Sound Management Of Chemicals Research Articles

Integrated Transfer Learning and Multitask Learning Strategies to Construct Graph Neural Network Models for Predicting Bioaccumulation Parameters of Chemicals.

Accurate prediction of parameters related to the environmental exposure of chemicals is crucial for the sound management of chemicals. However, the lack of large data sets for training models may result in poor prediction accuracy and robustness. Herein, integrated transfer learning (TL) and multitask learning (MTL) was proposed for constructing a graph neural network (GNN) model (abbreviated as TL-MTL-GNN model) using n-octanol/water partition coefficients as a source domain. The TL-MTL-GNN model was trained to predict three bioaccumulation parameters based on enlarged data sets that cover 2496 compounds with at least one bioaccumulation parameter. Results show that the TL-MTL-GNN model outperformed single-task GNN models with and without the TL, as well as conventional machine learning models trained with molecular descriptors or fingerprints. Applicability domains were characterized by a state-of-the-art structure-activity landscape-based (abbreviated as ADSAL) methodology. The TL-MTL-GNN model coupled with the optimal ADSAL was employed to predict bioaccumulation parameters for around 60,000 chemicals, with more than 13,000 compounds identified as bioaccumulative chemicals. The high predictive accuracy and robustness of the TL-MTL-GNN model demonstrate the feasibility of integrating the TL and MTL strategy in modeling small-sized data sets. The strategy holds significant potential for addressing small data challenges in modeling environmental chemicals.

Unveiling underutilized public data on Canadian industrial pollutant transfers and disposals

ABSTRACT Over 50 nations worldwide have Pollutant Release and Transfer Registers (PRTRs), including Canada’s National Pollutant Release Inventory (NPRI), which comprise large public datasets of chemical releases to air, water, and land and also transfers to various on and off-site waste management practices. These inventories aim to support a myriad of audiences in pollution-related decision-making. While the Organisation for Economic Cooperation and Development (OECD) framed a role for PRTRs as indicators for Sustainable Development Goal (SDG) 12 - the sound management of chemicals and wastes, research to date has focused only on air and water releases, omitting vast PRTR data on pollutant transfers to waste management. For Canada’s NPRI, 30 years of waste management transfers data on 250+ chemicals has been collected but rarely used in environmental research. Here we show how this overlooked NPRI data may be used to inform snapshots and trends in progress towards SDG 12 using the OECD’s framework. Results show that over 28 million (M) tonnes (t) of NPRI pollutants have been transferred from industrial facilities to various waste management practices from 2006 to 2021, of which ~10M t were transferred off-site for waste management operations both within and outside Canada. Time trends show pollutant transfer quantities are increasing, driven by on-site disposals to tailings and waste rock management (of mainly phosphorous, manganese and other metals) and underground injection (of mainly hydrogen sulphide). New route maps reveal that interprovincial and international pollutant transfers are common, and that chain of custody analyses is a burgeoning opportunity but hampered by data limitations. The findings create a state of the knowledge launching point for mainstreaming the use of this overlooked data from both Canada’s NPRI and PRTRs around the world, to better track both national and international progress towards sound management of chemicals in waste and SDG 12. Implications: Pollutant transfers and disposals data has not previously been widely used in environmental research. This paper shows how it can be, in the context of SDG 12. Doing so can inspire uptake by researchers and a range of other public users, both strengthening the justification for collecting this data, and bolstering public participation in environmental decision-making from a local to global scale. Doing so also provides the foundation for more in-depth analysis on the domestic and international transboundary movement of Canadian industrial pollutants in waste in the lens of SDG 12 – a topic that was beyond scope here but addressed elsewhere.

Composition and release rates of chemicals in inkjet fabrics determined by non-targeted screening and targeted analysis

Unveiling composition and release rates of chemicals in chemical-intensive products (CIPs) such as inkjet fabrics that are applied extensively in advertising and publicizing industries, is of importance to sound management of chemicals. This study tentatively identified 212 compounds from 69 inkjet fabric samples using gas chromatograph coupled with quadrupole time-of-flight mass spectrometry (GC-QTOF-MS). Contents of six phthalate esters (PAEs) were quantified to range from 3.0 × 102 mg/kg to 3.1 × 105 mg/kg with GC-MS. Bis(2-ethylhexyl) phthalate was predominantly detected to average 96 g/kg. The inkjet fabrics collected from southern China contained fewer non-intentionally added substances (NIASs) than from northern China. Annual mass release rates (RM) of the 6 PAEs from inkjet fabrics to air were estimated to range from 1.4 × 10−2 kg/year to 2.8 × 104 kg/year in China in 2020, and the mean indoor RM was comparable with the outdoor one. Equilibrium partition coefficients of the compounds between the product and air, ambient temperature, and concentrations of chemicals in the product, are key factors leading to RM with the high variance. The findings indicate that contents of the NIASs in the CIPs should be minimized, and the refining concept should be adopted in design of the CIPs, so as to control the release of chemicals from the CIPs.

Applicability Domains Based on Molecular Graph Contrastive Learning Enable Graph Attention Network Models to Accurately Predict 15 Environmental End Points.

In silico models for predicting physicochemical properties and environmental fate parameters are necessary for the sound management of chemicals. This study employed graph attention network (GAT) algorithms to construct such models on 15 end points. The results showed that the GAT models outperformed the previous state-of-the-art models, and their performance was not influenced by the presence or absence of compounds with certain structures. Molecular similarity density (ρs) was found to be a key metrics characterizing data set modelability, in addition to the proportion of compounds at activity cliffs. By introducing molecular graph (MG) contrastive learning, MG-based ρs and molecular inconsistency in activities (IA) were calculated and employed for characterizing the structure-activity landscape (SAL)-based applicability domain ADSAL{ρs, IA}. The GAT models coupled with ADSAL{ρs, IA} significantly improved the prediction coefficient of determination (R2) on all the end points by an average of 14.4% and enabled all the end points to have R2 > 0.9, which could hardly be achieved previously. The models were employed to screen persistent, mobile, and/or bioaccumulative chemicals from inventories consisting of about 106 chemicals. Given the current state-of-the-art model performance and coverage of the various environmental end points, the constructed models with ADSAL{ρs, IA} may serve as benchmarks for future efforts to improve modeling efficacy.

A Graph Neural Network Model with a Transparent Decision-Making Process Defines the Applicability Domain for Environmental Estrogen Screening.

The application of deep learning (DL) models for screening environmental estrogens (EEs) for the sound management of chemicals has garnered significant attention. However, the currently available DL model for screening EEs lacks both a transparent decision-making process and effective applicability domain (AD) characterization, making the reliability of its prediction results uncertain and limiting its practical applications. To address this issue, a graph neural network (GNN) model was developed to screen EEs, achieving accuracy rates of 88.9% and 92.5% on the internal and external test sets, respectively. The decision-making process of the GNN model was explored through the network-like similarity graphs (NSGs) based on the model features (FT). We discovered that the accuracy of the predictions is dependent on the feature distribution of compounds in NSGs. An AD characterization method called ADFT was proposed, which excludes predictions falling outside of the model's prediction range, leading to a 15% improvement in the F1 score of the GNN model. The GNN model with the AD method may serve as an efficient tool for screening EEs, identifying 800 potential EEs in the Inventory of Existing Chemical Substances of China. Additionally, this study offers new insights into comprehending the decision-making process of DL models.

New Sustainability Perspectives on Pollutant Releases from Canada's Nuclear Sector.

This novel characterization of new Canadian radionuclide release data aims to both deepen the understanding of the nature and magnitude of present-day emissions from nuclear facilities and accelerate the tracking of this sector's progress toward United Nations Sustainable Development Goal (SDG) 12 (responsible consumption and use patterns) and target 12.4 (environmentally sound chemicals management). Further novel perspectives on the role of this data as an indicator of sustainability are discussed by merging it with other pollutant releases from this sector, as reported to the National Pollutant Release Inventory (NPRI), to fill gaps in the latter's substance coverage. These public data sets are processed and analyzed using Tableau software and the Organization for Economic Cooperation and Development's framework for using pollutant release and transfer (PRTR) data in sustainability analysis. Findings confirm that radionuclide emissions to air and direct discharges to water from present-day Canadian nuclear facilities do not contribute significantly to national-scale radionuclide contamination. Moreover, findings validate the usefulness of combining various PRTR (and similar) data to address substance coverage gaps and set a global precedent for strengthening PRTR indicator power in SDG 12 evaluation. This work underscores the value of interoperable data in accelerating knowledge translation of PRTRs in the lens of sustainable development.

Machine Learning Model for Screening Thyroid Stimulating Hormone Receptor Agonists Based on Updated Datasets and Improved Applicability Domain Metrics.

Machine learning (ML) models for screening endocrine-disrupting chemicals (EDCs), such as thyroid stimulating hormone receptor (TSHR) agonists, are essential for sound management of chemicals. Previous models for screening TSHR agonists were built on imbalanced datasets and lacked applicability domain (AD) characterization essential for regulatory application. Herein, an updated TSHR agonist dataset was built, for which the ratio of active to inactive compounds greatly increased to 1:2.6, and chemical spaces of structure-activity landscapes (SALs) were enhanced. Resulting models based on 7 molecular representations and 4 ML algorithms were proven to outperform previous ones. Weighted similarity density (ρs) and weighted inconsistency of activities (IA) were proposed to characterize the SALs, and a state-of-the-art AD characterization methodology ADSAL{ρs, IA} was established. An optimal classifier developed with PubChem fingerprints and the random forest algorithm, coupled with ADSAL{ρs ≥ 0.15, IA ≤ 0.65}, exhibited good performance on the validation set with the area under the receiver operating characteristic curve being 0.984 and balanced accuracy being 0.941 and identified 90 TSHR agonist classes that could not be found previously. The classifier together with the ADSAL{ρs, IA} may serve as efficient tools for screening EDCs, and the AD characterization methodology may be applied to other ML models.

IUPAC´s Role in UN Panel on Chemicals, Waste, and Pollution Prevention

Abstract The United Nations Environment Assembly (UNEA), in its Resolution 5/8, decided on the creation of the Intergovernmental Panel on Chemicals, Waste, and Pollution Prevention (IPCP) [1]. This resolution stated that a science-policy panel should be established to contribute further to the sound management of chemicals and waste, prevent pollution, and to convene—subject to the availability of resources—an ad hoc Open-Ended Working Group (OEWG). The OEWG started its work in 2022, with the ambition of completing it by the end of 2024 [2]. The next steps for this OEWG include the establishment of the panel, the selection of panel members, and the development of the assessment process. The panel members will be selected based on their expertise in the field of chemical pollution and their ability to provide a balanced and objective assessment of the state of chemical pollution. The assessment process will include a comprehensive review of the scientific literature, as well as input from stakeholders.

CHEMICAL HEALTH RISK ASSESSMENT IN THE METALLURGY DEPARTMENT OF A MINING COMPANY, INDONESIA : A CASE STUDY

ABSTRACT Background: Miners are routinely exposed to various hazardous chemicals entering the body through inhalation, dermal, and ingestion. Although, likely, the long-term health impacts of certain chemicals will only become evident in years to come, the utilization of hazardous chemicals will continue to increase in the coming years, leading to a higher disease burden. Therefore, effective controls for the sound management of chemicals at the workplace shall be implemented. Purpose: This study aimed to assess the chemicals used and analyze the health risks related to the use of the chemicals in the flotation process. Methods: This study was a descriptive case study conducted in Indonesia's gold and copper mining company. We ran the assessment using the Chemical Health Risk Assessment method published by the Department of Safety and Health, Malaysia, Year 2018. The chemicals analyzed were limited to the reagents used in the flotation process in the metallurgy department, which have two possible exposure, inhalation, and dermal routes. Results: The results show three out of seven chemicals have the potential to expose workers through inhalation, which is categorized under medium risk, where xanthate has the highest risk rating (RR=12), which potentially causes health effects related to acute toxicity, specific target organs, and reproductive toxicity. Meanwhile, five out of seven chemicals have the potential to expose workers through dermal, where three chemicals fell into the high-risk category: promoter, frother, and lime (H2), and two chemicals fell into the moderate-risk category: xanthate and solutrix (M2). Adverse health effects from chemical exposures to dermal include irritation, corrosion, and sensitization. Conclusion: The reagents used in the flotation plant exhibited a significant health risk of inhalation and dermal contact with hazardous chemical exposure. The company shall evaluate the hazard and risk from the hazardous chemicals used in the flotation plant and implement adequate controls, considering elimination, substitution, engineering, administrative, and personal protective equipment (PPE) controls to minimize the workers' inhalation and dermal exposure.

Coupled Dynamic Material Flow, Multimedia Environmental Model, and Ecological Risk Analysis for Chemical Management: A Di(2-ethylhexhyl) Phthalate Case in China.

Di(2-ethylhexhyl) phthalate (DEHP) is a widely used plasticizer that has adverse effects on ecosystems and human health. However, data about its stocks, flows, emission rates, as well as ecological risks are generally unknown in China, one of the world's largest producers of chemicals including DEHP, limiting sound management of chemicals. Herein, dynamic material flow analysis, coupled with a multimedia environmental model and ecological risk analysis, was performed to fill the data gap about DEHP in China mainland from 1956 to 2020. Results indicate that the in-use stocks of DEHP increased from 6.54 × 106 kg in 1956 to 8.40 × 109 kg in 2020. With growth in the emission rates, DEHP concentrations in air, soil, water, and sediment kept increasing from 1956 to 2010, which declined after 2010 and regrew after 2015. Sediment was a main sink of DEHP with the highest ecological risk quotient of >10 after 1999, necessitating measures for controlling the risk, for example, technology innovation to reduce DEHP emission rates, and substitution of DEHP with low-toxic alternatives. The coupled models that connect socio-economic data with ecological risk output may provide a systematic methodology for verification of the data necessary for risk control of chemicals.

International approaches to reducing the risk of highly hazardous chemicals exposure on human health and to the selection criteria for substitution by safer analogues (literature review)

Introduction. Replacing hazardous substances with less hazardous ones is a basic principle of any good chemical risk management. At the first stage of scientific research on the development of a national concept for the replacement of highly hazardous chemicals in the composition of products (food, synthetic detergents and household chemicals, pesticides, paints and varnishes, basic chemicals) with safer analogues, the purpose of the research was: •to study international and domestic approaches to the organization of monitoring and regulation of substances highly hazardous to human health and the environment; •analysis, selection and scientific substantiation of criteria for selecting chemicals for their replacement with safer analogues. Materials and methods. materials, used for the analysis were the literature sources from the bibliographic databases Web of Science, MedLine, EMBASE, Global Health, PubMed, Scopus, RSCI. The guidelines and recommendations of the OECD, WHO, ILO, FAO, UNEP on the organization and implementation of monitoring of highly hazardous chemicals in environmental objects have been studied and analyzed. A number of international agreements are considered, which are based on criteria for prohibiting or restricting the use of substances on the market that cause an unacceptable risk. Results. An analysis of international approaches to the sound management of chemicals has shown that the identification of causal relationships between health and/or environmental conditions and exposure to a chemical factor is a trigger for the concept of substitution. In this regard, the tools and databases of socio-hygienic monitoring carried out by Rospotrebnadzor can serve as the basis for identifying highly hazardous substances that require management decisions to be made to ban, restrict circulation and replace them with safe analogues. The study of foreign and domestic materials on the selection of priority criteria for identifying substances of greatest concern for the purpose of risk assessment and further regulation showed that the following indicators are the main ones: •biological activity (carcinogens, mutagens, GHS class 1A and 1B reprotoxicants, endocrine disruptors), •stability in the environment, •bioaccumulative potential (bioconcentration factor BCF >2000, partition coefficient n-octanol/water Log Kow ≥ 4), •the possibility of cross-media transfer (air, water flows), •toxicity to representatives of aquatic biota (acute and chronic toxicity of hazard class 1 in accordance with GHS), •production volumes (volumes of emissions and discharges), •number of contacts.

Graph Attention Network Model with Defined Applicability Domains for Screening PBT Chemicals.

In silico models for screening environmentally persistent, bio-accumulative, and toxic (PBT) substances are necessary for sound management of chemicals. Due to the complex structure-activity landscapes (SALs) on the PBT attributes, previous models for screening PBT chemicals lack either applicability domain (AD) characterizations or interpretability, restricting their applications. Herein, graph attention networks (GATs), a novel neural network architecture, were introduced to construct models for screening PBT chemicals. Results show that the GAT model not only outperformed those in previous studies but also exhibited interpretability since it optimizes attention weight parameters (PAW) that indicate contributions of each atom to the PBT attributes. An AD characterization termed ADFP-AC, which considers both molecular fingerprint (FP) similarities and compounds at activity cliffs (ACs) of SALs, was proposed to describe the ADs, which further assured the performance of the GAT model. Eight previously unidentified classes of compounds were identified as PBT chemicals from the Inventory of Existing Chemical Substances in China. The GAT model together with the ADFP-AC characterization may serve as efficient tools for screening PBT chemicals, and the modeling methodology can be applied to other physicochemical, environmental, behavioral, and toxicological parameters of chemicals that are necessary for their risk assessment and management.

Роль Специального докладчика по токсичным веществам СПЧ ООН в международном управлении в сфере обращения с химическими веществами

Environmentally sound management of chemicals is one of the urgent environmental problems today at the present stage of development of social relations. The quality of the environment and human well-being depends on the compliance with environmentally sound chemicals management. One of the international legal mechanisms to facilitate the realization of this task is the special procedures of the Human Rights Council - the mandate of the Special Rapporteur on the implications for human rights of the environmentally sound management and disposal of hazardous substances and wastes. The mandate of the Special Rapporteur was established on the threshold of the establishment and development of the international regime on management of chemicals, and over this period, the mandate holder has undertaken extensive work on the impact of toxic pollution on human right to access to justice, right to information, and right to life, health and bodily integrity. The present article attempts to define the role and significance of, and assess the Special Rapporteur's contribution to, the international legal governance on management of chemicals based on a study of the Special Rapporteur's reports and academic contributions. It is concluded that the mandate of the Special Rapporteur should be supported in the pursuit of the ideal environmentally sound world.

Institutional Framework for the Sound Management of Chemicals and Chemical Industries in Nigeria

Nigeria can harness its human, social, and economic potential only through adequate use of its abundant natural and human resources. Chemicals are the most critical agent of Nigeria’s industrial revolution. They are useful in manufacturing and economic development and occupy a pivotal role in many industrial sectors. This study assesses Nigeria’s framework for sound management of chemicals in chemical industries and the local threats limiting its developmental strides. It reviews past and current regulations, as well as guidelines and processes for compliance by chemical importers, exporters, distributors, and end users. Highlighted are the Nigerian chemical supply chain measures to enhance sound chemical management nationwide. Also discussed are the need to strengthen these regulations and laws and the necessary steps to place the country on the path of development and to ensure the socioeconomic wellbeing of its citizens and environment.

Multi-Objective Sustainable Planning of Chemical Production Chains

Sound management of chemicals is one of key contributors for achieving of the Sustainable Development Goals. The aim of this work is developing a multi-objective planning framework on the early planning and decision-making for chemical production processes, in order to obtain the maximum economic benefits, minimize the risk of possible chemical accidents to people, and minimize the environmental risk of production routes. GM (1, 1) model was established to predict the price trend and demand change of chemical products from 2020 to 2030. Combining three objective functions with the production chain constraints, a mixed integer linear programming model is developed. Polyvinyl Chloride production chains are taken as an example.

Framework for Sound Management of Chemicals: Toward a Sustainable Chemicals Industry in Bangladesh

Bangladesh entered the group of middle-income countries in 2015. The ever-increasing manufacturing sector is the key player behind its GDP growth, which led to a substantial rise in chemical demand...

Solid waste-derived biodegradable keratin sponges for removal of chromium: A circular approach for waste management in leather industry

Abstract Sustainable consumption and production falls under UN sustainable development goal 12, which require environmentally sound management of chemicals and all wastes; and to reduce waste generation through prevention, reduction, recycling and reuse. Leather industry produce huge solid keratinous waste and chromium contaminated wastewater. In this study, a sponge is obtained from keratinous waste that is tested to remove chromium from wastewater. Removal experiments are carried out at simulated leather industry discharge conditions. Characteristics features of sponge are; its multimodal pore structure with mesopores of 3.83 nm (BET surface area 5.52 m2/g) and 66.5% of macropores as obtained by nitrogen adsorption porosimetry and volume displacement methods, respectively. Removal of chromium was optimized by multivariate method and procedure was applied for removal of chromium from synthetic and real tanning industry wastewater that showed 91% and 87.13% removal efficiency. Langmuir adsorption capacity of 270 mg/g was obtained. Desorption study showed that at pH 10 or above, sponge can be reused, and recovery of chromium is feasible. Sorption mechanism and continuous flow removal experiments were also carried out to understand the interaction and industrial viability of method. Overall finings support ‘utilization of solid waste for treatment of liquid waste’ which would add into environmentally sustainable production in leather industry.

Identifying Practices and Prospects of Chemical Safety and Security in the Bangladesh Textiles Sector

The textile sector is the backward linkage industry for the $30 billion USD apparel sector of Bangladesh, which is the key contributor to the rapid growth of Bangladesh’s economy. This major export-oriented sector of the country consumes a large amount of complex chemical substances for different stages of its processing. It is estimated that almost 1.47 million metric tons of chemicals have been consumed by the textile sector of Bangladesh in fiscal year 2017–2018, which is worth over about $2 billion USD. The chemicals that are consumed by the textile sector are mostly imported from other countries. Alongside imported chemicals, locally produced chemicals are also consumed. This rapidly growing sector has faced hurdles regarding safe, sound, and secure management of chemicals. Textile industries depend on chemical management regulations provided by buyers. There is no existing government policy on textile chemical safety and security. Local textile industries are heavily dependent on chemical management regulations provided by multinational apparel brands, commonly known as buyers. There are stringent regulations based on different private and global chemical management programs, policies, treaties, and frameworks, which mainly address chemical safety and occupational health and safety but hardly address chemical security. Buyers have limited control over chemical formulators, mode of transportation of potentially hazardous chemicals from port to factory gate, and internal transportation of chemicals within the facility and chemical storage. There are significant lapses in electric surveillance, maintaining “live” inventory, and proper segregation in chemical storage units onsite. These practices possess threats of chemical accidents, theft of chemicals, and siege or sabotage within textile industries. This article represents the current practices and prospects of chemical safety and security management in textile industries of Bangladesh. It provides a detailed analysis of textile chemicals consumed in Bangladesh, particularly in knit and woven dyeing industries. Different global and sectoral chemical safety and security frameworks have been analyzed with respect to textile chemicals here. The article aims at cross-linking global, private, and public frameworks for chemical safety and security and addressing key elements of a national textile chemical safety and security policy for Bangladesh. This study could be useful to develop chemical security policies that are missing in the national legal instrument of Bangladesh and to support buyers in efforts to incorporate chemical security and modify safety aspects in the regulations.

Experimenting with TripleCOPs: Productive innovation or counterproductive complexity?

The Basel, Rotterdam, and Stockholm Conventions are engaged in a path-breaking “synergies” initiative that coordinates and even integrates parts of their administration, operation, and implementation. This includes holding TripleCOPs during which their Conference of the Parties meet together in sequential and simultaneous sessions. This article provides a preliminary analysis of this unprecedented experimentation. We find several important positive and negative procedural, political, and policy consequences of the new format, including: countries with large delegations hold a variety of advantages; developing countries can potentially leverage negotiating strength in one convention to advance concerns in another; it is easier to address the environmentally sound management of chemicals and wastes holistically as well as specific technical issues that involve two or more of the treaties; and new opportunities exist for brinkmanship, obstruction, and cross-treaty negotiating that can make reaching agreement on some issues more difficult.

Fats and Oils as Renewable Feedstock for the Chemical Industry

Fats and Oils as Renewable Feedstock for the Chemical Industry