Grantee Research Project Results
2017 Progress Report: NCCLC: Network for Rapid Assessment of Chemical Life Cycle Impact
EPA Grant Number: R835579Title: NCCLC: Network for Rapid Assessment of Chemical Life Cycle Impact
Investigators: Suh, Sangwon , Keller, Arturo A. , Doherty, Michael , Seshadri, Ram , Scott, Susannah
Current Investigators: Suh, Sangwon , Keller, Arturo A. , Scott, Susannah , Seshadri, Ram , Doherty, Michael
Institution: University of California - Santa Barbara
EPA Project Officer: Aja, Hayley
Project Period: December 1, 2013 through November 30, 2017 (Extended to November 30, 2019)
Project Period Covered by this Report: December 1, 2016 through November 30,2017
Project Amount: $4,887,644
RFA: EPA/NSF Networks for Characterizing Chemical Life Cycle (NCCLCs) (2013) RFA Text | Recipients Lists
Research Category: Chemical Safety for Sustainability
Objective:
The Network for Rapid Assessment of Chemical Life Cycle Impact of UCSB, which is internally referred to as the Chemical Life Cycle Collaborative (CLiCC) is developing a framework to enable early assessment of the life cycle impacts of novel chemicals and materials. The framework can also be used to fill-in data gaps in the prediction of life cycle impacts for existing chemicals with large data gaps.
Progress Summary:
To date, the CLiCC team has 1) developed a Predictive Life Cycle Impact Assessment Module to estimate the impact of chemicals across cumulative energy demand, aquatic eco-toxicity, eco-indicator, ozone layer depletion, acidification and water demand; 2) developed a Life Cycle Inventory Module that recursively models chemical manufacturing processes to estimate heat and energy requirement for synthesizing a chemical product. 3) Finalized the underlying Life Cycle Inventory database containing substantial amount of chemical manufacturing information; 4) conducted preliminary sensitivity analyses for the Life Cycle Inventory module 5) created a module that connects chemical functional uses and product applications together to generate realistic estimates of releases; 6) developed two fate and transport modules to predict the dynamic long term fate of organic chemicals and nanomaterials in the natural environment; 7) validated the organic chemical fate and transport model against the Level III standard; 8) completed regional data, allowing users to make reports for specific locations, for 12 local regions and one global region; 8) completed three human exposure modules for outdoor exposure, indoor air exposure, and dermal exposure scenarios; 9) collected and integrated an extensive database of experimental toxicity data; 10) built internal capabilities for three QSAR property modeling tools; 11) developed a module that accommodates in a consistent way the different sources of uncertainty and allows a coherent uncertainty and sensitivity characterization across all Risk Assessment modules in CLiCC; 12) completed case studies for Sustainable Apparel Coalition, Ecolab, and Sherwin-Williams, incorporating Predictive Life Cycle Impact Assessment, Quantitative Structure-Activity Relationship Toxicity, Fate & Transport, and Exposure Assessment as integral components of the CLiCC Tool; 13) continuously performed various outreach activities such as webinars introducing CLiCC, summer workshops for graduate students and professionals, stakeholder meetings and surveys, etc; 14) validated fate and transport module outputs with real-world emissions and measurements of pesticides in the environment; 15) optimized CLiCC modules for running on the backend of the online CLiCC Tool to minimize runtime for users; 16) Programmed an extensive Application Programming Interface (API) allowing CLiCC’s back-end modules to connect with the front-end of the online CLiCC Tool; 17) Contracted and collaborated with professional software engineers and web developers who have created and ensured the security of a beta version of the online CLiCC Tool; and 18) Written detailed draft Technical User Guides for the Exposure and the Fate and Transport modules.
The team's extensive fate and transport, life cycle inventory, and life cycle impact estimation modules have been embedded into a beta version of user-friendly public online tool. The tool allows multiple levels of analysis, for both the layperson and the advanced user to benefit from. Workshops and scholarly achievements in terms of papers and presentations are summarized below with the detailed list in the accompanied progress report.
Future Activities:
First, we will finalize the life cycle inventory module methodology, continue to curate and integrate additional external databases for experimental toxicity values, and expand the uncertainty characterization to applicable sections of all modules. Second, we will compare and analyze the outputs of our modules to those from other similar models developed by other researchers. Eventually an outside expert/organization will be employed to evaluate the validity of out modules.Third, we will continue to improve the CLiCC Tool by designing and performing a robust round of user testing that adheres to industry best practices. This feedback will be used to refine the usability and functionality of the CLiCC Tool. Tutorial content and videos will be developed to aid inexperienced users, and detailed guides for users will be refined to make the CLiCC Tool more transparent.Fourth, we will promote the CLiCC Tool to the widest potential audience possible. We will continue to host training sessions with other organizations, distribute promotion materials at professional events, hold special sessions highlighting the CLiCC Tool at international conferences, etc.
Journal Articles on this Report : 15 Displayed | Download in RIS Format
| Other project views: | All 34 publications | 24 publications in selected types | All 24 journal articles |
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Bergesen JD, Suh S. A framework for technological learning in the supply chain: a case study on CdTe photovoltaics. Applied Energy 2016;169:721-728. |
R835579 (2015) R835579 (2016) R835579 (2017) R835579 (2018) |
Exit Exit Exit |
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Cucurachi S, Suh S. A moonshot for sustainability assessment. Environmental Science & Technology 2015;49(16):9497-9498. |
R835579 (2016) R835579 (2017) R835579 (2018) |
Exit Exit Exit |
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Cucurachi S, Suh S. Cause-effect analysis for sustainable development policy. Environmental Reviews 2017;25(3):358-379. |
R835579 (2016) R835579 (2017) R835579 (2018) |
Exit Exit |
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Fabin D. Quantifying the potential for lead pollution from halide perovskite photovoltaics. The Journal of Physical Chemistry Letters 2015;6(18):3546-3548. |
R835579 (2016) R835579 (2017) R835579 (2018) |
Exit Exit Exit |
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Garner KL, Suh S, Lenihan HS, Keller AA. Species sensitivity distributions for engineered nanomaterials. Environmental Science & Technology 2015;49(9):5753-5759. |
R835579 (2015) R835579 (2016) R835579 (2017) R835579 (2018) |
Exit Exit Exit |
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Garner KL, Suh S, Keller AA. Assessing the risk of engineered nanomaterials in the environment: development and application of the nanoFate model. Environmental Science & Technology 2017;51(10):5541-5551. |
R835579 (2015) R835579 (2016) R835579 (2017) R835579 (2018) |
Exit Exit Exit |
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Ghadbeigi L, Harada JK, Lettiere BR, Sparks TD. Performance and resource considerations of Li-ion battery electrode materials. Energy & Environmental Science 2015;8(6):1640-1650. |
R835579 (2015) R835579 (2016) R835579 (2017) R835579 (2018) |
Exit Exit Exit |
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Katelhon A, Bardow A, Suh S. Stochastic technology choice model for Consequential Life Cycle Assessment. Environmental Science & Technology 2016;50(23):12575-12583. |
R835579 (2015) R835579 (2016) R835579 (2017) R835579 (2018) |
Exit Exit Exit |
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Katelhon A, von der Assen N, Suh S, Jung J, Bardow A. Industry-cost-curve approach for modeling the environmental impact of introducing new technologies in life cycle assessment. Environmental Science & Technology 2015;49(13):7543-7551. |
R835579 (2016) R835579 (2017) R835579 (2018) |
Exit Exit Exit |
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Qin Y, Suh S. What distribution function do life cycle inventories follow? The International Journal of Life Cycle Assessment 2017;22(7):1138-1145. |
R835579 (2016) R835579 (2017) R835579 (2018) |
Exit Exit |
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Song R, Qin Y, Suh S, Keller AA. Dynamic model for the stocks and release flows of engineered nanomaterials. Environmental Science & Technology 2017;51(21):12424-12433. |
R835579 (2016) R835579 (2017) R835579 (2018) R835800 (Final) |
Exit Exit Exit |
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Song R, Keller AA, Suh S. Rapid life-cycle impact screening using artificial neural networks. Environmental Science & Technology 2017;51(18):10777-10785. |
R835579 (2016) R835579 (2017) R835579 (2018) |
Exit Exit Exit |
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Suh S, Qin Y. Pre-calculated LCIs with uncertainties revisited. International Journal of Life Cycle Assessment 2017;22(5):827-831. |
R835579 (2015) R835579 (2016) R835579 (2017) R835579 (2018) |
Exit Exit |
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Tsang MP, Li D, Garner KL, Keller AA, Suh S, Sonnemann GW. Modeling human health characterization factors for indoor nanomaterial emissions in life cycle assessment: a case-study of titanium dioxide. Environmental Science: Nano 2017;4(8):1705-1721. |
R835579 (2016) R835579 (2017) R835579 (2018) |
Exit Exit |
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Yang Y, Suh S. Changes in environmental impacts of major crops in the US. Environmental Research Letters 2015;10(9):094016. |
R835579 (2016) R835579 (2017) R835579 (2018) |
Exit Exit Exit |
Relevant Websites:
The CLiCC Tool is now live in beta Exit
We have built a website to showcase our progress and achievements Exit
Progress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.
Project Research Results
- Final Report
- 2018 Progress Report
- 2016 Progress Report
- 2015 Progress Report
- 2014 Progress Report
- Original Abstract
24 journal articles for this project