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The effects of temperature, pH, and gas-to-liquid-volume-ratio on ammonia recovery via gas–liquid stripping have been widely studied. However, there is a lack of a structured approach towards characterising the stripping process. Furthermore, limited information is available on the effect of the composition of the stripping gas on ammonia recovery. This study includes the application of a factorial design of experiments to ammonia stripping. The outcome is a mathematical relationship for ammonia recovery as a function of process conditions. The temperature was found to have the highest influence on ammonia recovery. With respect to the influence of the stripping gas, similar ammonia recoveries were reported when using air, CH4, and N2 (96, 92, and 95%, respectively). This was attributed to their similar influences on the pH of the digestate, and subsequently, on the free ammonia equilibrium. In addition, the presence of CO2 in the stripping gas had a critical effect on ammonia recovery due to its influence on the total ammonia equilibrium in the digestate. These results showed the possibility of using different stripping gases interchangeably to obtain similar ammonia recoveries, with a critical emphasis on their CO2 content.

The effects of temperature, pH, and gas-to-liquid-volume-ratio on ammonia recovery via gas–liquid stripping have been widely studied. However, there is a lack of a structured approach towards characterising the stripping process. Furthermore, limited information is available on the effect of the composition of the stripping gas on ammonia recovery. This study includes the application of a factorial design of experiments to ammonia stripping. The outcome is a mathematical relationship for ammonia recovery as a function of process conditions. The temperature was found to have the highest influence on ammonia recovery. With respect to the influence of the stripping gas, similar ammonia recoveries were reported when using air, CH4, and N2 (96, 92, and 95%, respectively). This was attributed to their similar influences on the pH of the digestate, and subsequently, on the free ammonia equilibrium. In addition, the presence of CO2 in the stripping gas had a critical effect on ammonia recovery due to its influence on the total ammonia equilibrium in the digestate. These results showed the possibility of using different stripping gases interchangeably to obtain similar ammonia recoveries, with a critical emphasis on their CO2 content.

To assess the potential environmental impact of human/industrial systems, life cycle assessment (LCA) is a very common method. There are two prominent types of LCA, namely attributional (ALCA) and consequential (CLCA). A lot of literature covers these approaches, but a general consensus on what they represent and an overview of all their differences seems lacking, nor has every prominent feature been fully explored. The two main objectives of this article are: (1) to argue for and select definitions for each concept and (2) specify all conceptual characteristics (including translation into modelling restrictions), re-evaluating and going beyond findings in the state of the art. For the first objective, mainly because the validity of interpretation of a term is also a matter of consensus, we argue the selection of definitions present in the 2011 UNEP-SETAC report. ALCA attributes a share of the potential environmental impact of the world to a product life cycle, while CLCA assesses the environmental consequences of a decision (e.g., increase of product demand). Regarding the second objective, the product system in ALCA constitutes all processes that are linked by physical, energy flows or services. Because of the requirement of additivity for ALCA, a double-counting check needs to be executed, modelling is restricted (e.g., guaranteed through linearity) and partitioning of multifunctional processes is systematically needed (for evaluation per single product). The latter matters also hold in a similar manner for the impact assessment, which is commonly overlooked. CLCA, is completely consequential and there is no limitation regarding what a modelling framework should entail, with the coverage of co-products through substitution being just one approach and not the only one (e.g., additional consumption is possible). Both ALCA and CLCA can be considered over any time span (past, present & future) and either using a reference environment or different scenarios. Furthermore, both ALCA and CLCA could be specific for average or marginal (small) products or decisions, and further datasets. These findings also hold for life cycle sustainability assessment.

To assess the potential environmental impact of human/industrial systems, life cycle assessment (LCA) is a very common method. There are two prominent types of LCA, namely attributional (ALCA) and consequential (CLCA). A lot of literature covers these approaches, but a general consensus on what they represent and an overview of all their differences seems lacking, nor has every prominent feature been fully explored. The two main objectives of this article are: (1) to argue for and select definitions for each concept and (2) specify all conceptual characteristics (including translation into modelling restrictions), re-evaluating and going beyond findings in the state of the art. For the first objective, mainly because the validity of interpretation of a term is also a matter of consensus, we argue the selection of definitions present in the 2011 UNEP-SETAC report. ALCA attributes a share of the potential environmental impact of the world to a product life cycle, while CLCA assesses the environmental consequences of a decision (e.g., increase of product demand). Regarding the second objective, the product system in ALCA constitutes all processes that are linked by physical, energy flows or services. Because of the requirement of additivity for ALCA, a double-counting check needs to be executed, modelling is restricted (e.g., guaranteed through linearity) and partitioning of multifunctional processes is systematically needed (for evaluation per single product). The latter matters also hold in a similar manner for the impact assessment, which is commonly overlooked. CLCA, is completely consequential and there is no limitation regarding what a modelling framework should entail, with the coverage of co-products through substitution being just one approach and not the only one (e.g., additional consumption is possible). Both ALCA and CLCA can be considered over any time span (past, present & future) and either using a reference environment or different scenarios. Furthermore, both ALCA and CLCA could be specific for average or marginal (small) products or decisions, and further datasets. These findings also hold for life cycle sustainability assessment.

Due to an increase in international demand, quinoa production has boomed tremendously in the Andes since the early 2010s. This has led to significant investments into developing improved varieties for large-scale agribusinesses, but breeding programs are not tailored to the needs of smallholders. In this paper, we study farmers’ preferences and willingness to pay for improved quinoa varieties in the Junín region in Peru. We use data from a choice experiment among 458 smallholders and estimate generalized multinomial logit models to control for preference and scale heterogeneity. We find that farmers generally prefer improved varieties over traditional varieties, with mildew-resistance as the most important crop trait. In general, farmers prefer varieties that are characterized by larger grain sizes, higher yield levels, lower levels of saponin, and a reduced maturation period. Yet, food-insecure farmers are found to be indifferent to early maturity and a larger grain size, which can be explained by a lower degree of commercialization among these farmers. Our results imply that developing mildew-tolerant and higher-yielding varieties with a medium to low saponin content is a priority if investments in quinoa technologies are to benefit small-scale and food insecure farmers in the Andean highlands of Peru.

Due to an increase in international demand, quinoa production has boomed tremendously in the Andes since the early 2010s. This has led to significant investments into developing improved varieties for large-scale agribusinesses, but breeding programs are not tailored to the needs of smallholders. In this paper, we study farmers’ preferences and willingness to pay for improved quinoa varieties in the Junín region in Peru. We use data from a choice experiment among 458 smallholders and estimate generalized multinomial logit models to control for preference and scale heterogeneity. We find that farmers generally prefer improved varieties over traditional varieties, with mildew-resistance as the most important crop trait. In general, farmers prefer varieties that are characterized by larger grain sizes, higher yield levels, lower levels of saponin, and a reduced maturation period. Yet, food-insecure farmers are found to be indifferent to early maturity and a larger grain size, which can be explained by a lower degree of commercialization among these farmers. Our results imply that developing mildew-tolerant and higher-yielding varieties with a medium to low saponin content is a priority if investments in quinoa technologies are to benefit small-scale and food insecure farmers in the Andean highlands of Peru.

Due to the similarity in skills and assets, Civilian-Military collaboration has emerged as one of the most reliable partnerships during the disaster and public health emergency management to address all necessary elements of surge capacity, i.e., staff, stuff, structure (space), and systems. This study aimed to evaluate this collaboration before and during the coronavirus 2019 pandemic. The outcomes of the systematic review revealed several published reports on successful civilian-military collaboration and proposed a need for further improvement. One hundred sixty-six individuals from 19 countries responded to nine questions, included in an online survey with the possibility to leave comments if necessary. The questionnaire referred to elements such as command and control, safety, communication, assessment, triage, treatment, and transport, as the crucial components of emergency management. The comprehensive examination of the survey results together with registered comments revealed a possible improvement in collaboration particularly on the strategic levels, i.e., meetings at the command-and-control level, safety, communication, and networking issues. While logistic collaboration seemed to be unchanged, the practical parts of the collaboration, i.e., clinical and non-clinical operational partnership (Triage and Treatment), mutual education, training, and operational understanding of each organization remained unchanged. In conclusion, although the current pandemic may have facilitated a more intense collaboration between civilian and military healthcare organizations, it lacks practical partnership and operative engagement, representing two crucial elements necessary for harmony and compatibility of both systems. Such collaboration may require a political will and perhaps a mutual civilian-military authority.