A routine method for the semi-quantitative determination of porous carbonaceous material in wastewater matrices

The EU directive 2024/1990/EU mandates a fourth treatment stage for micropollutant removal in wastewater treatment plants (WWTPs). Porous carbonaceous materials (PCMs) such as powdered activated carbon (PAC) and biochar are effective but require monitoring. This study presents a semi-quantitative, low-cost method for PCM determination in wastewater matrices using total carbon (TC) analysis. Calibration curves were established for activated sludge (AS), excess sludge (ES) and anaerobic digestion sludge (ADS) matrices, yielding high linearity (R² > 0.99). Limits of detection (LOD) and quantification (LOQ) ranged from 0.10 to 0.25 gPCM/gDM. Recovery rates exceeded 95% for AS and ES matrices, with relative standard deviations below 5%, demonstrating excellent precision. Comparison with chemical oxygen demand (COD)-based estimates showed strong correlation, validating the approach. The method enables routine PCM monitoring in WWTPs lacking sophisticated analytical equipment, supporting regulatory compliance and process optimization. While native organic carbon cannot be excluded and the relatively high LOD reduces sensitivity, the method still delivers robust semi-quantitative estimates for basic routine PCM monitoring.

GRAPHICAL ABSTRACT

GRAPHICAL ABSTRACT | Graphical Abstract of semi-quantitative determination of porous carbonaceous materials (PCMs) in wastewater treatment plant (WWTP) sludge matrices using total carbon (TC) analysis. The workflow comprises PCM-spiked sludge preparation, drying, TC measurement, and calibration against known PCM concentrations, enabling cost-effective monitoring for micropollutant removal applications.

Highlights

• Practical TC-based screening method to estimate porous carbonaceous material in WWTP sludges.

• Simple sample preparation (centrifugation, drying at 90 °C, grinding) with linear spike-recovery in three matrices.

• Applicable to medium-to-high PCM loads; detection limits of 0.10–0.25 g PCM/g DM.

• Matrix effects require site-/PCM-specific calibration.

1 Introduction

Eliminating micropollutants from municipal wastewater has become a key environmental challenge across Europe. European Union Directive 2024/1990/EU stipulates that wastewater treatment plants (WWTPs) serving populations of over 100,000 must implement advanced treatment processes to remove micropollutants (Proposal for a Directive of the European Parliament and of the Council concerning urban waste water treatment (recast)-13857/23;/2022/0345(COD). Council of the European Union (2023). These contaminants, which include pharmaceutical residues, pesticides, personal care products and industrial chemicals, persist through conventional biological treatment processes and pose risks to aquatic ecosystems and drinking water resources (Rawat et al., 2024).

Adsorption of micropollutants onto porous carbonaceous materials (PCM) is one of the available technologies for fourth treatment in WWTP that has demonstrated particular effectiveness (Bansal and Goyal, 2005; Jagadeesh and Sundaram, 2023). PCM in various forms, such as powdered activated carbon (PAC) and granular activated carbon (GAC) offers high specific surface areas (500–2,000 m²/g) combined with broad-spectrum adsorption capacity for diverse organic micropollutants including diclofenac, sulfamethoxazole, benzotriazole, metoprolol and various heavy metals (UVEK, 2016). In particular, PAC systems have gained widespread acceptance due to their operational flexibility; typical dosing rates of 10–30 mg/L achieve substantial removal of target compounds (Bosch et al., 2025). Compared to membrane processes (Sugiyama et al., 2022) or ozonation (Issaka et al., 2022), PCM-based treatments are cost-effective, chemically stable and easy to integrate (e.g., activated sludge tanks).

However, the optimal operation of PCM-based treatment stages requires precise control of dosing rates. Overdosing leads to unnecessary chemical consumption, increased operational costs, and potential complications in downstream sludge handling and disposal (Wang et al., 2023). Conversely, insufficient dosing compromises the removal of micropollutants and may result in non-compliance with discharge standards (Altmann et al., 2015). Routine monitoring of PCM concentrations in the treatment system is therefore necessary to verify applied dosages, support mass balance calculations, optimise process control and document regulatory compliance.

Existing analytical methods for quantifying PCMs in wastewater matrices present significant barriers to their routine implementation. While thermogravimetric analysis (TGA) provides detailed compositional information through differential thermal decomposition, it requires specialised instrumentation, trained personnel and extended analysis times (60–90 min per sample) (Dittmann et al., 2018). Scanning electron microscopy (SEM) offers visual confirmation, but remains qualitative and labour-intensive (Paetsch et al., 2017). Although chemical oxygen demand (COD) measurements are routine in WWTPs, they reflect total oxidisable matter rather than specifically quantifying carbonaceous materials, and they suffer from matrix interferences. Also hydrogen pyrolysis, benzene polycarboxylic acid (BPCA) analysis, wet chemical oxidation and ¹³C-labelled tracers, are primarily designed to detect black carbon in soils and sediments (Hardy et al., 2022; Schmidt et al., 2001; Xie et al., 2024). Furthermore, most methods only quantify the carbon fraction of the material, which may not accurately reflect the total mass of PCMs due to the variable ash and volatile content of PCMs derived from different sources (Margreiter et al., 2024). None of these approaches provides the combination of simplicity, cost-effectiveness and adequate precision required for routine operational monitoring in typical WWTP laboratories.

Despite the growing implementation of PCM-based treatment stages, there is currently no validated method for the simple, semi-quantitative determination of PCM using standard WWTP laboratory equipment. This discrepancy between operational requirements and analytical capabilities impedes process optimisation and increases reliance on manufacturer dosing recommendations rather than facility-specific data.

This study addresses this issue by developing and validating a new approach based on total carbon (TC) analysis. Unlike previous studies that have employed TC analysis for the general characterisation of carbonaceous materials, this study is the first to systematically validate a method for the quantification of PCM in complex wastewater matrices. The method uses TC analysers that are already present in many WWTP laboratories for regulatory reporting, requires only conventional sample preparation (drying and grinding) and provides results within 24 h. Through comprehensive validation of calibration linearity, limit of detection (LOD) and limit of quantification (LOQ), recovery, precision and matrix effects, we demonstrate that this approach achieves >95% recovery with relative standard deviations below 5%, which is sufficient for making operational decisions regarding PCM dosing.

The specific objectives of this work were to establish and validate TC-based calibration curves for determining PCMs in sludge and extruded sorbent matrices, to define the method’s performance characteristics, including LOD and LOQ, recovery, and precision, across operationally relevant concentration ranges, and to compare TC-based results with independent COD-based estimates for cross-validation. Additionally, the study aimed to evaluate the limitations and applicability boundaries of the method and to provide practical guidance for its implementation in WWTP laboratories.

The validated method enables the cost-effective, routine monitoring of PCM concentrations, thereby supporting the optimisation of processes, ensuring regulatory compliance and facilitating the economic operation of treatment stages.

2 Materials and methods

2.1 Porous carbonaceous material (PCM)

The PCM used in this study was a gasification residue (GR), a by-product of the wood gasification process. It was produced in a fixed-bed floating reactor developed by SynCraft and operated by Innsbrucker Kommunalbetriebe AG (IKB) at the municipal WWTP Innsbruck/Rossau, Austria (Huber et al., 2016). The feedstock for the IKB char consisted of regionally sourced forest residues.

The GR material was dried overnight at 105 °C, ground, and sieved to a particle size of ≤1 mm to ensure homogeneity. A detailed physicochemical characterization of the PCM, including specific surface area (SSA), total pore volume (Vtotal), pore size distribution, and elemental composition, is provided in (Margreiter et al., 2024) and summarized in Tables 1, 2.

Table 1

Table 1. Gas adsorption properties of the PCM, measured via BET analysis.

Table 2

Table 2. Physicochemical properties of the PCM.

2.2 Wastewater matrices

Sludge samples for method validation were obtained from two identical pilot-scale WWTP systems, each comprising a nitrification tank (100 L), denitrification tank (50 L), secondary clarifier (100 L), sand filter, and anaerobic digestion unit (3.5 L). Both plants were continuously fed with municipal wastewater sourced from the full-scale Zirl WWTP (population equivalent: ∼61,500) to simulate realistic operating conditions. While both units operated under identical parameters (inflow: 12.5 L/h), one plant received additional PCM dosing at a concentration of 100 mg/L.

Activated sludge (AS), excess sludge (ES) and anaerobic digestion sludge (ADS) samples were collected on three different sampling days. Fresh samples were characterized immediately upon arrival and summarized in Table 3. Including baseline TC value which represents the native organic carbon in activated sludge without PCM addition and serves as the reference for all spiked samples.

Table 3

Table 3. Physicochemical properties of AS, ES, ADS.

2.3 Sample preparation

To demonstrate a representative TC content of the various matrices with TC analysis, a series of sample preparations were conducted to determine TC and DM as shown in Figure 1. After determining the tare weight of the Falcon tube (Figure 1/1), 50 mL of matrix was transferred to the tube, weighed (Figure 1/2, 1/3) and centrifuged at 5,000 rpm for 10 min (Figure 1/4). After removal of the supernatant (Figure 1/5), the mass was dried at 90 °C for 12 h (Figure 1/6), weighed for DM determination (Figure 1/7) and ground (Figure 1/8).

Figure 1

Figure 1. Sample preparation protocol for wastewater matrices prior to TC analysis. After determining the tare weight of the Falcon tube (1), 50 mL of matrix was transferred to the tube, weighed (2, 3), and centrifuged (4). After removal of the supernatant (5), the pellet was slowly dried (6), weighed (7), and ground (8).

2.3.1 Spiking procedure

PCM-spiked samples were prepared by adding known masses of PCM to AS, ES and ADS matrices to achieve target concentrations ranging from 0.5–20.0 g/L. For each spiking level, triplicate samples were prepared independently to assess method precision. The spiking procedure was as follows:

1. Accurately weigh fresh activated sludge.

2. Add precisely measured PCM mass using analytical balance (±0.001 g precision).

3. Homogenize for 15 min using magnetic stirrer at 300 rpm.

4. Allow equilibration for 30 min at room temperature.

5. Subsample for TC analysis.

2.4 Analysis

Approximately 40–50 mg of each ground, dried sample was subjected to TC analysis using a Shimadzu SSM-5000A TC solid sample module (Shimadzu Europa GmbH, 2022) with ceramic boats. Samples were combusted at 900 °C in an oxygen carrier gas stream (0.2 L/min). TC instrument was calibrated with solid glucose standards (Roth, Germany; 15–100 mg; linear fit R² > 0.99) yielded measured carbon masses of 39.66 ± 0.42 mg, consistent with the theoretical ∼40% carbon fraction of glucose. Full calibration data per batch are archived in the laboratory SOP and available upon request. Method validation, including calibration slopes and performance metrics, was conducted using PCM-spiked sludge matrices (AS, ES, ADS).All results were expressed on a dry-matter basis: after gravimetric determination of dry matter (wet sample weighed, dried at 90 °C for 12 h, reweighed), concentrations were calculated from the dried aliquot as PCM in g/gDM using each sample’s own DM fraction (m_dry/m_wet); volumetric concentrations, where reported, were derived as (PCM g/gDM) × (total dry solids, gDM/L). PCM-associated carbon was expressed as delta TC (g/gDM) by subtracting the controls from the spiked samples, thereby removing background from native organic/inorganic carbon.

To assess potential interference from organic matter, COD was additionally measured using standard photometric kits (Macherey-Nagel). COD values were determined both for a defined PCM/RO water solution and for sludge samples containing PCM from the pilot WWTP.

2.5 Validation and statistical analysis

All statistical analyses and data visualizations were performed in R (R Core Team, 2022), using packages tidyverse (Wickham et al., 2019), carData (Fox et al., 2022), dplyr (Wickham et al., 2023), and broom (Robinson et al., 2024). The code and calculations are available in the Supplementary Material. A 95% confidence interval (α = 0.05) was applied. The 5-point calibration was utilised for the calibration process.

• Linearity was assessed by linear regression of TC [%] vs. known PCM concentration [g/gDM], with the coefficient of determination (R²) reported.

• Limit of detection (LOD) and limit of quantification (LOQ) were calculated as follows (Equations 1, 2):

$LOD=3.3·standarderror/slope(1)$

$LOQ=10·standarderror/slope(2)$

• Accuracy was evaluated as the deviation between measured and known PCM concentrations.

• Precision was calculated as the standard deviation of the accuracy across replicates.

• Robustness was tested using one-way analysis of variance (ANOVA) to identify significant differences between matrix types.

• Recovery was assessed as the percentage of the measured value relative to the true spiked value.

• Repeatability and consistency were evaluated via standard deviation of replicate TC values and measured PCM concentrations, respectively.

• Calibration factors for each sludge type were derived from the slope of the regression lines, and 95% confidence intervals were calculated using the t-distribution.

3 Results and discussion

3.1 Linearity

The approach used to determine PCM concentrations in different wastewater matrices via TC analysis demonstrated strong linearity, with coefficients of determination (R²) of 0.968 for ES, 0.986 for AS, and 0.932 for ADS, as shown in Figure 2. These high R² values confirm that TC measurements reliably reflect the actual PCM content across all tested matrices, validating the method’s applicability for quantifying carbonaceous materials in complex sludge environments.

Figure 2

Figure 2. Linear regression of TC (%) against PCM (g/gDM) for AS (A), ES (B), and ADS (C); lines indicate best-fit models with associated statistics.

3.2 Baseline controls and intercept

To account for native sludge carbon variability, baseline control samples without PCM addition were analysed (Table 4). The baseline total carbon of the controls was 37.24% (95% CI 36.27–38.21; RSD 6.44%) for ES, 37.52% (95% CI 37.16–37.88; RSD 2.24%) for AS, and 25.94% (95% CI 25.55–26.33; RSD 2.84%) for ADS. Because delta TC subtracts matched controls, the intercept is theoretically zero; the estimated intercept 95% CI includes zero for all matrices.

Table 4

Table 4. Summary statistics by matrix: slope (95% CI), R², ANOVA F/p and (mean, RSD, 95% CI). Regression of delta TC (g/gDM) against PCM dose (g/gDM), after matched-control subtraction.

3.3 Recovery and precision

Recovery validation confirmed accurate baseline subtraction with mean recovery 97.2% ± 3.1% (ES), 98.8% ± 2.8% (AS), and 95.9% ± 3.9% (ADS). These results demonstrate method precision and operational fitness; the higher RSD in ADS reflects matrix-specific variability without compromising the semi-quantitative suitability for plant monitoring.

3.4 Operational delta TC for PCM

TC quantifies total carbon non-selectively. PCM was reported as the operational increment on a DM basis. This was done by subtracting controls from spiked samples (delta TC, g/gDM). Across the spike series, the difference in TC increased linearly with dose in all matrices (AS, ES, ADS; R² values reported), indicating that the control-subtraction framework isolates the spike-associated carbon from the native background. Replicate precision (reported as RSD %) remained within acceptable limits for routine monitoring (AS/ES ≤ 5%; higher in ADS due to matrix heterogeneity), supporting the robustness of TC for semi-quantitative estimation. Baseline TC (DM basis) varied by matrix (AS, ES, ADS), but this variability is explicitly accounted for by matched-control subtraction, which removes constant background contributions from native organic/inorganic carbon.

Reporting PCM as the difference in TC (g/gDM) relative to matched controls mitigates TC’s non-specificity by removing matrix background and, given the observed linearity, recovery, and precision, reliably captures the spike-associated signal within the tested range. While delta TC remains operational and may include carbonate/Extracellular Polymeric Substances (EPS) effects, control subtraction together with linear dose response and acceptable precision mitigates overestimation for typical WWTP PCM loads. Additional speciation steps (e.g., total organic carbon (TOC)/total inorganic carbon (TIC)) or chemothermal oxidation) would further resolve carbon sources but substantially increase analysis time and complexity, making them less suitable for rapid routine monitoring.

For routine operation without a true zero, we monitor dosing via delta TC relative to matched contemporaneous controls and estimate absolute PCM in sludge by combining delta TC with an operational baseline (rolling mean/CI from controls or recent non-dosing periods), providing a transparent, uncertainty-aware approximation of total PCM under real plant conditions.

3.5 LOD/LOQ and operational relevance

Matrix-specific LOD and LOQ were determined (Table 5), yielding values of 0.17/0.52 g/gDM for ES, 0.10/0.31 g/gDM for AS, and 0.25/0.75 g/gDM for ADS. These detection limits are consistent with the operating regime expected for a fourth treatment stage. At influent doses of around 50 mg/L PCM, steady-state retention in the sludge line is expected to result in accumulations of around 2 gPCM/L (higher in anaerobic digesters due to solids concentration and longer Solid Retention Time). Using the conversion factor g/gDM ≈ (gPCM/L sludge)/(gDM g/L sludge) and typical dry solids of 8–15 gDM/L for activated/excess sludge and 20–40 gDM/L for anaerobic digestate, 2 gPCM/L corresponds to approximately 0.13–0.25 g/gDM for activated/excess sludge and 0.05–0.10 g/gDM for anaerobic digestate (higher if accumulation exceeds 2 g/L) (Metcalf and Eddy, Inc and AECOM, 2014). Accordingly, LODs of 0.10–0.25 g/gDM are adequate for medium-to-high-load operation, whereas trace-level monitoring would require more sensitive methods.

Table 5

Table 5. Matrix-specific LOD and LOQ for PCM (g/gDM) derived from calibration for AS, ES, and ADS.

3.6 Accuracy

Accuracy, expressed as deviation from known PCM concentrations, was excellent for AS (−0.013 g/gDM) and ES (0.026 g/gDM), while ADS showed a slightly higher deviation (0.328 g/gDM), likely due to its greater matrix complexity. Corresponding variances were low for AS (0.362) and ES (0.434), with a higher value observed for ADS (2.605). The 95% confidence intervals further confirmed the method’s precision in AS and ES, while highlighting increased uncertainty in ADS. It is worth noting that higher TC contents in complex matrices like ADS tend to amplify measurement variability; however, the overall method accuracy remained high across all samples.

3.7 Recovery

Recovery rates were calculated as 97.52% for AS, 97.97% for ES, and 92.10% for ADS, demonstrating that TC-based quantification aligns closely with actual PCM concentrations. Statistical significance (p < 0.01) was confirmed across all matrices. Repeated ANOVA testing showed no statistically significant variation over time within the same matrix (AS: F = 0.11, p = 0.997; ES: F = 0.45, p = 0.911; ADS: F = 0.29, p = 0.915), thereby confirming the method’s robustness.

3.8 Calibration factors

Regression analyses were used to calculate the calibration factors for each matrix, defined here as 100 × slope of delta TC versus dose. The calibration factors were 36.9 (95% CI 35.4–38.4) for AS, 32.2 (95% CI 30.5–33.9) for ES, and 54.6 (95% CI 48.8–60.4) for ADS. Calibration factors are matrix- and PCM-specific. We recommend site-specific calibration, especially where PCM ash/volatile content differs substantially.

3.9 Matrix comparison

Matrix differences were tested with ANCOVA (Table 6) (delta TC ∼ dose + matrix + dose:matrix): the interaction was not significant (F = 2.11, p = 0.124), indicating no slope differences across AS, ES, ADS. Baseline TOC% (controls) showed no significant matrix effect (ANOVA F = 1.87, p = 0.161). The pooled dose effect was highly significant (F = 1.965, p < 0.001), supporting a robust, matrix-independent method.

Table 6

Table 6. Overview of matrix comparison analyses. It covers slope comparisons across matrices (ANCOVA), baseline TOC % differences (one-way ANOVA on controls), and the overall dose–response effect (pooled regression).

3.10 External validation (COD)

Additional validation was performed using chemical oxygen demand (COD) as a reference. A 1 g/L solution of the applied PCM yielded a COD of 1912.83 ± 129.57 mgO₂ (n = 6). For ES from the bypass treatment plant, COD increased from 8040 ± 276.22 mgO₂/L (without PCM) to 12356.67 ± 391.71 mgO₂/L (with PCM), corresponding to an estimated PCM concentration of 2.25 g/L. TC-based analysis of the same samples yielded 1.94 ± 0.12 g/L (n = 3), confirming a close agreement between the two methods and further validating the TC approach. Minor deviations are attributed to matrix heterogeneity and non-carbon organic fractions contributing to COD.

3.11 Sample preparation rationale

Alternative sample preparation methods were explored, including drying in Petri dishes and filter-based approaches. However, these methods were less effective: the samples adhered strongly to the dishes and the carbon fines were retained in the filters, which introduced bias. The chosen preparation protocol (centrifugation in Falcon tubes, removal of the supernatant, drying at 90 °C and grinding) was found to be superior (Figure 1). This method effectively compacted the solids and minimized sample loss, enabling representative and reproducible measurements. We selected a drying temperature of 90 °C to minimise the thermal loss of volatile and semi-volatile compounds, while ensuring that the turnaround time for routine PCM determination remained practical. This approach is supported by sludge-drying research, which shows that measurable volatilisation and thermal degradation effects only increase markedly at elevated temperatures. In particular, (Vesilind and Ramsey, 1996), demonstrated that significant losses of volatile solids and heating value predominantly occur once drying temperatures exceed the conventional range of 103 °C–105 °C and accelerate further at temperatures above 150 °C. Below 100 °C, however, changes in volatile matter were minor, indicating that moderate drying conditions largely preserve the organic fraction. In light of this, it is expected that drying at 90 °C will reduce thermal bias relative to 105 °C, while remaining close enough to standard conditions to avoid excessive drying times. Consequently, the 90 °C protocol is a conservative yet defensible compromise: it operates below the threshold at which temperature-induced volatilisation becomes analytically significant. Yet it still allows for same-day sample processing and reproducible PCM quantification.

3.12 Method comparison to other techniques

The present protocol minimises the risk of volatilisation of organic compounds by employing controlled drying at 90 °C (O'Kelly, 2005), unlike chemothermal oxidation methods (Elmquist et al., 2006), which operate at elevated temperatures (285 °C–375 °C). Furthermore, untreated sludge samples without prior phase separation can produce inconsistent TC values due to variable water content. However, by separating the liquid and solid phases and grinding the samples after drying, homogeneity and measurement accuracy are enhanced.

Unlike other quantification techniques, such as DSC (Hardy et al., 2022) or BPCA-based methods (Schmidt et al., 2001), which only detect thermally stable or aromatic carbon fractions, the TC method captures all types of carbon (including aliphatic and partially charred components). Microscopic methods (Paetsch et al., 2017) suffer from high visual ambiguity and fail to distinguish between similar looking carbonaceous particles. They also lack quantitative reliability. In contrast, the TC approach provides a comprehensive and robust quantification of PCMs in sludge matrices, regardless of the carbon source or structural complexity.

A relevant methodological comparison can be made with the study by Dittmann et al. (2018), in which TGA was used to quantify PAC in sludge, achieving a low LOD of 0.012 g/g. Although TGA does not rely on chemical markers, it distinguishes between substances based on their thermal degradation behaviour. However, the thermal decomposition ranges of PAC can overlap with those of native organic matter in sewage sludge, which may limit its selectivity and lead to misestimation. In contrast, the TC-based method developed in this study quantifies total carbon content directly, independently of thermal behaviour, making it more robust and applicable across heterogeneous wastewater matrices.

3.13 Limitations and future work

Delta TC is a non-specific operational measure. Despite matched-control subtraction, minor contributions from carbonate and EPS may remain. Variance is higher in ADS due to matrix heterogeneity, but this can be mitigated through composite sampling, increased replication and inter-lab checks. Calibration factors are matrix- and PCM specific. Site-specific calibration with periodic verification and uncertainty reporting is recommended. The LOD/LOQ are adequate for medium-to-high loads, but trace-level monitoring may require preconcentration or complementary methods (e.g., TOC/TIC or TGA). COD provides supportive, albeit non-specific, validation, and additional carbon-specific cross-checks would strengthen accuracy.

3.14 Summary

In summary, the optimized TC method presented here offers a reliable solution for quantifying PCMs. It is also reproducible and practical. This is especially true when PCMs are analysed in diverse wastewater treatment matrices. Its simplicity, cost-effectiveness and adaptability to WWTP operations make it ideal for research and routine monitoring, particularly when considering the integration of PCMs as a fourth treatment stage.

4 Conclusion

This work addresses the operational need for routine PCM monitoring in WWTP sludge by validating a simple, TC based approach with matched control subtraction (delta TC). Across activated, excess, and anaerobic digestion sludges, the method showed strong linearity, high recoveries (≥95% AS/ES), acceptable precision, matrix-specific calibration factors, and close agreement with COD estimates. Practically, it enables dosing verification, mass balance support, and compliance using existing WWTP instrumentation. In the absence of a true zero, dosing is tracked via delta TC against contemporaneous controls and absolute PCM approximated with a rolling baseline. Limitations include the operational, non-specific nature of delta TC (potential minor carbonate/EPS contributions), higher variance in ADS, and limited trace sensitivity (LOD ∼0.10–0.25 g/gDM); calibration remains matrix-/PCM-specific. Future work should partition TOC/TIC, evaluate matrix-matched calibration and in-house standards with interlaboratory comparison, extend validation across PCM types and seasons, and establish a formal uncertainty budget. Overall, the TC-based delta-TC framework is reproducible, economical, and fit for medium-to-high load monitoring, supporting data-driven dosing control in fourth-stage treatment.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

CM: Formal Analysis, Methodology, Conceptualization, Visualization, Writing - original draft, Investigation, Writing - review and editing. AH: Project administration, Conceptualization, Validation, Writing - review and editing, Supervision, Funding acquisition. AW: Project administration, Validation, Writing - review and editing, Conceptualization, Funding acquisition, Supervision.

Funding

The author(s) declared that financial support was received for this work and/or its publication. Christian Margreiter received a fellowship from the Christian Doppler Research Association. The financial support by the Austrian Federal Ministry of Economy, Energy and Tourism, the National Foundation for Research, Technology and Development, the Christian Doppler Research Association as well as the participating companies is gratefully acknowledged. Publication was made possible by Publikationsfonds der Universität Innsbruck.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenve.2026.1719988/full#supplementary-material

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