Development and application of screening tools for biodegradation in water-sediment systems and soil

Read-Across of Biotransformation Potential between Activated Sludge and the Terrestrial Environment: Toward Making It Practical and Plausible

Recent emphasis on the development of safe-and-sustainable-by-design chemicals highlights the need for methods facilitating the early assessment of persistence. Activated sludge experiments have been proposed as a time- and resource-efficient way to predict half-lives in simulation studies. Here, this persistence "read-across" approach was developed to be more broadly and robustly applicable. We evaluated 21 previously used reference plant protection products (PPPs) for their broader applicability in calibrating regression and classification models for predicting half-lives in soil (DT50OECD307) and water-sediment systems (DT50OECD308) based on their half-life in sludge and the organic carbon-water partition coefficient KOC as predictors. The calibrated regression models showed satisfactory predictions of DT50OECD307 for another 22 test PPPs. Performance was less satisfying for the prediction of DT50OECD308 for 46 active pharmaceutical ingredients (APIs), suggesting a need for expanding the set of calibration substances and more experimental KOC values. The classification models mostly correctly classified persistent and non-persistent test compounds for both PPPs and APIs, which is relevant for early-stage screening of persistence. Transformation products of the reference compounds in activated sludge samples were consistent with the reported degradation pathways in soil, particularly with respect to major aerobic, enzyme-catalyzed transformation reactions. Overall, "reading across" biotransformation in environmental compartments such as soils or sediments from experiments with activated sludge outperformed three widely used in silico approaches for estimating half-lives and hence has immediate potential to support early assessment of biodegradability when aiming to develop chemicals that are safe and sustainable by design.

Improving predictions and understanding of primary and ultimate biodegradation rates with machine learning models

This study aimed to develop machine learning based quantitative structure biodegradability relationship (QSBR) models for predicting primary and ultimate biodegradation rates of organic chemicals, which are essential parameters for environmental risk assessment. For this purpose, experimental primary and ultimate biodegradation rates of high consistency were compiled for 173 organic compounds. A significant number of descriptors were calculated with a collection of quantum/computational chemistry software and tools to achieve comprehensive representation and interpretability. Following a pre-screening process, multiple QSBR models were developed for both primary and ultimate endpoints using three algorithms: extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR). Furthermore, a unified QSBR model was constructed using the knowledge transfer technique and XGBoost. Results demonstrated that all QSBR models developed in this study had good performance. Particularly, SVM models exhibited high level of goodness of fit (coefficient of determination on the training set of 0.973 for primary and 0.980 for ultimate), robustness (leave-one-out cross-validated coefficient of 0.953 for primary and 0.967 for ultimate), and external predictive ability (external explained variance of 0.947 for primary and 0.958 for ultimate). The knowledge transfer technique enhanced model performance by learning from properties of two biodegradation endpoints. Williams plots were used to visualize the application domains of the models. Through SHapley Additive exPlanations (SHAP) analysis, this study identified key features affecting biodegradation rates. Notably, MDEO-12, APC2D1_C_O, and other features contributed to primary biodegradation, while AATS0v, AATS2v, and others inhibited it. For ultimate biodegradation, features like No. of Rotatable Bonds, APC2D1_C_O, and minHBa were contributors, while C1SP3, Halogen Ratio, GGI4, and others hindered the process. Also, the study quantified the contributions of each feature in predictions for individual chemicals. This research provides valuable tools for predicting both primary and ultimate biodegradation rates while offering insights into the mechanisms.

Bioremediation of aniline aerofloat wastewater at extreme conditions using a novel isolate Burkholderia sp. WX-6 immobilized on biochar

Aniline aerofloat (AAF) is a refractory organic pollutant in floatation wastewater. Little information is currently available on its biodegradation. In this study, a novel AAF-degrading strain named Burkholderia sp. WX-6 was isolated from mining sludge. The strain could degrade more than 80% of AAF at different initial concentrations (100-1000 mg/L) within 72 h. AAF degrading curves were fitted well with the four-parameter logistic model (R² >0.97), with the degrading half-life ranging from 16.39 to 35.55 h. This strain harbors metabolic pathway for complete degradation of AAF and is resistant to salt, alkali, and heavy metals. Immobilization of the strain on biochar enhanced both tolerance to extreme conditions and AAF removal, with up to 88% of AAF removal rate in simulated wastewater under alkaline (pH 9.5) or heavy metal pollution condition. In addition, the biochar-immobilized bacteria removed 59.4% of COD in the wastewater containing AAF and mixed metal ions within 144 h, significantly (P < 0.05) higher than those by free bacteria (42.6%) and biochar (48.2%) only. This work is helpful to understand AAF biodegradation mechanism and provides viable references for developing practical biotreatment technique of mining wastewater.

mibPOPdb: An online database for microbial biodegradation of persistent organic pollutants

Microbial biodegradation of persistent organic pollutants (POPs) is an attractive, ecofriendly, and cost-efficient clean-up technique for reclaiming POP-contaminated environments. In the last few decades, the number of publications documenting POP-degrading microbes, enzymes, and experimental data sets has continuously increased, necessitating the development of a dedicated web resource that catalogs consolidated information on POP-degrading microbes and tools to facilitate integrative analysis of POP degradation data sets. To address this knowledge gap, we developed the Microbial Biodegradation of Persistent Organic Pollutants Database (mibPOPdb) by accumulating microbial POP degradation information from the public domain and manually curating published scientific literature. Currently, in mibPOPdb, there are 9215 microbial strain entries, including 184 gene (sub)families, 100 enzymes, 48 biodegradation pathways, and 593 intermediate compounds identified in POP-biodegradation processes, and information on 32 toxic compounds listed under the Stockholm Convention environmental treaty. Besides the standard database functionalities, which include data searching, browsing, and retrieval of database entries, we provide a suite of bioinformatics services to facilitate comparative analysis of users' own data sets against mibPOPdb entries. Additionally, we built a Graph Neural Network-based prediction model for the biodegradability classification of chemicals. The predictive model exhibited a good biodegradability classification performance and high prediction accuracy. mibPOPdb is a free data-sharing platform designated to promote research in microbial-based biodegradation of POPs and fills a long-standing gap in environmental protection research. Database URL: http://mibpop.genome-mining.cn/.

Compartment-Specific Screening Tools for Persistence: Potential Role and Application in the Regulatory Context

The persistence assessment under the European Union regulation Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) relies on compartment-specific degradation half-lives derived from laboratory simulation studies with surface water, aquatic sediment, or soil. Although these data are given priority, they are not available for most of the compounds. Therefore, according to the Integrated Assessment and Testing Strategy (ITS) for persistence assessment, results from ready biodegradability tests (RBTs) are used within a persistence screening to decide whether a substance is considered as "not persistent" or "potentially persistent." However, ready biodegradability is currently tested only in water. Consequently, there is a lack of approaches that include the soil and sediment compartments for persistence assessment at the screening level. In previous studies, compartment-specific screening tools for water-sediment (Water-Sediment Screening Tool [WSST]) and soil (Soil Screening Tool [SST]) were developed based on the existing test guideline Organisation for Economic Development and Co-operation (OECD TG 301C [MITI (Ministry of International Trade and Industry, Japan) test]). The test systems MITI, WSST, and SST were successfully applied to determine sound and reliable biodegradation data for 15 test compounds. In the present study, these results are used within the scope of a new alternative persistence screening approach, the Compartment-Specific Persistence Screening (CSPS). Compared to the persistence screening under REACH, the CSPS is a more conservative approach that provides additional reasonable results, particularly for compounds that sorb to sediment and soil, and for which the current standard persistence screening might be insufficient. Thus, the CSPS can be used to identify potentially persistent and nonpersistent compounds in the regulatory context by a comprehensive assessment that includes water, soil, and sediment. Moreover, experimentally determined half-lives from the compartment-specific screening tools can be used as input for multimedia models that estimate, for example, overall persistence (P_ov ). The application of fixed half-life factors to extrapolate from water to soil and sediment, which is here demonstrated to be inappropriate, can thereby be avoided. Integr Environ Assess Manag 2019;00:000-000. © 2019 SETAC.

A comparison of methods for early prediction of anaerobic biogas potential on biologically treated municipal solid waste

Anaerobic gas production tests, generically Biochemical Methane Potential (BMP) or Biogas Potential (BP) tests, are often used to assess biodegradability, though long duration limits their utility. This research investigated whether simple modelling approaches could provide a reliable earlier prediction of total biogas production. Data were assessed from a non-automated biogas test on a large number of both fresh and processed municipal solid waste (MSW) samples, sourced from a mechanical biological treatment (MBT) plant. Non-linear models of biogas production curves were useful in identifying a suitable test endpoint, supporting a test duration of 50 days. Biogas production at 50 days (B₅₀) was predicted using the first 14 days of test data, using (a) linear correlation, (b) a new linearisation process, and (c) non-linear kinetic models. Prediction errors were quantified as relative root mean squared error of prediction (rRMSEP), and bias. Predictions from most models were improved by removing the initial exponential increase phase. Linear correlation gave the most precise and accurate predictions at 14 days (rRMSEP = 2.8%, bias under 0.05%) and allowed acceptable prediction (rRMSEP <10%) both at 8 days, and at 6 days using separate correlations for each sample type. Of the other predictions, the new linearisation process gave the lowest rRMSEP (10.6%) at 14 days. More complex non-linear models conferred no advantage in prediction of B₅₀. These results demonstrate that early prediction of anaerobic gas production is possible for a well-optimised test, using only basic equipment and without recourse to external data sources or complex mathematical modelling.

PBT assessment under REACH: Screening for low aquatic bioaccumulation with QSAR classifications based on physicochemical properties to replace BCF in vivo testing on fish

Aquatic bioconcentration factors (BCFs) are critical in PBT (persistent, bioaccumulative, toxic) and risk assessment of chemicals. High costs and use of more than 100 fish per standard BCF study (OECD 305) call for alternative methods to replace as much in vivo testing as possible. The BCF waiving scheme is a screening tool combining QSAR classifications based on physicochemical properties related to the distribution (hydrophobicity, ionisation), persistence (biodegradability, hydrolysis), solubility and volatility (Henry's law constant) of substances in water bodies and aquatic biota to predict substances with low aquatic bioaccumulation (nonB, BCF<2000). The BCF waiving scheme was developed with a dataset of reliable BCFs for 998 compounds and externally validated with another 181 substances. It performs with 100% sensitivity (no false negatives), >50% efficacy (waiving potential), and complies with the OECD principles for valid QSARs. The chemical applicability domain of the BCF waiving scheme is given by the structures of the training set, with some compound classes explicitly excluded like organometallics, poly- and perfluorinated compounds, aromatic triphenylphosphates, surfactants. The prediction confidence of the BCF waiving scheme is based on applicability domain compliance, consensus modelling, and the structural similarity with known nonB and B/vB substances. Compounds classified as nonB by the BCF waiving scheme are candidates for waiving of BCF in vivo testing on fish due to low concern with regard to the B criterion. The BCF waiving scheme supports the 3Rs with a possible reduction of >50% of BCF in vivo testing on fish. If the target chemical is outside the applicability domain of the BCF waiving scheme or not classified as nonB, further assessments with in silico, in vitro or in vivo methods are necessary to either confirm or reject bioaccumulative behaviour.

Assessing the suitability of a manometric test system for determining the biodegradability of volatile hydrocarbons

Manometric test systems, adapted from those used to measure biochemical oxygen demand (BOD), and the OxiTop^® test system in particular, are being increasingly used to determine the biodegradability of chemicals in accordance to OECD 301F guidelines. In this study, the suitability of the OxiTop^® test system for determining the biodegradability of volatile hydrophobic substances has been explored. Experiments in biotic and abiotic systems were conducted with readily biodegradable complex aliphatic hydrocarbons covering a range of volatilities. Results indicated that abiotic losses of test substances were occurring due to sorption of the test substance to plastic components used in the OxiTop^® system. A further 'plastic-free' biodegradation test system was designed using PreSens optical dissolved oxygen (DO) sensors. This significantly improved the measured biodegradation due to reduced abiotic losses and better retention of the test substance. These results highlight the importance of considering the physico-chemical properties of test substances when selecting test methods and equipment. They also highlight the value of incorporating chemical analysis and abiotic controls to improve the interpretation of biodegradation studies.