Biodegradable plastics are often considered to exhibit superior environmental performance compared to conventional recalcitrant plastics. Here, we assess the greenhouse gas (GHG) emissions of selected biodegradable and recalcitrant plastics made from both fossil and biogenic carbon (C) as disposed in a national average U.S. landfill. This average landfill incorporates consideration of size, precipitation, landfill gas management, and gas collection installation schedule. The GHG emissions of an 80% biodegradable polycaprolactone (PCL _f ) made from fossil C and a 2% biodegradable poly(butylene succinate) (PBS _b ) made from biogenic C were evaluated to represent the range of anaerobic biodegradabilities. The 2% biodegradable PBS _b has lower GHG emissions than the 80% biodegradable PCL _f in the national average landfill. In the best case, which includes aggressive gas collection, conversion of gas to energy, and disposal in a large landfill, the PCL _f results in 2423 kg CO _2 e/mt, which is well above PBS _b (−1956 kg CO _2 e/mt), a hypothetical biogenic and 80% biodegradable PCL _b (4739 kg CO _2 e/mt), and recalcitrant fossil plastic (0 kg CO _2 e/mt). From a disposal perspective, a recalcitrant biogenic plastic is optimal given the long-term storage of carbon. This study informs the direction of materials research to develop materials that minimize their overall environmental footprint at end-of-life.

Solid waste generation and its accumulation is increasing at an alarming pace due to population growth and urbanization posing severe risks to health, safety, and natural ecosystems. This review strategically addresses the challenges and solutions to increasing the sustainability footprint of solid waste management (SWM) systems by revealing multipronged approaches that reduce solid waste and handling costs while generating revenue and reducing greenhouse gas and related emissions. For example, the United States sends ∼150 million tons of waste to landfills, which is composed of over 75% organic and recyclable materials having a potential to be diverted to alternative scenarios. The emergence of an automated upstream and downstream sorting process for solid waste to increase material diversion from landfills is a promising approach for creating sustainable SWM. The utilization of artificial-intelligence-enabled smart and automated systems at the home and industrial scales, comprehensive public re-education including awareness of the adverse effects of landfilled waste on the ecosystem, and more eco-friendly product development are required to significantly reduce landfills and their negative footprint.

This paper describes a novel open-source life-cycle optimization framework for solid waste and sustainable materials management applications named solid waste optimization life-cycle framework in Python (SwolfPy). The current version includes life-cycle models for landfills, mass burn waste-to-energy, gasification, centralized composting, home composting, anaerobic digestion, material recovery facilities, refuse-derived fuel facilities, material recycling, transfer stations, and single-family collection. Compared to existing frameworks, SwolfPy streamlines data input/output processes, improves model integration and modularity, provides a wide variety of data visualization and customization, speeds up uncertainty analysis and optimization, and has a user-friendly graphical user interface (GUI). SwolfPy's GUI allows users to define solid waste management networks and scenarios as well as perform comparative life cycle assessments (LCAs), contribution analyses, uncertainty analyses, and optimization. SwolfPy is implemented in Python using Pandas, NumPy, and SciPy for computational tasks, PySide2 for creating the GUI, and Brightway2 for storing life-cycle inventory data and performing the LCA calculations. SwolfPy is modular and flexible, which enables it to be easily coupled with other packages and to facilitate the addition of new processes, materials, environmental flows and impacts, and methodologies. SwolfPy uses sequential least-squares programming for constrained nonlinear optimization to find systems and strategies that minimize cost or environmental emissions and impacts while meeting user-defined constraints. An illustrative case study with 44 materials, 4 collection processes, and 6 treatment processes is presented, and SwolfPy performs 10,000 Monte Carlo iterations in 16 min and finds optimal solutions in 10–25 min on a Windows 10 machine with a CPU speed of 3.60 GHz and 8 logical processors. This article met the requirements for a Gold-Gold Badge. JIE data openness badge described at http://jie.click/badges.

There is increasing interest in recovering nutrients and energy from the organic fraction of municipal solid waste (OFMSW). Given the costs associated with separate collection of OFMSW, and the potential difficulty in finding clean feedstocks, there are potential benefits in beneficial recovery of OFMSW as part of residual MSW. Therefore, this study compared the life-cycle impacts associated with management alternatives for recovering energy and/or nutrients from the OFMSW through mixed waste processing systems. The considered treatment alternatives include landfilling, mass burn waste-to-energy, gasification and syngas combustion (GC) for electricity production, gasification Fischer-Tropsch (GFT) for transportation fuel production, aerobic composting (AC), and anaerobic digestion (AD). Seven environmental impacts include global warming potential (GWP), cumulative energy demand, acidification, eutrophication, photochemical oxidation, ecotoxicity, and human toxicity were assessed for five sets of state and one U.S. national waste compositions. The mass burn waste-to-energy and GC scenarios generally have the lowest environmental impacts, while landfilling and GFT have the greatest impacts. Separating out organics for AC increased environmental impacts compared to sending them to GC, while sending them to AD decreased GWP and increased the other impacts. Sensitivity analyses suggest that these conclusions are generally robust to uncertainty in input values.

Landfills receive over half of all U.S. municipal solid waste (MSW) and are the third largest source of anthropogenic methane emissions. Life-cycle assessment (LCA) of landfills is complicated by the long duration of waste disposal, gas generation and control, and the time over which the engineered infrastructure must perform. The objective of this study is to develop an LCA model for a representative U.S. MSW landfill that is responsive to landfill size, regulatory thresholds for landfill gas (LFG) collection and control, practices for LFG management (i.e., passive venting, flare, combustion for energy recovery), and four alternative schedules for LFG collection well installation. Material production required for construction and operation contributes 68-75% to toxicity impacts, while LFG emissions contribute 50-99% to global warming, ozone depletion, and smog impacts. The current non-methane organic compound regulatory threshold (34 Mg yr-1) reduces methane emissions by <7% relative to the former threshold (50 Mg yr-1). Requiring landfills to continue collecting LFG until the flow rate is <10 m3 min-1 reduces emissions by 20-52%, depending on the waste decay rate. In general, for landfills already required to collect gas, collecting gas longer is more important than collecting gas earlier to reduce methane emissions.

The primary goal of the project is to leverage recent developments in smart water technologies to detect and reduce water leakages in large water distribution networks with the aid of neural networks. A cost effective, non-invasive solution to detect leakages in transmission pipelines is needed by many water utilities, as it will lead to significant water savings and reduced pipe breakage frequencies, especially in older infrastructure systems. The eventual goal of the project is to test the ANN model on a real network using field measured pressure and pipe breakage data after tuning and developing the model with simulated data. In this project, we propose building a regression model, based on multi-layer perceptron (MLP) algorithm, which is a class of feedforward artificial neural networks (ANNs) to detect the leak locations within a proposed network. The model should be able to learn the structure, i.e., mapping of various leak nodes and sensor nodes in an area, such that it can detect the leak nodes based on the pressure values with significant accuracy.

There is increasing interest in diverting the organic fraction of municipal solid waste from landfills to biological treatment processes that result in compost. Due to variations in compost quality and available markets, it is not always possible for compost to be beneficially used on soil. In such cases, compost may be used as alternative daily cover (ADC) in landfills. The objective of this study is to compare the environmental impacts of using compost as a soil amendment, accounting for its beneficial substitutions for fertilizer and peat, to its use as ADC. Monte Carlo simulation and parametric sensitivity analyses were performed to evaluate the effects of uncertainty in input values on the environmental performance. The ADC scenario outperforms the soil amendment scenario in terms of global warming potential, acidification, and eutrophication in ∼63, ∼77, and ∼100% of simulations, respectively, while the soil amendment scenario is better in terms of cumulative energy demand and abiotic resource depletion potential ∼94 and ∼96% of the time, respectively. Therefore, we recommend that using compost as ADC be considered, especially when site-specific factors such as feedstock contamination or a lack of markets make it difficult to find appropriate applications for compost as a soil amendment.

The authors previously analysed a real-world solid waste management (SWM) system using the solid waste optimization life-cycle framework (SWOLF) to identify optimal SWM strategies that meet modelled objectives (e.g.cost, environmental impacts, landfill diversion). While mathematically optimal strategies can support SWM decision making, they may not be readily implementable because of unmodelled objectives (e.g.practical limitations, social preferences, political and management considerations). A mathematical programming technique extending SWOLF is used to systematically identify, for several scenarios, different 'optimal' SWM strategies that are maximally different from each other in terms of waste flows, while meeting modelled objectives and constraints. The performance with respect to unmodelled issues was analysed to demonstrate the flexibility in potential strategies. Practitioner feedback highlighted implementation challenges due to existing practices; however, insights gained from this exercise led to more plausible and acceptable strategies by incrementally modifying the initial SWM alternatives generated.

Life-cycle assessments (LCAs) of municipal solid waste management (MSWM) systems are time- and data-intensive. Reducing the data requirements for inventory and impact assessments will facilitate the wider use of LCAs during early system planning and design. Therefore, the objective of this study is to develop a systematic framework for streamlining LCAs by identifying the most critical impacts, life-cycle inventory emissions, and inputs based on their contributions to the total impacts and their effect on the rankings of 18 alternative MSWM scenarios. The scenarios are composed of six treatment processes: landfills, waste-to-energy combustion, single-stream recycling, mixed waste recycling, anaerobic digestion, and composting. The full LCA uses 1752 flows of resources and emissions, 10 impact categories, 3 normalization references, and 7 weighting schemes, and these were reduced using the streamlined LCA approach proposed in this study. Human health cancer, ecotoxicity, eutrophication, and fossil fuel depletion contribute 75-83% to the total impacts across all scenarios. It was found that 3.3% of the inventory flows contribute ≥95% of the overall environmental impact. The highest-ranked strategies are consistent between the streamlined and full LCAs. The results provide guidance on which impacts, flows, and inputs to prioritize during early strategy design.

Smart city programs provide a range of technologies that can be applied to solve infrastructure problems associated with ageing infrastructure and increasing demands. The potential for infrastructure and urban improvement remains unrealized, however, due to technical, financial, and social constraints and criticisms that limit the implementation of smart cities concepts for infrastructure management. The discussion presented here provides a review of smart technologies including sensors, crowdsourcing and citizen science, actuators, data transmission, Internet of Things, big data analytics, data visualization, and blockchain, which can be used for infrastructure management. Smart infrastructure programs are reviewed to explore how enabling technologies have been applied across civil engineering domains, including transportation systems, water systems, air quality, energy infrastructure, solid waste management, construction engineering and management, structures, and geotechnical systems. Gaps in the application of smart technologies for infrastructure systems are identified, and we highlight how the civil engineering profession can adopt new roles toward the development of smart cities applications. These roles are: (1) master designer: civil engineers can identify ready applications of enabling technologies to improve the delivery of urban resources and services; (2) steward: civil engineers must account for both the environmental and societal impacts of smart infrastructure applications; (3) innovator and integrator: civil engineers should integrate across diverse sectors and groups of experts to develop smart infrastructure programs; (4) manager of risk: civil engineers should manage existing and growing risks of natural disasters, emergencies, and climate change; they should also manage new vulnerabilities in the privacy and security of individuals and households that are introduced through smart technologies; and (5) leader and decision maker: civil engineers can take a lead in smart infrastructure discussions and policy development.

Life cycle assessment (LCA) has been used in waste management for the last two decades and hundreds of journal papers have been published. The use of LCA in waste management has provided a much-improved holistic view of waste management including waste flows and potential environmental impacts. Although much knowledge has been obtained from LCA studies, there is still a need to use LCA models in integrated waste management. This paper describes six areas where LCA is expected to play a role in waste management in the future: 1) understanding an existing waste management system; 2) improving existing waste management systems; 3) comparing alternative technologies/ technology performance; 4) technology development/prospective technologies; 5) policy development/strategic development; and 6) reporting. Illustrative examples are provided for each application area.

Solid waste management (SWM) is a key function of local government and is critical to protecting human health and the environment. Development of effective SWM strategies should consider comprehensive SWM process choices and policy implications on system-level cost and environmental performance. This analysis evaluated cost and select environmental implications of SWM policies for Wake County, North Carolina using a life-cycle approach. A county-specific data set and scenarios were developed to evaluate alternatives for residential municipal SWM, which included combinations of a mixed waste material recovery facility (MRF), anaerobic digestion, and waste-to-energy combustion in addition to existing SWM infrastructure (composting, landfilling, single stream recycling). Multiple landfill diversion and budget levels were considered for each scenario. At maximum diversion, the greenhouse gas (GHG) mitigation costs ranged from 30 to 900 $/MTCO2e; the lower values were when a mixed waste MRF was used, and the higher values when anaerobic digestion was used. Utilization of the mixed waste MRF was sensitive to the efficiency of material separation and operating cost. Maintaining the current separate collection scheme limited the potential for cost and GHG reductions. Municipalities seeking to cost-effectively increase landfill diversion while reducing GHGs should consider waste-to-energy, mixed waste separation, and changes to collection.

Landfills are a major contributor of anthropogenic CH4 emissions. Since the greenhouse gas (GHG) emissions associated with landfilling waste can occur over decades to centuries, the standard static approach to estimating global warming impacts may not accurately represent the global warming impacts of landfills. The objective of this study is to assess the implications of using 100-yr and 20-yr static and dynamic global warming potential (GWP) approaches to estimate the global warming impacts from municipal solid waste landfills. A life-cycle model was developed to estimate GHG emissions for three gas treatment cases (passive venting, flare, CH4 conversion to electricity) and four decay rates. For the 100-yr GWP, other model uncertainties (e.g., static GWP values, decay rate, moisture content, or gas collection efficiency) generally had a larger effect on the estimated global warming impact than the choice of static versus dynamic GWP methods. This shows that when comparing single-point GWP values, the choice of static versus dynamic is relatively unimportant for most landfills. While dynamic GWPs consider temporal variance and provide useful estimates for the warming over a set time horizon, for most comparative analyses, static values provide reasonable bounds for the actual 100-yr warming impact.

Waste managers struggle to comply with the European legislation that regulates the handling of organic waste. A waste management system that aims at recovering nutrients from the municipal organic waste generated in the Spanish region of Cantabria was modeled by combining material flow analysis, life cycle assessment, and life cycle costing. The model was optimized to find system configurations that minimize the total annual cost (TAC) and the global warming impacts (GW) and maximize the circularity indicators of nitrogen and phosphorus (CIN and CIP). The developed superstructure is composed of waste management unit processes and unit processes related to the land application of the recovered products (compost, digestate, (NH4)2SO4, and NH4MgPO4·6H2O) and industrial fertilizers to grow corn. The results of the optimization indicate that increasing CIN and minimizing GW raises the TAC, because of the investment in new technologies, although high CIP values can be achieved at low TACs. The economic margin that enables the organic fertilizers to compete in the market with industrial fertilizers was estimated. Cooperation between waste managers, the farmers that purchase the recovered products, and the policy-makers that set the waste management taxes can minimize the costs that hinder the transition toward a circular economy.

Purpose Large data amounts are required in an LCA, but often, site-specific data are missing and less representative surrogate data must be used to fill data gaps. No standardized rules exist on how to address data gaps and process completeness. We suggest a systematic evaluation of process completeness, identification of data gaps, and application of surrogate values to fill the gaps. The study focus on foreground process data. Methods A solid waste management (SWM) scenario was used to illustrate the suggested method. The expected input and output flows in a waste incineration model were identified based on legislation and expert judgment, after which process completeness scores were calculated and missing flows identified. To illustrate the use of different types of surrogate data to fill data gaps, data gaps were selected for 16 different parameters in five SWM processes. We compared the global warming potential (GWP) from using surrogate data, and from leaving the gap, to identify the data gaps where representative surrogate data should be used. Results and discussion The completeness score for the material inputs to waste incineration was 78%, and the missing flows were auxiliary fuels and precipitation chemicals. The completeness score for air emissions were between 38 and 50% with and without expert judgment. If only greenhouse gases were considered (CO2, CH4, and N2O), the completeness score would be 67%. Applying weighting factors according to the greenhouse gas contribution in the USA gave a completeness score of 94%. The system-wide data gaps, where representative surrogate data should be applied, were the CH4 release from composting; electricity generation efficiency of incineration; recovery efficiencies at a material recovery facility; and composition of the plastic, metal, and paper fractions in the household waste; in these cases, leaving the gap changed the GWP results by > 5%. Conclusions Completeness evaluation should take into account the relevance and importance of flows; relevance depends on the considered life cycle impact methods and importance depends on the weighting of the different flows. The set of expected flows and evaluation of relevance and importance must be documented in a transparent manner. The choice of surrogate values to fill data gaps depends on the availability of secondary data and on whether the data gap matters, i.e., significantly affects the LCA results. The suggested method can be used to properly document the identification of missing flows and to select and apply surrogate values to fill the data gaps.

The field of engineered nanomaterial (ENM) risk analysis has matured significantly in the past decade. While there is a suite of new, emerging tools to evaluate ENM risks and make decisions regarding these risks, there has not yet been thorough testing of these tools. This analysis applies and tests three risk screening tools (NanoRiskCat, LICARA nanoSCAN, NanoGRID) using a common case study focused on ENMs designed for water treatment technologies, compares results generated, and highlights key lessons learned and best practices for stakeholders involved in developing and/or applying ENM risk screening tools. NanoRiskCat was found to be most useful for providing a visual aid to characterize the potential exposure and health impact profiles of the ENMs, while LICARA nanoSCAN was most useful for providing guidance on proceeding with ENM-enabled innovations. NanoGRID was helpful for characterizing data on potential ENM exposure and hazards and providing detailed guidance for subsequent laboratory-based testing. At the same time, several key challenges were identified during tool application and testing phases, ranging from minor inconveniences to more complex, foundational issues. Key lessons learned and potential best practices gleaned from this analysis include: i) risk screening tools can be used together in a complementary manner; ii) risk managers and other users should be clear on the selection of underlying data and impacts on results; iii) multidisciplinary teams are essential for tool completion; and iv) continued testing and validation of emerging risk analysis tools for ENMs is a continued research need.

The development of sustainable solid waste management (SWM) systems requires consideration of both economic and environmental impacts. Societal life-cycle costing (S-LCC) provides a quantitative framework to estimate both economic and environmental impacts, by including "budget costs" and "externality costs". Budget costs include market goods and services (economic impact), whereas externality costs include effects outside the economic system (e.g., environmental impact). This study demonstrates the applicability of S-LCC to SWM life-cycle optimization through a case study based on an average suburban U.S. county of 500 000 people generating 320 000 Mg of waste annually. Estimated externality costs are based on emissions of CO2, CH4, N2O, PM2.5, PM10, NOx, SO2, VOC, CO, NH3, Hg, Pb, Cd, Cr (VI), Ni, As, and dioxins. The results indicate that incorporating S-LCC into optimized SWM strategy development encourages the use of a mixed waste material recovery facility with residues going to incineration, and separated organics to anaerobic digestion. Results are sensitive to waste composition, energy mix and recycling rates. Most of the externality costs stem from SO2, NOx, PM2.5, CH4, fossil CO2, and NH3 emissions. S-LCC proved to be a valuable tool for policy analysis, but additional data on key externality costs such as organic compounds emissions to water would improve future analyses.

The objective of this review is to critically analyze literature, data, and models on the environmental releases from the uncontrolled disposal and burning of solid waste. Major concerns include releases of greenhouse gases, particulate matter, and leachate. Many factors influence these releases including waste composition, site depth, and climate. While the impact of these factors is understood qualitatively, there is little data and considerable uncertainty in model predictions. One limitation is that in general, predicted emissions are not responsive to changes in waste composition. Estimating impacts to human health and the environment from the predicted emissions results in additional uncertainty.

Landfills are the final stage in the life cycle of many products containing per- and polyfluoroalkyl substances (PFASs) and their presence has been reported in landfill leachate. The concentrations of 70 PFASs in 95 samples of leachate were measured in a survey of U.S. landfills of varying climates and waste ages. National release of PFASs was estimated by coupling measured concentrations for the 19 PFASs where more than 50% of samples had quantifiable concentrations, with climate-specific estimates of annual leachate volumes. For 2013, the total volume of leachate generated in the U.S. was estimated to be 61.1 million m(3), with 79% of this volume coming from landfills in wet climates (>75 cm/yr precipitation) that contain 47% of U.S. solid waste. The mass of measured PFASs from U.S. landfill leachate to wastewater treatment plants was estimated to be between 563 and 638 kg for 2013. In the majority of landfill leachate samples, 5:3 fluorotelomer carboxylic acid (FTCA) was dominant and variations in concentrations with waste age affected total estimated mass. There were six PFASs that demonstrated significantly higher concentrations in leachate from younger waste compared to older waste and six PFAS demonstrated significant variation with climate.