China 
Africa 
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Microplastics in Freshwater
Published in Leo M. L. Nollet, Khwaja Salahuddin Siddiqi, Analysis of Nanoplastics and Microplastics in Food, 2020
Mohammad Rashid, Shariq Shamsi, Khwaja Salahuddin Siddiqi
As a first approach for MP assessment in freshwater environments, Miklos et al. [100] suggested a modular system initiating with the quantification of selected indicator polymers. When the concentration of these polymers exceeds a certain level, more specific analyses should be conducted. These subsequent analyses can take various criteria (such as polymer type, size, shape, additives, etc.) into consideration to further categorize the particles and support the selection of adequate mitigation measures. Here, approaches from chemical regulation might serve as examples where chemicals can be categorized based on molecular similarities (e.g., PAHs, PCBs, etc.), by field application (e.g., pesticides), or according to their mode of action (e.g., endocrine disruptors). Similarly, MPs can also be grouped together based on their physicochemical properties (e.g., polymer type, density), by their application (e.g., cosmetics, carrier bags, electrical devices) or by (eco)toxicological impacts.
Aquatic Communities: Pesticide Impacts
Published in Brian D. Fath, Sven E. Jørgensen, Megan Cole, Managing Water Resources and Hydrological Systems, 2020
David P. Kreutzweiser, Paul K. Sibley
The risk of adverse effects on aquatic communities may also be decreased by intentional selection and use of pesticides that are inherently safer to the environment. This would include so-called reduced-risk pesticides that are bioactive compounds usually with unique modes of action and derived from microbial, plant, or other natural sources. These are generally thought to be less persistent and toxic to non-target organisms than conventional synthetic pesticides.[47] Examples include the bacteria-derived insecticide Bt (Bacillus thuringiensis), the plant-derived insecticide neem, and the microbe-derived herbicide phosphinothricin. However, Thompson and Kreutzweiser[48] caution that it cannot be assumed that this group of pesticides is inherently safer or more environmentally acceptable than synthetic counterparts and that full environmental risk evaluations must be conducted to ensure their environmental safety.
On Error Measures for Validation and Uncertainty Estimation of Predictive QSAR Models
Published in Agnieszka Gajewicz, Tomasz Puzyn, Computational Nanotoxicology, 2019
Supratik Kar, Kunal Roy, Jerzy Leszczynski
Quantitative structure–activity relationships (QSARs) have received tremendous attention in drug discovery, materials designing, property and toxicity prediction, and environmental fate modeling of chemicals and pharmaceuticals [1–4]. The term “QSAR” is interchangeably used with QSPR/QSTR (quantitative structure–property relationship and quantitative structure–toxicity relationship) by many researchers when the response or dependent variable is property and toxicity, respectively. Not only that, on the basis of the type of compounds modeled, new derived terms are generated, like nano-QSAR and QNAR (quantitative nanostructure–activity relationship), for nanomaterials modeling [5–8]. Searching using the terms “QSAR,” “QSPR,” and “QSTR” in Scopus within the time frame from 2007 to 2016, one can find a total of 11,549 publications, which is no doubt a huge number. A graphical yearwise distribution of all publications is illustrated in Fig. 10.1. A good number of QSAR articles demonstrated the following objectives [4]: (i) design of new chemicals or pharmaceuticals with enhanced property or activity profile, (ii) improved understanding and exploration of the mode of action of chemicals and pharmaceuticals regarding their response and toxicity, (iii) optimization of the lead molecule among congeners with reduced toxicity, (iv) rationalization of synthetic experimentation as an alternative to the medium-throughput in vitro and low-throughput in vivo assays, (v) cutback in cost, time, and manpower constraint through the development of more effective and less exhaustive approaches, and finally (vi) an alternative to animal experimentation in conformity with the REACH1 guidelines [9] and the 3Rs concept [10] signifying “reduction, replacement, and refinement” of animal experiments. Therefore, without any doubt, the QSAR technique has emerged as an efficient tool for the designing, development, and/or screening of new drug molecules/chemicals.
Adverse Outcome Pathway for Antimicrobial Quaternary Ammonium Compounds
Published in Journal of Toxicology and Environmental Health, Part A, 2022
The Mode of Action (MOA) of a chemical or chemical class is defined as a series of biologically plausible events that lead to an effect. The World Health Organization’s International Programme on Chemical Safety (WHO IPCS) developed a framework to organize and present chemical-specific MOA information (Boobis et al. 2008; Meek et al. 2014; Seed et al. 2005; Sonich-Mullin et al. 2001). This led to development of the AOP analysis (OECD 2017, 2018) based upon the concept that toxicity results from the chemical reaching a critical target and triggering a molecular initiating event (MIE). Given sufficient dose this is followed by downstream key events (KE) leading to an adverse outcome. An understanding of the AOP may be used to develop DDEFs to replace default uncertainty factors (UF) for risk assessment (EPA 2014). These DDEFs address interspecies variability, intraspecies variability, and varying durations of exposure (short, intermediate, and long term).