Pilot-Scale Comparative Evaluation of the Filtration Performance of Dual-Media Filters

Pilot-Scale Comparative Evaluation of the Filtration Performance of Dual-Media Filters | 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 Pilot-Scale Comparative Evaluation of the Filtration Performance of Dual-Media Filters Xuefan Rao, Wang Ya, Naixin Kang, Qiang Zhang, Yanhui Fan, Zhengong Tong, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7971198/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 Filtration is a critical process in drinking water treatment, with dual-media filters widely recognised for their superior performance compared to single-medium filters. However, comprehensive comparative studies evaluating different media combinations remain limited. This pilot-scale study systematically assessed the filtration performance of four dual-media filters: anthracite–quartz sand (AQ), anthracite–garnet (AG), ceramsite–quartz sand (CQ) and ceramsite–garnet (CG). By monitoring key indicators including turbidity, particle count, UV 254 , head loss and pollutant retention capacity, the study revealed that ceramsite-based filters (CQ and CG) perform considerably better than anthracite-based filters (AQ and AG). Specifically, the CQ filter showed 18%–54% higher pollutant retention capacity, 21%–28% slower head loss growth rate and substantially longer filtration cycles. Both Rank sum ratio and JP evaluation methods consistently showed that CQ is the optimal media combination, followed by CG. The results indicate that greater differences in physical characteristics between upper and lower media layers promote better utilisation of the capacity of the filter bed. These findings provide valuable theoretical insights and practical guidance for optimising media selection in dual-media filters and inform the upgrading of single-medium filters. Dual-media filter Filter media comparison and selection Filtration performance Rank sum ratio evaluation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction In the production of drinking water, conventional processes (i.e. coagulation–sedimentation–filtration) are the most widely adopted treatment technology globally, particularly in developing countries (Cococeanu et al., 2021; Tian et al., 2021 ). Filtration serves as one of the pivotal unit processes in this scheme, playing a critical role in ensuring water quality (Razali et al., 2023 ). This process involves the separation and retention of suspended particles from water as it passes through porous media (e.g. quartz sand, anthracite and ceramsite) or filter screens (Kim et al., 2020 ; Meinel et al., 2015 ). It is primarily employed to remove particulate matter (clay and silt), microorganisms, metal ions and organic precipitates, with a specific focus on water quality indicators including turbidity, colour, organic matter and particle count (Cheremisinoff, 2002 ; Wang et al., 2021 ). Early slow sand filtration, which relies on a combination of mechanical straining and biological activity, produces high-quality effluent; however, its large footprint and low production capacity limit its application in densely populated areas (Noredinvand et al., 2016 ; Shirakawa et al., 2022 ). To meet the requirements of large-scale production, rapid sand filters have been developed, with single-medium sand filters being the most prevalent (Shin et al., 2006; Mahanna et al., 2020 ). However, in response to growing water demand, single-medium filters often have to be operated beyond their design capacity, leading to issues such as rapid filter clogging, shortened filtration cycles, increased operational energy consumption and poor effluent quality. Compared with single-medium filters, multi-media filters, owing to their coarse-particle layer, exhibit greater retention capacity towards solids. This effectively prevents impurities from penetrating the finer media layer, thereby increasing the filtration loading rate, extending the filtration cycle and reducing backwashing frequency (Brandt et al., 2017 ; Sanyaolu, 2010 ; Xie et al., 2022 ). These advantages can, to a certain extent, compensate for the limitations of single-medium filters and alleviate the pressure from high hydraulic and pollutant loads. Common media used in multi-media filters include anthracite, quartz sand, garnet and magnetite, among which the combination of anthracite and quartz sand is a typical choice for dual-media filters (Cescon et al., 2020; Hoko et al., 2024 ). Although numerous comparative studies on the performance of single-medium versus multi-layer (dual-media) filters have been conducted (Kazemi et al., 2016; Zouboulis et al., 2007 ), comparative studies evaluating different media combinations within dual-media filters and their impact on filtration performance are still lacking. Herein, four dual-media filter columns (anthracite–quartz sand (AQ), anthracite–garnet (AG), ceramsite–quartz sand (CQ) and ceramsite–garnet (CG)) for a drinking water treatment plant (DWTP) were designed and constructed. Indicators, including turbidity and particle count in the initial filtration stage, as well as turbidity, UV₂₅₄, head loss increase rate and pollutant retention capacity in the main filtration stage, were monitored. The differences in filtration performance among the different media combinations were compared and analysed. Furthermore, the rank sum ratio (RSR) method was employed to evaluate the filtration performance of the four dual-media filter columns, and the results were compared with those obtained through the JP method. Therefore, this study aimed to fill this knowledge gap by systematically comparing the performance of four representative dual-media combinations under realistic conditions. Beyond a direct performance comparison, we used a comprehensive evaluation framework (RSR method) to integrate multiple key performance indicators, providing a more robust basis for media selection. The findings of the present study are expected to offer concrete guidance for the optimisation of filter media selection in dual-media filters and the upgrading of single-medium filters. 2. Materials and Methods 2.1. Experimental Setup Pilot-scale filtration units were installed at a DWTP that draws water from the Ganjiang River in Nanchang, Jiangxi Province, southern China. The DWTP has a treatment capacity of 100,000 m³/d and operates a conventional treatment sequence comprising coagulation, sedimentation, sand filtration and disinfection. The physical diagram and process schematic of the pilot-scale facility are shown in Fig. 1 . The main unit (filter column) is made of organic glass, with an internal diameter of 150 mm and a height of 3000 mm. From bottom to top, the column consists of the water distribution chamber, support layer and filter media layer, with heights of 300, 300 and 800 mm, respectively. The thickness of both the upper and lower filter media layers is 400 mm. The height above the top of the upper media layer is 1600 mm. The support layer comprises three layers of pebbles arranged in order of increasing size from top to bottom. Sampling ports (for media) and pressure measurement ports are located on both sides of the column, starting 150 mm above the bottom of the support layer, with intervals of 150 mm between each port. During the experiment, the influent to the filter columns was taken from the effluent of the sedimentation tank of the DWTP. The settled water had a pH of 6.98–7.35, temperature of 18.4–31.7°C, turbidity of 0.73–4.56 NTU and UV₂₅₄ of 0.027–0.057 cm⁻¹. The experimental process was: sedimentation tank effluent → intermediate tank → feed pump → filter column → filtered effluent. 2.2. Experimental Methods 2.2.1. Filter Column Construction The filter columns were constructed in accordance with the design parameters for dual-media filters specified in the Chinese national standard (GB50013-2018). Four dual-media filter columns were constructed: AQ, AG, CQ and CG. The filter media were purchased from Zhengzhou Haoda Environmental Protection (China). The basic parameters of the media are listed in Table 1 . Table 1 Basic parameters of the filter media. Filter media Size range (mm) Effective size (mm) Uniformity coefficient Porosity (%) Density (g·cm − 1 ) Anthracite 0.8–1.8 0.89 1.39 53.0 1.60 Quartz Sand 0.5–1.0 0.58 1.45 43.0 2.60 Ceramsite 1.0–3.0 1.26 1.52 56.0 1.60 Garnet 0.5–1.0 0.54 1.63 47.0 3.50 2.2.2. Filtration and Backwashing During the experiment, the filtration rate was controlled at approximately 8.0 m/h using a UPVC ball valve. Filtration was stopped for backwashing when the maximum head loss of 1.5 m was reached. Single-water backwashing was performed with a washing intensity of 16.0 L/(m²·s) and a duration of 8.0 min. 2.2.3. Water and Filter Media Sampling After the operation of the filter column stabilised, water samples were collected. In the initial filtration stage (0–7 min, at 1-min intervals and at 20 and 30 min), initial filtrate water was collected. During the main filtration stage, influent and effluent water were collected every hour until the end of the filtration cycle. After filtration termination, filter media samples were collected using a simple sampler at each sampling port: 50 mL of media were gathered and stored in centrifuge tubes for later analysis. 2.3. Analytical Methods The surface morphology of the filter media was analysed using scanning electron microscopy (TESCAN MIRA LMS, Czech Republic). Turbidity was measured with a turbidimeter (HACH TU5200). UV₂₅₄ was detected using a UV spectrophotometer (HACH DR6000). Particle counts were determined using a KZ-2 particle counter (Tianjin Luogen Technology). Head loss was measured using manometer tubes. The pollutant retention capacity was analysed in accordance with the methodology reported by Zhou et al. ( 2013 ). 2.4. Methods for Evaluating Filtration Performance 2.4.1. RSR Method The RSR method is an effective evaluation method that is widely used for multi-index comprehensive assessment (Wang et al., 2023 ). In this method, the values of each indicator are first converted to dimensionless rank values. Then, the entropy weight method is used to assign weights to each evaluation indicator. Finally, the weighted RSR for each evaluation object is calculated. Objects are sorted based on this ratio, with a higher RSR value indicating better performance (Xiao et al., 2025). The specific steps are outlined below. In the context of the evaluated subjects, with each possessing a set of indicators, the initial step entails the calculation of the rank value R ij for the j- th indicator value X i of the i- th subject. This calculation was conducted using Eq. ( 1 ). $$\:{\text{R}}_{\text{ij}}\text{=1+}\left(\text{n-1}\right)\frac{\Phi}{\left({\text{X}}_{\text{max}}\text{-}{\text{X}}_{\text{min}}\right)}$$ 1 where Φ takes the value of X max − X i or X i − X min , with X max and X min being the maximum and minimum values of the indicator, respectively. In instances where X i is a high-priority indicator, where a higher value is preferable, the value of Φ is set to X i − X min . Conversely, when X i is a low-priority indicator, the value of Φ is set to X max − X i . In the second step, entropy values are assigned to each indicator, with standardisation performed through a 0–1 transformation, as shown in Eq. ( 2 ). where λ takes the value of \(\:{\text{b}}_{\text{ij}}\text{-}\underset{\text{j}}{\text{min}}{\text{b}}_{\text{ij}}\) or \(\:\underset{\text{j}}{\text{max}}{\text{b}}_{\text{ij}}\text{-}{\text{b}}_{\text{ij}}\) , with \(\:{\text{b}}_{\text{ij}}\text{-}\underset{\text{j}}{\text{min}}{\text{b}}_{\text{ij}}\) and \(\:\underset{\text{j}}{\text{max}}{\text{b}}_{\text{ij}}\text{-}{\text{b}}_{\text{ij}}\) representing the maximum and minimum entropy values, respectively; when X i is a high-priority indicator (where a higher indicator value is preferable), λ is set to \(\:{\text{b}}_{\text{ij}}\text{-}\underset{\text{j}}{\text{min}}{\text{b}}_{\text{ij}}\) ; in the other case, λ is set to \(\:\underset{\text{j}}{\text{max}}{\text{b}}_{\text{ij}}\text{-}{\text{b}}_{\text{ij}}\) . Subsequently, in accordance with the standardised decision matrix, the characteristic weight for the j -th indicator for the i -th evaluation sample, denoted as P ij , is calculated using Eq. ( 3 ), where 0 < P ij < 1. $$\:{\text{p}}_{\text{ij}}\text{=}\frac{{\text{b}}_{\text{ij}}^{\text{*}}}{\sum\:_{\text{i=1}}^{\text{m}}{\text{b}}_{\text{ij}}^{\text{*}}}$$ 3 The corrected entropy value for the j -th indicator (denoted by g j ), the entropy weight for the j -th indicator (denoted by w j ) and the weighted rank ratio for the i -th evaluation object (denoted by \(\:{\text{δ}}_{\text{WRSRi}}\) ) are calculated using Equations ( 4 ), ( 5 ) and (6), respectively. 2.4.2. JP Method Jing et al. ( 2000 ) proposed the JP method for evaluating filter performance. The evaluation method is expressed in Eq. ( 7 ). A smaller JP value indicates better filtration performance (Jing et al., 2000 ). $$\:\text{JP=}\frac{{\text{H}}_{\text{t}}{\text{LC}}_{\text{e}}}{{\text{V}}^{\text{2}}{\text{T}}^{\text{2}}\left({\text{C}}_{\text{0}}\text{-}{\text{C}}_{\text{e}}\right)}$$ 7 where H t is the head loss increase at the end of the filtration cycle (m), L is the filter bed thickness (m), V is the filtration rate (m/h), T is the duration of the filtration cycle (h) and C 0 and C e are the average turbidities of the influent and effluent (NTU), respectively. 3. Results and Discussion 3.1. Initial Filtration Stage 3.1.1. Turbidity and Particle Count Variations in the turbidity and particle count of the initial filtrate from the four dual-media filter columns over time are shown in Fig. 2 ; the correlation between turbidity and particle count in the initial filtrate is shown in Fig. 3 . The turbidity and particle count of the initial filtrate from all four dual-media columns showed an oscillating downwards trend over time, rapidly decreasing within the first 7 min. The trends for both parameters are generally consistent, showing a significant positive correlation (R² = 0.78, P < 0.05), which agrees with previous research findings (Cescon et al., 2020; Zamani et al., 2009). Notably, the turbidity and particle count in the effluent from the AQ group were lower than those for the CQ group, and those for the AG group were lower than those for the CG group. Furthermore, the AQ and AG groups required less time for the effluent turbidity to meet the internal control limit of the plant (0.5 NTU). Pairwise comparisons of the upper and lower layer media in the four columns revealed that when the lower heavy media were the same, the upper lightweight anthracite exhibited better pollutant retention capability than ceramsite. When the upper lightweight media were the same, the difference in pollutant retention capacity between the lower heavy quartz sand and garnet media was not significant. This indicates that in the initial filtration stage, the overall pollutant retention performance of the dual media is primarily determined by the upper lightweight layer. Stronger surface retention capability leads to better overall initial filtration performance. 3.1.2. Particle Size Distribution To further investigate the composition of particles of different sizes in the initial stage, the particle size distribution in the influent and effluent at the end of the initial stage (30 min) in the four columns was examined. The distribution of particle numbers for different size ranges is shown in Fig. 4 . Similar to the influent, the proportions of particles in different size ranges in the effluent from the four columns significantly varied, with particles sized 3–5 µm being the most abundant (64%–78%). The order of particle count reduction for the four columns was: AG > AQ > CQ > CG. The removal efficiency for particles larger than 5 µm was greater than that for particles sized 2–3 and 3–5 µm in all columns, indicating that particles in the 2–5 µm range are more difficult to remove. This phenomenon was attributed to the presence of particles with a size too fine to be retained by the filter column (Wakeman, 2007 ). 3.2 Main Filtration Stage 3.2.1. Turbidity Variations in the turbidities of the influent and effluent from the four dual-media filter columns are shown in Fig. 5 . Although the influent turbidity slightly fluctuated during filtration, the effluent turbidity remained relatively stable, mostly below the internal control limit of the plant (0.5 NTU). The turbidity removal efficiency decreased in the order of AG > AQ > CG > CQ, with an average removal rate of approximately 88.6%, indicating good turbidity removal performance for all columns. The comparison revealed two key aspects. First, when the upper lightweight media were the same, the rougher surface and larger specific surface area of the lower heavy media (quartz sand or garnet; as shown in Fig. 6 ) provided more opportunities for contact with turbidity-causing substances and their capture. Second, when the lower heavy media were the same, the smaller average particle size of the upper lightweight media (ceramsite or anthracite; Table 1 ) led to better turbidity retention, but also caused faster clogging because of rapid deposition, potentially shortening the filtration cycle (Zouboulis et al., 2007 ). 3.2.2. UV 254 Figure 7 shows the box plots of the influent and effluent UV₂₅₄ for the four dual-media filter columns. The UV 254 removal efficiency decreases in the order of CQ > CG > AQ > AG, with total removal rates ranging from 10.1% to 16.7%. These removal rates are slightly to moderately higher than those typically reported for conventional single-layer sand filters (often < 10%, Kazemi et al., 2016). This was attributed to the rough surface of the upper lightweight ceramsite and anthracite media (Fig. 8 ). The larger specific surface area is more conducive to biofilm attachment, forming a biological layer that removes organic matter through biodegradation (Altmann et al., 2016 ; Wang et al., 2021 ). 3.2.3. Head Loss The variations in head loss with filtration time for the four dual-media filter columns are shown in Fig. 9 . The head loss for all four dual-media columns increased over time. The head loss for the CG and CQ groups initially increased slowly, then rapidly, with the increase being more uniform throughout the cycle for the AG and AQ groups. At the beginning of filtration, the initial head loss order was AG > AQ > CG > CQ. This order remained unchanged after the same filtration time. However, at the point of maximum head loss, the order of filtration cycle duration was reversed: CQ > CG > AQ > AG. This implies that a greater amount of pollutants retained per unit time inevitably shortens the filtration cycle. These phenomena indicate that the filtration head loss of the dual-media filter is closely related to the duration of the filtration cycle. At the same time, the initial head loss is related to the media composition (Mahanna et al., 2020 ). When the lower heavy media are the same, larger particle size and porosity of the upper lightweight media result in larger water passage channels and weaker flow resistance, leading to a smaller initial head loss. When the upper lightweight media were the same, rougher surfaces and a larger specific surface area of the lower heavy media increase the flow resistance, also resulting in a larger initial head loss. 3.2.4. Pollutant Retention Capacity Figure 10 shows variations in pollutant retention capacity (measured as turbidity) with filter bed depth for the four dual-media filter columns. The maximum retention capacity for all columns was observed within the 0–5 cm depth, gradually decreasing along the bed depth, consistent with the deep-bed filtration theory (Zamani et al., 2009). Using the area enclosed by the depth and turbidity axes to represent the dirt-holding capacity, the order for the four columns was CQ > CG > AG > AQ. As shown in Table 1 , the average particle size and porosity of the upper lightweight media were greater than those of the lower media for all dual-media configurations. Considering the retention capacity in the lower heavy media layer (35–65 cm), only the CQ group showed a significant difference in capacity between its upper and lower layers. This indicates that pollutants retained in the upper layer of the CQ column migrated to the lower layer, allowing the more effective utilisation of the lower heavy media for particle retention, thereby enhancing the overall retention performance of the column. This suggests that a significant contrast in physical characteristics (e.g. particle size, porosity) between the upper and lower layers facilitates the migration of retained flocs into the deeper bed, thereby promoting more effective utilisation of the filter capacity. 3.3. Evaluation of Filtration Performance The filtration performance of the four dual-media filter columns was comprehensively evaluated using the RSR method described in Section 1.4. Six evaluation metrics were selected for analysis: initial filtered water turbidity, total turbidity removal rate, UV 254 removal rate, pollutant retention capacity, head loss growth rate and filtration cycle duration. The entropy weights calculated for these indicators were 16.5%, 22.1%, 13.5%, 16.7%, 14.3% and 15%, respectively. The numerical values for each indicator in the RSR method are listed in Table 2 ; the results of RSR evaluation are presented in Table 3 , and those obtained using the JP method are provided in Table 4 . Table 2 Numerical values of various evaluation indicators in the RSR method. Filter columns Initial filtered water turbidity (NTU) Total turbidity removal rate (%) UV 254 removal rate (%) Pollutant retention capacity (NTU) Head loss growth rate (cm/h) Filtration cycle duration (h) AQ 0.30 89.2 9.1 645.6 3.13 43.2 AG 0.27 90.5 11.8 698.8 3.42 39.4 CQ 0.46 86.6 16.7 996.6 2.45 54.0 CG 0.43 86.8 15.6 858.8 2.63 50.1 Table 3 RSR results. Filter columns Weighted rank Weighted rank sum ratio Rank Initial filtered water turbidity Total turbidity removal rate UV 254 removal rate Pollutant retention capacity Head loss growth rate Filtration cycle duration AQ 0.62 0.66 0.14 0.17 0.27 0.27 0.531 Ⅳ AG 0.72 0.88 0.28 0.26 0.14 0.15 0.609 Ⅲ CQ 0.18 0.22 0.54 0.67 0.57 0.60 0.695 Ⅰ CG 0.25 0.26 0.48 0.52 0.49 0.48 0.621 Ⅱ Table 4 JP method results. Filter columns Increase in head loss (m) Effluent turbidity (NTU) Filtration rate (m/h) Filtration cycle duration (h) Influent turbidity(NTU) JP value (10 − 6 ) Rank AQ 1.35 0.192 8.0 43.2 1.970 0.98 Ⅲ AG 1.32 0.167 8.0 39.4 1.920 1.01 Ⅳ CQ 1.41 0.277 8.0 54.0 2.130 0.91 Ⅰ CG 1.37 0.254 8.0 50.1 2.068 0.96 Ⅱ Tables 3 and 4 indicate that the results obtained via the two above methods are basically consistent. The CQ combination showed the best filtration performance, with a good organic matter removal rate, small head loss growth rate, long filtration cycle and higher pollutant retention capacity. The weighted ranks for these four indicators reached 0.54, 0.57, 0.60 and 0.67, respectively, far exceeding those for the turbidity-related indicators (0.18 and 0.22). The CG combination ranked second. Its weighted ranks for organic matter removal, head loss growth rate, filtration cycle duration and pollutant retention capacity were 0.48, 0.49, 0.48 and 0.52, also substantially higher than those for turbidity (0.25 and 0.26). In contrast, the AQ and AG combinations showed smaller weighted ranks (only around 0.14–0.28) for these key performance indicators. The obtained results indicate that relying solely on turbidity indicators is insufficient to comprehensively evaluate the performance of dual-media filters. A comprehensive evaluation should incorporate indicators such as organic matter removal rate, head loss growth rate, filtration cycle duration and pollutant retention capacity. Furthermore, considering the discussion in Sections 3.1 and 3.2 , the pollutant retention capacity of the upper-layer media considerably affects filtration performance in dual-media configurations. Moreover, greater differences in characteristics such as particle size and porosity between the upper and lower layers facilitate the migration of impurities to the lower layer, allowing for a more complete utilisation of the overall filtration capacity of the media bed, resulting in better performance. 4. Conclusions This study systematically evaluated the filtration performance of four pilot-scale dual-media filter columns with different media combinations. The principal conclusions are as follows: (1) During the initial filtration stage, all four filter columns showed good turbidity removal, with the effluent turbidity being below the plant control limit (0.5 NTU). Particle count removal rates of the four columns were similar (83.4%–91.4%), with particles sized 2–5 µm being more difficult to remove. A significant correlation (R² = 0.78, P < 0.05) was found between particle count and turbidity in the initial filtrate. (2) In the main filtration stage, the overall turbidity removal rates for the four columns ranged from 86.6% to 90.5%, and UV₂₅₄ removal rates ranged from 10.1% to 16.7%. The CQ group exhibited the largest pollutant retention capacity and the slowest increase in head loss, indicating better filtration performance. Greater differences in particle size and porosity between the upper and lower filter layers promoted the migration of pollutants from the upper to the lower layer, allowing for more complete utilisation of the retention capacity of the lower layer. (3) The evaluation results obtained using the RSR method and the JP method were generally consistent. The CQ and CG combinations showed better filtration performance than the AQ and AG combinations. When evaluating the filtration performance of dual-media filters, a comprehensive assessment should be conducted by incorporating indicators such as head loss growth rate, filtration cycle duration and pollutant retention capacity. This study highlights that the optimal selection of dual-media should not solely pursue the highest turbidity removal efficiency in the initial stage, but aim for a combination of high pollutant retention capacity, slow head loss development and long service cycle, as exemplified by the ceramsite-based media. Thus, future research should focus on the long-term stability of ceramsite-based media and the economic assessment of their full-scale application. Declarations Author Contribution R. X., W. Y., and K. N. wrote the main manuscript text; K. N. and Z. Q. conducted the main experiment, W. Y. and F. Y. provided guidance on specific experiments, T. Z. provided analytical insights and writing guidance, W. Y. and J. L. oversaw the project and guaranteed funding. All authors reviewed the manuscript. Acknowledgement This work was supported by The National Natural Science Foundation of China (52060006), the Jiangxi Province Graduate Student Innovation Special Fund Project (YC2025-S489), and the Jiangxi Association for Science Education Graduate Student Projects (2025KXJYS296). References Altmann, J., Rehfeld, D., Träder, K., Sperlich, A., & Jekel, M. 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Water Supply and Drainage, 2013, 49(09), 33-38. https://doi.org/10.13789/j.cnki.wwe1964.2013.09.023 Zouboulis, A., Traskas, G., & Samaras, P. Comparison of single and dual media filtration in a full-scale drinking water treatment plant. Desalination, 2007, 213(1-3), 334-342. https://doi.org/10.1016/j.desal.2006.02.102 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7971198","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":536369049,"identity":"d5bcff7e-42ae-436e-b7ee-d374aef8834b","order_by":0,"name":"Xuefan Rao","email":"","orcid":"","institution":"East China Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Xuefan","middleName":"","lastName":"Rao","suffix":""},{"id":536369050,"identity":"bc95b76f-fe47-48f9-a320-50410e635436","order_by":1,"name":"Wang Ya","email":"","orcid":"","institution":"East China Jiaotong 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1","display":"","copyAsset":false,"role":"figure","size":257597,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Physical diagram and (b) process schematic of the pilot-scale facility.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7971198/v1/b6edd820a77ee53d6bf7905b.png"},{"id":94725358,"identity":"2f5c47e5-7f9f-4d20-8d36-c751ab8744ec","added_by":"auto","created_at":"2025-10-30 06:28:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3488336,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the turbidity and particle count of filtrate after the initial stage through (a) AQ, (b) AG, (c) CQ and (d) CG dual-media filter columns.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7971198/v1/c0c61bbd4aa210acaefbdef0.png"},{"id":94725360,"identity":"2ee2a45a-24d1-42e6-9232-1da9ee34f204","added_by":"auto","created_at":"2025-10-30 06:28:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2323748,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between turbidity and particle number for the initial filtered water.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7971198/v1/d4bfd75bd05d24a1d30537d0.png"},{"id":94730430,"identity":"1b0f56a7-decb-40e5-8db8-19f97b55b305","added_by":"auto","created_at":"2025-10-30 07:05:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1315450,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of particle numbers for different particle sizes. The abbreviations AQ, AG, CQ and CG denote anthracite–quartz sand, anthracite–garnet, ceramsite–quartz sand and ceramsite–garnet, respectively.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7971198/v1/4c44ce6e88e151cb3d19db9a.png"},{"id":94729524,"identity":"69ae2891-7dd9-419b-87cc-0b9583c54ace","added_by":"auto","created_at":"2025-10-30 07:05:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4648063,"visible":true,"origin":"","legend":"\u003cp\u003eTurbidity before and after filtration using (a) AQ, (b) AG, (c) CQ and (d) CG dual-media filter columns.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7971198/v1/b970279b1cb2e9a22a05e4b3.png"},{"id":94725364,"identity":"9a747e6a-e65e-436c-86d1-8ff4c8f1ea0e","added_by":"auto","created_at":"2025-10-30 06:28:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":877946,"visible":true,"origin":"","legend":"\u003cp\u003eSurface morphology of quartz sand filter material (left) and garnet filter material (right).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7971198/v1/84c7cb16c0c7c369b2568133.png"},{"id":94725378,"identity":"4cf63018-4508-457b-a852-0c765a7e0607","added_by":"auto","created_at":"2025-10-30 06:28:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":656568,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots of influent and effluent UV₂₅₄ for (a) AQ, (b) AG, (c) CQ and (d) CG dual-media filter columns.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7971198/v1/a134d3ac672a9aed97b948c6.png"},{"id":94730162,"identity":"1f63b840-3f80-4076-ab10-f0da5c877ebb","added_by":"auto","created_at":"2025-10-30 07:05:43","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":293354,"visible":true,"origin":"","legend":"\u003cp\u003eSurface morphology of the anthracite filter material (left) and ceramic filter material (right).\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7971198/v1/d2dc4400a3c3478bf74c54e9.png"},{"id":94730004,"identity":"0ac6b008-9837-4bd2-b70d-146bec21c928","added_by":"auto","created_at":"2025-10-30 07:05:34","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":700624,"visible":true,"origin":"","legend":"\u003cp\u003eHead loss of four dual-media filter columns against filtration time. The abbreviations AQ, AG, CQ and CG denote anthracite–quartz sand, anthracite–garnet and ceramsite–garnet, respectively.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7971198/v1/a695780917fd53b6d5bdf370.png"},{"id":94729421,"identity":"24b87395-7462-49fa-94c7-85b65353b15c","added_by":"auto","created_at":"2025-10-30 07:04:56","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":591769,"visible":true,"origin":"","legend":"\u003cp\u003eVariations in pollutant retention capacity with filter depth for the four dual-media filter columns. The abbreviations AQ, AG, CQ and CG denote anthracite–quartz sand, anthracite–garnet, ceramsite–quartz sand and ceramsite–garnet, respectively.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7971198/v1/9a1a4e60d92850bc443f4857.png"},{"id":96245471,"identity":"25f72123-fe4e-4580-9352-9de8969f5953","added_by":"auto","created_at":"2025-11-19 07:20:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16149059,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7971198/v1/fb39cee8-195a-4904-974e-059ef0d591bd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pilot-Scale Comparative Evaluation of the Filtration Performance of Dual-Media Filters","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn the production of drinking water, conventional processes (i.e. coagulation\u0026ndash;sedimentation\u0026ndash;filtration) are the most widely adopted treatment technology globally, particularly in developing countries (Cococeanu et al., 2021; Tian et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Filtration serves as one of the pivotal unit processes in this scheme, playing a critical role in ensuring water quality (Razali et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This process involves the separation and retention of suspended particles from water as it passes through porous media (e.g. quartz sand, anthracite and ceramsite) or filter screens (Kim et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Meinel et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). It is primarily employed to remove particulate matter (clay and silt), microorganisms, metal ions and organic precipitates, with a specific focus on water quality indicators including turbidity, colour, organic matter and particle count (Cheremisinoff, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Early slow sand filtration, which relies on a combination of mechanical straining and biological activity, produces high-quality effluent; however, its large footprint and low production capacity limit its application in densely populated areas (Noredinvand et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Shirakawa et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To meet the requirements of large-scale production, rapid sand filters have been developed, with single-medium sand filters being the most prevalent (Shin et al., 2006; Mahanna et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, in response to growing water demand, single-medium filters often have to be operated beyond their design capacity, leading to issues such as rapid filter clogging, shortened filtration cycles, increased operational energy consumption and poor effluent quality.\u003c/p\u003e\u003cp\u003eCompared with single-medium filters, multi-media filters, owing to their coarse-particle layer, exhibit greater retention capacity towards solids. This effectively prevents impurities from penetrating the finer media layer, thereby increasing the filtration loading rate, extending the filtration cycle and reducing backwashing frequency (Brandt et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sanyaolu, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These advantages can, to a certain extent, compensate for the limitations of single-medium filters and alleviate the pressure from high hydraulic and pollutant loads. Common media used in multi-media filters include anthracite, quartz sand, garnet and magnetite, among which the combination of anthracite and quartz sand is a typical choice for dual-media filters (Cescon et al., 2020; Hoko et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although numerous comparative studies on the performance of single-medium versus multi-layer (dual-media) filters have been conducted (Kazemi et al., 2016; Zouboulis et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), comparative studies evaluating different media combinations within dual-media filters and their impact on filtration performance are still lacking.\u003c/p\u003e\u003cp\u003eHerein, four dual-media filter columns (anthracite\u0026ndash;quartz sand (AQ), anthracite\u0026ndash;garnet (AG), ceramsite\u0026ndash;quartz sand (CQ) and ceramsite\u0026ndash;garnet (CG)) for a drinking water treatment plant (DWTP) were designed and constructed. Indicators, including turbidity and particle count in the initial filtration stage, as well as turbidity, UV₂₅₄, head loss increase rate and pollutant retention capacity in the main filtration stage, were monitored. The differences in filtration performance among the different media combinations were compared and analysed. Furthermore, the rank sum ratio (RSR) method was employed to evaluate the filtration performance of the four dual-media filter columns, and the results were compared with those obtained through the JP method. Therefore, this study aimed to fill this knowledge gap by systematically comparing the performance of four representative dual-media combinations under realistic conditions. Beyond a direct performance comparison, we used a comprehensive evaluation framework (RSR method) to integrate multiple key performance indicators, providing a more robust basis for media selection. The findings of the present study are expected to offer concrete guidance for the optimisation of filter media selection in dual-media filters and the upgrading of single-medium filters.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Experimental Setup\u003c/h2\u003e\u003cp\u003ePilot-scale filtration units were installed at a DWTP that draws water from the Ganjiang River in Nanchang, Jiangxi Province, southern China. The DWTP has a treatment capacity of 100,000 m\u0026sup3;/d and operates a conventional treatment sequence comprising coagulation, sedimentation, sand filtration and disinfection. The physical diagram and process schematic of the pilot-scale facility are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The main unit (filter column) is made of organic glass, with an internal diameter of 150 mm and a height of 3000 mm. From bottom to top, the column consists of the water distribution chamber, support layer and filter media layer, with heights of 300, 300 and 800 mm, respectively. The thickness of both the upper and lower filter media layers is 400 mm. The height above the top of the upper media layer is 1600 mm. The support layer comprises three layers of pebbles arranged in order of increasing size from top to bottom. Sampling ports (for media) and pressure measurement ports are located on both sides of the column, starting 150 mm above the bottom of the support layer, with intervals of 150 mm between each port.\u003c/p\u003e\u003cp\u003eDuring the experiment, the influent to the filter columns was taken from the effluent of the sedimentation tank of the DWTP. The settled water had a pH of 6.98\u0026ndash;7.35, temperature of 18.4\u0026ndash;31.7\u0026deg;C, turbidity of 0.73\u0026ndash;4.56 NTU and UV₂₅₄ of 0.027\u0026ndash;0.057 cm⁻\u0026sup1;. The experimental process was: sedimentation tank effluent \u0026rarr; intermediate tank \u0026rarr; feed pump \u0026rarr; filter column \u0026rarr; filtered effluent.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Experimental Methods\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1. Filter Column Construction\u003c/h2\u003e\u003cp\u003eThe filter columns were constructed in accordance with the design parameters for dual-media filters specified in the Chinese national standard (GB50013-2018). Four dual-media filter columns were constructed: AQ, AG, CQ and CG. The filter media were purchased from Zhengzhou Haoda Environmental Protection (China). The basic parameters of the media are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" 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\u003eBasic parameters of the filter media.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFilter media\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSize range (mm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEffective size (mm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUniformity coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePorosity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDensity (g\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnthracite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.8\u0026ndash;1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e53.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartz Sand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.5\u0026ndash;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e43.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCeramsite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.0\u0026ndash;3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e56.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGarnet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.5\u0026ndash;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e47.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.50\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=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2. Filtration and Backwashing\u003c/h2\u003e\u003cp\u003eDuring the experiment, the filtration rate was controlled at approximately 8.0 m/h using a UPVC ball valve. Filtration was stopped for backwashing when the maximum head loss of 1.5 m was reached. Single-water backwashing was performed with a washing intensity of 16.0 L/(m\u0026sup2;\u0026middot;s) and a duration of 8.0 min.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3. Water and Filter Media Sampling\u003c/h2\u003e\u003cp\u003eAfter the operation of the filter column stabilised, water samples were collected. In the initial filtration stage (0\u0026ndash;7 min, at 1-min intervals and at 20 and 30 min), initial filtrate water was collected. During the main filtration stage, influent and effluent water were collected every hour until the end of the filtration cycle. After filtration termination, filter media samples were collected using a simple sampler at each sampling port: 50 mL of media were gathered and stored in centrifuge tubes for later analysis.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Analytical Methods\u003c/h2\u003e\u003cp\u003eThe surface morphology of the filter media was analysed using scanning electron microscopy (TESCAN MIRA LMS, Czech Republic). Turbidity was measured with a turbidimeter (HACH TU5200). UV₂₅₄ was detected using a UV spectrophotometer (HACH DR6000). Particle counts were determined using a KZ-2 particle counter (Tianjin Luogen Technology). Head loss was measured using manometer tubes. The pollutant retention capacity was analysed in accordance with the methodology reported by Zhou et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Methods for Evaluating Filtration Performance\u003c/h2\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.4.1. RSR Method\u003c/h2\u003e\u003cp\u003eThe RSR method is an effective evaluation method that is widely used for multi-index comprehensive assessment (Wang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this method, the values of each indicator are first converted to dimensionless rank values. Then, the entropy weight method is used to assign weights to each evaluation indicator. Finally, the weighted RSR for each evaluation object is calculated. Objects are sorted based on this ratio, with a higher RSR value indicating better performance (Xiao et al., 2025).\u003c/p\u003e\u003cp\u003eThe specific steps are outlined below. In the context of the evaluated subjects, with each possessing a set of indicators, the initial step entails the calculation of the rank value \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e for the \u003cem\u003ej-\u003c/em\u003eth indicator value \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e of the \u003cem\u003ei-\u003c/em\u003eth subject. This calculation was conducted using Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{\\text{R}}_{\\text{ij}}\\text{=1+}\\left(\\text{n-1}\\right)\\frac{\\Phi}{\\left({\\text{X}}_{\\text{max}}\\text{-}{\\text{X}}_{\\text{min}}\\right)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eΦ\u003c/em\u003e takes the value of \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e \u0026minus; \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e or \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e \u0026minus; \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003emin\u003c/em\u003e\u003c/sub\u003e, with \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003emin\u003c/em\u003e\u003c/sub\u003e being the maximum and minimum values of the indicator, respectively. In instances where \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is a high-priority indicator, where a higher value is preferable, the value of \u003cem\u003eΦ\u003c/em\u003e is set to \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e \u0026minus; \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003emin\u003c/em\u003e\u003c/sub\u003e. Conversely, when \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is a low-priority indicator, the value of \u003cem\u003eΦ\u003c/em\u003e is set to \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e \u0026minus; \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e.\u003c/p\u003e\u003cp\u003eIn the second step, entropy values are assigned to each indicator, with standardisation performed through a 0\u0026ndash;1 transformation, as shown in Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 510px; height: 64.3458px;\" width=\"510\" height=\"64.3458\"\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eλ\u003c/em\u003e takes the value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{b}}_{\\text{ij}}\\text{-}\\underset{\\text{j}}{\\text{min}}{\\text{b}}_{\\text{ij}}\\)\u003c/span\u003e\u003c/span\u003e or \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\underset{\\text{j}}{\\text{max}}{\\text{b}}_{\\text{ij}}\\text{-}{\\text{b}}_{\\text{ij}}\\)\u003c/span\u003e\u003c/span\u003e, with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{b}}_{\\text{ij}}\\text{-}\\underset{\\text{j}}{\\text{min}}{\\text{b}}_{\\text{ij}}\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\underset{\\text{j}}{\\text{max}}{\\text{b}}_{\\text{ij}}\\text{-}{\\text{b}}_{\\text{ij}}\\)\u003c/span\u003e\u003c/span\u003erepresenting the maximum and minimum entropy values, respectively; when \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is a high-priority indicator (where a higher indicator value is preferable), λ is set to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{b}}_{\\text{ij}}\\text{-}\\underset{\\text{j}}{\\text{min}}{\\text{b}}_{\\text{ij}}\\)\u003c/span\u003e\u003c/span\u003e; in the other case, \u003cem\u003eλ\u003c/em\u003e is set to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\underset{\\text{j}}{\\text{max}}{\\text{b}}_{\\text{ij}}\\text{-}{\\text{b}}_{\\text{ij}}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eSubsequently, in accordance with the standardised decision matrix, the characteristic weight for the \u003cem\u003ej\u003c/em\u003e-th indicator for the \u003cem\u003ei\u003c/em\u003e-th evaluation sample, denoted as \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e, is calculated using Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), where 0\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e \u0026lt; 1.\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{\\text{p}}_{\\text{ij}}\\text{=}\\frac{{\\text{b}}_{\\text{ij}}^{\\text{*}}}{\\sum\\:_{\\text{i=1}}^{\\text{m}}{\\text{b}}_{\\text{ij}}^{\\text{*}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe corrected entropy value for the \u003cem\u003ej\u003c/em\u003e-th indicator (denoted by \u003cem\u003eg\u003c/em\u003e\u003csub\u003ej\u003c/sub\u003e), the entropy weight for the \u003cem\u003ej\u003c/em\u003e-th indicator (denoted by w\u003csub\u003ej\u003c/sub\u003e) and the weighted rank ratio for the \u003cem\u003ei\u003c/em\u003e-th evaluation object (denoted by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{\u0026delta;}}_{\\text{WRSRi}}\\)\u003c/span\u003e\u003c/span\u003e) are calculated using Equations (\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), (\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and (6), respectively.\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 526px; height: 172.644px;\" width=\"526\" height=\"172.644\"\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.4.2. JP Method\u003c/h2\u003e\u003cp\u003eJing et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) proposed the JP method for evaluating filter performance. The evaluation method is expressed in Eq.\u0026nbsp;(\u003cspan refid=\"Equ7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). A smaller JP value indicates better filtration performance (Jing et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\:\\text{JP=}\\frac{{\\text{H}}_{\\text{t}}{\\text{LC}}_{\\text{e}}}{{\\text{V}}^{\\text{2}}{\\text{T}}^{\\text{2}}\\left({\\text{C}}_{\\text{0}}\\text{-}{\\text{C}}_{\\text{e}}\\right)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e is the head loss increase at the end of the filtration cycle (m), \u003cem\u003eL\u003c/em\u003e is the filter bed thickness (m), \u003cem\u003eV\u003c/em\u003e is the filtration rate (m/h), \u003cem\u003eT\u003c/em\u003e is the duration of the filtration cycle (h) and \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e are the average turbidities of the influent and effluent (NTU), respectively.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Initial Filtration Stage\u003c/h2\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1. Turbidity and Particle Count\u003c/h2\u003e\u003cp\u003eVariations in the turbidity and particle count of the initial filtrate from the four dual-media filter columns over time are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; the correlation between turbidity and particle count in the initial filtrate is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The turbidity and particle count of the initial filtrate from all four dual-media columns showed an oscillating downwards trend over time, rapidly decreasing within the first 7 min. The trends for both parameters are generally consistent, showing a significant positive correlation (R\u0026sup2; = 0.78, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which agrees with previous research findings (Cescon et al., 2020; Zamani et al., 2009).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNotably, the turbidity and particle count in the effluent from the AQ group were lower than those for the CQ group, and those for the AG group were lower than those for the CG group. Furthermore, the AQ and AG groups required less time for the effluent turbidity to meet the internal control limit of the plant (0.5 NTU). Pairwise comparisons of the upper and lower layer media in the four columns revealed that when the lower heavy media were the same, the upper lightweight anthracite exhibited better pollutant retention capability than ceramsite. When the upper lightweight media were the same, the difference in pollutant retention capacity between the lower heavy quartz sand and garnet media was not significant. This indicates that in the initial filtration stage, the overall pollutant retention performance of the dual media is primarily determined by the upper lightweight layer. Stronger surface retention capability leads to better overall initial filtration performance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2. Particle Size Distribution\u003c/h2\u003e\u003cp\u003eTo further investigate the composition of particles of different sizes in the initial stage, the particle size distribution in the influent and effluent at the end of the initial stage (30 min) in the four columns was examined. The distribution of particle numbers for different size ranges is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Similar to the influent, the proportions of particles in different size ranges in the effluent from the four columns significantly varied, with particles sized 3\u0026ndash;5 \u0026micro;m being the most abundant (64%\u0026ndash;78%). The order of particle count reduction for the four columns was: AG\u0026thinsp;\u0026gt;\u0026thinsp;AQ\u0026thinsp;\u0026gt;\u0026thinsp;CQ\u0026thinsp;\u0026gt;\u0026thinsp;CG. The removal efficiency for particles larger than 5 \u0026micro;m was greater than that for particles sized 2\u0026ndash;3 and 3\u0026ndash;5 \u0026micro;m in all columns, indicating that particles in the 2\u0026ndash;5 \u0026micro;m range are more difficult to remove. This phenomenon was attributed to the presence of particles with a size too fine to be retained by the filter column (Wakeman, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Main Filtration Stage\u003c/h2\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1. Turbidity\u003c/h2\u003e\u003cp\u003eVariations in the turbidities of the influent and effluent from the four dual-media filter columns are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Although the influent turbidity slightly fluctuated during filtration, the effluent turbidity remained relatively stable, mostly below the internal control limit of the plant (0.5 NTU). The turbidity removal efficiency decreased in the order of AG\u0026thinsp;\u0026gt;\u0026thinsp;AQ\u0026thinsp;\u0026gt;\u0026thinsp;CG\u0026thinsp;\u0026gt;\u0026thinsp;CQ, with an average removal rate of approximately 88.6%, indicating good turbidity removal performance for all columns.\u003c/p\u003e\u003cp\u003eThe comparison revealed two key aspects. First, when the upper lightweight media were the same, the rougher surface and larger specific surface area of the lower heavy media (quartz sand or garnet; as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) provided more opportunities for contact with turbidity-causing substances and their capture. Second, when the lower heavy media were the same, the smaller average particle size of the upper lightweight media (ceramsite or anthracite; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) led to better turbidity retention, but also caused faster clogging because of rapid deposition, potentially shortening the filtration cycle (Zouboulis et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2. UV\u003csub\u003e254\u003c/sub\u003e\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the box plots of the influent and effluent UV₂₅₄ for the four dual-media filter columns. The UV\u003csub\u003e254\u003c/sub\u003e removal efficiency decreases in the order of CQ\u0026thinsp;\u0026gt;\u0026thinsp;CG\u0026thinsp;\u0026gt;\u0026thinsp;AQ\u0026thinsp;\u0026gt;\u0026thinsp;AG, with total removal rates ranging from 10.1% to 16.7%. These removal rates are slightly to moderately higher than those typically reported for conventional single-layer sand filters (often\u0026thinsp;\u0026lt;\u0026thinsp;10%, Kazemi et al., 2016). This was attributed to the rough surface of the upper lightweight ceramsite and anthracite media (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The larger specific surface area is more conducive to biofilm attachment, forming a biological layer that removes organic matter through biodegradation (Altmann et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3. Head Loss\u003c/h2\u003e\u003cp\u003eThe variations in head loss with filtration time for the four dual-media filter columns are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. The head loss for all four dual-media columns increased over time. The head loss for the CG and CQ groups initially increased slowly, then rapidly, with the increase being more uniform throughout the cycle for the AG and AQ groups. At the beginning of filtration, the initial head loss order was AG\u0026thinsp;\u0026gt;\u0026thinsp;AQ\u0026thinsp;\u0026gt;\u0026thinsp;CG\u0026thinsp;\u0026gt;\u0026thinsp;CQ. This order remained unchanged after the same filtration time. However, at the point of maximum head loss, the order of filtration cycle duration was reversed: CQ\u0026thinsp;\u0026gt;\u0026thinsp;CG\u0026thinsp;\u0026gt;\u0026thinsp;AQ\u0026thinsp;\u0026gt;\u0026thinsp;AG. This implies that a greater amount of pollutants retained per unit time inevitably shortens the filtration cycle.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese phenomena indicate that the filtration head loss of the dual-media filter is closely related to the duration of the filtration cycle. At the same time, the initial head loss is related to the media composition (Mahanna et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). When the lower heavy media are the same, larger particle size and porosity of the upper lightweight media result in larger water passage channels and weaker flow resistance, leading to a smaller initial head loss. When the upper lightweight media were the same, rougher surfaces and a larger specific surface area of the lower heavy media increase the flow resistance, also resulting in a larger initial head loss.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e3.2.4. Pollutant Retention Capacity\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows variations in pollutant retention capacity (measured as turbidity) with filter bed depth for the four dual-media filter columns. The maximum retention capacity for all columns was observed within the 0\u0026ndash;5 cm depth, gradually decreasing along the bed depth, consistent with the deep-bed filtration theory (Zamani et al., 2009). Using the area enclosed by the depth and turbidity axes to represent the dirt-holding capacity, the order for the four columns was CQ\u0026thinsp;\u0026gt;\u0026thinsp;CG\u0026thinsp;\u0026gt;\u0026thinsp;AG\u0026thinsp;\u0026gt;\u0026thinsp;AQ. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the average particle size and porosity of the upper lightweight media were greater than those of the lower media for all dual-media configurations. Considering the retention capacity in the lower heavy media layer (35\u0026ndash;65 cm), only the CQ group showed a significant difference in capacity between its upper and lower layers. This indicates that pollutants retained in the upper layer of the CQ column migrated to the lower layer, allowing the more effective utilisation of the lower heavy media for particle retention, thereby enhancing the overall retention performance of the column. This suggests that a significant contrast in physical characteristics (e.g. particle size, porosity) between the upper and lower layers facilitates the migration of retained flocs into the deeper bed, thereby promoting more effective utilisation of the filter capacity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Evaluation of Filtration Performance\u003c/h2\u003e\u003cp\u003eThe filtration performance of the four dual-media filter columns was comprehensively evaluated using the RSR method described in Section 1.4. Six evaluation metrics were selected for analysis: initial filtered water turbidity, total turbidity removal rate, UV\u003csub\u003e254\u003c/sub\u003e removal rate, pollutant retention capacity, head loss growth rate and filtration cycle duration. The entropy weights calculated for these indicators were 16.5%, 22.1%, 13.5%, 16.7%, 14.3% and 15%, respectively. The numerical values for each indicator in the RSR method are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; the results of RSR evaluation are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and those obtained using the JP method are provided in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\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\u003eNumerical values of various evaluation indicators in the RSR method.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFilter columns\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInitial filtered water turbidity\u003c/p\u003e\u003cp\u003e(NTU)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal turbidity removal rate\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUV\u003csub\u003e254\u003c/sub\u003e removal rate\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePollutant retention capacity\u003c/p\u003e\u003cp\u003e(NTU)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHead loss growth rate\u003c/p\u003e\u003cp\u003e(cm/h)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFiltration cycle duration\u003c/p\u003e\u003cp\u003e(h)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e645.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e43.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e698.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e39.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e996.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e54.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e858.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e50.1\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\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\u003eRSR results.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFilter columns\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003eWeighted rank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWeighted rank sum ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInitial filtered water turbidity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal turbidity removal rate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUV\u003csub\u003e254\u003c/sub\u003e removal rate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePollutant retention capacity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHead loss growth rate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFiltration cycle duration\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eⅣ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eⅢ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eⅠ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eⅡ\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\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\u003eJP method results.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\u003cp\u003eFilter columns\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncrease in head loss\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEffluent turbidity\u003c/p\u003e\u003cp\u003e(NTU)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFiltration rate\u003c/p\u003e\u003cp\u003e(m/h)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFiltration cycle duration\u003c/p\u003e\u003cp\u003e(h)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInfluent turbidity(NTU)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eJP value\u003c/p\u003e\u003cp\u003e(10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e43.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eⅢ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e39.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eⅣ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e54.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eⅠ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e50.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eⅡ\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\u003eTables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e indicate that the results obtained via the two above methods are basically consistent. The CQ combination showed the best filtration performance, with a good organic matter removal rate, small head loss growth rate, long filtration cycle and higher pollutant retention capacity. The weighted ranks for these four indicators reached 0.54, 0.57, 0.60 and 0.67, respectively, far exceeding those for the turbidity-related indicators (0.18 and 0.22). The CG combination ranked second. Its weighted ranks for organic matter removal, head loss growth rate, filtration cycle duration and pollutant retention capacity were 0.48, 0.49, 0.48 and 0.52, also substantially higher than those for turbidity (0.25 and 0.26). In contrast, the AQ and AG combinations showed smaller weighted ranks (only around 0.14\u0026ndash;0.28) for these key performance indicators.\u003c/p\u003e\u003cp\u003eThe obtained results indicate that relying solely on turbidity indicators is insufficient to comprehensively evaluate the performance of dual-media filters. A comprehensive evaluation should incorporate indicators such as organic matter removal rate, head loss growth rate, filtration cycle duration and pollutant retention capacity. Furthermore, considering the discussion in Sections \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e and \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e, the pollutant retention capacity of the upper-layer media considerably affects filtration performance in dual-media configurations. Moreover, greater differences in characteristics such as particle size and porosity between the upper and lower layers facilitate the migration of impurities to the lower layer, allowing for a more complete utilisation of the overall filtration capacity of the media bed, resulting in better performance.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThis study systematically evaluated the filtration performance of four pilot-scale dual-media filter columns with different media combinations. The principal conclusions are as follows:\u003c/p\u003e\u003cp\u003e(1) During the initial filtration stage, all four filter columns showed good turbidity removal, with the effluent turbidity being below the plant control limit (0.5 NTU). Particle count removal rates of the four columns were similar (83.4%\u0026ndash;91.4%), with particles sized 2\u0026ndash;5 \u0026micro;m being more difficult to remove. A significant correlation (R\u0026sup2; = 0.78, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was found between particle count and turbidity in the initial filtrate.\u003c/p\u003e\u003cp\u003e(2) In the main filtration stage, the overall turbidity removal rates for the four columns ranged from 86.6% to 90.5%, and UV₂₅₄ removal rates ranged from 10.1% to 16.7%. The CQ group exhibited the largest pollutant retention capacity and the slowest increase in head loss, indicating better filtration performance. Greater differences in particle size and porosity between the upper and lower filter layers promoted the migration of pollutants from the upper to the lower layer, allowing for more complete utilisation of the retention capacity of the lower layer.\u003c/p\u003e\u003cp\u003e(3) The evaluation results obtained using the RSR method and the JP method were generally consistent. The CQ and CG combinations showed better filtration performance than the AQ and AG combinations. When evaluating the filtration performance of dual-media filters, a comprehensive assessment should be conducted by incorporating indicators such as head loss growth rate, filtration cycle duration and pollutant retention capacity.\u003c/p\u003e\u003cp\u003eThis study highlights that the optimal selection of dual-media should not solely pursue the highest turbidity removal efficiency in the initial stage, but aim for a combination of high pollutant retention capacity, slow head loss development and long service cycle, as exemplified by the ceramsite-based media. Thus, future research should focus on the long-term stability of ceramsite-based media and the economic assessment of their full-scale application.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR. X., W. Y., and K. N. wrote the main manuscript text; K. N. and Z. Q. conducted the main experiment, W. Y. and F. Y. provided guidance on specific experiments, T. Z. provided analytical insights and writing guidance, W. Y. and J. L. oversaw the project and guaranteed funding. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by The National Natural Science Foundation of China (52060006), the Jiangxi Province Graduate Student Innovation Special Fund Project (YC2025-S489), and the Jiangxi Association for Science Education Graduate Student Projects (2025KXJYS296).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAltmann, J., Rehfeld, D., Tr\u0026auml;der, K., Sperlich, A., \u0026amp; Jekel, M. Combination of granular activated carbon adsorption and deep-bed filtration as a single advanced wastewater treatment step for organic micropollutant and phosphorus removal. Water Research, 2016, 92, 131\u0026ndash;139. https://doi.org/10.1016/j.watres.2016.01.051\u003c/li\u003e\n\u003cli\u003eBrandt, M.J., Johnson, K.M., Elphinston, A.J., \u0026amp; Ratnayaka, D.D. Water Filtration. In Twort\u0026apos;s Water Supply (Seventh Edition), 2017, 367-406. https://doi.org/10.1016/B978-0-08-100025-0.00009-0=-99055.00009\u003c/li\u003e\n\u003cli\u003eCescon, A., \u0026amp; Jiang, J.Q. Filtration process and alternative filter media material in water treatment. Water, 2020, 12(12), 3377. https://doi.org/10.3390/w12123377\u003c/li\u003e\n\u003cli\u003eCheremisinoff, N.P. Handbook of Water and Wastewater Treatment Technologies, Chapter 2 - What Filtration is All About. Butterworth-Heinemann: Oxford, UK, 2002. https://doi.org/10.1016/B978-075067498-0/50005-X\u003c/li\u003e\n\u003cli\u003eCococeanu, A.L., Man, T.E. Methods and Characteristics of Conventional Water Treatment Technologies. In: Vaseashta, A., Maftei, C. (eds) Water Safety, Security and Sustainability. Advanced Sciences and Technologies for Security Applications. Springer, Cham. 2021. https://doi.org/10.1007/978-3-030-76008-3_14\u003c/li\u003e\n\u003cli\u003eHoko, Z., Musima, B.T., \u0026amp; Mapenzauswa, C.F. Exploring the feasibility of dual media filtration at Morton Jaffray Water Works (Harare, Zimbabwe). Journal of Applied Water Engineering and Research, 2024, 12(1), 14-26. https://doi.org/10.1080/23249676.2023.2191348\u003c/li\u003e\n\u003cli\u003eJing, Y.H., Jin, T.G., \u0026amp; Fan, J.C. Evaluation indicators for direct filtration performance of homogeneous filter media (in Chinese). Water Supply and Drainage, 2000, (03), 13-16. https://doi.org/10.13789/j.cnki.wwe1964.2000.03.006\u003c/li\u003e\n\u003cli\u003eKazemi Noredinvand, B., Takdastan, A., \u0026amp; Jalilzadeh Yengejeh, R. Removal of organic matter from drinking water by single and dual media filtration: a comparative pilot study. Desalination and Water Treatment, 2016, 57(42), 20792-20799. https://doi.org/10.1080/19443994.2015.1110718\u003c/li\u003e\n\u003cli\u003eKim, T.H., Oh, H., Eom, J., Park, C.H. A column study of effect of filter media on the performance of sand filter. Membrane and Water Treatment, 2020, 1I(4), 247-255. https://doi.org/10.12989/mwt.2020.11.4.247\u003c/li\u003e\n\u003cli\u003eMahanna, H., Fouad, M., Radwan, K., \u0026amp; Elgamal, H. Modeling of headloss through deep bed sand filters. MEJ Mansoura Engineering Journal, 2020, 40(4), 1\u0026ndash;10. https://doi.org/10.21608/bfemu.2020.102444\u003c/li\u003e\n\u003cli\u003eMeinel, F.,Ruhl, A.S., Sperlich, A., Zietzschmann, E. Pilot-Scale Investigation of Micropollutant Removal with Granular and Powdered Activated Carbon. Water Air and Soil Pollution, 2015, 226, 1. https://doi.org/10.1007/s11270-014-2260-y\u003c/li\u003e\n\u003cli\u003eNoredinvand, B.K., Takdastan, A., Yengejeh, R.J. 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Desalination, 2007, 213(1-3), 334-342. https://doi.org/10.1016/j.desal.2006.02.102\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Dual-media filter, Filter media comparison and selection, Filtration performance, Rank sum ratio evaluation","lastPublishedDoi":"10.21203/rs.3.rs-7971198/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7971198/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFiltration is a critical process in drinking water treatment, with dual-media filters widely recognised for their superior performance compared to single-medium filters. However, comprehensive comparative studies evaluating different media combinations remain limited. This pilot-scale study systematically assessed the filtration performance of four dual-media filters: anthracite\u0026ndash;quartz sand (AQ), anthracite\u0026ndash;garnet (AG), ceramsite\u0026ndash;quartz sand (CQ) and ceramsite\u0026ndash;garnet (CG). By monitoring key indicators including turbidity, particle count, UV\u003csub\u003e254\u003c/sub\u003e, head loss and pollutant retention capacity, the study revealed that ceramsite-based filters (CQ and CG) perform considerably better than anthracite-based filters (AQ and AG). Specifically, the CQ filter showed 18%\u0026ndash;54% higher pollutant retention capacity, 21%\u0026ndash;28% slower head loss growth rate and substantially longer filtration cycles. Both Rank sum ratio and JP evaluation methods consistently showed that CQ is the optimal media combination, followed by CG. The results indicate that greater differences in physical characteristics between upper and lower media layers promote better utilisation of the capacity of the filter bed. These findings provide valuable theoretical insights and practical guidance for optimising media selection in dual-media filters and inform the upgrading of single-medium filters.\u003c/p\u003e","manuscriptTitle":"Pilot-Scale Comparative Evaluation of the Filtration Performance of Dual-Media Filters","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 06:28:41","doi":"10.21203/rs.3.rs-7971198/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":"ac45af14-e177-4c99-a7ca-2226cd95d470","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-14T19:53:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 06:28:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7971198","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7971198","identity":"rs-7971198","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}