# Developing a cost and time efficient strategy for assessing novel materials for wastewater treatment with non-target screening analysis

Alba Rodriguez-Otero, Selina Tisler, Lisa M Reinhardt, Mathias B Jørgensen, David I Pattison, Brice Bouyssiere, Jan H Christensen

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## ► To cite this version:

Alba Rodriguez-Otero, Selina Tisler, Lisa M Reinhardt, Mathias B Jørgensen, David I Pattison, et al.. Developing a cost and time efficient strategy for assessing novel materials for wastewater treatment with non-target screening analysis. Journal of Hazardous Materials, 2026, 503, pp.141115. 10.1016/j.jhazmat.2026.141115. hal-05457348

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Submitted on 14 Jan 2026

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# Developing a cost and time efficient strategy for assessing novel materials for wastewater treatment with non-target screening analysis

Alba Rodriguez-Otero<sup>a,b,*</sup>, Selina Tisler<sup>a</sup>, Lisa M. Reinhardt<sup>a</sup>, Mathias B. Jørgensen<sup>c</sup>, David I. Pattison<sup>a,b</sup>, Brice Bouyssiere<sup>b</sup>, Jan H. Christensen<sup>a,b</sup>

## HIGHLIGHTS

* Analysing removal behaviours with NTS can predict efficiency, uncover properties, and prioritize compounds missed by target analysis.

* Under screening conditions, RH-SiO₂ exhibits distinct removal profiles for large molecules (e.g., macrolides) and cationic compounds, regardless of charge.

* The proposed methodological framework facilitates the identification of material-specific behaviors, providing a foundation for evaluating emerging treatment technologies in lab-scale.

## GRAPHICAL ABSTRACT

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## ARTICLE INFO

**Keywords:**

Wastewater advanced treatment
Organic micropollutants
Adsorption
Nontarget analysis

## ABSTRACT

This study presents a methodological screening strategy for assessing the removal organic micropollutants (OMPs) using a non-target screening (NTS) workflow integrated with liquid chromatography high resolution mass spectrometry (LC-HRMS). To demonstrate this approach, three distinct adsorbent materials—a novel rice husk-derived silica (RH-SiO₂) and commercial adsorbents (GAC and Resinex)—were used in lab-scale column experiments with secondary effluent wastewater from one wastewater treatment plant (WWTP). Hierarchical cluster analysis of the non-target features was employed to characterize diverse removal patterns across the three materials under specific empty bed contact time (EBCT) conditions. The NTS approach enabled the identification of unique removal patterns, such as the capture of poly(propylene glycols) (PPGs) and their carboxylate metabolites by RH-SiO₂, which would have been overlooked in traditional lab-scale evaluation of novel adsorbents. Furthermore, the identification of 37 compounds with different confidence levels allowed for an exploratory correlation between removal behaviours and their physicochemical properties. By moving beyond traditional

<footnote><sup>a</sup> Analytical Chemistry Group, Department of Plant and Environmental Science, Faculty of Science, University of Copenhagen, Thorvaldsensvej 40, Frederiksberg 1871, Denmark</footnote>

<footnote><sup>b</sup> Université de Pau et des Pays de l'Adour, E2S UPPA, CNRS, IPREM, UMR5254, Technopôle Hélioparc, 2 Avenue du Président Angot, Pau 64053, France</footnote>

<footnote><sup>c</sup> BIOFOS A/S, Refshalevej 250, Copenhagen 1432, Denmark</footnote>

<footnote>* Corresponding author at: Analytical Chemistry Group, Department of Plant and Environmental Science, Faculty of Science, University of Copenhagen, Thorvaldsensvej 40, Frederiksberg 1871, Denmark.</footnote>

<footnote>E-mail address: alba@plen.ku.dk (A. Rodriguez-Otero).</footnote>
target-based methods, this study outlines a strategic pathway for using NTS to characterize the screening potential of new treatment technologies and extract valuable information on chemical-material interactions, providing a foundation for future, more intensive assessments.

# 1. Introduction

The growing prevalence of emerging contaminants (ECs) in aquatic ecosystems poses a significant challenge. Chemicals such as household and synthetic plastic products with complex physicochemical properties pose a risk to public health due to their persistence in wastewater treatment plants (WWTPs), where removal efficiencies vary with concentration, process and compound [1,2]. To illustrate this point, a global study of WWTPs effluent samples revealed negative EC removal efficiencies of up to 5731 % [3], highlighting the pressing need for new treatment strategies, as there is currently no single treatment effectively removes all ECs [4].

The construction of WWTPs specifically designed to remove ECs is directed in combining both conventional and advanced treatment techniques to target elusive contaminants more effectively [5]. Current research on developing these advanced treatment strategies is being made on designing adsorption filters to remove residual contaminants and transformation products (TPs) that escape traditional processes [1, 6].

The conventional laboratory approach to assessing the efficacy of new adsorption filters typically involves the evaluation of the removal efficiency of specific target compounds [7–11]. Accurately assessing the removal efficiency of all ECs present in WWTP effluent, would require the analysis of several thousand compounds, resulting in costly, time-consuming tests and potentially overlooking unexpected TPs or new compounds [12].

A solution to this challenge is non-target screening (NTS), which is based on high-resolution mass spectrometry (HRMS). Unlike traditional methods that focus on predefined contaminants, NTS allows for the detection of unknown pollutants, providing a more comprehensive analysis of water quality and treatment effectiveness [13,14]. Additionally, using open-source tools like MS-DIAL facilitate wider access to NTS, increasing worldwide data availability [15].

Although NTS is commonly used for water quality monitoring, it can also be applied to evaluate advanced wastewater treatment techniques specifically designed for the elimination of ECs [12,16-18]. NTS workflows can help to prioritize the annotation of relevant features, which can then be used to detect unknown compounds in complex matrices with low removal efficiencies [19]. Micropollutant removal efficiencies are typically calculated by comparing the concentration of target compounds before and after treatment. Although NTS does not provide quantitative data, detected signals and their peak areas can still be compared relative and used to assess treatment efficacy [16] or even to predict breakthrough behavior in WWTPs [20].

Schollée et al. [19] applied data analysis strategies for NTS to samples treated with ozone treatment and post-treatments to investigate the behavior of unknown compounds, such as OTPs. They used hierarchical clustering analysis (HCA) to compare the removal efficiency of different treatment technologies for non-target features. Later, Schollée et al. [17] developed an automated trend detection tool to track the removal of non-target features through ozone and GAC treatments. Tisler et al. [12] used a combination of suspect screening and NTS to study the impact of advanced oxidation processes on groundwater, followed by biological AC (BAC) filtration. They identified the formation of OTPs and found that BAC filtration was ineffective at removing the most polar compounds. In Tisler et al. [18] they demonstrated the critical need for a comprehensive evaluation of adsorption treatment processes, making possible the detection of new industrial compounds that were being removed and unexpected migration from resin filters.

NTS has been shown to provide critical information about the

efficiency of advanced treatments for EC removal. In this study, NTS was applied to evaluate a new filter material based on rice husk waste for removal of ECs from secondary effluent wastewater. This evaluation can make use of NTS's time- and cost-efficiency, enabling the rapid identification of the strengths and limitations of new treatments. The aim is to develop a new strategy for evaluating emerging treatment technologies at laboratory scale by combining NTS and compound property analysis. HCA was used to identify the most relevant features, specifically, those distinctively removed by one of the adsorbents. This approach sought to identify removal patterns in clusters of compounds with analogous treatment responses and physicochemical properties, which could potentially be used to predict the removal behavior of unknown compounds.

# 2. Materials and methods

The removal profiles of RH-SiO₂, GAC, and Resinex were investigated in packed column experiments using secondary effluent wastewater to assess the utility of the non-target screening approach. Details of the chemicals and solvents used are provided in *Supplementary Information* (SI-1).

## 2.1. Secondary effluent wastewater sampling

The secondary effluent wastewater used in adsorption experiments was collected from the secondary clarifier at the Avedøre wastewater treatment plant in Copenhagen, Denmark, in June 2023, just before its discharge into the environment. The effluent had an average pH of 7.2 and total organic carbon (TOC) concentration of 13.7 ± 1.6 mg/L. A 20-liter grab sample was manually collected in two 10-liter glass containers and vacuum-filtered through a 0.7 µm filter on the same day. The water was then stored in pre-washed and heated at 550 °C for 4 h 3-liter laboratory Duran glass bottles at 4 °C for the 6-day duration of the adsorption experiments.

## 2.2. Adsorbent materials

Mesoporous silica was extracted from rice husk obtained from *Arrocerca del Pirineo - Brazal*, a commercial mill in Zaragoza, Spain. The silica was produced through a thermochemical process involving acid leaching and calcination. The material characterization is detailed elsewhere [21]. Nitrogen adsorption isotherm analysis revealed that the RH-silica had a BET surface area of 329 m²/g, a pore volume of 0.35 mL/g, an average pore diameter of 6.4 nm, and a dry density of 0.32 g/cm³. RH-SiO₂ exhibited a broad size distribution, with particles of 100–200 µm, of 50 µm and some of 5–30 µm confirmed by SEM [22]. The material was passed through a 120-mesh sieve to eliminate larger particles. The point of zero charge (pH<sub>pzc</sub>) of RH-silica was determined by measuring the zeta potential (DDL Zetasizer, Malvern Panalytical, UK). The method, as described elsewhere [22], consisted of measuring the zeta potential of solutions in triplicates of suspended RH-silica at different pH levels (2–9) prepared with 0.1 M HNO₃ or 0.1 M NaOH (25 °C). Using the Smoluchowski theory approximation, the pH<sub>pzc</sub> was determined to be approximately 2 (Fig. S1).

The commercial GAC AquaSorb™ 2000 (12 × 40 mesh), supplied by Jacobi Group, has been extensively characterized in previous work [23–25]. Detailed analysis of the GAC's textural properties, including its pore distribution, is provided in Sanchez-Cano et al. [24]. GAC Aqua-sorb™ 2000 had a BET surface area of 950 m²/g, a mean particle size of 1 mm, pore diameter of 1.85 nm, pore volume of 0.48 mL/g and a dry
density of 0.45 g/cm³. The pH<sub>pzc</sub> was determined elsewhere to be 9.3 [23].

The anionic exchange resin 'Resinex PFCR-1', also from Jacobi Group, is a strongly basic, gel-type resin developed for selective PFAS removal from potable water, even in the presence of high sulphate concentrations. It is a crosslinked polystyrene divinylbenzene with tributylamine functional groups, featuring macroporous spherical beads and a true density of 1.00 – 1.10 g/cm³ [18].

## 2.3. Experimental set-up for column elution studies with wastewater effluent and sample preparation

The experimental protocol involved eluting secondary effluent wastewater (pH 7.2) through three Omnifit EZ Benchmark columns (150 x ID 10 mm) from Merck (Darmstadt, Germany). Each column was packed with different adsorbent material with a bed volume (BV) of 3.1 mL. One liter of effluent wastewater was pumped from three separate 2 L glass containers into each column using either pump 1 (Waters 515 HPLC pump) or a quaternary pump (Agilent 1100 LC), which operated as Pump 2 A and Pump 2B for the other two columns (Fig. 1). Hydrodynamic properties of the columns were analyzed and are described in detail in SI-1.1.

The flow rate for all pumps was set to 2.5 mL/min, a low empty bed contact time of 1.2 min. From each of the three columns, two 100 mL samples were collected: the first sample (T1), representing 8 – 40 specific BVs, where the filter was fresh, and the second sample (T2), representing 290 – 323 specific BVs, where the filter was pre-loaded. The aim of the two sampling time points was to compare the removal potential of the three adsorbents at two distinct phases while using NTS to reveal differences in feature-specific removal patterns. Method blanks were prepared using Milli-Q water with 300 mg/L CaCl₂, and controls were prepared by pumping effluent wastewater through empty columns. Samples were taken from the secondary effluent wastewater at both T1 and T2 time points

A typical daily setup involved: pump 1 delivering Milli-Q water, pump 2 A pumping effluent wastewater through a column packed with RH-SiO₂, and pump 2B pumping effluent wastewater through a column packed with GAC. The experiment order was randomized and performed on different days to minimize bias.

The 100 mL samples were pre-concentrated to an enrichment factor of 67 using a vacuum-assisted evaporation (VEC) system (BUCHI SyncorePlus Evaporator) as described by Rodriguez-Otero et al. [22]. Lastly, the concentrates were reconstituted in methanol and adjusted to a final volume of 1.5 mL, then stored in 1.5 mL HPLC vials in the freezer until analysis by LC-HRMS.

## 2.4. Chemical and data analysis of micro-pollutants in the samples

The samples were analyzed in triplicate using LC-HRMS, operated in MS<sup>e</sup> continuum mode which allowed the simultaneous acquisition of

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Schematic representation of the setup for secondary effluent water experiments on GAC, RH-SiO₂, and Resinex adsorption in fixed bed columns.</figcaption></figure>

full-spectrum accurate mass data from both parent and fragment ions in a single run, with both positive and negative electrospray ionization (ESI+ and ESI-). Details of the chemical analysis, data processing (including feature detection, compounds annotation and identification, and filtering), and quality control are provided in **Supplementary information** (SI-1.3, SI-1.4, and SI-2.1). The workflow was designed to prioritize relevant compounds that had significantly different trends across the three adsorption treatments. Since the goal was to assess the performance of new adsorbent material compared to two commercial alternatives, the focus was on identifying characteristic removal capacities of RH-SiO₂. A summary of the workflow can be found in **Supplementary Information** (Fig. S2). Integrated peak areas were used to determine removal efficiency based on the variation in feature peak areas (A) between the inlet and outlet (RE = (1 - (A<sub>v</sub> / A<sub>0</sub>))). With the objective of obtaining a relative comparison of features, all samples were processed under identical conditions ensuring any preparation bias was uniform across treatments.

# 3. Results and discussion

## 3.1. Non-target analysis of features by removal efficiencies

### 3.1.1. General overview

In total, 2673 non-target features were detected in the positive electrospray ionization (ESI+) mode, after the filtering process detailed in **Supplementary Information** (SI-2.1.1). A general analysis of the features in ESI+ (Fig. 2) reveals several correlation clusters (Fig. 2a), with features grouping according to their similarity in removal efficiencies across the three filters. Four main clusters were identified, with over 70 % of the features showing correlation with others. Removal efficiency ratios were computed for each feature, and the average of the triplicates for each sample was used to create the HCA heatmap shown in Fig. 2b.

GAC demonstrated the highest general removal efficiency in the GAC T1 sample, with an average of 91 %, which dropped to 55 % in the GAC T2 samples. RH-SiO₂ followed, with average removal efficiency of 75 % in RH-SiO₂ T1 and 33 % in RH-SiO₂ T2. Comparable removal efficiencies for both materials were observed in region B. However, in region D, 600 features removed by the aged GAC (with an average removal efficiency (RE) of 54 %) were not removed in RH-SiO₂ T2 sample, though these features had initially been removed by the fresh filter. In contrast, in region A, 104 features were retained in both T1 and T2 by RH-SiO₂

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** (a) Correlation heatmap of 2673 non-target features detected in ESI+ mode. Red indicates high correlation in their removal patterns, while white indicates no correlation. (b) HCA heatmap of removal efficiency ratios in T1 and T2 for all features detected in ESI+. Dark red indicates the highest removal efficiencies, while dark blue represents no removal. Key regions of interest (A-D) are highlighted and discussed in the text.</figcaption></figure>
(>90% RE), whereas in T2 for GAC, these features were no longer effectively removed (<20% RE). Region B groups features that were similarly removed by both GAC and RH-SiO₂.

Resinex generally shows limited removal efficiency; however, in region C, there is a group of 375 features efficiently removed in both T1 (> 90% RE) and T2 (> 80% RE). These features likely originate from negatively charged compounds, since Resinex is an anionic-exchange resin. This was observed in Fig. S9b, where the features removed were detected in negative ionization mode (ESI-) in the T2 sample were 97% removed by Resinex alone. RH-SiO₂, with its negatively charged surface at the wastewater pH of 7.2 (pH<sub>pzc</sub> 2), tends to attract cationic compounds, while repelling negatively charged ones. This likely explains why the features removed by Resinex in region C are not removed by RH-SiO₂ [21]. Conversely, GAC's surface has a globally basic character, indicated by its high pH<sub>pzc</sub> of 9.3. Based on this bulk property, one would anticipate a targeted removal towards a specific group of features as positively charged Resinex. However, GAC surface is heterogeneous, containing both basic and acidic functional groups, and its surface complexity leads to a broader removal regardless of compounds charge as discussed by Guillossou et al. [23] and further addressed below in Section 3.3 with the identified features.

Lastly, some calculated RE were negative, indicating a higher feature peak area after treatment than before. This phenomenon could be linked to differential matrix effects observed by Mechelke et al. [26] with VEC's tendency towards ion suppression; consequently, the variable reduction in dissolved organic carbon across treatments would result in less suppression in the cleaner, treated samples. This yields a conservative estimation of RE of the detected features.

### 3.1.2. Prioritization of non-target features

To investigate which type of features uniquely behaved through each treatment, features with a removal efficiency greater than (> 80% RE) were visualized using a Venn diagram (Fig. 3). Consistent with the HCA results in Fig. 2, the number of features completely removed during the initial specific BVs is higher for GAC (Fig. 3a). However, a comparison of Fig. 3a and Fig. 3b reveals that the features removed at the later specific BVs are more selective to each material.

Interestingly, in the sample collected at T2 (Fig. 3b), RH-SiO₂ maintains the highest removal efficiency compared to the other two treatments. Based on this finding, the 677 features uniquely removed by RH-SiO₂ in the T2 sample are prioritized for identification in the next section.

### 3.1.3. Identification and fate of prioritized features: polypropylene glycol and its metabolites

Distinct removal signatures were observed where specific features were retained by RH-SiO₂ but showed low affinity for GAC (region A,

Fig. 2). Analysis of the 677-feature cluster (Fig. 3b) revealed homologous series (Fig. S10), demonstrating the selective behavior captured by the NTS approach [27]. These series consisted of polypropylene glycols (PPGs) congeners with mass differences of either 14.0157 Da or 58.0418 Da, and multiples of the latter (116.080 Da and 174.172 Da), corresponding to methylene (CH₂) and propylene oxide (PO: CH₂CH(CH₃)O) units [28]. Chromatographic peaks separated by 58.0418 Da (one propylene oxide group) were found, and their mass spectrometry data aligned with sodium adducts (SI-2.5, Fig. S11).

Additionally, other homologous series with a mass difference of 13.9781 Da (representing +O and -2H) were detected (Fig. S12). The retention time difference between PPGs and their carboxylated (COOH) metabolites was 0.2 min. Homologue series of PPGs and PPGs-COOH are shown in Fig. S13 and S14. All identified compounds are listed in Table S6, in SI-2.5.

The presence of PPGs and PPGs-COOH in wastewater has been previously reported, though the available literature is limited [27]. PPGs are widely used as surfactants in cosmetics, pharmaceuticals, lubricants, and hydraulic fracturing fluids. Short chain PPGs and mono-carboxylated species were detected as degradation products [27, 28]. Although PPGs are not known to be toxic to aquatic organisms, they can serve as environmental tracers [28,29].

Initially, longer chain PPGs and PPGs-COOH were detected in the cluster with RE > 80% for RH-SiO₂. However, shorter-chain compounds (PPG-6, PPG-(6-8)-COOH), which were not part of this cluster, were also detected. As shown in Fig. 4a, these shorter-chain compounds were less efficiently removed by RH-SiO₂ compared to the longer chains (N > 8). In contrast, GAC showed consistent removal efficiency across different chain lengths for both PPGs-COOH (Fig. 4a) and PPGs (Fig. 4b), though its overall removal efficiency was lower than that of RH-SiO₂. Interestingly, Resinex removed only the longer-chain PPGs, with a removal efficiency of less than 50% (Fig. 4b).

RH-SiO₂ showed the lowest removal efficiency for smaller PPG-COOHs These compounds are negatively charged at the wastewater pH of 7.2 (SI-2.6, Table S7). The longer the chain, the lower the electrostatic repulsion between the deprotonated silica surface and the carboxyl group. Adsorption likely occurs through the hydroxyl group at the opposite end of the chain, suggesting the involvement of physical adsorption interactions through hydrogen bonding, or ion-dipole interactions. Also, the penetration in the pores of 6.4 nm size of RH-SiO₂ favours the trapping of the molecules and their access to sorption sites. On the contrary, GAC had a much lower pore size (1.8 nm), which will hardly allow the entrance of long polymers.

## 3.2. Identification of target and suspect compounds

All identified compounds are summarized in Table 1. Pattern-level observations (removal trends) were derived from feature-level data and should not be interpreted as a confirmed identification. Compound-level claims are restricted to the 37 compounds summarized in Table 1, with confidence levels assigned according to Schymanski et al. [30]. Of these, 10 compounds were confirmed with analytical standards (SI-1.2, Table S2) and assigned confidence level 1. For 27 compounds, no standards were available, but MSMS_PUBLIC_EXP_Pos_VS17 library was used, and characteristic fragments were matched, leading to level 2a assignments. MS2 spectra for each compound are provided in the Supplementary Information (SI-2.3 for level 1 and SI-2.4 for level 2a confidence).

The majority of identified target and suspect compounds were pharmaceuticals and their metabolites, with a total of 30 compounds. These included antibiotics, antihistamines, beta blockers, steroids, and other pharmaceuticals. Additionally, three compounds commonly found in sunscreen formulations were identified: sulisobenzone, 2-[4-(diethylamino)-2-hydroxybenzoyl] benzoic acid, and ensulizole. Two industrial compounds (1,3-diphenylguanidine and 1H-benzotriazole), one flavonoid (isosakuranetin), and one insecticide (DEET) were also

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Venn diagram of the number of features removed (>80% RE) in T1 samples (a) and in T2 samples (b) by each treatment detected in positive ionization mode.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 4. Removal efficiency (RE%) for PPGs (a) and PPGs-COOH (b) by each material in the T2 sample.</figcaption></figure>

Table 1

List of assignments for target and suspect compounds, including PubChem identifier (CID), charge at wastewater pH (7.2), retention time (RT), monoisotopic mass, detected ion, measured mass, calculated mass, mass deviation, and confidence level according to [30].

<table><thead><tr><th>Metabolite name</th><th>CID</th><th>Charge</th><th>RT (min)</th><th>Monoisotopic mass</th><th>Ion</th><th>Measured mass</th><th>Theoretical mass</th><th>Δm/z (ppm)</th><th>confidence level</th></tr></thead><tbody><tr><td>Sulfapyridine</td><td>5336</td><td>-0.9016</td><td>2.37</td><td>249.05720</td><td>[M+H]+</td><td>250.065</td><td>250.065</td><td>1.3</td><td>1</td></tr><tr><td>Carbamazepine</td><td>2554</td><td>0.0000</td><td>5.93</td><td>236.09496</td><td>[M+H]+</td><td>237.103</td><td>237.103</td><td>0.4</td><td>1</td></tr><tr><td>Amitriptyline</td><td>2160</td><td>0.9864</td><td>6.17</td><td>277.18305</td><td>[M+H]+</td><td>278.193</td><td>278.191</td><td>6.7</td><td>1</td></tr><tr><td>Citalopram</td><td>2771</td><td>0.9993</td><td>5.23</td><td>324.16379</td><td>[M+H]+</td><td>325.173</td><td>325.172</td><td>3.3</td><td>1</td></tr><tr><td>Erythromycin</td><td>12560</td><td>0.9845</td><td>5.11</td><td>733.46124</td><td>[M+H]+</td><td>734.469</td><td>734.469</td><td>0.2</td><td>1</td></tr><tr><td>Metoprolol</td><td>4171</td><td>0.9915</td><td>3.22</td><td>267.18344</td><td>[M+H]+</td><td>268.192</td><td>268.191</td><td>3.4</td><td>1</td></tr><tr><td>O-desmethylvenlafaxine</td><td>125017</td><td>0.9770</td><td>2.83</td><td>263.18853</td><td>[M+H]+</td><td>264.197</td><td>264.196</td><td>1.0</td><td>1</td></tr><tr><td>Sulfamethoxazole</td><td>5329</td><td>-0.9562</td><td>4.21</td><td>253.05211</td><td>[M+H]+</td><td>254.060</td><td>254.056</td><td>0.5</td><td>1</td></tr><tr><td>1H-Benzotriazole</td><td>7220</td><td>-0.9016</td><td>2.80</td><td>119.04835</td><td>[M+H]+</td><td>120.056</td><td>120.056</td><td>1.5</td><td>1</td></tr><tr><td>Valsartan</td><td>60846</td><td>-1.9557</td><td>8.21</td><td>435.22704</td><td>[M+H]+</td><td>436.234</td><td>436.235</td><td>1.9</td><td>1</td></tr><tr><td>Ensurilazole</td><td>33919</td><td>-0.9979</td><td>1.87</td><td>274.04121</td><td>[M+H]+</td><td>275.049</td><td>275.049</td><td>1.4</td><td>2a</td></tr><tr><td>Losartan carboxylic acid</td><td>108185</td><td>-1.9546</td><td>7.62</td><td>436.14145</td><td>[M+H]+</td><td>437.148</td><td>437.149</td><td>2.0</td><td>2a</td></tr><tr><td>Trenbolone</td><td>25015</td><td>0.0000</td><td>5.68</td><td>270.16198</td><td>[M+H]+</td><td>271.168</td><td>271.170</td><td>5.6</td><td>2a</td></tr><tr><td>2-[4-(Diethylamino)-2-hydroxybenzoyl] benzoic acid</td><td>79878</td><td>-1.0706</td><td>8.86</td><td>313.13141</td><td>[M+H]+</td><td>314.139</td><td>314.139</td><td>0.1</td><td>2a</td></tr><tr><td>Fexofenadine</td><td>3348</td><td>-0.0082</td><td>6.09</td><td>501.28791</td><td>[M+H]+</td><td>502.297</td><td>502.296</td><td>1.7</td><td>2a</td></tr><tr><td>Losartan</td><td>3961</td><td>-0.9568</td><td>6.57</td><td>422.16219</td><td>[M+H]+</td><td>423.169</td><td>423.170</td><td>2.1</td><td>2a</td></tr><tr><td>17alpha-Hydroxyprogesterone</td><td>6238</td><td>0.0000</td><td>6.77</td><td>330.21949</td><td>[M+H]+</td><td>331.227</td><td>331.227</td><td>2.0</td><td>2a</td></tr><tr><td>DEET</td><td>4284</td><td>0.0000</td><td>6.93</td><td>191.13101</td><td>[M+H]+</td><td>192.138</td><td>192.139</td><td>3.0</td><td>2a</td></tr><tr><td>Dydrogesterone</td><td>9051</td><td>0.0000</td><td>7.60</td><td>312.20893</td><td>[M+H]+</td><td>313.216</td><td>313.217</td><td>1.9</td><td>2a</td></tr><tr><td>N-Desmethyltramadol</td><td>198555</td><td>0.9980</td><td>2.18</td><td>249.17288</td><td>[M+H]+</td><td>250.181</td><td>250.181</td><td>0.5</td><td>2a</td></tr><tr><td>Progesterone</td><td>5994</td><td>0.0000</td><td>8.01</td><td>314.22458</td><td>[M+H]+</td><td>315.231</td><td>315.232</td><td>4.4</td><td>2a</td></tr><tr><td>Trimethoprim</td><td>5578</td><td>0.9001</td><td>2.36</td><td>290.13789</td><td>[M+H]+</td><td>291.145</td><td>291.146</td><td>2.3</td><td>2a</td></tr><tr><td>1,3-Diphenylguanidine</td><td>7594</td><td>0.9935</td><td>2.89</td><td>211.11095</td><td>[M+H]+</td><td>212.119</td><td>212.119</td><td>0.7</td><td>2a</td></tr><tr><td>2-Ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine</td><td>5378015</td><td>0.1633</td><td>5.56</td><td>277.18305</td><td>[M+H]+</td><td>278.191</td><td>278.191</td><td>0.8</td><td>2a</td></tr><tr><td>Bupivacaine</td><td>65770</td><td>0.8959</td><td>4.36</td><td>288.22016</td><td>[M+H]+</td><td>289.227</td><td>289.228</td><td>2.3</td><td>2a</td></tr><tr><td>Flecainide</td><td>3356</td><td>0.9962</td><td>5.31</td><td>414.13781</td><td>[M+H]+</td><td>415.144</td><td>415.146</td><td>3.8</td><td>2a</td></tr><tr><td>Lidocaine</td><td>3676</td><td>0.7819</td><td>2.74</td><td>234.17321</td><td>[M+H]+</td><td>235.181</td><td>235.181</td><td>0.1</td><td>2a</td></tr><tr><td>Norquetiapine</td><td>11369918</td><td>0.9769</td><td>4.26</td><td>295.11432</td><td>[M+H]+</td><td>296.122</td><td>296.122</td><td>1.6</td><td>2a</td></tr><tr><td>Propranolol</td><td>4946</td><td>0.9915</td><td>4.69</td><td>259.15723</td><td>[M+H]+</td><td>260.165</td><td>260.165</td><td>0.0</td><td>2a</td></tr><tr><td>Tramadol</td><td>33741</td><td>0.9770</td><td>3.24</td><td>263.18853</td><td>[M+H]+</td><td>264.196</td><td>264.196</td><td>0.3</td><td>2a</td></tr><tr><td>Venlafaxine</td><td>5656</td><td>0.9847</td><td>4.15</td><td>277.20418</td><td>[M+H]+</td><td>278.213</td><td>278.212</td><td>3.2</td><td>2a</td></tr><tr><td>D617 Verapamil metabolite</td><td>93168</td><td>0.9999</td><td>4.50</td><td>290.19943</td><td>[M+H]+</td><td>291.207</td><td>291.207</td><td>0.2</td><td>2a</td></tr><tr><td>Cetirizine dihydrochloride</td><td>2678</td><td>-0.1449</td><td>6.23</td><td>388.15537</td><td>[M+H]+</td><td>389.163</td><td>389.163</td><td>1.1</td><td>2a</td></tr><tr><td>Lamotrigine</td><td>3878</td><td>0.3197</td><td>3.15</td><td>255.00785</td><td>[M+H]+</td><td>256.015</td><td>256.016</td><td>1.4</td><td>2a</td></tr><tr><td>6beta-Hydroxy-21-desacetyl Deflazacort</td><td>29981402</td><td>0.0000</td><td>10.16</td><td>415.19949</td><td>[M+H]+</td><td>416.206</td><td>416.207</td><td>3.9</td><td>2a</td></tr><tr><td>Sulisobenzone</td><td>19988</td><td>-1.3004</td><td>4.34</td><td>308.03546</td><td>[M-H]-</td><td>307.028</td><td>307.028</td><td>0.0</td><td>2a</td></tr><tr><td>Isosakuranetin</td><td>160481</td><td>-0.1816</td><td>4.87</td><td>286.08412</td><td>[M-H]-</td><td>285.078</td><td>285.076</td><td>6.2</td><td>2a</td></tr></tbody></table>
detected. These pharmaceuticals, personal care products, industrial chemicals, and insecticide compounds are common micropollutants in wastewater, as reported in the NORMAN Substance Database [31]. Isosakuranetin, a natural xenoestrogen derivative of the flavanone naringenin, has also been detected in surface waters [32].

### 3.3. Evaluation of removal efficiencies by correlation clustering of identified compounds

The removal efficiencies of all identified compounds, including target, suspect and non-target, were evaluated using correlation

clustering as done with NTS features in Fig. 2a in Section 3.1.1. This method grouped compounds exhibiting similar behavior and explored correlations with their physicochemical properties (Fig. 5). A notable correlation was observed based on the charge of the compounds. This method can also aid in confirming compound identifications—once a relevant physicochemical property is identified, similar drugs should show similar behavior. Compounds are clustered based on their removal efficiency trends, and their physicochemical characteristics can be correlated (SI-2.6, Table S7). The selected clusters were analysed to identify removal trends for each (Fig. 6).

Cluster A consists of negatively charged compounds (e.g., losartan

<figure><img src="image_8.png" /><figcaption>Fig. 5. Correlation clustering of target, suspect and NTS compounds. Main clusters are labelled with letters from A to D.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Boxplot of removal efficiency distributions for each sample across selected clusters from the correlation heatmap. (Sample abbreviations: RH=RH-SiO₂, AC=GAC and Resin=Resinex).</figcaption></figure>

carboxylic acid, valsartan, isosakuranetin). They were not retained by RH-SiO₂, while both GAC and Resinex exhibited high RE for the fresh adsorbents (T1). However, the average RE moderately decreased for both at T2. This trend primarily groups compounds in their negative form, such as carboxylic and sulfonic acids. The neutral compound trenbolone and the nearly neutral benzotriazole have a log D greater than 1, while the negatively charged compounds showed a broad range of lipophilicities. Valsartan is lipophilic (log D > 2), while compounds like ensulizole and losartan carboxylic acid have log D values below 1. Negatively charged compounds can be repelled by the negatively charged surface of silica, while the anionic exchange resin is selective for negatively charged compounds [22].

**Cluster B** includes neutral compounds and shows a similar trend to Cluster A for GAC and Resinex samples. However, there is a notable breakthrough for RH-SiO₂ samples. The neutral and positively charged compounds exhibit a wide range of log D values, from -1–4. Progesterone and DEET, neutral compounds with high log D values (4.15 and 2.50, respectively), are well retained by GAC due to its lipophilic character, but RH-SiO₂ does not retain them effectively at T2.

**Cluster C** contains large, positive charged compounds and long-chain PPG-COOHs. This cluster includes compounds that are well retained by RH-SiO₂ in both T1 and T2 samples, while some show minor breakthrough with GAC, and none are retained by Resinex due to molecular repulsion. The group comprises negatively charged long-chain PPGs-COOH (n9–14) and the positively charged macrolide erythromycin. These compounds have log D values ranging from 0.75 to 2.99 and masses greater than 600 m/z. As mentioned in Section 3.1.3, long-chain PPGs-COOH were adsorbed by RH-SiO₂ without breakthrough. GAC, on the other hand, loses its efficiency over time, as observed with another large molecule, erythromycin. Interestingly, Resinex did not retain PPGs-COOH, despite their anionic nature.

**Cluster D** consists of positively charged molecules, PPGs, and short-chain PPG-COOHs. This larger cluster shows a broad range of removal efficiencies. It is characterized by the presence of PPG-8-COOH and PPG-9-COOH, which are negatively charged and hydrophilic, along with neutral and hydrophobic PPGs. Additionally, it includes positively charged molecules with masses below 400 m/z, which show affinity for RH-SiO₂ and GAC.

In summary, the removal efficiencies of the identified compounds display distinct patterns that align with those observed in the HCA heatmap for NTS (Fig. 2b). These patterns include compounds efficiently removed by RH-SiO₂, those removed equally well by RH-SiO₂ and GAC, compounds showing significant breakthrough with RH-SiO₂, and those not removed by RH-SiO₂ at all. The novel adsorbent RH-SiO₂ demonstrated a particular attraction towards positively charged molecules, as expected with its negatively charged surface, exhibiting selective attraction irrespective of lipophilicity—a trend previously observed in spiked water tests [22]. Conversely, negatively charged compounds showed minimal to no removal by RH-SiO₂ compared to Resinex, suggesting that further investigation into the role of ESI- features could enhance understanding of this disparity.

Regarding GAC's efficiency, it initially adsorbed all identified compounds effectively in T1, contrasting with the more selective removal patterns seen in Resinex and RH-SiO₂. The complex surface and hydrophobic nature of GAC are key factors that explain a less charge-dependent adsorption. Guillossou et al. [23] observed the same heterogeneous adsorption and suggested that there's not one predominant mechanism. Not only does the GAC surface contain both acidic and basic groups, but the adsorption of negatively charged natural organic matter (NOM) could alter the adsorbent global charge by enhancing the adsorption of positive OMPs [23,33]. To confirm this mechanism, specific experiments to further characterize GAC's surface chemical composition and the continuous monitoring of NOM in the treated samples should be performed in future studies.

Nevertheless, this efficiency diminished significantly over time (T2), particularly for high molecular weight compounds. This decline was surprising given the low volume of water filtered (BV). This reduction in performance can be explained by various contributing factors. As previously mentioned, the hydrophobic nature of GAC can also absorb NOM present in wastewater, which could clog its pores and limits access to adsorption sites [23,33]. Another explanation for the observed performance is steric hindrance explained by the adsorbent microporous structure. The removal efficiency of large molecular weight micropollutants (such as long-chain PPGs-COOH or antibiotic macrolides in Cluster C) is constrained by the GAC's microporous structure, which has a mean pore diameter of 1.85 nm, physically restricting access for these larger molecules. Conversely, the mesoporous structure of RH-SiO₂ (mean pore diameter of 6.4 nm) would allow it to accommodate these larger molecules more effectively [34]. Activated carbons with a higher mesopore content have been discussed by other authors as being more appropriate for the removal of long-chain compounds compared to the material used in this study [35,36]. Finally, the most likely explanation and highest limitation of this study is the short EBCT and flow behavior. The observed low adsorption efficiency of GAC is linked to the short EBCT of 1.2 min used in the filter, mirroring observations made by Crowe et al. [37] in wastewater remediation studies using similar EBCTs for GAC. Regarding flow behavior, the analysis of bed porosity (ε) and pressure drop (ΔP) (SI, Table S1), for the adsorbent media under an identical superficial velocity, revealed that GAC had the lowest pressure drop (ΔP = 143 Pa) with a high bed porosity (ε = 0.77). A lower flow resistance within the column could lead to unequal flow and channeling. Such effects often contribute to limited mass transfer and ultimately to premature breakthrough as observed by Terrigeol and Trifilieff [38]. In this context, differences in particle size, porosity, and packing behavior across the media mean that—without column-level replicates or dispersion metrics—observed variations should be attributed to physical column effects rather than inherent adsorbent chemistry. Future work should address these limitations by matching EBCTs to the design properties of GAC using the Rapid Small-Scale Column Tests (RSSCT) approach, as originally proposed by Crittenden et al. [39]. This approach allows the obtention of breakthrough curves for OMPs simulating the behavior of a larger-scale pilot plant by scaling down the column dimensions, reducing both EBCTs and adsorbent particle size. This approach also would allow to run for longer BVs, in a shorter
period, allowing more feasible sampling after 10,000 BVs, which is necessary to learn the long-term efficiency of the adsorbent [40]. Different matrices as well as pilot scale should be explored to assess how variations in wastewater composition and scaling-up might affect performance. However, the increased data volume should be balanced with the practical limits of sample analysis and data processing with NTS.

### 3.4. Cost and time-efficiency comparison with conventional targeted approach

The strategic approach presented in this study for lab-scale evaluation of novel adsorbents combining NTS with HR-MS for chemical analysis and VEC for sample pre-concentration, offers substantial gains in the amount and depth of information obtained (Table 2).

Using VEC for sample preparation significantly reduces the consumption of organic solvents compared to traditional solid-phase extraction (SPE), providing a more environmentally friendly and cost-effective alternative [26]. When sample concentrations are sufficiently high, large-volume injection is another option that can further minimize preparation time and costs [41].

Compared to conventional methods, where the number of compounds tested is constrained by the availability of standards, as well as the time, cost, and labor required for sample preparation, NTS shifts the main limitation to the cost of instrumentation and labor-intensive data processing and interpretation.

Unlike targeted analysis, which often requires multiple methods, NTS achieves this in a single HR-MS run. Its scope could be further expanded to cover a broader range of detected compounds by incorporating complementary NTS approaches [42,43]. While this would enhance applicability, it must be weighed against the associated costs. Critically, this single run can assess over 1000 detected features, enabling a comprehensive evaluation of adsorbent performance in one workflow rather than relying on removal data for fewer than 10 compounds [7–11]. The use of NTS enables a more holistic evaluation of wastewater treatment performance and help mitigate biases introduced by the selection of a limited set of target compounds [44]. NTS provides detailed insights into the differences in compound removal across treatments, as observed for the novel adsorbent in this study.

Screening experiments are particularly valuable for novel

<caption>**Table 2**</caption>

Key differences between conventional evaluation of novel adsorbents and the strategy proposed in this study.

<table><thead><tr><th>Aspect</th><th>Conventional evaluation</th><th>Proposed strategy</th></tr></thead><tbody><tr><td>N° of compounds assessed</td><td>10–50</td><td>&gt; 1000 (features detected)</td></tr><tr><td>Number of BV windows</td><td>&gt; 10 (full curve)</td><td>2 (start and end)</td></tr><tr><td>Analysis method</td><td>MS/MS</td><td>HRMS (single workflow)</td></tr><tr><td>Sample prep. complexity</td><td>High (SPE: solvent intensive)</td><td>Low (VEC, no additional solvents)</td></tr><tr><td>Time analysis per sample</td><td>More than one method for multiple compounds</td><td>One single &lt; 30 min run</td></tr><tr><td>Additional reactive</td><td>Standards per compounds</td><td>No standards, libraries</td></tr><tr><td>Risk of overlooking unknowns</td><td>High (blind to unknowns/TPs)</td><td>Low (detects all features above the limit and allows for retrospective analysis)</td></tr><tr><td>Performance insight</td><td>Limited, biased evaluation</td><td>Holistic evaluation, retrospective analysis</td></tr><tr><td>Instrument cost</td><td>Low (LC-MS)</td><td>Very high (LC-HRMS instrumentation)</td></tr><tr><td>Analysis cost (sample/day)</td><td>Low (per method)</td><td>High</td></tr><tr><td>Operational burden</td><td>Lower, simple data integration</td><td>Energy intensive and complex data treatment (requires specialized software and know-how)</td></tr></tbody></table>

compounds with limited chemical information, where targeted analysis is not feasible, or for prioritizing substances in regulatory monitoring programs [45]. Moreover, as noted by Hollender et al. [13], retrospective screening is possible, meaning that if new legislation emerges, preliminary insights into its impact on treatment can be obtained without additional laboratory work.

Although this study has limitations discussed in Section 3.3, an important question remains: is a full breakthrough curve necessary, or is a general overview of treatment performance sufficient? The trade-off between data volume and the level of detail required must be considered carefully. With the rapid adoption of NTS in environmental research, declining HRMS costs, improved instrument performance, and streamlined workflows through advanced software, the balance is clearly shifting toward maximizing information acquisition [13].

### 4. Conclusions

In this study, a novel adsorbent material (RH-SiO₂) was evaluated alongside two commercial materials (GAC and Resinex) in column experiments using a LC-HRMS-NTS workflow. This approach served as a methodological screening to characterize the removal behaviors of organic micropollutants in secondary effluent wastewater in a laboratory scale.

The presented workflow identified distinct, material-specific removal profiles that vary based on physicochemical properties. GAC exhibited higher initial removal efficiency, removing both positively and negatively charged compounds as well as neutral ones, with less charge selectivity than the other adsorbents. While RH-SiO₂ showed a specialized affinity for positively changed compounds, large macrolides and long-chain neutral PPGs, even under the constrained EBCT conditions of this screening.

This study was strategically designed to learn how NTS can enrich the evaluation of novel adsorbents. Instead of maximizing BVs, we focused on early-stage treatment behavior to observe distinct removal patterns. While this resulted in a limited BV range and relatively low EBCT, these parameters were chosen to enable rapid screening and establish removal profiles. Future studies should incorporate longer run times, more realistic EBCTs and different water matrices and scales to validate long-term performance and scalability.

Ultimately, NTS was instrumental in identifying removal behaviors that would have been missed in traditional target analysis, such as RH-SiO₂'s affinity for larger compounds. Typically, adsorbent evaluation focuses on a limited set of target molecules, but by assessing hundreds of features simultaneously, this approach helps prioritize features for further investigation. This method also highlights treatment behaviors that reveal key physicochemical properties of wastewater contaminants, offering potential for predicting treatment efficiency based on chemical structures.

### Environmental Implication

This study proposes a strategy for the assessment of novel adsorbent materials in wastewater treatment using NTS with LC-HRMS. The approach outlined here facilitates a more informed material selection and broadens the knowledge on the material limited by target-based analysis, by revealing removal patterns of thousands of micropollutants. This strategy, when applied to early-stage adsorbents such as rice husk silica, promotes sustainable innovation and minimises experimental waste and resource use. This contributes to the development of more effective and environmentally responsible solutions for micropollutants in wastewater.

### CRediT authorship contribution statement

**Pattison David:** Validation, Supervision, Methodology. **Jørgensen Mathias:** Investigation. **Reinhardt Lisa:** Methodology. **Selina Tisler:**
Writing – review & editing, Validation, Supervision, Methodology, Investigation, Conceptualization. **Rodríguez Otero Alba:** Writing – review & editing, Writing – original draft, Visualization, Validation, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Christensen Jan:** Writing – review & editing, Supervision, Resources, Methodology, Funding acquisition, Conceptualization. **Brice Bouyssiere:** Writing – review & editing, Supervision, Project administration, Funding acquisition, Conceptualization.

## Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Alba Rodriguez Otero reports financial support was provided by European Union. Jan H. Christensen reports financial support was provided by Innovation Fund Denmark. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgement

This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 945416; and Innovation Fund Denmark project VANDALF, Grant Number: 9067-00032A.

## Supporting information (pdf)

SI-1. Materials and methods; SI-1.1. Adsorbent materials, SI-1.2. Chemicals and solvents; SI-1.3. Chemical analysis; SI-1.3.1. Measurement by LC-HRMS of target OMPs in secondary effluent wastewater; SI-1.4. Data Processing; SI-1.4.1. MS-DIAL parameters; SI-2. Results; SI-2.1. Quality control; SI-2.1.1. Data filtering evaluation; SI-2.1.2. PCA scores plot of features after standardization with influent samples; SI-2.2. Prioritization of compounds based on removal efficiencies; SI-2.3. Level 1 identifications and MS/MS spectrum match; SI-2.4. Level 2 annotations and MS/MS spectrum match; SI-2.5. Level 3 identification of prioritized features: PPG and PPG-COOH; SI-2.6. Cluster analysis of trends among identified compounds.

Mass spectrometry datasets (link to repository in SI).

Supporting information B (excel file).

ESI+ Feature list sample and blank maximum intensities and calculated ratio (Table SIB-1), ESI- Feature list sample and blank maximum intensities and calculated ratio (Table SIB-2), ESI+ Feature list with *m/z* and retention time values with intensities in each sample after filtering (Table SIB-3), ESI- Feature list with *m/z* and retention time values with intensities in each sample after filtering (Table SIB-4).

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.jhazmat.2026.141115.

## Data Availability

Data will be made available on request.

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