To ensure compliance with regulatory standards, it is important to examine the potential of treatment technologies to enhance trace metal removal from wastewater. This study investigated the effectiveness of coagulation–flocculation at removing trace metals from humus effluent with ferric chloride (FeCl₃), the synthetic polymer polyethyleneimine (PEI) and the biopolymers chitosan and floculan. Effluent samples were collected from a trickling filter treatment works operating in the UK and contained 21 ± 4 μg/L Cu, 0.8 ± 0.1 μg/L Pb, 4 ± 1 μg/L Ni and 43 ± 9 μg/L Zn. The influence of coagulant dosage and the velocity and time of the slow mixing stage were studied via a series of jar tests. Chitosan and PEI had a moderate effect on the removal of trace metals (≤ 35%). FeCl₃ removed 48% Cu, 56% Pb and 41% Zn at the optimised dose of 0.10 mg/L. At the optimised dose of 0.25 mg/L, floculan removed 77% Cu, 68% Pb and 42% Zn. The dominant mechanism for particle removal by FeCl₃ was enmeshment in the precipitates (i.e. sweep flocculation), whereas, for floculan, inter-particle bridging was the dominant removal mechanism. Overall, FeCl₃ and floculan were found to be most effective at removing trace metals from wastewater.

To better understand the impacts of Corexit 9500 on the structure and activity levels of hydrocarbon-degrading microbial communities, we analyzed next-generation 16S rRNA gene sequencing libraries of hydrocarbon enrichments grown at 5 and 25°C using both DNA and RNA extracts as the sequencing templates. Oil biodegradation patterns in both 5 and 25°C enrichments were consistent with those reported in the literature (i.e., aliphatics were degraded faster than aromatics). Slight increases in biodegradation were observed in the presence of Corexit at both temperatures. Differences in community structure were observed between treatment conditions in the DNA-based libraries. The 25°C consortia were dominated by Vibrio , Idiomarina , Marinobacter , Alcanivorax , and Thalassospira species, while the 5°C consortia were dominated by several species of the genera Flavobacterium , Alcanivorax , and Oleispira . Most of these genera have been linked to hydrocarbon degradation and have been observed after oil spills. Colwellia and Cycloclasticus , known aromatic degraders, were also found in these enrichments. The addition of Corexit did not have an effect on the active bacterial community structure of the 5°C consortia, while at 25°C, a decrease in the relative abundance of Marinobacter was observed. At 25°C, Thalassospira , Marinobacter , and Idiomarina were present at higher relative abundances in the RNA than DNA libraries, suggesting that they were active in degradation. Similarly, Oleispira was greatly stimulated by the addition of oil at 5°C. IMPORTANCE While dispersants such as Corexit 9500 can be used to treat oil spills, there is still debate on the effectiveness on enhancing oil biodegradation and its potential toxic effect on oil-degrading microbial communities. The results of this study provide some insights on the microbial dynamics of hydrocarbon-degrading bacterial populations in the presence of Corexit 9500. Operational taxonomic unit (OTU) analyses indicated that several OTUs were inhibited by the addition of Corexit. Conversely, a number of OTUs were stimulated by the addition of the dispersant, many of which were identified as known hydrocarbon-degrading bacteria. The results highlight the value of using RNA-based methods to further understand the impact of dispersant on the overall activity of different hydrocarbon-degrading bacterial groups.

Understanding the distribution, behaviour and interactions of complex chemical mixtures is key for providing the evidence necessary to make informed decisions and implement robust remediation strategies. Much of the current risk assessment frameworks applied to manage land contamination are based on total contaminant concentrations and the exposure assessments embedded within them do not explicitly address the partitioning and bioavailability of chemical mixtures. These oversights may contribute to an overestimation of both the eco-toxicological effects of the fractions and the mobility of contaminants. In turn, this may limit the efficacy of risk frameworks to inform targeted and proportionate remediation strategies. In this review we analyse the science surrounding bioavailability, its regulatory inclusion and the challenges of incorporating bioavailability in decision making process. While a number of physical and chemical techniques have proven to be valuable tools for estimating bioavailability of organic and inorganic contaminants in soils, doubts have been cast on its implementation into risk management soil frameworks mainly due to a general disagreement on the interchangeable use of bioavailability and bioaccessibility, and the associated methods which are still not standardised. This review focuses on the role of biotic and abiotic factors affecting bioavailability along with soil physicochemical properties and contaminant composition. We also included advantages and disadvantages of different extraction techniques and their implications for bioavailability quantitative estimation. In order to move forward the integration of bioavailability into site-specific risk assessments we should (1) account for soil and contaminant physicochemical characteristics and their effect on bioavailability; (2) evaluate receptor's potential exposure and uptake based on mild-extraction; (3) adopt a combined approach where chemical-techniques are used along with biological methods; (4) consider a simplified and cost-effective methodology to apply at regulatory and industry setting; (5) use single-contaminant exposure assessments to inform and predict complex chemical mixture behaviour and bioavailability.

Empirical data from a 6-month mesocosms experiment were used to assess the ability and performance of two machine learning (ML) models, including artificial neural network (NN) and random forest (RF), to predict temporal bioavailability changes of complex chemical mixtures in contaminated soils amended with compost or biochar. From the predicted bioavailability data, toxicity response for relevant ecological receptors was then forecasted to establish environmental risk implications and determine acceptable end-point remediation. The dataset corresponds to replicate samples collected over 180 days and analysed for total and bioavailable petroleum hydrocarbons and heavy metals/metalloids content. Further to this, a range of biological indicators including bacteria count, soil respiration, microbial community fingerprint, seeds germination, earthworm's lethality, and bioluminescent bacteria were evaluated to inform the environmental risk assessment. Parameters such as soil type, amendment (biochar and compost), initial concentration of individual compounds, and incubation time were used as inputs of the ML models. The relative importance of the input variables was also analysed to better understand the drivers of temporal changes in bioavailability and toxicity. It showed that toxicity changes can be driven by multiple factors (combined effects), which may not be accounted for in classical linear regression analysis (correlation). The use of ML models could improve our understanding of rate-limiting processes affecting the freely available fraction (bioavailable) of contaminants in soil, therefore contributing to mitigate potential risks and to inform appropriate response and recovery methods.