In microfluidics, typical laboratory processes can be condensed to a miniature device. This reduces analysis time and required volumes of samples and reagents, increases mobility and flexibility, and is cost effective. In this work, the environmental impacts of three microfluidic devices for glucose detection are assessed using a comparative life cycle assessment (LCA) from cradle-to-grave. The three devices are a polydimethylsiloxane (PDMS) device manufactured through soft lithography, a paper device manufactured through wax stamping, and a polylactic acid (PLA) device manufactured through 3D printing. The environmental impacts are determined for two manufacturing scenarios: on laboratory-scale and commercial-scale. The functional unit is 1 act of glucose detection performed on a human sample using 1 microfluidic glucose detection device.
Assuming laboratory-scale manufacturing, the paper device has the lowest environmental impacts, whereas the PLA device has the highest impact. The main contributing processes are those pertaining to the devices' manufacture. For the PDMS device, these are the processes for soft lithography, for the paper device it is the paraffin use, while for the PLA device it is the 3D printing. Assuming commercial-scale manufacturing, the PLA device has the lowest environmental impact, whereas the PDMS device has the highest impact. This scenario was modelled by improving efficiencies in the manufacturing of the PDMS and paper device, and substituting 3D printing for injection moulding for the PLA device.
To reduce the devices' environmental impacts, a general recommendation is to transition to an electricity mix based on renewables rather than fossil fuels. For the PDMS device, a recommendation is to redesign the device such that the user can apply reagents before use, thereby avoiding emissions by cutting out a need for refrigeration, which is present in the current design. For the paper device a recommendation is to minimise the waste of paraffin, as it is the main contributor to the device's environmental impacts. Lastly, for the paper and PLA device it is recommended to consider alternate manufacturing methods when upscaling production. Their current manufacturing methods are ideal for prototyping, but are inefficient on a commercial scale.
Some limitations are that several data points were estimated, cut off, or secondary. Data gaps were bridged through the use of proxies and stoichiometry for chemicals, which affected the accuracy of the model. Furthermore, recycling and chance of failure during manufacture are not accounted for. Lastly, as microfluidic devices are designed in many different ways, the results cannot be translated one-to-one to other devices. They can only provide a general idea of what the impacts for other devices might be.
A continuation of this work could simulate the synthesis of chemicals using process design software, for increased accuracy. Another opportunity for further study is to collect and implement primary data and assess how that affects the results. Future research could investigate fields of microfluidics other than diagnostics. Generally, more research is necessary to model a proper ex-ante LCA with various scenarios. With enough research, microfluidics might fully reach its potential, while being environmentally responsible.

The aim of this study was to identify the most sustainable way to produce char from sewage sludge that later can be used as a coal substitute for the steel industry. For this purpose, a literature review was conducted on the latest technological developments for the production of char, which were subsequently simulated in Aspen Plus to obtain process-specific details. The results of the process simulation are then combined with the Life-Cycle-Assessment (LCA) database EcoInvent to assess the environmental impact of the entire value chain for all countries of the European Union using the LCA methodology according to the ISO 14040 framework and the environmental footprint impact family. This family consists of 19 impact categories of which five relevant categories were chosen: (I) climate change, (II) acidification, (III) freshwater ecotoxicity, (IV) freshwater eutrophication and (V) marine eutrophication. In a second analysis the LCA is combined with the integrated assessment model REMIND, which represents the technological evolution in the future. This allows to evaluate the environmental impact in the years 2030 and 2040.
From the literature review, two technology developments that significantly can reduce the overall electricity consumption of the process were identified: an industrial dryer that can dry sewage sludge at 90% efficiency and a heat pump that can convert moist exhaust air into process heat with an efficiency of 400%. Furthermore, the Torwash process was identified, as a pre-processing step for sewage sludge, which increases the efficiency of the filter process and the subsequent drying of the sludge. Based on the results of the literature research, three different technology scenarios were defined. (I) In the baseline scenario, the gases and bio-oils formed during the slow pyrolysis reaction are burned to produce sufficient process heat for the pyrolysis unit and the drying step. (II) In the Torwash scenario, the feedstock is thermally treated before the filtration, which allows a higher solid content in the filter cake and thus reduces the energy consumption in the drying step. (III) The bio-oil scenario considers the condensation of bio-oils and provides the missing process heat with electric heaters.
The process simulation in Aspen Plus was used to determine several process-specific details, including the energy content of the combustion process and the resulting CO2 emissions. Moreover, the overall energy balance of the different scenario was calculated and the need for external heat was determined. The process simulation additionally revealed a factor not described in the literature yet that influences the energy consumption of the pyrolysis reaction. The higher the ash content of the feedstock, the less energy is required for the pyrolysis process.
In the LCA, the simulation results were combined with further background processes and environmental information from the LCA database EcoInvent and compared against a business-as-usual alternative. In this alternative, the sludge is incinerated and coal with a comparable heating value to the produced char is provided. The results show that the baseline scenario performs better than the business-as-usual alternative in all impact categories. The Torwash scenario, due to the high energy requirements of the pretreatment, performs worse than the baseline scenario in all impact categories and in all but freshwater eutrophication also worse than the business-as-usual alternative. For the bio-oil scenario, the type of heat supply and the CO2 intensity of the electricity grid is of great importance. If an inefficient resistant heater is used, the bio-oil scenario performs better than the baseline scenario in only half of the European countries, depending on the specific CO2 intensity of the national electricity mixes. If, instead, a highly efficient heat pump is used, the environmental impact is lower for all EU member states for all impact categories. The difference between the baseline and the bio-oil scenario becomes even larger when the environmental impact is determined for 2030 or 2040. If a conservative integrated assessment model which leads to 2.5°C warming by 2050 is used as the basis for technological change, the emissions of the bio-oil scenario are 3.020 kg CO2eq per ton of char, more than 9% lower than the baseline scenario and almost 42% lower than the business-as-usual alternative. In addition, to the ton of char, 120 l of bio-oil are produced, with an economic value that offsets the additional cost of electricity.
Therefore, the strong recommendation is expressed to produce char of sewage sludge according to the bio-oil scenario, with includes the latest drying and heat-pump technologies as well as the co-production of bio-oil.

The activities of humans are increasingly influencing the Earth's systems. The climate is changing and the ecosystems are being affected by our actions. If we stay in the same path, the consequences could be catastrophic. For this reason, there is a growing focus on how to minimise our negative impacts on the planets and develop in a more sustainable manner. To aid in this objective, computers are a useful tool. Computational techniques can offer solutions to complex problems in sustainability, which often involve uncertainty, optimisation and decision making. However, the potential of classical computers is expected to reach a barrier, since the size of computer chips is reaching its physical limit. This requires new solutions that can provide better computational results in the future. A promising paradigm that is emerging is quantum computing, which works with quantum bits (or qubits) to make calculations and produce new solutions.

Given the potential of quantum computers, it is interesting to consider how they could help industrial ecology. Up to date, there have been no studies that focus on these two scientific disciplines together. For this reason, the first aim of this work is to identify what industrial ecology problems can benefit from quantum computing. The second aim is to provide a practical case study to illustrate how to work with quantum computers. In particular, the case study focuses on how to optimise vehicle routings in order to minimise emissions. Although routing problems have been extensively studied from the sustainability point of view, there are no such studies done on quantum computing yet. Overall, the main research question is "how can quantum computing benefit industrial ecology and how can it be applied to a relevant problem?'".

Regarding the methodology, it is separated into different parts. Firstly, a literature review is conducted to identify possible applications for industrial ecology. This is done by considering relevant industrial ecology problems and investigating if any gains can be obtained by using quantum computers. The second part of the methodology focuses on the more practical side of quantum computing. It starts by benchmarking two state-of-the-art variational quantum algorithms (Rosalin and LCB CMA-ES) to test the performance and evaluate the produced results. Following the benchmarking, a green routing problem will be encoded and optimised using the mentioned algorithms. All the coding is done in Python with the help of Cirq and Openfermion, which are two packages for quantum computing developed by Google.

After following the methodology, several results were obtained. The literature review highlighted a series of problems that can benefit from quantum computing. Examples are the optimisation of flows in an industrial park and the management of data centres to reduce energy needs. Moreover, the review illustrated different techniques to tackle these problems, such as quantum machine learning and quantum information. Secondly, as for the benchmarking, both algorithms perform well in the test cases and there is no clear advantage of one over the other. One is slightly better for some test cases and the other slightly better in the other cases. In the logistics problem, the problem is encoded into a hamiltonian that is more complex than the test cases. To minimise the expectation of the hamiltonian, the previously mentioned algorithms are tested with different quantum circuits and the results are compared. LCB CMA-ES with circuit of depth 1 gives the best results, while the other LCB CMA-ES experiments do not do as well. This is unexpected because all the LCB CMA-ES experiments are able to optimise successfully the expectation of the hamiltonian. This indicates that it is possible that the encoding of the routing problem is not optimal. Regarding the Rosalin experiments, they are not able to achieve a desirable result due to the adaptive nature of this algorithm, which does not allow a sufficient number of iterations of the optimisation process.

Concluding, although there is potential for using quantum computing in industrial ecology problems, it is clear that for the moment, classical computers produce the best results. However, quantum computing is evolving rapidly, and there may be a point where the tables are turned and quantum computers become the norm. As for now, the obtained results are far from ideal, but these could improve in the coming years.

The textile and fashion industry is one of the most polluting and resource-wasting industries in the world. At the moment, approximately 55% of all end-of-life textiles are incinerated in the Netherlands as they end up in residual household waste. It is becoming imperative to examine effective ways to improve individual’s textile recycling behaviour to facilitate a more sustainable and circular textile industry. This research seeks to understand how two nudging interventions, designed based on a framework from environmental psychology, can effectively increase textile recycling in the municipality of Leiden, which was used as a local case study. First, an online survey (n = 182) was conducted to explore the determinants of citizen’s textile recycling intention (Study 1). The study used a comprehensive model in which the Norm Activation Model (NAM) and the Theory of Planned Behaviour (TPB) were combined. The multiple regression analysis reveals that attitude was the strongest determinant of intention to recycle textile, followed by outcome efficacy. Guided by the insights from the online survey and nudging theory, an informational nudge and an outcome efficacy nudge were designed and evaluated (Study 2). The effect of the nudging interventions was assessed by conducting a field experiment. The descriptive analysis shows a percentual increase in textile recycling in both treatments groups compared to the control group, however, the findings of the one-way ANOVA analysis demonstrate that this increase was non-statistically significant. Nevertheless, this research shows that the integration of TPB and NAM contributes to a more comprehensive model to encourage textile recycling behaviour. Future studies should investigate the application of the two nudging interventions at a larger scale and the long-term effects. Consequently, the implications of the findings are also discussed and suggestions are provided for local authorities to improve textile recycling schemes and campaigns.

Global plastic production increased from 200 million metric tons in 2002, to 359 million metric tons in 2019. It is estimated that plastics in the marine environment weigh more than 150 million metric tons and by 2050 could even outweigh the mass of fish in the ocean. Over half of the world population (55%) nowadays lives in urban areas and consumes large amounts of materials and turns them into waste, including plastics. Thus, it necessary to understand the plastic mass balance of a city. The goal of this thesis is to assess plastic flows and stocks for an urban area, using the well-established method of Material Flow Analysis (MFA). By using MFA, material flows through society can be assessed in a systematic way. This research answers the following research question: “How can the plastic mass balance of an urban area be modelled using Material Flow Analysis?” This is done by developing a generic framework, which includes eight consumption sectors, three distinguishable littering processes and two subsystems (surface water system and soil). A bottom-up approach is applied and the municipality of Leiden in the Netherlands is used as a case study. The surface water system is analysed as a subsystem, which stores plastics but also transports them through physical forces, like water flow outside of the system. Crowd-sourced data is analysed and its usage for urban MFA assessed. The analysis shows that the biggest plastic flows are packaging flows (4 146 t), plastics in building waste (1 342 t) and plastics in End-of-life transportation systems (570 t). The highest plastic stocks are in buildings (86 080 t), transportation (14 020 t) and electronics devices (10 326 t). It is concluded that the crowd-sourced data set Litterati includes for 2019 for the case of Leiden to little data points in order to represent the plastic litter quantity in the system accurately. This thesis adds to MFA theory by investigating the surface water system and its connection to the environment through physical forces like water flow and wind.

Environmental emissions from transports need to be reduced in order to construct a sustainable society. Nowadays, gasoline and diesel account for 21% of the total carbon emissions in the world. In the Netherlands, important projects are present to develop a hydrogen economy by 2050. Hydrogen can be used as a sustainable fuel alternative for transports as its consumption produces only water. However, the current technologies for hydrogen production possess environmental emissions such as CO2, based on steam reforming and coal gasification. If hydrogen is to be used massively as an energy carrier, a shift in its production paradigm is necessary.
The thesis compares the environmental performances of the two strongest potentials for green hydrogen (without harmful emissions) production: the alkaline and PEM electrolysers. Both of these technologies are based on the same principle: water is split with electricity into oxygen and hydrogen. To produce green hydrogen, the electricity comes from the wind energy, the largest renewable energy source potential in the NL. Both electrolysers are used at a pilot-scale currently. The main question studied in the thesis is to see how the environmental profiles of these two alternatives may evolve when a shift is operated pilot-scale to large-scale implementation. The Life Cycle Assessment tool is used to achieve the assessment and to put the focus on environmental emissions. LCAs at a pilot-scale were constructed based on a literature review. Analyses of the environmental performances from alkaline and PEM electrolysers enabled to settle a list of potentially relevant parameters to consider during upscaling process.
Several technology analyses were conducted (workshop, interviews, collaboration with a company) and combined with the General Morphological Approach (GMA) to create scenarios for 2050. By combining the scenarios constructed with the GMA approach and the pilot-scale LCA models, prospective LCA models were created for potential future electrolyser’s plants. A special focus was made on electrolysers’ parameters to understand clearer their potential influences.
Analyses of the prospective (named “ex-ante”) LCA models and comparisons were made between alkaline and PEM electrolysers. The alkaline electrolyser performs slightly better than PEM, even though the two alternatives possess virtually the same environmental performances. The differences in environmental performances between alkaline and PEM electrolysers are most of the time non-significant. The largest contributor in environmental emissions remains the electricity production (from Dutch wind turbines). That said, other perspective, such as geopolitical ones, may favour more one technology than the other (e.g. the PEM electrolyser consumes noble metals).
Overall, a combination of LCA and GMA methodologies has been applied in the thesis to assess the environmental performances for two electrolysers in 2050, for green hydrogen production. Using renewable energy source decreases significantly the environmental impacts but the electricity production will likely remain the biggest contributor. The electrolysers are unlikely to influence significantly the environmental profile of green hydrogen production. A focus should be made on the electricity’s production.

Life cycle assessment (LCA) has been the primary tool for the quantification and
comparison of the environmental impact of product systems. Despite ongoing
development of the LCA methodology, uncertainties are often not adequately
addressed in LCA research. The large variety of approaches to treat uncertainties and the lack of uniform guidelines prevent a widespread adoption of uncertainty analysis, while its importance is generally acknowledged. Especially in the agricultural sector, where LCA is often used as policy support, uncertainties are of great importance. This thesis aims to provide guidelines for practitioners of agricultural LCA in developing countries, as agriculture is often an important sector there. To this end four different method of uncertainty propagation are tested on a case study of sugarcane cultivation methods in Thailand. These methods are compared and evaluated on their suitability for agricultural LCA in developing countries, complemented by an expert survey in a Thai LCA research network. The results show that there is a general lack of knowledge among LCA practitioners on uncertainty analysis, while primary data is often abundant. Also, sampling methods can provide great insight into the output uncertainties, but are also complex and data intensive. Analytical or fuzzy interval methods could be a good alternative when knowledge or resources are lacking. LCA practitioners should be better guided in choosing and performing the most suitable propagation method for their situation. To this end, a decision tree for choosing the most suitable method depending on the user and the type of study closes this thesis.