End-of-life (EoL) chemical flow analysis (CFA) is essential for assessing environmental releases, exposure pathways, and promoting a safer circular economy for chemicals. This study introduces a hydrid multi-scale computational method that integrates data engineering, generic scenario analysis, and process systems engineering (PSE) techniques to evaluate CFA during the EoL stage of commercial chemicals. Using methyl methacrylate (MMA), a key component in resin and plastic manufacturing, as a case study, the method examines the EoL supply and management chain of plastics to perform a comprehensive CFA. The method identifies potential chemical transfers across EoL activities (recycling, recovery, and disposal), environmental releases (air, water, and land), and occupational exposure scenarios, highlighting inter-EoL activity transfers before final disposal or reuse (e.g., biosolids to landfills and incineration) that may lead to unintended environmental releases. Also, the detection of co-occurring chemicals alongside MMA in EoL streams suggests the inadvertent recycling of toxic substances and environmental releases during recycling and wastewater treatment processes. However, data limitations and reporting variability introduce uncertainties that could affect CFA accuracy. To address these challenges, this study underscores the need of integrating facility, process, and equipment-level data to enhance release estimates and exposure assessments. Future research should focus on hybrid modeling approaches by combining top-down regulatory data with bottom-up PSE insights, and employ graph-based methods for simulating chemical supply chains. By advancing and merging data-driven CFA and PSE methodologies, this research aims to provide a scientific basis to support regulatory implementation, safer circular economy strategies, and sustainable chemical management.

Plastic pyrolysis is widely promoted as a techno-economic industrial scale recycling strategy. Nevertheless, the fate and reactivity of plastic chemical additives during pyrolysis are mostly overlooked in product quality and environmental release assessments. Here, we present an integrated modeling framework to elucidate the role of additives in plastic pyrolysis and evaluate the implications of their transformation products and environmental releases. Using high-density polyethylene (HDPE) as a case study, chemical additives of concern are selected based on occurrence, concentration data, and potential risk to human health and the environment. Bond dissociation energies are predicted using a machine learning model to identify dominant radical species formed under pyrolytic conditions. These additive-derived radicals are incorporated into an automatic chemical reaction mechanism generator that constructs kinetic models composed of elementary chemical reaction steps. These kinetic models are simulated using kinetic Monte Carlo (kMC) methods to predict product distributions and yields. The results show that common additives readily form stabilized alkyl and aryl radicals at energies accessible during pyrolysis, enabling their active participation in polymer degradation pathways. These interactions influence product formation and may contribute to the generation of environmentally relevant by-products. Overall, this study provides a mechanistic and risk-informed perspective on plastic pyrolysis, emphasizing the importance of explicitly accounting for additive chemistry in the development of safer and more sustainable chemical recycling technologies.Disclaimer: The views expressed in this work are those of the authors and do not necessarily represent the views or policies of the EPA.

Chemical release data are essential for performing chemical risk assessments to understand the potential exposures arising from industrial processes. Often, these data are unknown or unavailable and must be estimated. A case study of volatile organic compound releases during extrusion-based additive manufacturing is used here to explore the viability of various regression methods for predicting chemical releases to inform chemical assessments. The methods assessed in this work include linear Least Squares, Least Absolute Shrinkage and Selection Operator (LASSO) and Ridge regression, classification and regression tree, random forest model, and neural network analysis. Secondary data describing polymeric extrusion in multiple applications are curated and assembled in a dataset to support regression modeling using default parameters for the various approaches. The potential to add noise to the dataset and improve regression is evaluated using synthetic data generation. Evaluation of model performance for a common test set found all methods were able to achieve predictions within 10%-error for up to 98% of the test sample population. The degree to which this level of performance was maintained when varying the number and type of features for regression was dependent on the model type. Linear methods and neural network analysis predicted the most test samples within 10%-error for smaller numbers of features while tree-based approaches could accommodate a larger number of features. The number and type of features can be important if the desire is to make chemical-specific release predictions. The inclusion of release data from related processes generally improved test set predictions across all models while the use of synthetic data as implemented here resulted in smaller increases in test sample predictions within 10%-error. Future work should focus on improving access to primary data and optimizing models to achieve maximum predictive performance of environmental releases to support chemical risk assessment.

Plastics are widely used for their affordability and versatility in various applications. However, the end-of-life (EoL) management stage can often lead to the release of hazardous chemical additives and degradation products into the environment, which leads to ecological and human exposure risks. The increasing demand for plastics is expected to escalate the frequency of material releases during plastic EoL management activities, creating a challenge for policymakers, consumers, manufacturers, and communities to ensure proper material segregation, reuse, recycling, and disposal. Effective management is crucial for achieving a safer and sustainable circular economy (CE) for plastics, enhancing recycling efficiency and promoting material reuse. End-of-life plastic research efforts often overlook chemical additives, which are crucial for understanding the environmental and health implications of plastic usage. Therefore, chemical additive content and release must be assessed and considered when designing and implementing technologies, supply chains, incentives, and regulations for plastic CE management solutions. This research offers a Python-based EoL plastic and additive flow tracker tool (EoLPAFT) to support decision-makers in analyzing the holistic impacts and benefits of potential plastic EoL management solutions, considering the chemical additives within EoL plastics, their releases, and occupational exposure scenarios. The utility of the tool was tested through two hypothetical case scenarios, including (1) nationwide adoption of an extended producer responsibility (EPR) program and (2) maintaining a CE for plastic. The analyses projected by the tool can ease the prediction of long-term outcomes, offering technical knowledge and insight for policymakers and stakeholders seeking to minimize the environmental, social, economic, and health impacts of plastic pollution while seeking a safer and more sustainable CE of plastics considering chemical additives.

Efforts to constrain the negative environmental impacts of excess nitrogen (N) and phosphorus (P) are costly and challenging, due in part to inconsistent reporting of nutrient sources at temporal and spatial scales relevant for local decision making. To meet this challenge, the U.S. Environmental Protection Agency's National Nutrient Inventory provides estimates of major agricultural, urban, atmospheric, and natural nutrient fluxes for the contiguous United States at county and HUC12 scales annually from 1987 (from 1950 for agriculture) to 2017. Since the late 1980s, total N emissions and atmospheric N deposition have declined 22% and 15%, respectively, despite increased agricultural emissions. Over the same period, municipal wastewater N and P loads remained largely stable, despite population increases, through wastewater treatment upgrades and the phaseout of phosphorus-containing detergents. Improved agricultural efficiency allowed for dramatic increases in agricultural production and crop harvest since 1987 (∼25% for N and P), with little change in surplus nutrients left on fields. Overall, a combination of innovative technologies and management has stemmed or even decreased major sources of nutrient pollution to the environment over the last several decades, representing an important shift that, if continued, may contribute to improved air, land, and water quality and human health.

Publicly owned treatment works (POTWs) provide a vital service in treating wastewater from rural, urban, and industrial sources. The inflow of industrial wastewater to POTWs introduces a complex mixture of conventional and emerging contaminants, creating challenges for effective treatment and posing potential environmental and health risks. This study presents ChemTEAPOTW, a Python-based simulation model developed to track and estimate the fate and transport of chemicals of concern (CoC) in POTWs while also integrating inhalation and dermal occupational exposure pathways. The model comprehensively simulates chemical partitioning, removal processes, and environmental releases, incorporating sub-models for clarifiers, activated sludge reactors, anaerobic digesters, and sludge handling. Model predictions for CoC partitioning across air, water, biosolids, and biodegradation pathways showed strong agreement with literature-reported data, with approximately 75 % of values falling within one standard deviation of published means. By simulating the transport and fate of these CoC, this model serves as a valuable tool for designing, evaluating, and enhancing POTW operations. It provides a comprehensive understanding of end-of-life industrial material transfers to POTWs and describes the implications for environmental and public health impact assessments.

Additive manufacturing (AM) methods enable complex, customized, and on-demand production of many products from different material types across various industries. The growing demand for flexible and more sustainable manufacturing solutions places AM in the mix of processes considered for non-commodities. However, AM processes also present unintentional environmental releases in end-of-life (EoL) material management, compromising overall sustainability. Data availability to assess the sustainability of individual EoL material management from individual AM processes is limited. Even so, EoL materials generated across AM practices frequently overlap, supporting high-level assessment as an alternative approach. Therefore, a holistic AM EoL material management sustainability analysis was completed using a customized list of efficiency, environmental, energy, and economic indicators from the Gauging Reaction Effectiveness for the Environmental Sustainability of Chemistries with a multi-Objective Process Evaluator (GREENSCOPE) methodology. Subsequently, this assessment identified low material recycling rates and high energy costs in some EoL material management processes, such as incineration and recovery. Subsequently, a trade-off analysis was performed to determine process modification opportunities, including implementing recycling to reduce the amount of hazardous waste at the expense of additional energy and cost investment. The AM EoL-specific sustainability analysis serves as a resource to offer insights and empower policymakers and stakeholders to enhance pollution prevention strategies and optimize the existing EoL material management processes.

Nutrient pollution and cyanobacteria harmful algal blooms (cyanoHABs) are critical challenges shared among surface waters, largely driven by nutrient releases from nonpoint sources. Tools that inform the selection of nutrient source control and/or timing of implementation would further efforts to reduce nutrient pollution and public health impacts. We provide a modeling framework that uses both mechanistic and statistical models for quantifying the relative importance of external and internal phosphorus (P) loads on cyanoHAB severity and identifying subwatersheds with potential upstream legacy stores. We demonstrated the framework using data from a freshwater lake and found that recently added P from the internal load was significant in explaining cyanoHAB severity (24%), more so than recently added P from external sources (1.1%). Using counterfactual scenarios, we found that a 90% reduction in the recently added internal P load would significantly reduce cyanobacteria cell densities, leading to less severe blooms. Notably, we found that the relative importance of the internal and external P loads varied among years, which can infer when nutrient control strategies may be more/less successful. As such, this framework can help identify the most significant source of P across time and space to better inform nutrient source control.

Chemicals play a critical role in many products. To manage their environmental impact, chemical risk assessment (CRA) and material flow analysis (MFA) are commonly used. However, data gaps, particularly at the end-of-life (EoL) stage, hinder these efforts. This paper examines how software and data systems can support CRA and MFA by integrating regulatory databases, extracting information from academic sources via natural language processing, and incorporating real-time data streams. These advances improve the understanding of the EoL supply and management chain through seamless data integration and automated tracking. Furthermore, the manuscript explores the role of graph neural networks and transfer learning to enhance model representation and predictive performance.

Modeling the fate of chemicals across their life cycle when considering all potential uses can be challenging because of the data gaps arising from issues like confidential business information (data accessibility) and complex processing schemes (involvement in formulations, reactions, and separations) across multiple industries, products, and applications. Thus, assessing chemicals for safety and/or sustainability requires developing an extensive knowledge of chemical releases along the various conditions of use (CoU) to identify potential impacts on human health and the environment. The first step in this process is mapping the flow of a chemical throughout its various downstream uses, which can be time-intensive. Here, a chemical mapping methodology is developed to qualitatively assess the allocation of a chemical of interest from its manufacture through its CoU in consumer, commercial, and industrial products. The chemical flow mapping combines knowledge from searches of publicly available data sources based on the chemical's Chemical Abstracts Service number to determine viable chemical flow paths. Examples of data sources when applying this approach to chemicals in the United States include the Chemical Data Reporting database, the Toxics Release Inventory, the North American Industry Classification System, and the Chemical and Products Database. The methodology is demonstrated using case studies of methylene chloride and triphenyl phosphate. The value of this approach is its ability to be automated and enable rapid determination of the life cycle chemical flow for expedited chemical assessment.

Increased global plastic consumption and production boosted the amount of end-of-life (EoL) plastic. Also, 90 % of plastic EoL is either landfilled or incinerated. These unsustainable EoL pathways impact the environment and human health and waste valuable materials. Thus, improvements to the existing recycling infrastructure for sustainable plastic management are needed to enhance plastic circularity. Therefore, this contribution addresses optimizing cost-effective pathways for plastic recycling within the supply chain. The research uses mathematical optimization and the P-graph theoretical framework to calculate recycling costs, encompassing both capital expenditure and operational expenditure for various pathways of plastic recycling. The proposed methodology is applied through a detailed case study in Miskolc, Hungary, revealing estimated recycling costs ranging from 54.9 to 59.28 EUR/ton. This finding provides crucial insights into the economic implications of diverse recycling methods. Also, the study highlights the P-graph model's untapped potential as a resource for decision-makers in plastic recycling, particularly the enumeration of options for further consideration. The work's utility and novelty lie in the model's capability to design cost-effective pathways, offering a tangible contribution to the plastic recycling supply chain. Finally, this contribution offers economic solutions needed to ensure cost-effective sustainable plastic management solutions.

Sustainability and circular economy enclose initiatives to achieve economic systems and industrial value chains by improving resource use, productivity, reuse, recycling, pollution prevention, and minimizing disposed material. However, shifting from the traditional linear economic production system to a circular economy is challenging. One of the most significant hurdles is the absence of sustainable end-of-life (EoL)/manufacturing loops for recycling and recovering material while minimizing negative impacts on human health and the environment. Overcoming these challenges is critical in returning materials to upstream life cycle stage facilities such as manufacturing. Chemical flow analysis (CFA), sustainability evaluation, and process systems engineering (PSE) can supply chemical products and processes performances from environmental, economic, material efficiency, energy footprint, and technology perspectives. These holistic evaluation techniques can improve productivity, source material reduction, reuse, recycling, and prevent and minimize releases and disposal rates. Therefore, this contribution offers a computational framework that covers CFA, sustainability assessment, and risk evaluation for quantifying the benefits and challenges of chemical circular economy routes versus conventional linear systems. Finally, this contribution shows promising techniques and challenges for employing CFA, sustainability evaluation, and PSE as multicriteria decision-making tools for designing a closed-loop chemical management infrastructure and transforming the US chemical industry sector from linear to circular.

Sustainability, chemical safety, and circular economy represent holistic frameworks that support chemical synthesis, design, manufacturing, use, reuse, recycling, and disposal to create a closed-loop economic production system.Also, these frameworks target minimizing waste generation, energy consumption, and fresh raw material needs, maximizing resource efficiency, and economic, social, and environmental benefits.Therefore, designing a safer economy for chemicals includes the contribution of all chemical life cycle stages, starting from utilizing safer renewable feedstocks, developing safer chemical products, implementing sustainable chemical manufacturing systems, effective regulatory foundations, and non-destructive end-of-life management systems.

Plastics are widely used for their affordability and versatility across many consumer and industrial applications. However, the end-of-life (EoL) management stage can often lead to releasing hazardous chemical additives and degradation products into the environment. The increasing demand for plastics is expected to increase the frequency of material releases throughout the plastic EoL management activities, creating a challenge for policymakers, including ensuring proper material segregation and disposal management and increasing recycling efficiency and material reuse. This research designed a Python-based EoL plastic management tool to support decision-makers in analyzing the holistic impacts of potential plastic waste management policies. The constructed tool was developed to reduce the complexity of material flow analysis calculations, estimating material releases, and environmental impacts. The utility of the tool was tested through the hypothetical nationwide adoption of an extended producer responsibility (EPR) program. The decision-making capability of the tool can facilitate the prediction of long-term outcomes, offering technical knowledge and insight for policymakers seeking to mitigate the environmental and health impacts of plastic pollution.

Additive manufacturing (AM) offers a variety of material manufacturing techniques for a wide range of applications across many industries. Most efforts at process optimization and exposure assessment for AM are centered around the manufacturing process. However, identifying the material allocation and potentially harmful exposures in end-of-life (EoL) management is equally crucial to mitigating environmental releases and occupational health impacts within the AM supply chain. This research tracks the allocation and potential releases of AM EoL materials within the US through a material flow analysis. Of the generated AM EoL materials, 58% are incinerated, 33% are landfilled, and 9% are recycled by weight. The generated data set was then used to examine the theoretical occupational hazards during AM EoL material management practices through generic exposure scenario assessment, highlighting the importance of ventilation and personal protective equipment at all stages of AM material management. This research identifies pollution sources, offering policymakers and stakeholders insights to shape pollution prevention and worker safety strategies within the US AM EoL management pathways.

Phosphorus is a nonrenewable material essential for ensuring food security whose global reserves are controlled by a limited number of nations. Potential phosphorus insecurity prompted some countries to develop regulations to support a circular phosphorus economy from end-of-life materials. However, the impact of phosphorus recovery costs on the economy of communities served by wastewater resource recovery facilities stays unquantified. We analyze the socioeconomic impact of phosphorus recovery at the wastewater resource recovery facilities of Canada and the continental United States. We found that phosphorus recovery results in a cost gap between urban and rural areas due to the differences in the treatment level and scale of wastewater resource recovery facilities. This cost disparity could lead to the emergence of deprived social groups bearing the economic burden of transitioning toward a circular phosphorus economy. However, the environmental remediation costs avoided by the recovered phosphorus offset the recovery costs, providing an economic driver for phosphorus recovery.

In the evaluation and analysis of candidate process and product designs, several studies are performed including parametric and topological optimization to select operating conditions, raw materials, types of utilities, equipment, and specific flowsheet configurations based on defined criteria. However, these studies typically only provide the potential economic performance of the process, leaving stakeholders with little insight into the energy footprint, transformation efficiency, and potential impacts on the environment and human health. In this work, GREENSCOPE (Gauging Reaction Effectiveness for the ENvironmental Sustainability of Chemistries with a multi-Objective Process Evaluator) is introduced as a tool to perform comprehensive sustainability analysis and assess environmental and human health impact benefits by pollution prevention and source reduction efforts. In particular, the data requirements, metrics, and interfaces currently available in GREENSCOPE are summarized. Additionally, this tool is demonstrated with new case studies and process systems applications including the assessment of an acetic acid process with two separation schemes, a novel biorefinery process when operated at steady state, and the dynamic assessment for optimization and control of a coal/biomass co-gasification process.

Anthropogenic pollution of hydrological systems affects diverse communities and ecosystems around the world. Data analytics and modeling tools play a key role in fighting this challenge, as they can help identify key sources as well as trace transport and quantify impact within complex hydrological systems. Several tools exist for simulating and tracing pollutant transport throughout surface waters using detailed physical models; these tools are powerful, but can be computationally intensive, require significant amounts of data to be developed, and require expert knowledge for their use (ultimately limiting application scope). In this work, we present a graph modeling framework -- which we call ${\tt HydroGraphs}$ -- for understanding pollutant transport and fate across waterbodies, rivers, and watersheds. This framework uses a simplified representation of hydrological systems that can be constructed based purely on open-source data (National Hydrography Dataset and Watershed Boundary Dataset). The graph representation provides an flexible intuitive approach for capturing connectivity and for identifying upstream pollutant sources and for tracing downstream impacts within small and large hydrological systems. Moreover, the graph representation can facilitate the use of advanced algorithms and tools of graph theory, topology, optimization, and machine learning to aid data analytics and decision-making. We demonstrate the capabilities of our framework by using case studies in the State of Wisconsin; here, we aim to identify upstream nutrient pollutant sources that arise from agricultural practices and trace downstream impacts to waterbodies, rivers, and streams. Our tool ultimately seeks to help stakeholders design effective pollution prevention/mitigation practices and evaluate how surface waters respond to such practices.

Chemical flow analysis (CFA) can be used for collecting life-cycle inventory (LCI), estimating environmental releases, and identifying potential exposure scenarios for chemicals of concern at the end-of-life (EoL) stage. Nonetheless, the demand for comprehensive data and the epistemic uncertainties about the pathway taken by the chemical flows make CFA, LCI, and exposure assessment time-consuming and challenging tasks. Due to the continuous growth of computer power and the appearance of more robust algorithms, data-driven modelling represents an attractive tool for streamlining these tasks. However, a data ingestion pipeline is required for the deployment of serving data-driven models in the real world. Hence, this work moves forward by contributing a chemical-centric and data-centric approach to extract, transform, and load comprehensive data for CFA at the EoL, integrating cross-year and country data and its provenance as part of the data lifecycle. The framework is scalable and adaptable to production-level machine learning operations. The framework can supply data at an annual rate, making it possible to deal with changes in the statistical distributions of model predictors like transferred amount and target variables (e.g., EoL activity identification) to avoid potential data-driven model performance decay over time. For instance, it can detect that recycling transfers of 643 chemicals over the reporting years (1988 to 2020) are 29.87%, 17.79%, and 20.56% for Canada, Australia, and the U.S. Finally, the developed approach enables research advancements on data-driven modelling to easily connect with other data sources for economic information on industry sectors, the economic value of chemicals, and the environmental regulatory implications that may affect the occurrence of an EoL transfer class or activity like recycling of a chemical over years and countries. Finally, stakeholders gain more context about environmental regulation stringency and economic affairs that could affect environmental decision-making and EoL chemical exposure predictions.