Effects of Distribution Choice on the Modeling of Life Cycle Inventory Uncertainty: An Assessment on the Ecoinvent v2.2 Database

Life cycle inventory data have multiple sources of uncertainty. These data uncertainties are often modeled using probability density functions, and in the ecoinvent database the lognormal distribution is used by default to model exchange uncertainty values. The aim of this article is to systematically measure the effect of this default distribution by changing from the lognormal to several other distribution functions and examining how this change affects the uncertainty of life cycle assessment results. Using the ecoinvent 2.2 inventory database, data uncertainty distributions are switched from the lognormal distribution to the normal, triangular, and gamma distributions. The effect of the distribution switching is assessed for both impact assessment results of individual products system, as well as comparisons between product systems. Impact assessment results are generated using 5,000 Monte Carlo iterations for each product system, using the Intergovernmental Panel on Climate Change (IPCC) 2001 (100‐year time frame) method. When comparing the lognormal distribution to the alternative default distributions, the difference in the resulting median and standard deviation values range from slight to significant, depending on the distributions used by default. However, the switch shows practically no effect on product system comparisons. Yet, impact assessment results are sensitive to how the data uncertainties are defined. In this article, we followed what we believe to be ecoinvent standard practice and preserved the “most representative” value. Practitioners should recognize that the most representative value can depart from the average of a probability distribution. Consistent default distribution choices are necessary when performing product system comparisons.     

Synthesis and characterization of 8-ethynyl-1,3-dihydro-benzo[b][1,4]diazepin-2-one derivatives: new potent non-competitive metabotropic glutamate receptor 2/3 antagonists. Part 1

A series of 1,3-dihydrobenzo[b][1,4]diazepin-2-one derivatives was evaluated as non-competitive mGluR2/3 antagonists. Attachment of an 8-(2-aryl)-ethynyl-moiety produced compounds inhibiting the binding of [³H]-LY354740 to rat mGluR2 with low nanomolar affinity and consistent functional effect at both mGluR2 and mGluR3.     

The Environmental Importance of Energy Use in Chemical Production

In many cases, policy makers and laymen perceive harmful emissions from chemical plants as the most important source of environmental impacts in chemical production. As a result, regulations and environmental efforts have tended to focus on this area. Concerns about energy use and greenhouse gas emissions, however, are increasing in all industrial sectors. Using a life cycle assessment (LCA) approach, we analyzed the full environmental impacts of producing 99 chemical products in Western Europe from cradle to factory gate. We applied several life cycle impact assessment (LCIA) methods to cover various impact areas. Our analysis shows that for both organic and inorganic chemical production in industrial countries, energy‐related impacts often represent more than half and sometimes up to 80% of the total impacts, according to a range of LCIA methods. Resource use for material feedstock is also important, whereas direct emissions from chemical plants may make up only 5% to 10% of the total environmental impacts. Additionally, the energy‐related impacts of organic chemical production increase with the complexity of the chemicals. The results of this study offer important information for policy makers and sustainability experts in the chemical industry striving to reduce environmental impacts. We identify more sustainable energy production and use as an important option for improvements in the environmental profile of chemical production in industrial countries, especially for the production of advanced organic and fine chemicals.     

Lifting Industrial Ecology Modeling to a New Level of Quality and Transparency: A Call for More Transparent Publications and a Collaborative Open Source Software Framework

Industrial ecology (IE) is a maturing scientific discipline. The field is becoming more data and computation intensive, which requires IE researchers to develop scientific software to tackle novel research questions. We review the current state of software programming and use in our field and find challenges regarding transparency, reproducibility, reusability, and ease of collaboration. Our response to that problem is fourfold: First, we propose how existing general principles for the development of good scientific software could be implemented in IE and related fields. Second, we argue that collaborating on open source software could make IE research more productive and increase its quality, and we present guidelines for the development and distribution of such software. Third, we call for stricter requirements regarding general access to the source code used to produce research results and scientific claims published in the IE literature. Fourth, we describe a set of open source modules for standard IE modeling tasks that represent our first attempt at turning our recommendations into practice. We introduce a Python toolbox for IE that includes the life cycle assessment (LCA) framework Brightway2, the ecospold2matrix module that parses unallocated data in ecospold format, the pySUT and pymrio modules for building and analyzing multiregion input‐output models and supply and use tables, and the dynamic_stock_model class for dynamic stock modeling. Widespread use of open access software can, at the same time, increase quality, transparency, and reproducibility of IE research.     

Synthesis and biological evaluation of fenobam analogs as mGlu5 receptor antagonists

Optimization of affinity and microsomal stability led to identification of the potent, metabolically stable fenobam analog 4l. Robust in vivo efficacy of 4l was demonstrated in four different models of anxiety. Additionally, a ligand based pharmacophore alignment of fenobam and MPEP is proposed.     

Are there infinitely many trucks in the technosphere,or exactly one? How independent sampling of instances of unit processes affects uncertainty analysis in LCA

Purpose

Product systems use the same unit process models to represent distinct but similar activities. This notably applies to activities in cyclic dependency relationships (or “feedback loops”) that are required an infinite number of times in a product system. The study aims to test the sensitivity of uncertainty results on the assumption made concerning these different instances of the same activities. The default assumption assumes homogeneous production, and the same parameter values are sampled for all instances (e.g., there is one truck). The alternative assumption is that every instance is distinct, and parameter values are independently sampled for different instances of unit processes (e.g., there are infinitely many trucks). Intuitively, sampling the same values for each instance of a unit process should result in more uncertain results.

Methods

The results of uncertainty analyses carried out under either assumption are compared. To simulate models where each instance of a unit process is independent, we convert network models to acyclic LCI models (tree models). This is done three times: (1) for a very simple product system, to explain the methodology; (2) for a sample product system from the ecoinvent database, for illustrative purposes; and (3) for thousands of product systems from ecoinvent databases.

Results and discussion

The uncertainty of network models is indeed greater than that of corresponding tree models. This is shown mathematically for the analytical approximation method to uncertainty propagation and is observed for Monte Carlo simulations with very large numbers of iterations. However, the magnitude of the difference in indicators of dispersion is, for the ecoinvent product systems, often less than a factor of 1.5. In few extreme cases, indicators of dispersion are different by a factor of 4. Monte Carlo simulations with smaller numbers of iterations sometimes give the opposite result.

Conclusions

Given the small magnitude of the difference, we believe that breaking away from the default approach is generally not warranted. Indeed, (1) the alternative approach is not more robust, (2) the current default approach is conservative, and (3) there are more pressing challenges for the LCA community to meet. This being said, the study focused on ecoinvent, which should normally be used as a background database. The difference in dispersion between the two approaches may be important in some contexts, and calculating the uncertainty of tree models as a sensitivity analysis could be useful.

     

Synthesis of heterocyclic enol ethers and their use as group 2 metabotropic glutamate receptor antagonists.

Heterocyclic enol ethers of type 1 were studied with respect to the inhibition of 1S,3R-ACPD (10 microM)-stimulated GTP gamma35S binding on rat mGluR2 transfected cell membranes. The structure activity relationship with regard to the substitution pattern of the phenyl ring, the oxygen substituent and the nature of the heterocycle is discussed.     

Synthesis and characterization of 8-ethynyl-1,3-dihydro-benzo[b][1,4]diazepin-2-one derivatives: part 2. New potent non-competitive metabotropic glutamate receptor 2/3 antagonists

A series of 1,3-dihydro-benzo[b][1,4]diazepin-2-one derivatives was evaluated as non-competitive mGluR2/3 antagonists. Replacement of a cyano group by a five-membered heterocycle produced compounds inhibiting the binding of [(3)H]-LY354740 to rat mGluR2 with low nanomolar affinity and consistent functional effect at both mGluR2 and mGluR3. Further modification to improve the physicochemical properties led eventually to compounds with the ability to reverse LY354740-mediated inhibition of field excitatory postsynaptic potentials in the rat dentate gyrus.