. 3 Results The results for the daily microplastic counts passing through the nodes of Bristol’s water system are summarised in Table 3 and contain the highest, lowest and average counts of microplastics expected. The highest values are found for the sludge, while the lowest are found for the groundwater (no data for the fertilizer). It is evident that the variation in data collected is high, meaning that the spread of values presented for each node across the sources used varies. This is also demonstrated in Fig. 2 where a comparison of the coefficient of variation for each node is presented. Table 3 Results of the microplastics quantities per node in the Bristol water system MP concentration [MP/L] MP quantity [MP/day] Node Node Volume [l/day] Highest Lowest Average Highest Lowest Average Rivers 116,880,000 15 0 2.29 1,753,200,000 0 267,655,200 Reservoirs (Including Pumped-River Storage) 116,880,000 113 0 7.54 13,207,440,000 0 881,275,200 Groundwater Sources 41,250,000 0.17 0 0.045 7,012,500 0 1,856,250 DWTP Influent 275,000,000 13 5 9 3,685,000,000 1,347,500,000 2,516,250,000 Tap Water 275,000,000 74 0 27 20,350,000,000 30,250 7,427,750,000 WWTP Influent 275,000,000 1,227 0 424 337,425,000,000 0 116,462,500,000 WWTP Effluent 274,960,000 2 0 1 522,424,000 0 253,650,600 Sludge 164,000 44,714,587 401,580 N/A 7,333,192,268,000 65,859,120,000 N/A Fertiliser 132,000 N/A 0 0 N/A Overflow 42,500 N/A N/A 365 N/A N/A 15,512,500 The mean CV values across the data used is 213.64%, suggesting that the standard deviation is likely to be twice the magnitude of the average. However, this varies greatly between nodes, with the data for rivers varying almost 3 times the magnitude of its average, and the data for DWTP influent varying by 6 times the magnitude of its average. This suggests that an average value for these nodes may not be indicative of the true value, which may fall anywhere within its wide range of potential values. The value for any of these nodes may also fall beyond the range specified given the small sample sized procured from the literary sources available. Another important distinction is that the CV does not capture all the relevant information to comprehensively analyse the dataset. The tap water node for example, presents the lowest CV of 71.5% suggesting a relatively low variance from the average within the dataset, but if a relative rage is taken (where range is divided by the mean), outlying values give a value of 300% of the average. When the same principle is applied to sludge, the highest outlying values push the relative range value to over 280,000% (Fig. 2 ). This means that large outlying values may not be picked up when analysing the CV and might signify a greater level of variability than is captured by this analysis. 3.1 Mass Balance Model The full mass balance model presented in Fig. 3 brings the data together in a visualised format. In general, the values tend to increase through the system from inflows to outflows. However, the first node in the system, i.e. the inflow to the drinking water treatment, is only reflecting a mixing of water with a relatively high microplastic load (river water) with water of lower microplastic load (reservoir water and groundwater). It is surprising that the model shows an increase in microplastic concentration from the drinking water treatment inflow to the tap water though one would expect the treatment system to reduce microplastic concentrations and that microplastics released from pipes, tanks and fittings in the treatment and distribution works are minimal. However, the range of microplastic loads in tap water range over several orders of magnitude and an occasional use of larger proportions of river water might cause the highest concentrations recorded, while when considering the minimum values recorded, the treatment system reduces the microplastic load considerably, from 1.3*10 9 to 3*10 4 MP/day. The increase from the tap water microplastic counts to the wastewater microplastic counts is 1.1*10 11 MP/day (using average quantity) to 3.2*10 11 MP/day (using maximum quantity) which is 15.6 to 16.5 times the load after domestic use than before leaving the household tap. This supports the notion presented by Mintenig et al. ( 2017 ) and others that urban water systems may be a net source of microplastics into terrestrial and marine aquatic environments and can be attributed to the large influx observed in the wastewater stage of the cycle. The wasterwater treatment system removes 1.2*10 11 MP/day (using average quantity) to 3.4*10 11 MP/day (using maximum quantity), which indicates a removal efficiency of 99.8%. This is correlative to the general academic consensus, that the microplastic removal rate in wastewater treatment plants is 99.9% (Water UK, 2019 ). The microplastic concentration of the effluent to the environment is also significantly lower than the microplastic concentration in rivers. While wastewater treatment has a high potential to harbour large microplastic concentrations, though given that one source cited a concentration of 0 MP/L this is likely subject to a very high degree of variability. However, a large portion of microplastics are transferred from wastewater to the sludge (56%, using average values) which returns microplastic back into the environment. A large portion is lost at this stage from the mass balance, ending up in unaccounted waste disposal, e.g. on submerged aerated filters. Sludge repeatedly presented high concentrations throughout the data collected, suggesting some certainty as to its substantial role in the flow of microplastics within the urban water system. Overall, untreated wastewater microplastic concentrations are relatively understudied and a wider range of data would be required to produce an accurate analysis of its role in the flux of microplastics into the terrestrial water environment. Though, given the values presented for corresponding nodes (such as wastewater) this may fairly be assumed to be high. The same can be said for sludge derived fertiliser, for which direct studies are necessary if concentrations are to be attained and analysed. The large range of values for each node in the system signifies the high degree of variance across and within the studies analysed. The model is effective at highlighting this variance by utilising the full set of values across an available set of studies. The main significant variance in the data utilised, which is highlighted in the model, can be explored in the context of tap water. The data presented by Johnson et al. ( 2020 ) was obtained via 10 µm filtration and gave extremely low MP/L values, however the newer study by Al-Mansoori et al. ( 2025 ) which utilised 0.45 µm filtration presented far higher values, as presented in Fig. 4 . The switch to 0.45 µm corresponding magnitudinous increase in particle concentration casts significant doubt on current testing regimens and the broad scientific and industrial focus on microplastics as a point of concern. It instead suggests that a more critical point of study may be upon the quantification of nano-plastics (particle sizes of 1-1000 nm). 4 Discussion The results obtained within this investigation, present a basis for discussion within multiple contexts. Discrepancies of the concentrations and counts at various nodes are reflecting on the one hand fragmentation, secondary sources, and biological uptake, but also a lack of systematic microplastic monitoring and lack of standardised methods. These uncertainties are discussed below. 4.1 Data Variation Discrepancies Fragmentation - The results of the mass balance model display a wide range of values, many of which vary by orders of magnitude between sources even when regarding the same node. A large proportion of microplastics in aquatic environments are secondary, produced due to fragmentation and degradation (Ziani et al., 2023 ). This degradation can occur an arbitrary number of times, often resulting in micro- to nano-plastics (Yee et al., 2021 ). Particles of this size may have been below the range of quantification for some studies used and may have been accounted for in others skewing the data between sources even regarding the same node. This is supported when comparing the values obtained for tap water, where Johnson et al. ( 2020 ) used a 10 µm filter and observed a maximum concentration of 0.0244 MP/L, whereas Al-Mansoori et al. ( 2025 ) used a 0.45 µm filter and observed a maximum concentration of 74 MP/L. This suggests that most particles within tap water are of a sub 10 µm scale, and that this factor may be significant throughout the data utilised within this analysis. Most of the studies used to inform this research utilised filters greater than 5 µm and so, if this trend correlates, the majority of microplastic particles may have been missed entirely. As well as this, the continual fragmentation process is also likely to have continued within the water system as microplastics have been shown to undergo chemical bond breakage and molecular cleavage within water systems (Ma et al., 2024 ), adding an addition layer of complexity to the quantification process. Secondary Sources – This observation can be extended to plastics which may present as macro-scale particles at earlier stages of the water system but are broken down later. This could include larger plastic items, but also fibres shed from polyester materials (such as sanitary products) (Browne et al., 2011 ). Alternative transport dynamics, via air or biota (Gasperi et al., 2018 ) which are not considered in the model may present as alternative sources or sinks. Urban and roadway stormwater runoff is also presented as a significant inflow of microplastics, especially near industrial areas (Liu et al., 2019 ). Due to the widespread nature of road surface runoff, nuanced quantification techniques would be required for analysis. Biological Uptake - Van Cauwenberghe and Janssen ( 2014 ) identifies the uptake of microplastics in commercially grown bivalves. Given the biological digestion techniques used in the processing of sewage sludge (Harley-Nyang et al., 2022 ) there may be a likelihood that smaller particles were digested by the bacteria used in this process. Additional research in this area would be suggested to confirm or deny the impact this could have on overall particle counts. Beyond these factors, mismanagement such as illegal dumping or spillages could make quantification more difficult (Wagner and Lambert, 2018 ). Full transparency and considerations would need to be adopted to account for these issues in the building of a fully comprehensive model. Overall, the unknown rates and proportions of microplastic degradation cause uncertainty in the presented mass balance model. In a strict sense, mass balances are only applicable to truly conservative tracer, but microplastics undergo process of degradation (Gambino et al., 2022 ). This includes biotic degradation occurring through exposure to bacteria, fungi and microbes or abiotic degradation from UV and heat exposure (Cai et al., 2023 ). However, these processes are very slow (Zhang et al., 2021 ). Further uncertainty is caused by the different magnitudes of microplastic concentrations and for some nodes the errors are larger (e.g. wastewater treatment inflow or sludge) than the range of values at other nodes (e.g. groundwater or treated wastewater release). A further source of uncertainty stems from the range of quantification methods used and the question arises if the mass flow within the studied system considered in fact the same material. Microplastic are defined as particles smaller than 5 mm (US EPA, 2016 ). However, for practical reasons and a lack of a standardised procedure, different membranes or filters are used to pass samples through (Table 4 ) and thus operationally considered as the lower size limit of microplastics (0.45 to 26 µm). This compilation indicates that, in general, higher concentrations are associated with lower filter opening sizes. Further to this, volumes of water samples are also not standardised and range from a few litres (e.g., 5 L by Erdem et al. ( 2024 ); or 10 L by Shen et al. ( 2021 )), to “several hundred litres” evidenced by Johnson et al. ( 2020 ). Table 4 Compilation of mesh/membrane sizes observed within the literature when determining microplastic concentrations in tap water Country Source Filter size [µm] MP concentration [MP/L] UK Adediran et al. ( 2024 ) 5 0.027 UK Al-Mansoori et al. ( 2025 ) 0.45 40 UK Johnson et al. ( 2020 ) 10 0.00011 Belgium Semmouri et al. ( 2022 ) 8 0.01 Czech Republic Halfar et al. ( 2024 ) 1 65–68.3 Denmark Feld et al. ( 2021 ) 10 0.88 Finland, France, Japan, USA, Germany Mukotaka et al. ( 2021 ) 26 46–97 Germany Mintening et al. (2019) 3 0.7 Saudi Arabia Almaiman et al. ( 2021 ) 25 1.9–47 Turkey Erdem et al. ( 2024 ) 0.45 470–2821 4.2 Urban Water Cycle – Source or Sink? Whether the urban water cycle is a source or a sink of microplastics is a continual area of debate. While our study indicates domestic households as a major source of microplastics to the urban water system, WWTP capture 99.8% of it. However, the transfer of microplastics from the wastewater to the sludge is a sink, but if the sludge is turned into fertilizer, this is to be considered as an important microplastic source for the wider environment. A simple analysis using the stated DWTP influent value given by Johnson et al. ( 2020 ) of 4.9 MP/L and the UKWIR ( 2019 ) effluent value of 5.1 MP/L, presents a system which would produce a net gain of over 54 million MP/ day. If the total volume of water through the DWTP and WWTP system was estimated to be 11 billion litres in the UK (DEFRA, 2002 ), this would give an annual net gain of 796 billion particles, from the UK alone. If the total estimated microplastic concentration on the surface ocean is 24.4 trillion (Kyushu University, 2021 ), this annual net gain output would represent 3.26% of the total global estimated abundance. If extrapolated to a global scale, this suggests that a figure such as 24.4 trillion may be a significant underrepresentation. 5 Conclusion This study highlighted the difficulty in effectively quantifying the numbers of microplastics within Bristol’s urban water system. The main sink is the sludge from the wastewater treatment node. The main sources of microplastics are from domestic households and rivers where environmental microplastics originate from the river inputs such as farmlands and smaller tributaries (Horton and Dixon, 2017 ) where the microplastics are transported towards the ocean. The applied mass balance shows overall that many particles enter from the environment, but that the drinking water system is effective in removing them, however given the very high particle counts identified in some sources this may still correlate with a large microplastic influx into the environment. Sludge based fertiliser was noted as a potential vehicle, transporting microplastics into terrestrial ecosystems, and beyond this, for example into the groundwater. The large quantities of microplastics identified in sludge are transferred into sludge-based fertiliser (Cusworth et al., 2024 ), meaning microplastics are being transferred into areas where crops are grown. The range of results of volumetric figures for microplastics show a very wide range of daily throughput values and this correlated with prior research. Substantial numerical differences were also cited, highlighting the potential unknown volumes of microplastics within the urban water system possibly due to the differences in sampling techniques used and the significant potential for micro- and nano-plastics to be undetected with the filters and microscopes commonly used in testing. It’s clear that further research regarding sampling techniques, standardised approaches, sophisticated detection, quantification and modelling is required for both the sampling methods and for water treatment systems. Once this is completed a more comprehensive understanding of this topic will be achieved. Most notably using standardised sampling with the possibility of uncovering far higher concentrations of nanoparticles and other sub-5 µm particles; the impacts of which remain unknown. The study of fertiliser as a pathway for terrestrial microplastics should also be a recommendation regarding public health policy. These findings echo the growing concern over microplastic impact on human health, by quantifying their extremely high abundance. Despite the statistical effectiveness of the water system studied, the high number of microplastics mean many millions still make their way into the environment each day. Significant investment in further research, and policy interventions as well as engineering solutions are a necessity for dealing with plastics in water treatment plants. Declarations Ethical Approval This is not applicable. Consent to Participate This is not applicable. Consent to Publish This is not applicable. Clinical trial number This is not applicable. Competing Interests Statement The authors confirm that we have no competing interests nor conflicts of interest Funding This research was undertaken for an undergraduate research project with no external funding. Author Contributions SE and HW conceptualised the work; SE undertook data reviewing, analytical work and model building SE prepared all figures; SE, HW and HB undertook the writing and editing; All authors reviewed the manuscript prior to submission. Acknowledgements This is not applicable. Data availability statement The data that supports the findings of this study are available on request from the author. 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07:43:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1680264,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8638531/v1/06f71b64-4b51-4554-bad2-94b09d3ee41a.pdf"},{"id":101443596,"identity":"30102efa-76a2-410c-b8b6-f870defd3846","added_by":"auto","created_at":"2026-01-29 17:44:11","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":128235,"visible":true,"origin":"","legend":"","description":"","filename":"GRAPHICALABSTRACT.png","url":"https://assets-eu.researchsquare.com/files/rs-8638531/v1/a0e8b6a76f32228aa33f8272.png"}],"financialInterests":"","formattedTitle":"Tracing and Quantifying Microplastics in Bristol’s Urban Water System","fulltext":[{"header":"Highlights","content":"\u003cp\u003e- Domestic households are a major source of microplastics.\u003c/p\u003e\u003cp\u003e- The mass balance model indicates that the WWPT removes 99.8% of microplastics.\u003c/p\u003e\u003cp\u003e- Despite this the UK still produces 3.26% of microplastic entering the ocean yearly.\u003c/p\u003e\u003cp\u003e- Removal rates are often misleading given that a large proportion of particle sizes are below the filtration and quantification thresholds.\u003c/p\u003e"},{"header":"1 Introduction","content":"\u003cp\u003eWith annual plastic production continually rising from 2\u0026nbsp;million tons in 1950 to over 400\u0026nbsp;million tons in 2023 (Ritchie et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) the infiltration of both primary and secondary microplastics into urban water systems has become a significant point of concern. A large proportion of microplastic research focuses on their presence in marine environments, though 80% of marine microplastics are sourced from terrestrial plastic mismanagement (Cole et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Jambeck et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Despite being the primary source, their presence in terrestrial water system is less understood and serves as a driving factor in this research. Water treatment systems in Europe have been investigated (e.g. Minteing et al., 2019) focusing on either drinking water (Adediran et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) or wastewater (e.g. Edo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), but not on the overall water system. This study explores the tracing and quantification of microplastics within Bristol\u0026rsquo;s urban water system as an example, identifying their sources and sinks from secondary sources while performing a mass balance and seeking to represent microplastic abundance at different stages within the water treatment system. With Bristol being the 8th largest city in the UK hosting an estimated population of 494,400 people (Bristol City Council, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), a conceptual model of this type may serve as a basis for furthering understanding of microplastics across urban water systems in the UK and as well as water systems globally. As the UK\u0026rsquo;s water systems operate under a series of regulatory frameworks, the outcome of the work aims to provide input into wider policy recommendations. The work also addresses the lack of system wide modelling of the raw quantities and concentrations of microplastics in urban water systems, the understanding of which will be vital in developing regulatory frameworks to reduce and mitigate the impacts of the plastic problem. With increasing research regarding the toxic effects of microplastics on human health (Ashrafy et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the model will give an informed overview of the current situation within the geographical region, along with the current limitations and inconsistencies in the data available. Clearly highlighting the need for standardised microplastic quantification methodology to be used for further research.\u003c/p\u003e"},{"header":"2 Methodology","content":"\u003cp\u003eThe data used to inform this model came from academic sources, from studies of water systems primarily in England and Wales. As these systems are governed by the same or similar regulations as Bristol, the data is expected to provide a more accurate representation than that from studies conducted in other regions. No primary data was collected, and thus the model represents a synthesis of data available from previous studies. The initial consideration was to determine the volume of water specific to Bristol\u0026rsquo;s urban and natural water system. This data provided the basis for converting the microplastic concentration values observed in the studies, from microplastics per litre, into microplastic particles per day. This culminated in a dataset of both volumetric sources (data on the volume of water or material passing through each part of the system per day), and microplastic concentration values measured in each of part of the system. Concentration data was standardised in the format microplastics per litre (MP/L) and this was multiplied by the litres of water throughput per day (million litres per day - ML/day), to give a final value in the form of microplastics per day (MP/day).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Water System Framework\u003c/h2\u003e \u003cp\u003eTo identify relevant focus points, the stages in Bristol\u0026rsquo;s water system were broken down into three sections: (a) inflows (rivers, reservoirs and groundwater), (b) the processes of drinking and wastewater treatment, and (c) the outflows such as sludge and processed water, forming a basic flowchart model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This approach was informed by Mitchell et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) who presented a systematic approach to modelling the urban water cycle identifying the inflows, processes and outflows. Using this as a starting point, a database containing the subsections for each of these elements was constructed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eInflows -\u003c/b\u003e In a drought plan for Bristol Water, Bristol Water (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) identified the three inflows of Bristol\u0026rsquo;s urban water system as river abstraction (from the River Severn), groundwater, and reservoirs. The report stated that around 15% of the water inflow came from groundwater, while reservoirs and river abstraction provide temporally varying proportion between 30% and 50%. For modelling efficiency, a value of 42.5% was used for both rivers and reservoirs. The sum inflow of these sources represented on average 275 ML of water per day (Bristol Water, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eProcesses -\u003c/b\u003e For this study, the term \u0026lsquo;processes\u0026rsquo; includes parts of the system where drinking water is processed, used by people and where wastewater is processed before it is released back into the environment. Bristol water operates 16 drinking water treatment plants (Bristol Water, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which collectively process around 275 ML/day (Bristol Water, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Once used domestically, the water is transferred to wastewater treatment plants. Water losses from the system due to stormwater discharge and subsequent releases of untreated sewage (caused by the system exceeding its water capacity) were also considered. Laville (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) estimated that these water losses (in 2023) totalled 50 ML/year within in the Wessex Water catchment area, with Bristol accounting for 31% of this total (Wessex Water, n.d.). From this, a daily average discharge of 0.0425 ML was calculated. It was assumed that the remaining 274.96 ML/day continued through wastewater treatment works.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOutflows -\u003c/b\u003e The primary outflow of wastewater treatment works was identified as processed water, known as effluent. The secondary outflow was the accumulation of solid particulates called sludge (Astaraie-Imani et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Using data from Wessex Water (n.d.), the daily effluent used for modelling was 274.96 ML/day. The volume for sludge was not subtracted from this, as sludge is caused by additional matter added to the system. Sludge produced by Wessex Water, stood at approximately 62,000 tons/year in 2022 (Wessex Water, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, with an average sludge density of 1.035 kg/L (Jang and Schuler, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) this corresponded to a daily output of around 0.164 ML. The final outflow was the conversion of sludge into fertiliser with GENeco (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) providing a maximum annual manufacture fertiliser mass of 70,000 tons/year. The unprocessed fertiliser was assumed to be of the same density as the sludge; thus, the daily sludge production would be 0.164 ML/day.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Database Compilation for Inflows, Wastewater Treatment, Processed Water Release, Fertilizer and Stormwater Overflow\u003c/h2\u003e \u003cp\u003eOnce the water and material volumes were allocated to each node in the water system, the database was developed to organise microplastic concentrations for each node. The database (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was subcategorised into inflows, processes and outflows, each of which was colour coded corresponding to the flowchart in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCompilation of microplastic concentrations for water inflows, processes and outflows relevant for the study area; STD meaning Standard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eMP concentration reported [MP/L]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHighest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLowest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSTD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRivers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJohnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRivers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGao et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.608 and 0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePristine Reservoirs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJohnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReservoirs (including pumped storage from lowland rivers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJohnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroundwater Sources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLapworth and Shockley (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking water treatment influent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater UK (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTap Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJohnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTap Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl-Mansoori et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTap Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater UK. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWastewater Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUKWIR (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWastewater Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUKWIR (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e423.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e560.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcessed Water Release\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUKWIR (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcessed Water Release\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUKWIR (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSludge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJohnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35,823,420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162,495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSludge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUKWIR (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2708227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStormwater Overflow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUKWIR (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRivers -\u003c/b\u003e No research articles in which the specific microplastic concentrations of the River Severn could be found, so other UK sources for river inflows to drinking water treatment plants (DWTP\u0026rsquo;s) were utilised. Johnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) provided a set of concentrations from various UK river abstraction sites, taken from the raw water inflows at eight water treatment works in England and Wales. The final set of results presented by the study for river abstraction were used for statistical analysis. As standards amongst DWTP\u0026rsquo;s will be governed by the same regulations across the UK, this data can be viewed as strongly representative of that in Bristol.\u003c/p\u003e \u003cp\u003eJohnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) utilised woven stainless steel 10 \u0026micro;m filters with a 500 cm\u003csup\u003e2\u003c/sup\u003e surface area for filtration. The highest value in the data is 15 MP/L and represents the extreme upper end for microplastic concentration in UK river water, thus was used to model the potential upper bound of the mass balance model. The lowest value of 0 MP/L opposingly represents the lower bound for potential concentrations. This is useful in highlighting the vast differences between sampled data (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The average value of 2.29 MP/L is useful in estimating long-term exposure levels but is susceptible to extreme values on both the lower and upper bound and given the relatively small sample size in this data set, a more definitive average value may be obtained through the inclusion of more data. This feature in the data is highlighted by the high standard deviation which signifies a high level of variance in the data.\u003c/p\u003e \u003cp\u003eThe review by Gao et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) presents several concentrations from rivers across Europe. The paper highlights that when extreme outliers are omitted, the mean concentrations given were 0.608 and 0.145 MP/L which correspond to the lower bound of values presented by Johnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, Gao et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) also presented extreme values as high as 222 MP/L found in research conducted along the Rhine River by Faith (2019). This shows a similar variability in data as with the study by Johnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), however the review covers European rivers with no UK sources, its data is less geographically relevant and is not incorporated in this mass balance model.\u003c/p\u003e \u003cp\u003e \u003cb\u003eReservoirs -\u003c/b\u003e Johnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also studies raw, pre-treated water sourced from one pristine upland reservoir located along the England/Wales boundary. As explained previously, this UK data should provide a similarity to Bristol\u0026rsquo;s water system given it falls under the same geographical and regulatory boundaries. In this study, the pristine upland reservoir presented no quantifiable microplastics. However, the study also presents values for \u0026lsquo;Lowland river-pumped storage\u0026rsquo; sites, which are described as abstracted river water stored in reservoirs. Given that this system is utilised within the Bristol area as detailed by Bristol Water (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), these values may be more relevant to the study. Pumped storage site values presented a high value of 113 MP/L, greater than that of direct river abstraction, though this value appears to be an outlier in the data as no other concentration above 0.2 MP/L was detected, suggesting that this value may be an outlier.\u003c/p\u003e \u003cp\u003eThe data from Johnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) suggests that upland pristine reservoirs are less likely than rivers to be a significant source of microplastic influx, but that river-pumped storage reservoirs may contribute. However, this is difficult to present with certainty given the high standard deviation in the data provided and the likelihood of large outlying concentrations skewing the average values upwards. If the value of 113 MP/L is omitted from the river-pumped storage and pristine reservoir data, the average concentration drops significantly. This aligns with the consensus presented by Watkins et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), that reservoirs generally \u0026lsquo;trap\u0026rsquo; microplastics in their sediment acting as a filter and thus have a lower concentration suspended within their body of water.\u003c/p\u003e \u003cp\u003eJohnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) quantified particles of size greater than 25 \u0026micro;m and did not provide size distribution details specific to river or reservoir sources, though the general size distributions presented for raw water spike at the lower end of the size range. This is highlighted by the study as indicating that a significant number of sub-25 \u0026micro;m particles may be present in the samples but could not be quantified.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGroundwater Sources \u0026ndash;\u003c/b\u003e The data used to model groundwater microplastics in the mass balance model was precured by Lapworth and Shockley (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The study took samples from 8 sites including public and private boreholes, a private well and two Environment Agency (EA) water level monitoring wells. The average particle size detected was 80 \u0026micro;m with just 28% of particles below 50 \u0026micro;m, and thus well above the filter size of 5 \u0026micro;m. This can be contrasted with the data given by Johnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in which no particles were detected in raw groundwater, despite using a filter size of 10 \u0026micro;m. Given that Johnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) states England and Wales as geographical locations for groundwater sampling, the geographical variations between these sources are difficult to state. Though as highlighted by Liu et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) physical aquifer properties such as material type and particle size may influence the migration of particles into groundwater.\u003c/p\u003e \u003cp\u003eThe average microplastic concentration identified in the groundwater sites is 0.045MP/L which is approximately half that of the reservoir sources, and with a lower standard deviation; indicating fewer statistical anomalies (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This means that in this case, the average value may be more indicative than the highest or lowest values.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWastewater Treatment \u0026ndash;\u003c/b\u003e Wastewater treatment represents the particle concentration in raw used water from the urban sewage system as it enters the wastewater treatment plant. The data used from UKWIR (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) was specifically relevant to the modelling process as it was procured from a Wessex Water WWTP in Saltford near Bath. In their study, sample water filtered through a 500 cm\u003csup\u003e2\u003c/sup\u003e, 5 \u0026micro;m stainless steel cartridge filter. Although this site mainly processes water from Bath and the surrounding area, not Bristol (Wessex Water, n.d.), the geographical relevance and the fact that it is operated by the same company gives significance to this study.\u003c/p\u003e \u003cp\u003eThe range of 1227 MP/L this data indicates a substantial variation in results obtained between sampling periods. This is also highlighted by the high standard deviation, demonstrating that the average value of 423.5 MP/L (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) may not be truly indicative of the average value, thus a wider sample set would be required to obtain a more accurate representation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eProcessed Water Release \u0026ndash;\u003c/b\u003e UKWIR (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) presents an average value for the whole of UK for processed water releases of 5.1 MP/L. In their study, water was filtered through a 500 cm\u003csup\u003e2\u003c/sup\u003e, 5 \u0026micro;m stainless steel cartridge filter. However, the values presented specifically for Wessex Water\u0026rsquo;s Saltford plant may be more indicative of those for Bristol and were used to inform the model instead of the UK average.\u003c/p\u003e \u003cp\u003eIn the Saltford data from UKWIR (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), even the highest concentration recorded is far lower than the overall average presented. This could indicate that removal by Wessex Water and thus within the Bristol water system is more effective than the UK average. However, the small sample size and relatively high variance in data means this cannot be stated as a certainty.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFertiliser\u003c/b\u003e \u003cb\u003e\u0026ndash;\u003c/b\u003e The microplastic pathway from sludge derived fertiliser to agricultural fields and subsequently groundwater is highlighted by Harley-Nyang et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), however no direct values for fertiliser are available. Assumptions could be made if considering sludge concentrations and using them to predict the particle quantities transferred into the final fertiliser products, however, given the influx of values presented at the sludge stage (which may be due to further breaking down of particles) such a result may not be accurate and thus will be omitted from the scope of this paper.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStormwater Overflow\u003c/strong\u003e \u003cp\u003eWater released in an \u0026lsquo;overflow\u0026rsquo; situation is untreated and is due to the water system exceeding its volume capacity often due to heavy rain. This is to relieve pressure on the system and water commonly flows into rivers (Environment Agency, 2021). The value of 365 MP/L presented by UKWIR (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) is an average across the year from water sampled in Exmouth. As it is untreated, its value may be representative of the Bristol region, given that the average concentrations of untreated sewage may be similar nationally.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2.3 Calculations of MP quantities for the study area for Drinking Water Treatment Influent, Tap Water and Sludge\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDrinking Water Treatment Influent\u003c/b\u003e \u003cb\u003e-\u003c/b\u003e Based on the water sources in the study area, and their relative contribution as source water, an average microplastic concentration of 13.4 MP/L is calculated using their percentual contributions (42.5% from rivers; 42.5% from reservoirs and 15% from groundwater) and average microplastic concentrations:\u003c/p\u003e \u003cp\u003eCalculation: (42.5% \u0026times; 31.5 MP/L) + (42.5% \u0026times; 0.1022 MP/L) + (15% \u0026times; 0.045 MP/L)\u0026thinsp;=\u0026thinsp;13.4 MP/L\u003c/p\u003e \u003cp\u003eThis calculation yields a higher concentration than the value of 4.9 MP/L provided by Water UK (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) as a UK average, though the inclusion of potentially outlying values in the average for river water (signified by the high standard deviation) may skew the data upwards. However, Water UK (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) does not state the methodology used for obtaining the value. This average value can be used for comparison with the inflow concentrations in the previous section.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTap Water -\u003c/b\u003e Given that the data from Johnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) contained tap water samples from river, reservoir and groundwater sources, a weighted average of the concentrations provided was produced to more accurately model Bristol\u0026rsquo;s water sources. Using the percentual contributions and upper and lower microplastic concentrations (tap water from river source: 0.001\u0026ndash;0.0244 MP/L; from reservoir sources 0.0005\u0026ndash;0.0028 MP/L; from groundwater sources: 0.00011\u0026ndash;0.002 MP/L) an upper and lower bound was determined from the data set.\u003c/p\u003e \u003cp\u003eUpper Bound = (0.0244 MP/L \u0026times; 42.5%) + (0.0028 MP/L \u0026times; 42.5%) + (0.002 MP/L \u0026times; 15%)\u0026thinsp;=\u0026thinsp;0.01186 MP/L\u003c/p\u003e \u003cp\u003eLower Bound = (0.001 MP/L \u0026times; 42.5%) + (0.0005 MP/L \u0026times; 42.5%) + (0.00011 MP/L \u0026times; 15%)\u0026thinsp;=\u0026thinsp;0.000654 MP/L\u003c/p\u003e \u003cp\u003eWhen compared to the drinking water concentration from Water UK (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) of 0.00011 MP/L, the lower bound determined above is 6 times greater and the upper bound over 100 times greater. Another order of magnitude is brought into consideration by the values presented by Al-Mansoori et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) in which 12 cities analysed produced an average concentration of 36 MP/L, with a lowest concentration of 6 MP/L and high of 74 MP/L. This once again highlights the great variability in data between sources.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSludge -\u003c/b\u003e Using data from Bristol Water (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the inflow proportions between river abstraction and reservoirs was taken as 42.5% each. Using this weighting, a weighted upper and lower bound for sludge was calculated. The data from Johnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) gave a range of values, with LR1 (Lowland River), LR3 and LRS1 being sludge from river abstraction derived wastewater, and UR (Upland Reservoir) being sludge from reservoir derived wastewater. The values are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMicroplastics per gram for sludge derived from river abstraction and reservoirs according to Johnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLR2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLR3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLRS1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMP/g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1092 to 85,729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e265 to 676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo weight the values and produce an upper and lower bound, the upper and lower values for each source were first identified Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As the weighting in the final sludge value for Bristol\u0026rsquo;s water is 42.5% both rivers and reservoirs, both can be viewed as equally proportionate in the final sludge produced.\u003c/p\u003e \u003cp\u003eMin = (50% \u003cb\u003e\u0026times;\u003c/b\u003e 511) + (50% \u003cb\u003e\u0026times;\u003c/b\u003e 265)\u0026thinsp;=\u0026thinsp;388 MP/g\u003c/p\u003e \u003cp\u003eMax = (50% \u003cb\u003e\u0026times;\u003c/b\u003e 676) + (50% \u003cb\u003e\u0026times;\u003c/b\u003e 85,729)\u0026thinsp;=\u0026thinsp;43,202 MP/g\u003c/p\u003e \u003cp\u003eNext, using the average sludge density of 1.035 kg/L (Jang and Schuler, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) the range has been converted into the units MP/L. Using this conversion, the lower and upper bounds for MP/L in sludge are 401,580 MP/L and 44,714,587 MP/L.\u003c/p\u003e \u003cp\u003eDaily counts of microplastics per node have been calculated using the daily water volume multiplied with the concentration ([MP/L] \u0026times; [L/Day] = [MP/Day]). The calculation has been carried out for the highest concentration for each node, along with the lowest and the average. Standard deviation and Coefficient of Variation (CV = (Standard Deviation/Average) \u0026times; 100) have been calculated for the daily microplastic quantities per node.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eThe results for the daily microplastic counts passing through the nodes of Bristol\u0026rsquo;s water system are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and contain the highest, lowest and average counts of microplastics expected. The highest values are found for the sludge, while the lowest are found for the groundwater (no data for the fertilizer). It is evident that the variation in data collected is high, meaning that the spread of values presented for each node across the sources used varies. This is also demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e where a comparison of the coefficient of variation for each node is presented.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the microplastics quantities per node in the Bristol water system\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eMP concentration [MP/L]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eMP quantity [MP/day]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNode Volume [l/day]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHighest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLowest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHighest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLowest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRivers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116,880,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,753,200,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e267,655,200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReservoirs (Including Pumped-River Storage)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116,880,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13,207,440,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e881,275,200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroundwater Sources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41,250,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7,012,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,856,250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDWTP Influent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e275,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,685,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,347,500,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,516,250,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTap Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e275,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20,350,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30,250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7,427,750,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWWTP Influent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e275,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e337,425,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e116,462,500,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWWTP Effluent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e274,960,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e522,424,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e253,650,600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSludge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e164,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44,714,587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e401,580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7,333,192,268,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65,859,120,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFertiliser\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e132,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverflow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15,512,500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe mean CV values across the data used is 213.64%, suggesting that the standard deviation is likely to be twice the magnitude of the average. However, this varies greatly between nodes, with the data for rivers varying almost 3 times the magnitude of its average, and the data for DWTP influent varying by 6 times the magnitude of its average. This suggests that an average value for these nodes may not be indicative of the true value, which may fall anywhere within its wide range of potential values. The value for any of these nodes may also fall beyond the range specified given the small sample sized procured from the literary sources available.\u003c/p\u003e \u003cp\u003eAnother important distinction is that the CV does not capture all the relevant information to comprehensively analyse the dataset. The tap water node for example, presents the lowest CV of 71.5% suggesting a relatively low variance from the average within the dataset, but if a relative rage is taken (where range is divided by the mean), outlying values give a value of 300% of the average. When the same principle is applied to sludge, the highest outlying values push the relative range value to over 280,000% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This means that large outlying values may not be picked up when analysing the CV and might signify a greater level of variability than is captured by this analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Mass Balance Model\u003c/h2\u003e \u003cp\u003eThe full mass balance model presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e brings the data together in a visualised format. In general, the values tend to increase through the system from inflows to outflows. However, the first node in the system, i.e. the inflow to the drinking water treatment, is only reflecting a mixing of water with a relatively high microplastic load (river water) with water of lower microplastic load (reservoir water and groundwater). It is surprising that the model shows an increase in microplastic concentration from the drinking water treatment inflow to the tap water though one would expect the treatment system to reduce microplastic concentrations and that microplastics released from pipes, tanks and fittings in the treatment and distribution works are minimal. However, the range of microplastic loads in tap water range over several orders of magnitude and an occasional use of larger proportions of river water might cause the highest concentrations recorded, while when considering the minimum values recorded, the treatment system reduces the microplastic load considerably, from 1.3*10\u003csup\u003e9\u003c/sup\u003e to 3*10\u003csup\u003e4\u003c/sup\u003e MP/day. The increase from the tap water microplastic counts to the wastewater microplastic counts is 1.1*10\u003csup\u003e11\u003c/sup\u003e MP/day (using average quantity) to 3.2*10\u003csup\u003e11\u003c/sup\u003e MP/day (using maximum quantity) which is 15.6 to 16.5 times the load after domestic use than before leaving the household tap. This supports the notion presented by Mintenig et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and others that urban water systems may be a net source of microplastics into terrestrial and marine aquatic environments and can be attributed to the large influx observed in the wastewater stage of the cycle. The wasterwater treatment system removes 1.2*10\u003csup\u003e11\u003c/sup\u003e MP/day (using average quantity) to 3.4*10\u003csup\u003e11\u003c/sup\u003e MP/day (using maximum quantity), which indicates a removal efficiency of 99.8%. This is correlative to the general academic consensus, that the microplastic removal rate in wastewater treatment plants is 99.9% (Water UK, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The microplastic concentration of the effluent to the environment is also significantly lower than the microplastic concentration in rivers.\u003c/p\u003e \u003cp\u003eWhile wastewater treatment has a high potential to harbour large microplastic concentrations, though given that one source cited a concentration of 0 MP/L this is likely subject to a very high degree of variability. However, a large portion of microplastics are transferred from wastewater to the sludge (56%, using average values) which returns microplastic back into the environment. A large portion is lost at this stage from the mass balance, ending up in unaccounted waste disposal, e.g. on submerged aerated filters.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSludge repeatedly presented high concentrations throughout the data collected, suggesting some certainty as to its substantial role in the flow of microplastics within the urban water system. Overall, untreated wastewater microplastic concentrations are relatively understudied and a wider range of data would be required to produce an accurate analysis of its role in the flux of microplastics into the terrestrial water environment. Though, given the values presented for corresponding nodes (such as wastewater) this may fairly be assumed to be high. The same can be said for sludge derived fertiliser, for which direct studies are necessary if concentrations are to be attained and analysed.\u003c/p\u003e \u003cp\u003eThe large range of values for each node in the system signifies the high degree of variance across and within the studies analysed. The model is effective at highlighting this variance by utilising the full set of values across an available set of studies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe main significant variance in the data utilised, which is highlighted in the model, can be explored in the context of tap water. The data presented by Johnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) was obtained via 10 \u0026micro;m filtration and gave extremely low MP/L values, however the newer study by Al-Mansoori et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) which utilised 0.45 \u0026micro;m filtration presented far higher values, as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The switch to 0.45 \u0026micro;m corresponding magnitudinous increase in particle concentration casts significant doubt on current testing regimens and the broad scientific and industrial focus on microplastics as a point of concern. It instead suggests that a more critical point of study may be upon the quantification of nano-plastics (particle sizes of 1-1000 nm).\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe results obtained within this investigation, present a basis for discussion within multiple contexts. Discrepancies of the concentrations and counts at various nodes are reflecting on the one hand fragmentation, secondary sources, and biological uptake, but also a lack of systematic microplastic monitoring and lack of standardised methods. These uncertainties are discussed below.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Data Variation Discrepancies\u003c/h2\u003e \u003cp\u003e \u003cb\u003eFragmentation\u003c/b\u003e - The results of the mass balance model display a wide range of values, many of which vary by orders of magnitude between sources even when regarding the same node. A large proportion of microplastics in aquatic environments are secondary, produced due to fragmentation and degradation (Ziani et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This degradation can occur an arbitrary number of times, often resulting in micro- to nano-plastics (Yee et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Particles of this size may have been below the range of quantification for some studies used and may have been accounted for in others skewing the data between sources even regarding the same node. This is supported when comparing the values obtained for tap water, where Johnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) used a 10 \u0026micro;m filter and observed a maximum concentration of 0.0244 MP/L, whereas Al-Mansoori et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) used a 0.45 \u0026micro;m filter and observed a maximum concentration of 74 MP/L. This suggests that most particles within tap water are of a sub 10 \u0026micro;m scale, and that this factor may be significant throughout the data utilised within this analysis. Most of the studies used to inform this research utilised filters greater than 5 \u0026micro;m and so, if this trend correlates, the majority of microplastic particles may have been missed entirely.\u003c/p\u003e \u003cp\u003eAs well as this, the continual fragmentation process is also likely to have continued within the water system as microplastics have been shown to undergo chemical bond breakage and molecular cleavage within water systems (Ma et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), adding an addition layer of complexity to the quantification process.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSecondary Sources\u003c/b\u003e \u0026ndash; This observation can be extended to plastics which may present as macro-scale particles at earlier stages of the water system but are broken down later. This could include larger plastic items, but also fibres shed from polyester materials (such as sanitary products) (Browne et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Alternative transport dynamics, via air or biota (Gasperi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) which are not considered in the model may present as alternative sources or sinks. Urban and roadway stormwater runoff is also presented as a significant inflow of microplastics, especially near industrial areas (Liu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Due to the widespread nature of road surface runoff, nuanced quantification techniques would be required for analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBiological Uptake\u003c/b\u003e - Van Cauwenberghe and Janssen (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) identifies the uptake of microplastics in commercially grown bivalves. Given the biological digestion techniques used in the processing of sewage sludge (Harley-Nyang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) there may be a likelihood that smaller particles were digested by the bacteria used in this process. Additional research in this area would be suggested to confirm or deny the impact this could have on overall particle counts.\u003c/p\u003e \u003cp\u003eBeyond these factors, mismanagement such as illegal dumping or spillages could make quantification more difficult (Wagner and Lambert, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Full transparency and considerations would need to be adopted to account for these issues in the building of a fully comprehensive model.\u003c/p\u003e \u003cp\u003eOverall, the unknown rates and proportions of microplastic degradation cause uncertainty in the presented mass balance model. In a strict sense, mass balances are only applicable to truly conservative tracer, but microplastics undergo process of degradation (Gambino et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This includes biotic degradation occurring through exposure to bacteria, fungi and microbes or abiotic degradation from UV and heat exposure (Cai et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, these processes are very slow (Zhang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Further uncertainty is caused by the different magnitudes of microplastic concentrations and for some nodes the errors are larger (e.g. wastewater treatment inflow or sludge) than the range of values at other nodes (e.g. groundwater or treated wastewater release).\u003c/p\u003e \u003cp\u003eA further source of uncertainty stems from the range of quantification methods used and the question arises if the mass flow within the studied system considered in fact the same material. Microplastic are defined as particles smaller than 5 mm (US EPA, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, for practical reasons and a lack of a standardised procedure, different membranes or filters are used to pass samples through (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and thus \u003cem\u003eoperationally\u003c/em\u003e considered as the lower size limit of microplastics (0.45 to 26 \u0026micro;m). This compilation indicates that, in general, higher concentrations are associated with lower filter opening sizes. Further to this, volumes of water samples are also not standardised and range from a few litres (e.g., 5 L by Erdem et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); or 10 L by Shen et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)), to \u0026ldquo;several hundred litres\u0026rdquo; evidenced by Johnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCompilation of mesh/membrane sizes observed within the literature when determining microplastic concentrations in tap water\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFilter size [\u0026micro;m]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMP concentration [MP/L]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdediran et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl-Mansoori et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJohnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelgium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSemmouri et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCzech Republic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHalfar et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65\u0026ndash;68.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFeld et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinland, France, Japan, USA, Germany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMukotaka et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46\u0026ndash;97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMintening et al. (2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaudi Arabia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlmaiman et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9\u0026ndash;47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eErdem et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e470\u0026ndash;2821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Urban Water Cycle \u0026ndash; Source or Sink?\u003c/h2\u003e \u003cp\u003eWhether the urban water cycle is a source or a sink of microplastics is a continual area of debate. While our study indicates domestic households as a major source of microplastics to the urban water system, WWTP capture 99.8% of it. However, the transfer of microplastics from the wastewater to the sludge is a sink, but if the sludge is turned into fertilizer, this is to be considered as an important microplastic source for the wider environment. A simple analysis using the stated DWTP influent value given by Johnson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) of 4.9 MP/L and the UKWIR (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) effluent value of 5.1 MP/L, presents a system which would produce a net gain of over 54\u0026nbsp;million MP/ day. If the total volume of water through the DWTP and WWTP system was estimated to be 11\u0026nbsp;billion litres in the UK (DEFRA, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), this would give an annual net gain of 796\u0026nbsp;billion particles, from the UK alone.\u003c/p\u003e \u003cp\u003eIf the total estimated microplastic concentration on the surface ocean is 24.4 trillion (Kyushu University, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), this annual net gain output would represent 3.26% of the total global estimated abundance. If extrapolated to a global scale, this suggests that a figure such as 24.4 trillion may be a significant underrepresentation.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study highlighted the difficulty in effectively quantifying the numbers of microplastics within Bristol\u0026rsquo;s urban water system. The main sink is the sludge from the wastewater treatment node. The main sources of microplastics are from domestic households and rivers where environmental microplastics originate from the river inputs such as farmlands and smaller tributaries (Horton and Dixon, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) where the microplastics are transported towards the ocean. The applied mass balance shows overall that many particles enter from the environment, but that the drinking water system is effective in removing them, however given the very high particle counts identified in some sources this may still correlate with a large microplastic influx into the environment. Sludge based fertiliser was noted as a potential vehicle, transporting microplastics into terrestrial ecosystems, and beyond this, for example into the groundwater. The large quantities of microplastics identified in sludge are transferred into sludge-based fertiliser (Cusworth et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), meaning microplastics are being transferred into areas where crops are grown.\u003c/p\u003e \u003cp\u003eThe range of results of volumetric figures for microplastics show a very wide range of daily throughput values and this correlated with prior research. Substantial numerical differences were also cited, highlighting the potential unknown volumes of microplastics within the urban water system possibly due to the differences in sampling techniques used and the significant potential for micro- and nano-plastics to be undetected with the filters and microscopes commonly used in testing. It\u0026rsquo;s clear that further research regarding sampling techniques, standardised approaches, sophisticated detection, quantification and modelling is required for both the sampling methods and for water treatment systems. Once this is completed a more comprehensive understanding of this topic will be achieved. Most notably using standardised sampling with the possibility of uncovering far higher concentrations of nanoparticles and other sub-5 \u0026micro;m particles; the impacts of which remain unknown. The study of fertiliser as a pathway for terrestrial microplastics should also be a recommendation regarding public health policy. These findings echo the growing concern over microplastic impact on human health, by quantifying their extremely high abundance. Despite the statistical effectiveness of the water system studied, the high number of microplastics mean many millions still make their way into the environment each day. Significant investment in further research, and policy interventions as well as engineering solutions are a necessity for dealing with plastics in water treatment plants.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical Approval\u003c/h2\u003e \u003cp\u003eThis is not applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Participate\u003c/strong\u003e \u003cp\u003eThis is not applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Publish\u003c/strong\u003e \u003cp\u003eThis is not applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003eThis is not applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests Statement\u003c/h2\u003e \u003cp\u003eThe authors confirm that we have no competing interests nor conflicts of interest\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was undertaken for an undergraduate research project with no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eSE and HW conceptualised the work; SE undertook data reviewing, analytical work and model building SE prepared all figures; SE, HW and HB undertook the writing and editing; All authors reviewed the manuscript prior to submission.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis is not applicable.\u003c/p\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e \u003cp\u003eThe data that supports the findings of this study are available on request from the author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdediran, J. 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Journal of Oceanology and Limnology 43: 372\u0026ndash;382. https://doi.org/10.1007/s00343-024-4138-1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Microplastic pollution, urban water systems, mass balance model","lastPublishedDoi":"10.21203/rs.3.rs-8638531/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8638531/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The extent of microplastic pollution is widespread, including aquatic ecosystems in urban environments. The aim here was to build a quantified mass balance model, exploring and tracing the microplastics within Bristol’s urban water system, filling a knowledge gap, specific to this geographical region but developing a model that can be applied more generally for urban water systems. A multitude of secondary data were compiled, reviewed, analysed and quantified to inform a mass balance model. The model represents the number of microplastics passing through each section of Bristol’s water system where one of the main findings indicates domestic households as a major source of microplastics to the urban water system, while wastewater treatment plants capture ~99.8% of microplastics. Our second finding implies the transfer of microplastics from wastewater to sludge is a sink, but if the sludge is turned into fertilizer, this is to be considered as an important microplastic source for the wider environment. A source of uncertainty stems from the range of quantification methods used in the reviewed data and points out the necessity of further research regarding standardised sampling techniques and approaches to feed into sophisticated detection, quantification, and modelling systems.","manuscriptTitle":"Tracing and Quantifying Microplastics in Bristol’s Urban Water System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 17:44:06","doi":"10.21203/rs.3.rs-8638531/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0b1b949a-76d2-48e8-a82d-df9cb4473424","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-03T07:42:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 17:44:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8638531","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8638531","identity":"rs-8638531","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}