Chemically enhanced primary treatment: Modelling and Resources Recovery

The chemically enhanced primary treatment (CEPT) process is gaining momentum for carbon redirection, thereby lowering the downstream liquid train load and maximizing energy recovery from the primary sludge. CEPT technique enhances coagulation and flocculation, that enable faster settling of particulate and colloidal solids and dissolved ions such as phosphate to enhance removal efficiency. Comprehending the dynamic behaviour of CEPT clarifiers is critical to not only develop a reliable whole plant simulation but also fully understand the efficacy of the treatment plant performance. To achieve such purposes, a great amount of efforts has been contributed to the advancement of primary clarifier models. Modelling and controlling the primary clarifier have vital impacts to characterize the downstream biological and sludge treatment performance accurately, and subsequently, the full plant modelling (WWTP). This research was conducted to compares and contrasts the performance of three primary clarifier models, including volume-less point separator, three-compartments clarifier and layered flux clarifier concerning the accuracy in describing the CEPT performance. Most importantly, the characterization focused on the models\u27 capability to accurately represent COD fractions, including colloidal COD (CCOD), soluble COD (SCOD), particulate COD(XCOD) and suspended solids (TSS) concentrations of the influent wastewater. For practical applications, our study has shown that among the three clarifier models, the three-compartments model accurately describes the effluent fractions, provides a better description of chemicals addition impacts, and comparatively a more straightforward calibration procedure. Furthermore, the impact of CEPT on the downstream solid train processes was also investigated. The experimental study was conducted on CEPT sludges with different pretreatments (ozonation and low-temperature thermal alkali pretreatments (LTTAP) in relation to the performance of anaerobic digestion for resource recovery. LTTAP process demonstrated the highest RP fraction of 53.33% in ferric based sludge (CEPT-I sludge), whereas ozonation process showed the highest RP fraction 76.38% in ferric alum- based sludge (CEPT-II sludge). Interestingly, after anaerobic digestion, not many differences were observed in the RP fraction from the pretreated samples for CEPT sludges and the control samples, implying pre-treatments may not be required due to the naturally occurred NRP conversion during digestion. Noteworthy, ferric-based sludge(control) produced 7% high methane yield than ferric alum-based sludge, indicated the inhibitory effect by the PACl coagulant on sludge digestion

Interaction of acetamiprid with extracellular polymeric substances (EPS) from activated sludge: A fluorescence study

Extracellular polymeric substances (EPS) are important components of activated sludge and it plays an important role in removing pollutants. The interaction between EPS and organic pollutants is still little known. In the present study, the interaction of soluble/bound EPS with acetamiprid, a neonicotinoid insecticide, was investigated using the three-dimensional excitation–emission matrix (EEM) fluorescence spectroscopy. The fluorescence spectra of EPS revealed that there were two classes of protein-like fluorophores in soluble/bound EPS and one class of fulvic acid-like fluorophore, in addition, in bound EPS. The quenching of protein-like fluorescence by acetamiprid indicated that static quenching (at peak B) and combined quenching (at peak A) occurred simultaneously. The interaction of acetamiprid with EPS was observed to have resulted in the formation of acetamiprid-EPS complexes. The binding constants of the soluble EPS for acetamiprid were greater than those of the bound EPS, indicating the soluble EPS had stronger binding capacity for acetamiprid than the bound EPS. This study confirmed that EPS (soluble/bound) play important roles in biosorption of organic pollutants by activated sludge and also indicated that they may serve as a protective barrier against toxic organic matter, for the microorganisms.Key words: Extracellular polymeric substance (EPS), activated sludge, fluorescence quenching, binding constant, acetamiprid

Applying federated learning to combat food fraud in food supply chains

Ensuring safe and healthy food is a big challenge due to the complexity of food supply chains and their vulnerability to many internal and external factors, including food fraud. Recent research has shown that Artificial Intelligence (AI) based algorithms, in particularly data driven Bayesian Network (BN) models, are very suitable as a tool to predict future food fraud and hence allowing food producers to take proper actions to avoid that such problems occur. Such models become even more powerful when data can be used from all actors in the supply chain, but data sharing is hampered by different interests, data security and data privacy. Federated learning (FL) may circumvent these issues as demonstrated in various areas of the life sciences. In this research, we demonstrate the potential of the FL technology for food fraud using a data driven BN, integrating data from different data owners without the data leaving the database of the data owners. To this end, a framework was constructed consisting of three geographically different data stations hosting different datasets on food fraud. Using this framework, a BN algorithm was implemented that was trained on the data of different data stations while the data remained at its physical location abiding by privacy principles. We demonstrated the applicability of the federated BN in food fraud and anticipate that such framework may support stakeholders in the food supply chain for better decision-making regarding food fraud control while still preserving the privacy and confidentiality nature of these data

Revisit the performance of MODIS and VIIRS leaf area index products from the perspective of time-series stability

As an essential vegetation structural parameter, leaf area index (LAI) is involved in many critical biochemical processes, such as photosynthesis, respiration, and precipitation interception. The MODerate resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imager Radiometer Suite (VIIRS) LAI sequence products have long supported various global climate, biogeochemistry, and energy flux research. These applications all rely on the accuracy of the product’s long time series. However, uncontrolled interferences (e.g., adverse observation conditions and sensor uncertainties) potentially introduce substantial uncertainties to time series in product applications. As one of the most sensitive areas in response to global climate change, the Tibet Plateau (TP) has been treated as a crucial testing ground for thousands of studies on vegetation. To ensure the credibility of the studies arising from MODIS/VIIRSLAI products, the temporal quality uncertainties of data need to be clarified. This article proposed a method to revisit the temporal stability of the MODIS (MOD and MYD) and VIIRS (VNP) LAI in the TP, expecting to provide useful information for better accounting for the uncertainties in this area. Results show that the MODIS and VIIRS LAI were relatively stable in time series and available to be used continuously, among which the temporal quality of the MODIS LAI was the most stable. Moreover, the MODIS and VIIRS LAI products performed similarly in both time-series stability and time-series anomaly distribution, magnitudes and fluctuations. The time-series stability evaluation strategy applied to the MODIS and VIIRS LAI can also be employed to other remote sensing products.Published versio

Global media as an early warning tool for food fraud; an assessment of MedISys-FF

Food fraud is a serious problem that may compromise the safety of the food products being sold on the market. Previous studies have shown that food fraud is associated with a large variety of food products and the fraud type may vary from deliberate changing of the food product (i.e. substitution, tampering, dilution etc.) to the manipulation of documents. It is therefore important that all actors within the food supply chain (food producers, authorities), have methodologies and tools available to detect fraudulent products at an early stage so that preventative measures can be taken. Several of such systems exist (i.e. iRASFF, EMA, HorizonScan, AAC-FF, MedISys-FF), but currently only MedISys-FF is publicly online available. In this study, we analyzed food fraud cases collected by MedISys-FF over a 6-year period (2015–2020) and show global trends and developments in food fraud activities. In the period investigated, the system has collected 4375 articles on food fraud incidents from 164 countries in 41 different languages. Fraud with meat and meat products were most frequently reported (27.7%), followed by milk and milk products (10.5%), cereal and bakery products (8.3%), and fish and fish products (7.7%). Most of the fraud was related to expiration date (58.3%) followed by tampering (22.2%) and mislabeling of country of origin (11.4%). Network analysis showed that the focus of the articles was on food products being frauded. The validity of MedISys-FF as an early warning system was demonstrated with COVID- 19. The system has collected articles discussing potential food fraud risks due to the COVID-19 crisis. We therefore conclude that MedISys-FF is a very useful tool to detect early trends in food fraud and may be used by all actors in the food system to ensure safe, healthy, and authentic food

Benchmarking the environmental performance of dairy farming systems

Milk production has a major impact on the environment and competes increasingly for scarce resources. As the demand for milk is expected to increase, these issues are likely to worsen. Benchmarking the environmental performance of dairy farming systems offers the opportunity to identify best farm practices and to provide guidance for reducing the environmental impact. Currently, benchmarking is hampered by the lack of an effective method that results in a set of indicators that is easily quantifiable and detects variations in environmental performance between farms. The aim of this thesis, therefore, was to develop a sound method to benchmark the environmental performance of dairy farming systems. This thesis focuses is on specialized dairy farming systems in Europe. The first challenge in benchmarking the environmental performance of dairy farming systems is to select a set of indicators that are relevant, measurable, valid, timely and understandable. Environmental indicators can be derived from various approaches, including a nutrient balance (NB) approach and a life cycle assessment (LCA). An NB is generally applied at farm level, and yields indicators that are relatively easy to quantify and communicate. We found that an NB at farm level can be used to benchmark dairy farming systems, if differences in on-farm losses are large and off-farm losses are relatively unimportant. Only if farms differ largely in the amount and/or type of purchased inputs, such as feed, the farm-based NB should be extended to a chain based NB or an LCA. An LCA, however, requires extensive data information, which can be difficult to collect. We, therefore, explored correlations between eight commonly used NB and LCA indicators with the system boundary from cradle-to-farm gate. We found that a set indicators, consisting of the nitrogen surplus, the phosphorus surplus, land use and energy use can be used as a proxy to benchmark the environmental performance of dairy farming systems, representing also global warming potential, acidification potential, freshwater eutrophication potential and marine eutrophication potential. The second challenge in benchmarking the environmental performance of dairy farming systems is to cope with data uncertainties. We therefore first evaluated the effect of epistemic uncertainty on benchmarking the nitrogen use efficiency of dairy systems. We found that ranking of farms based on this single indicator is not possible when the epistemic uncertainty of parameters is large and differences in N use efficiency are small. We, furthermore, identified the most influential parameters (e.g. input of concentrates, mineral fertilizer ) and found that reducing epistemic uncertainty of those parameters improved benchmarking results significantly. Afterwards, we demonstrated how to use fuzzy data envelopment analysis (DEA) to account for uncertainties of multiple indicators in benchmarking the eco-efficiency of dairy farming systems. With fuzzy DEA, the number of farms receiving the highest efficiency score was lower compared to standard DEA. In addition, fuzzy DEA identified a different set of peers than standard DEA. By taking uncertainty into account during the quantification processes, fuzzy DEA can contribute to increasing the reliability of results and prevent biased conclusions. Exploring correlations between environmental indicators can facilitate decision-makers to derive an effective set of indicators that can be used as proxies for benchmarking. In addition, decision-makers should acknowledge the effect of epistemic uncertainty on benchmarking results. When setting up reference values for penalty, for example, this value should be based on a range rather than a single value in order to account for epistemic uncertainty.</p

Mobile Apps for Green Food Practices and the Role for Consumers: A Case Study on Dining Out Practices with Chinese and Dutch Young Consumers

Mobile applications (apps) have become popular among consumers to facilitate their existing food practices like cooking, shopping, and dining out. However, the feasibility of using mobile apps to facilitate sustainability transitions in food consumption is not well researched. In this study, we, therefore, propose a conceptual framework to illustrate how mobile apps can be developed in linking everyday food practices with sustainability transitions. Through the case study of dining out and with the help of focus group discussions, we seek to illustrate that practice theory might serve as a useful starting point for understanding the dynamics of food practices, their relevant sustainability dimensions, and the ways in which mobile apps can be used for changing current food practices into more sustainable ones. Among our main results are the findings that consumers prefer the sustainability food app to be integrated with dominant or mainstream apps, which are already used by consumers in the context of dining out. Besides being simple, functional, flexible, and rewarding, the information provided by the app should be reliable and trustworthy. Moreover, both science-based and practice-based information is necessary to provide sufficient guidance to consumers on how changes in food practice can be operationalized and implemented

Thermal Decomposition Mechanism and Kinetics Study of Plastic Waste Chlorinated Polyvinyl Chloride

Chlorinated polyvinyl chloride (CPVC), as a new type of engineering plastic waste, has been used widely due to its good heat resistance, mechanical properties and corrosion resistance, while it has become an important part of solid waste. The pyrolysis behaviors of CPVC waste were analyzed based on thermogravimetric experiments to explore its reaction mechanism. Compared with polyvinyl chloride (PVC) pyrolysis, CPVC pyrolysis mechanism was divided into two stages and speculated to be dominated by the dehydrochlorination and cyclization/aromatization processes. A common model-free method, Flynn-Wall-Ozawa method, was applied to estimate the activation energy values at different conversion rates. Meanwhile, a typical model-fitting method, Coats-Redfern method, was used to predict the possible reaction model by the comparison of activation energy obtained from model-free method, thereby the first order reaction-order model and fourth order reaction-order model were established corresponding to these two stages. Eventually, based on the initial kinetic parameter values computed by model-free method and reaction model established by model-fitting method, kinetic parameters were optimized by Shuffled Complex Evolution algorithm and further applied to predict the CPVC pyrolysis behaviors during the whole temperature range

Synergistic Effects of Aluminum Diethylphosphinate and Melamine on Improving the Flame Retardancy of Phenolic Resin

A series of novel flame retardants (aluminum diethylphosphinate and melamine) were used to improve the fire performance of phenolic resin. Fourier transform infrared spectroscopy (FTIR) was used to characterize the modification results. Thermo-gravimetric analysis (TGA) was used to study the thermal decomposition of phenolic resin system, and the flame retardancy of phenolic resin system was tested by vertical combustion test (UL-94) and limiting oxygen index (LOI). The combustion properties of modified phenolic resin were further tested with a cone calorimeter(CCT). Finally, the structure of carbon residue layer was measured by scanning electron microscopy (SEM). The results show that with the introduction of 10 wt % aluminum diethylphosphinate in phenolic resin, the LOI reaches 33.1%, residual carbon content increase to 55%. The heat release rate (HRR) decreased to 245.6 kW/m2, and the total heat release (THR) decreased to 58.6 MJ/m2. By adding 10 wt % aluminum diethylphosphinate and 3 wt % melamine, the flame retardancy of the modified resin can pass UL-94 V-0 flame retardant grade, LOI reaches 34.6%, residual carbon content increase to 59.5%. The HRR decreases to 196.2 kW/m2 at 196 s, relatively pure phenolic resin decreased by 35.5%, and THR decreased to 51 MJ/m2. Compared with pure phenolic resin, the heat release rate and total heat release of modified phenolic resin decreased significantly. This suggests that aluminum diethylphosphinate and melamine play a nitrogen-phosphorus synergistic effect in the phenolic resin, which improves the thermal stability and flame retardancy of the phenolic resin