Hybrid modelling of water resource recovery facilities: status and opportunities.

Mathematical modelling is an indispensable tool to support water resource recovery facility (WRRF) operators and engineers with the ambition of creating a truly circular economy and assuring a sustainable future. Despite the successful application of mechanistic models in the water sector, they show some important limitations and do not fully profit from the increasing digitalisation of systems and processes. Recent advances in data-driven methods have provided options for harnessing the power of Industry 4.0, but they are often limited by the lack of interpretability and extrapolation capabilities. Hybrid modelling (HM) combines these two modelling paradigms and aims to leverage both the rapidly increasing volumes of data collected, as well as the continued pursuit of greater process understanding. Despite the potential of HM in a sector that is undergoing a significant digital and cultural transformation, the application of hybrid models remains vague. This article presents an overview of HM methodologies applied to WRRFs and aims to stimulate the wider adoption and development of HM. We also highlight challenges and research needs for HM design and architecture, good modelling practice, data assurance, and software compatibility. HM is a paradigm for WRRF modelling to transition towards a more resource-efficient, resilient, and sustainable future.

Global Sensitivity Analysis of Background Life Cycle Inventories.

In recent years many Life Cycle Assessment (LCA) studies have been conducted to quantify the environmental performance of products and services. Some of these studies propagated numerical uncertainties in underlying data to LCA results, and several applied Global Sensitivity Analysis (GSA) to some parts of the LCA model to determine its main uncertainty drivers. However, only a few studies have tackled the GSA of complete LCA models due to the high computational cost of such analysis and the lack of appropriate methods for very high-dimensional models. This study proposes a new GSA protocol suitable for large LCA problems that, unlike existing approaches, does not make assumptions on model linearity and complexity and includes extensive validation of GSA results. We illustrate the benefits of our protocol by comparing it with an existing method in terms of filtering of noninfluential and ranking of influential uncertainty drivers and include an application example of Swiss household food consumption. We note that our protocol obtains more accurate GSA results, which leads to better understanding of LCA models, and less data collection efforts to achieve more robust estimation of environmental impacts. Implementations supporting this work are available as free and open source Python packages.

A novel machine-learning approach for evaluating rebounds-associated environmental footprint of households and application to cooperative housing.

Multiple environmental policies aim to increase resource efficiency and reduce consumption of goods and services with high environmental impact. This may lead to cost-savings and, consequently, additional consumption with environmental impacts (rebound effects). In this study, a supervised machine-learning model (i.e. an application of random forest regression) is developed to quantify consumption rebound effects. In contrast to previous approaches, it is a versatile method, which allows to estimate any income-related rebound at household level considering specific household properties and the entire profile of consumption. Socio-economic properties (e.g. income, age group) of the households are used as the independent properties for the regressor to detect the dependent consumption expenses of the households. Thus, this method can be used as a bottom-up study for understanding rebounds and developing targeted measures to prevent or reduce rebound effects. To illustrate the application of the method, it is applied to the case of cooperative housing in Switzerland. In addition to environmental goals, the cooperative aims to provide affordable housing, and the reduced rent increases the disposable income of tenants. The results show that households tend to spend the 'extra' income on housing (e.g. for larger apartments) and travel. For the former, the cooperative already has a policy in place regulating the apartment area permitted per person, which delimits induced environmental impacts. For the latter, households with lower income particularly spend their extra-money on purchase and operation of vehicles, while higher-income groups rather spend it on recreation and package holidays. Travel, housing, clothing and personal care products have highest emissions per Swiss Franc (∼0.3-0.6 kg CO2-eq/CHF). Thus, it is recommended to provide incentives for shifting these expenses to other consumption, to avoid jeopardizing environmental goals. The method was also used for a range of other applications e.g. rebounds due to energy-efficient devices to illustrate its versatility.

Noise footprint from personal land-based mobility.

A large part of the world population is exposed to noise levels that are unhealthy. Yet noise is often neglected when impact assessment studies are conducted and when policy interventions are designed. In this study, we provide a way to calculate the noise footprint of citizens directly determined by their use of private and public transport on land. The study combines the results of the large transport simulation model MATSim applied to Switzerland, with a noise characterization model, N-LCA, developed in the context of life cycle assessment. MATSim results allow tracking the use of private and public transportation by agents in the model. The results after characterization provide a consumption-based noise footprint, thus the total noise and impacts that are caused by the private mobility demand of the citizens of Switzerland. Our results confirm that road transportation is the largest contributor to the total noise footprint of land-based mobility. We also included a scenario with a full transition to an electrified car fleet, which showed the potential for the reduction of impacts, particularly in urban areas, by about 55% as compared to the modeled regime with combustion engines.

Using Data Mining To Assess Environmental Impacts of Household Consumption Behaviors.

Household consumption is a main driver of economy and might be regarded as ultimately responsible for environmental impacts occurring over the life cycle of products and services. Given that purchase decisions are made on household levels and are highly behavior-driven, the derivation of targeted environmental measures requires an understanding of household behavior patterns and the resulting environmental impacts. To provide an appropriate basis in support of effective environmental policymaking, we propose a new approach to capture the variability of lifestyle-induced environmental impacts. Lifestyle-archetypes representing prevailing consumption patterns are derived in a two-tiered clustering that applies a Ward-clustering on top of a preconditioning self-organizing map. The environmental impacts associated with specific archetypical behavior are then assessed in a hybrid life cycle assessment framework. The application of this approach to the Swiss Household Budget Survey reveals a global picture of consumption that is in line with previous studies, but also demonstrates that different archetypes can be found within similar socio-economic household types. The appearance of archetypes diverging from general macro-trends indicates that the proposed approach might be useful for an enhanced understanding of consumption patterns and for the future support of policymakers in devising effective environmental measures targeting specific consumer groups.