WW LCI v2: A second-generation life cycle inventory model for chemicals discharged to wastewater systems

We present a second-generation wastewater treatment inventory model, WWLCI 2.0, which on many fronts represents considerable advances compared to its previous version WWLCI 1.0. WWLCI 2.0 is a novel and complete wastewater inventory model integratingWWLCI 1.0, i.e. a complete life cycle inventory, including infrastructure requirement, energy consumption and auxiliary materials applied for the treatment of wastewater and disposal of sludge and SewageLCI, i.e. fatemodelling of chemicals released to the sewer. The model is expanded to account for different wastewater treatment levels, i.e. primary, secondary and tertiary treatment, independent treatment by septic tanks and also direct discharge to natural waters. Sludge disposal by means of composting is added as a new option. The model also includes a database containing statistics on wastewater treatment levels and sludge disposal patterns in 56 countries. The application of the new model is demonstrated using five chemicals assumed discharged to wastewater systems in four different countries. WW LCI 2.0 model results shows that chemicals such as diethylenetriamine penta (methylene phosphonic acid) (DTPMP) and Diclofenac, exhibit lower climate change (CC) and freshwater ecotoxicity (FET) burdens upon wastewater treatment compared to direct discharge in all country scenarios. Results for Ibuprofen and Acetaminophen (more readily degradable) show that the CC burden depends on the country-specific levels of wastewater treatment. Higher treatment levels lead to lower CC and FET burden compared to direct discharge. WWLCI 2.0 makes it possible to generate complete detailed life cycle inventories and fate analyses for chemicals released to wastewater systems. Our test of the WWLCI 2.0 model with five chemicals illustrates how the model can provide substantially different outcomes, compared to conventional wastewater inventory models, making the inventory dependent upon the atomic composition of the molecules undergoing treatment as well as the country specific wastewater treatment levels. (c) 2017 Elsevier B.V. All rights reserved

Role of non-motorized transportation and buses in meeting climate targets of urban regions

Studies examining the potential of low-carbon modes of passenger transportation for achieving climate goals are limited. The study is one of the first to assess the potential of non-motorized transportation (NMT) and buses to meet regional climate targets representing 2 °C, 1.5 °C, and Intended Nationally Determined Contributions from 2018 to 2050. Also, the approach towards quantifying contribution from avoided trips and materials in holistically understanding the potential of NMT and buses is novel. Data from the transportation model of Mumbai Metropolitan Region\u27s Comprehensive Mobility Plan is used to assess multiple scenarios of upgrading NMT and bus infrastructure to reduce cumulative carbon dioxide emissions (CCE) from passenger transportation. The assessment is based on three push levels, i.e., conservative, moderate, and aggressive. Results show that upgrading bus infrastructure contributes higher to reducing CCE than NMT. As NMT also contributes significantly to decreasing CCE, it is recommended that bus and NMT development should be integrated. However, their combined contribution will not meet the climate targets. Since avoided materials contribute considerably more than avoided trips, high emission materials such as aluminum used in light-weighting should be questioned. The results provide policy guidance to authorities in prioritizing buses and NMT infrastructure development during city planning

PyTOPS: A Python based tool for TOPSIS

The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method determines the best solution from a set of alternatives with certain attributes. The best alternative is chosen based on its Euclidean distance from the ideal solution. TOPSIS is widely used in various multi-attribute decision making problems such as supply chain logistics, marketing management, environmental management or chemical engineering. Despite the extensive use of this method, there is no free and open-source software (FOSS) for TOPSIS with comprehensive post-analysis extensions. Therefore, this paper describes a Python-3 based tool PyTOPS for TOPSIS with the Berkeley Software Distribution (BSD)-3-Clause license. Keywords: Python, TOPSI

Life cycle based dynamic assessment coupled with multiple criteria decision analysis:A case study of determining an optimal building insulation level

This work looks at coupling Life cycle assessment (LCA) with a dynamic inventory and multiple criteria decision analysis (MCDA) to improve the validity and reliability of single score results for complex systems. This is done using the case study of a representative Danish single family home over the service life of the building. This case study uses both the established and the coupled MCDA assessment methods to quantify and assess the balance of impacts between the production of mineral wool insulation versus the production of space heat. The use of TOPSIS method for calculating single scores is proposed as an alternative to the ReCiPe single score impact assessment method. Based on the single score impact values obtained from both of these methods, various insulation levels are ranked to determine an ideal insulation level and gauge the effectiveness of environmental impact reduction measures in current Danish building regulations. Using a comparison of the results from the two methods, a preferred choice of impact assessment method is determined. The findings show that if the midpoint impacts for a particular scenario are strongly correlated with a climate change impact indicator, it does not matter which impact assessment is applied. However, for the scenarios where other impact categories vary inversely or independently from the climate change impact indicator, such as with renewable energy production, there is need for a more unconventional method, such as the TOPSIS method, for calculating single score impacts. (C) 2017 Elsevier Ltd. All rights reserved

Argumentation corrected context weighting-life cycle assessment: A practical method of including stakeholder perspectives in multi-criteria decision support for LCA

International audienceDespite advances in the data, models, and methods underpinning environmental life cycle assessment (LCA), it remains challenging for practitioners to effectively communicate and interpret results. These shortcomings can bias decisions and hinder public acceptance for planning supported by LCA. This paper introduces a method for interpreting LCA results, the Argumentation Corrected Context Weighting-LCA (ArgCW-LCA), to overcome these barriers. ArgCW-LCA incorporates stakeholder preferences, corrects unjustified disagreements, and allows for the inclusion of non-environmental impacts (e.g., economic, social, etc.) using a novel weighting scheme and the application of multi-criteria decision analysis to provide transparent and context-relevant decision support. We illustrate the utility of the method through two case studies: a hypothetical decision regarding energy production and a real-world decision regarding polyphenol extraction technologies. In each case, we surveyed a relevant stakeholder group on their environmental views and fed their responses into the model to provide decision support that is relevant to their perspective. We found marked differences between results using ArgCW-LCA and results from a conventional analysis using an equal-weighting scheme, as well as differentiation between stakeholder preference groups, indicating the importance of applying the perspective of the particular stakeholder group. For instance, there was a rank reversal of alternatives when comparing between an equal weighting approach for all environmental and economic dimensions and ArgCW-LCA. ArgCW-LCA provides opportunity for both public and private sector incorporation of LCA, such as in developing enlightened stakeholder value measures. This is achieved through enabling the LCA practition to provide public and private actors' interpreted LCA results in a manner that incorporates educated stakeholder perspectives. Furthermore, the method encourages stakeholder multiplicity through participatory design and policymaking that can enhance public backing of actions that can make society more sustainable