Granulated active carbon (GAC) is commonly used as a chemical barrier for the removal of organic micropollutants (OMPs) in drinking water treatment plants (DWTPs). However, little is known about the impact of dissolved organic carbon (DOC) and its long-term performance with regard to OMP removal efficiency. This study examined the performance of two GAC types (Norit 830W and Filtrasorb 400) in the removal of OMPs and DOC from natural lake water, in pilot-scale and full-scale tests run for almost one year. Potential early warning indicators of the exhaustion of GAC sorption capacity were also evaluated. The seven OMPs investigated (carbamazepine, lamotrigine, cetirizine, fexofenadine, oxazepam, fluconazole and N,N-diethyl-meta-toluamide (DEET)) all showed decreasing removal efficiencies after ~20,000 bed volumes (BV) in the pilot-scale Norit 830W and Filtrasorb 400 columns. However, columns with an 18-min empty bed contact time (EBCT) showed better performances than columns with 6-min EBCT. DEET was the OMP adsorbed most weakly. We found that DOC concentrations, methylene blue sorption kinetics, UV and fluorescence did not sufficiently explain the OMP breakthrough in the GAC columns. We concluded that carbamazepine, lamotrigine and fexofenadine can be used as indicators of decreasing GAC adsorption performance, due to their later breakthrough. Based on the results, UV and fluorescence removal could be used for the early detection of declining DOC removal, and online solid-phase extraction (SPE)–liquid chromatography–tandem mass spectrometry (SPE-LC-MS/MS) could be used for the early detection of OMPs in drinking water.

Two in situ optical sensors, a single-excitation fluorescence-based sensor (fDOM) mounted on a multi-parameter EXO2 sonde (YSI), and a stand-alone, multispectral absorbance-based instrument (spectro::lyser, scan Messtechnik GmbH), were evaluated for their capability to (i) estimate river dissolved organic carbon (DOC) concentrations and (ii) provide oversight of drinking water production. The sensors were deployed between March and November 2017 in the river Fyris, which drains a mixed forested and agricultural 2003 km² catchment and serves as a drinking water source by managed aquifer recharge. Grab samples were collected every 2 to 3 weeks and compared with logged sensor data collected at 15 minute intervals. The fDOM probe signal was used to estimate DOC concentrations in the range of 10.4 to 24.4 mg L⁻¹ using linear regression (R² = 0.71, RMSE = 2.5 mg L⁻¹), after correction for temperature, turbidity and inner-filter effects. Temporal changes in DOC character associated with the mixed land use landscape, as indicated by optical indices, reduced this sensor accuracy for estimating DOC concentration. Nevertheless, humic substance concentrations, the fraction of DOC that is preferentially removed during artificial infiltration, were well captured. The spectrolyser signal was used to establish a 2-component partial least square model that captured DOC fluctuations from 10.2 to 29.4 mg L⁻¹ (R² = 0.92; RMSE = 1.3 mg L⁻¹). This multiple-wavelength model (220 to 720 nm) effectively handled the changes in DOC composition while accurately estimating DOC concentrations. This study explores the advantages and limitations of optical sensors for their use in managed aquifer recharge and drinking water production in relation to DOC levels.

The long-term trend of increasing natural organic matter (NOM) in boreal and north European surface waters represents an economic and environmental challenge for drinking water treatment plants (DWTPs). High-frequency measurements from absorbance-based online spectrophotometers are often used in modern DWTPs to measure the chromophoric fraction of dissolved organic matter (CDOM) over time. These data contain valuable information that can be used to optimise NOM removal at various stages of treatment and/or diagnose the causes of underperformance at the DWTP. However, automated monitoring systems generate large datasets that need careful preprocessing, followed by variable selection and signal processing before interpretation. In this work we introduce AbspectroscoPY (“Absorbance spectroscopic analysis in Python”), a Python toolbox for processing time-series datasets collected by in situ spectrophotometers. The toolbox addresses some of the main challenges in data preprocessing by handling duplicates, systematic time shifts, baseline corrections and outliers. It contains automated functions to compute a range of spectral metrics for the time-series data, including absorbance ratios, exponential fits, slope ratios and spectral slope curves. To demonstrate its utility, AbspectroscoPY was applied to 15-month datasets from three online spectrophotometers in a drinking water treatment plant. Despite only small variations in surface water quality over the time period, variability in the spectrophotometric profiles of treated water could be identified, quantified and related to lake turnover or operational changes in the DWTP. This toolbox represents a step toward automated early warning systems for detecting and responding to potential threats to treatment performance caused by rapid changes in incoming water quality.

Fuel spills pose significant environmental risks, particularly to drinking water sources and aquatic ecosystems. The composition of fuels has changed over the decades to reduce fossil greenhouse gas emissions. In Sweden, although the number of spill incidents has declined, with around 600 cases reported annually, there remains limited knowledge on the environmental and health impacts of modern fuels. This study aimed to address this gap through comprehensive chemical analysis and ecotoxicological assessments of 31 fuel samples, including petrol, diesel, fuel oil, and marine gas oil. Using gas chromatography-mass spectrometry (GC-MS), we determined 53 substances, including aromatic and aliphatic hydrocarbons, ethers, esters, and 17 polycyclic aromatic hydrocarbons (PAHs). A key focus was on forming a stable water-accommodated fraction (WAF) to isolate non-dissolved fuel elements from water, which is crucial for assessing subsurface aquatic life and drinking water production impacts. Results indicated significant differences in fuel odor profiles, with ethers enhancing odor intensity. Petrol components showed higher water solubility than diesel, partly due to ethanol. Ecotoxicological tests revealed varying toxicity across fuels, with petrol showing the highest toxicity to aquatic organisms, although activated sludge exhibited resilience. Fuels containing water-soluble ethers posed the highest risks to drinking water, while modern diesel was of lower concern due to its low solubility and toxicity. In freshwater ecosystems, petrol and hydrophobic toxins in fuel oil had severe effects during spills. Overall, this study offers critical insights into the environmental impact of common fuels, supporting improved risk assessment and management strategies for spill mitigation and water resource protection.