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Vietnam is known as an agricultural country with a variety of agricultural crops. In addition to agricultural by-products, bio-waste is the by-product from livestock waste, forestry, industry, and daily life. They affect the soil, water, and air environment by self-degradation processes in the environment. Therefore, researchers have come up with ideas for the usage of the by-products to decrease the amount of waste and minimize the environmental effects. In Vietnam, the by-products were used by researchers to produce bio-ethanol, fertilizers, composites, and activated carbon (AC). AC is one of the materials used to rapidly reduce the number of agricultural by-products by researchers. The synthesis process is optimized for the highest yield, while the physicochemical properties are also clearly analyzed through the X-ray diffraction, Fourier transform infrared, and Bacterial endotoxin testing assays. The average recorded specific surface area was about 300 m2 g−1. The functional groups and surface structure showed that the material has an amorphous structure with –OH, –CH, –CC, –C═C, –C═O groups. The AC from agricultural waste had been studied and applied to treat pollutants present in water.

Arecaceae presents one of the plant families distributed mainly in the equatorial and subequatorial regions. Arecaceae are widely applied in many fields such as food, cosmetics, fuel, and chemical industries. However, a large amount of agricultural waste from the Arecaceae trees has been released into the environment. The objective of this report is to gain more insights into the potentials and applications of activated carbon (AC) from the Arecaceae trees in wastewater treatment, in which, the ability to handle organic pigments, metals, and antibiotics is focused. The physical properties and processability of AC are statistically evaluated. With a uniform structure, large specific surface area, processing ability according to Langmuir and pseudo-second-order models, we showed that ACs from Arecaceae trees are promising materials for water treatment applications. This is the basis for the development and reduction of by-products that affect the environment.

Data corroborated in this study highlights laundry wastewater as a primary source of microfibers (MFs) in the aquatic environment. MFs can negatively impact the aquatic ecosystem via five possible pathways, namely, acting as carriers of other contaminats, physical damage to digestive systems of aquatic organisms, blocking the digestive tract, releasing toxic chemicals, and harbouring invasive and noxious plankton and bacteria. This review shows that small devices to capture MFs during household laundry activities are simple to use and affordable at household level in developed countries. However, these low cost and small devices are unrealiable and can only achieve up to 40 % MF removal efficiency. In line filtration devices can achieve higher removal efficiency under well maintained condition but their performance is still limited compared to over 98 % MF removal by large scale centralized wastewater treatment. These results infer that effort to increase sanitation coverage to ensure adequate wastewater treatment prior to environmental discharge is likely to be more cost effective than those small devices for capturing MFs. This review also shows that natural fabrics would entail significantly less environmental consequences than synthetic materials. Contribution from the fashion industry to increase the share of natural frabics in the current textile market can also reduce the loading of plastic MFs in the environment.

Clean energy like hydrogen can be a promising strategy to solve problems of global warming. Photo-fermentation (PF) is an attractive technology for producing biohydrogen from various biowastes cost-effectively and environmentally friendly. However, challenges of low light conversion efficiency and small yields of biohydrogen production still limit its application. Thus, advanced strategies have been investigated to enhance photo-fermentative biohydrogen production. This review discusses advanced technologies that show positive outcomes in improving biohydrogen production by PF, including the following. Firstly, genetic engineering enhances light transfer efficiency, change the activity of enzymes, and improves the content of ATP, ammonium and antibiotic tolerance of photosynthetic bacteria. Secondly, immobilization technology is refined. Thirdly, nanotechnology makes great strides as a scientific technique and fourthly, integration of dark and photo-fermentation technology is possible. Some suggestions for further studies to achieve high levels of efficiency of photo-fermentative biohydrogen production are mentioned in this paper.

This research explores the possibilities of a dual-chamber microbial fuel cell as a biosensor to measure Bisphenol A (BPA) in wastewater. BPA is an organic compound and is considered to be an endocrine disruptor, affecting exposed organisms, the environment, and human health. The performance of the microbial fuel cells (MFCs) was first controlled with specific operational conditions (pH, temperature, fuel feeding rate, and organic loading rate) to obtain the best accuracy of the sensor signal. After that, BPA concentrations varying from 50 to 1000 μg L

Wastewater contains a significant amount of recoverable nitrogen. Hence, the recovery of nitrogen from wastewater can provide an option for generating some revenue by applying the captured nitrogen to producing bio-products, in order to minimize dangerous or environmental pollution consequences. The circular bio-economy can achieve greater environmental and economic sustainability through game-changing technological developments that will improve municipal wastewater management, where simultaneous nitrogen and energy recovery are required. Over the last decade, substantial efforts were undertaken concerning the recovery of nitrogen from wastewater. For example, bio-membrane integrated system (BMIS) which integrates biological process and membrane technology, has attracted considerable attention for recovering nitrogen from wastewater. In this review, current research on nitrogen recovery using the BMIS are compiled whilst the technologies are compared regarding their energy requirement, efficiencies, advantages and disadvantages. Moreover, the bio-products achieved in the nitrogen recovery system processes are summarized in this paper, and the directions for future research are suggested. Future research should consider the quality of recovered nitrogenous products, long-term performance of BMIS and economic feasibility of large-scale reactors. Nitrogen recovery should be addressed under the framework of a circular bio-economy.

Samples of the bimetallic-based NH2-MIL-125(Ti) at a ratio of Mn+/Ti4+ is 0.15 (Mn+: Ni2+, Co2+ and Fe3+) were first synthesized using the solvothermal method. Their fundamental properties were analyzed by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Raman spectra, scanning electron microscopy (SEM), N2 adsorption–desorption measurements, and UV–Vis diffuse reflectance spectroscopy (UV-Vis DRS). The as-acquired materials were used as high-efficiency heterogeneous photocatalysts to remove Rhodamine B (RhB) dye under visible light. The results verified that 82.4% of the RhB (3 × 10−5 M) was degraded within 120 min by 15% Fe/Ti−MOFs. Furthermore, in the purpose of degrading Rhodamine B (RhB), the rate constant for the 15% Fe/Ti-MOFs was found to be 2.6 times as fast as that of NH2-MIL-125(Ti). Moreover, the 15% Fe/Ti-MOFs photocatalysts remained stable after three consecutive cycles. The trapping test demonstrated that the major active species in the degradation of the RhB process were hydroxyl radicals (HO∙) and holes (h+).

Monitoring agricultural soil organic carbon (SOC) has played an essential role in sustainable agricultural management. Precise and robust prediction of SOC greatly contributes to carbon neutrality in the agricultural industry. To create more knowledge regarding the ability of remote sensing to monitor carbon soil, this research devises a state-of-the-art low cost machine learning model for quantifying agricultural soil carbon using active and ensemble-based decision tree learning combined with multi-sensor data fusion at a national and world scale. This work explores the use of Sentinel-1 (S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and an innovative machine learning (ML) approach using an integration of active learning for land-use mapping and advanced Extreme Gradient Boosting (XGBoost) for robustness of the SOC estimates. The collected soil samples from a field survey in Western Australia were used for the model validation. The indicators including the coefficient of determination (R2) and root - mean – square - error (RMSE) were applied to evaluate the model's performance. A numerous features computed from optical and SAR data fusion were employed to build and test the proposed model performance. The effectiveness of the proposed machine learning model was assessed by comparing with the two well-known algorithms such as Random Forests (RF) and Support Vector Machine (SVM) to predict agricultural SOC. Results suggest that a combination of S1 and S2 sensors could effectively estimate SOC in farming areas by using ML techniques. Satisfactory accuracy of the proposed XGBoost with optimal features was achieved the highest performance (R2 = 0.870; RMSE = 1.818 tonC/ha) which outperformed RF and SVM. Thus, multi-sensor data fusion combined with the XGBoost lead to the best prediction results for agricultural SOC at 10 m spatial resolution. In short, this new approach could significantly contribute to various agricultural SOC retrieval studies globally.

A high-resolution soil moisture prediction method has recently gained its importance in various fields such as forestry, agricultural and land management. However, accurate, robust and non- cost prohibitive spatially monitoring of soil moisture is challenging. In this research, a new approach involving the use of advance machine learning (ML) models, and multi-sensor data fusion including Sentinel-1(S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and ALOS Global Digital Surface Model (ALOS DSM) to predict precisely soil moisture at 10 m spatial resolution across research areas in Australia. The total of 52 predictor variables generated from S1, S2 and ALOS DSM data fusion, including vegetation indices, soil indices, water index, SAR transformation indices, ALOS DSM derived indices like digital model elevation (DEM), slope, and topographic wetness index (TWI). The field soil data from Western Australia was employed. The performance capability of extreme gradient boosting regression (XGBR) together with the genetic algorithm (GA) optimizer for features selection and optimization for soil moisture prediction in bare lands was examined and compared with various scenarios and ML models. The proposed model (the XGBR-GA model) with 21 optimal features obtained from GA was yielded the highest performance (R

This study investigated a dual-chamber microbial fuel cell-based biosensor (DC-MFC-B) for monitoring copper and arsenic in municipal wastewater. Operational conditions, including pH, flow rate, a load of organic substrate and external resistance load, were optimized to improve the biosensor's sensitivity. The DC-MFC-B's toxicity response was established under the electroactive bacteria inhibition rate function to a specific heavy metal level as well as the recovery of the DC-MFC-B. Results show that the DC-MFC-B was optimized at the operating conditions of 1000 Ω external resistance, COD 300 mg L

Ammonium nitrate is an important fertilizer and industrial explosive. The production of ammonium nitrate entails the generation of a large volume of condensate laden with nitrogen that must be treated before environment discharge. Results in this study show that through appropriate membrane selection, over 90% rejection of ammonium nitrate can be achieved by reverse osmosis (RO) filtration. Using RO (which is highly compact and efficient) to enrich ammonium nitrate in the condensate would significantly reduce the size of the evaporation separator for ammonia recovery. The results also highlight the importance of membrane selection for this application. Results reported here suggest that a low pressure RO membrane (e.g. ESPA2) is more suitable for the dilute condensate while a high pressure RO membrane (e.g. SW30) is recommended for the concentrated condensate to ensure adequate ammonia and nitrate rejection. Ammonia and nitrate rejections were dependent on key operating parameters including applied pressure (or water flux), temperature, feed solution pH, and initial ammonium nitrate concentration in the condensate. The impact of operating conditions on ammonia and nitrate rejections was more profound for low pressure (thus high flux) than high pressure RO membrane. An extended filtration experiment shows no evidence of membrane fouling. Results from this study are useful to the integration of a compact RO system to ammonium nitrate manufacturing for pollution prevention and improving product yield.

Le pediatre doit de plus en plus faire face a la decision de limiter ou d’arreter des traitements devenus inutiles (1). Au plan ethique, ce phenomene a entraine plusieurs discussions qui ont mene a des consensus et des lignes directrices (1–2). Ces guides de pratique donnent des balises pour les decisions, mais on connait peu les facteurs (ou valeurs) pris en compte lors de ces decisions de fin de vie. Les quelques etudes faites a ce sujet demontrent une grande variabilite dans le processus decisionnel des medecins faisant face a cette situation. Cette variabilite est influencee par beaucoup d’elements: la preference du patient, les caracteristiques personnelles du medecin, telles que l’âge, les attitudes du medecin et la specialite medicale (3–8). Nous avons voulu etudier le processus decisionnel des pediatres vis-a-vis d’un patient dont l’etat medical est deja tres compromis et dont la qualite de vie est diminuee. Comment raisonnent-ils lorsqu’ils doivent choisir entre deux options de traitement: intervenir ou s’abstenir d’entreprendre un traitement qui maintiendra le patient en vie sans assurement ameliorer son etat pathologique a long terme? Le but de l’etude est d’evaluer le processus decisionnel de fin de vie chez un enfant et l’importance accordee par les pediatres a l’opinion des parents par rapport a celle de l’enfant dans une telle situation.