● A global snapshot of plastic waste generation and disposal is analysed.

Plastic is considered one of the most indispensable commodities in our daily life. At the end of life, the huge ever-growing pile of plastic waste (PW) causes serious concerns for our environment, including agricultural farmlands, groundwater quality, marine and land ecosystems, food toxicity and human health hazards. Lack of proper infrastructure, financial backup, and technological advancement turn this hazardous waste plastic management into a serious threat to developing countries, especially for Bangladesh. A comprehensive review of PW generation and its consequences on environment in both global and Bangladesh contexts is presented. The dispersion routes of PW from different sources in different forms (microplastic, macroplastic, nanoplastic) and its adverse effect on agriculture, marine life and terrestrial ecosystems are illustrated in this work. The key challenges to mitigate PW pollution and tackle down the climate change issue is discussed in this work. Moreover, way forward toward the design and implementation of proper PW management strategies are highlighted in this study.

● Hybrid deep-learning model is proposed for water quality prediction.

Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants (WWTPs). However, some water quality metrics are not measurable in real time, thus influencing the judgment of the operators and may increase energy consumption and carbon emission. One of the solutions is using a soft-sensor prediction technique. This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit (BiGRU) combined with Gaussian Progress Regression (GPR) optimized by Tree-structured Parzen Estimator (TPE). TPE automatically optimizes the hyperparameters of BiGRU, and BiGRU is trained to obtain the point prediction with GPR for the interval prediction. Then, a case study applying this prediction method for an actual anaerobic process (2500 m³/d) is carried out. Results show that TPE effectively optimizes the hyperparameters of BiGRU. For point prediction of COD_eff and biogas yield, R² values of BiGRU, which are 0.973 and 0.939, respectively, are increased by 1.03%–7.61% and 1.28%–10.33%, compared with those of other models, and the valid prediction interval can be obtained. Besides, the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation. It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.

● Efficient carbon methanation and nitrogen removal was achieved in AnMBR-PN/A system.

An AnMBR-PN/A system was developed for mainstream sewage treatment. To verify the efficient methanation and subsequent chemolitrophic nitrogen removal, a long-term experiment and analysis of microbial activity were carried out. AnMBR performance was less affected by the change of hydraulic retention time (HRT), which could provide a stable influent for subsequent PN/A units. The COD removal efficiency of AnMBR was > 93% during the experiment, 85.5% of COD could be recovered in form of CH₄. With the HRT of PN/A being shortened from 10 to 6 h, nitrogen removal efficiency (NRE) of PN/A increased from 60.5% to 80.4%, but decreased to 68.8% when the HRT_PN/A further decreased to 4 h. Microbial analysis revealed that the highest specific ammonia oxidation activity (SAOA) and the ratio of SAOA to specific nitrate oxidation activity (SNOA) provide stable NO₂⁻-N/NH₄⁺-N for anammox, and anammox bacteria (mainly identified as Candidatus Brocadia) enriched at the bottom of Anammox-UASB might play an important role in nitrogen removal. In addition, the decrease of COD in Anammox-UASB indicated partial denitrification occurred, which jointly promoted nitrogen removal with anammox.

● EE2 photodegradation behavior in the presence of four WWTPs’ DOM was explored.

Dissolved organic matter (DOM) from each treatment process of wastewater treatment plants (WWTPs) contains abundant photosensitive substances, which could significantly affect the photodegradation of 17α-ethinylestradiol (EE2). Nevertheless, information about EE2 photodegradation behavior mediated by DOM from diverse WWTPs and the photosensitivity sources of such DOM are inadequate. This study explored the photodegradation behavior of EE2 mediated by four typical WWTPs’ DOM solutions and investigated the photosensitivity sources of DOM in the anaerobic-anoxic-oxic (A²/O) process. The parallel factor analysis identified three varying fluorescing components of these DOM, tryptophan-like substances or protein-like substances, microbial humus-like substances, and humic-like components. The photodegradation rate constants of EE2 were positively associated with the humification degree of DOM (P < 0.05). The triplet state substances were responsible for the degradation of EE2. DOM extracted from the A²/O process, especially in the secondary treatment process had the fastest EE2 photodegradation rate compared to that of the other three processes. Four types of components (water-soluble organic matter (WSOM), extracellular polymeric substance, humic acid, and fulvic acid) were separated from the A²/O process DOM. WSOM had the highest promotion effect on EE2 photodegradation. Fulvic acid-like components and humic acid-like organic compounds in WSOM were speculated to be important photosensitivity substances that can generate triplet state substances. This research explored the physicochemical properties and photosensitive sources of DOM in WWTPs, and explained the fate of estrogens photodegradation in natural waters.

● High fluorine is mainly HCO₃·Cl-Na and HCO₃-Na type.

Due to the unclear distribution characteristics and causes of fluoride in groundwater of Mihe-Weihe River Basin (China), there is a higher risk for the future development and utilization of groundwater. Therefore, based on the systematic sampling and analysis, the distribution features and enrichment mechanism for fluoride in groundwater were studied by the graphic method, hydrogeochemical modeling, the proportionality factor between conventional ions and factor analysis. The results show that the fluorine content in groundwater is generally on the high side, with a large area of medium-fluorine water (0.5–1.0 mg/L), and high-fluorine water is chiefly in the interfluvial lowlands and alluvial-marine plain, which mainly contains HCO₃·Cl-Na- and HCO₃-Na-type water. The vertical zonation characteristics of the fluorine content decrease with increasing depth to the water table. The high flouride groundwater during the wet season is chiefly controlled by the weathering and dissolution of fluorine-containing minerals, as well as the influence of rock weathering, evaporation and concentration. The weak alkaline environment that is rich in sodium and poor in calcium during the dry season is the main reason for the enrichment of fluorine. Finally, an integrated assessment model is established using rough set theory and an improved matter element extension model, and the level of groundwater pollution caused by fluoride in the Mihe-Weihe River Basin during the wet and dry seasons in the Shandong Peninsula is defined to show the necessity for local management measures to reduce the potential risks caused by groundwater quality.

● Dolomite-doped biochar/bentonite was synthesized for phosphate removal.

The removal of phosphate from wastewater using traditional biological or precipitation methods is a huge challenge. The use of high-performance adsorbents has been shown to address this problem. In this study, a novel composite adsorbent, composed of dolomite-doped biochar and bentonite (DO/BB), was first synthesized via co-pyrolysis. The combination of initial phosphate concentration of 100 mg/L and 1.6 g/L of DO/BB exhibited a high phosphate-adsorption capacity of 62 mg/g with a removal efficiency of 99.8%. It was also stable in complex water environments with various levels of solution pH, coexisting anions, high salinity, and humic acid. With this new composite, the phosphate concentration of the actual domestic sewage decreased from 9 mg/L to less than 1 mg/L, and the total nitrogen and chemical oxygen demand also decreased effectively. Further, the cross-flow treatment using a PVC membrane loaded with DO/BB (PVC-DO/BB), decreased the phosphate concentration from 1 to 0.08 mg/L, suggesting outstanding separation of phosphate pollutants via a combination of adsorption and separation. In addition, the removal of phosphate by the PVC-DO/BB membrane using NaOH solution as an eluent was almost 90% after 5 cycles. The kinetic, isotherm and XPS analysis before and after adsorption suggested that adsorption via a combination of electrostatic interaction, complexation and precipitation contributed to the excellent separation by the as-obtained membranes.

● P-rich carp residues-derived biochars presented excellent Cu sorption capacity.

Heavy metal pollution has attracted worldwide attention because of its adverse impact on the aquatic environment and human health. The production of biochar from biowaste has become a promising strategy for managing animal carcasses and remediating heavy metal pollution in the aquatic environment. However, the sorption and remediation performance of carp residue-derived biochar (CRB) in Cu-polluted water is poorly understood. Herein, batches of CRB were prepared from carp residues at 450–650 °C (CRB450–650) to investigate their physicochemical characteristics and performance in the sorption and remediation of Cu-polluted water. Compared with a relatively low-temperature CRB (e.g., CRB450), the high-temperature biochar (CRB650) possessed a large surface area and thermodynamic stability. CRB650 contained higher oxygen-containing functional groups and P-associated minerals, such as hydroxyapatite. As the pyrolytic temperature increased from 450 to 650°C, the maximum sorption capacity of the CRBs increased from 26.5 to 62.5 mg/g. The adsorption process was a type of monolayer adsorption onto homogenous materials, and the sorption of Cu²⁺ on the CRB was mainly based on chemical adsorption. The most effective potential adsorption mechanisms were in order of electrostatic attraction and cation-π interaction > surface complexation and precipitation > pore-filling and cation exchange. Accordingly, the CRBs efficiently immobilized Cu²⁺ and reduced its bioavailability in water. These results provide a promising strategy to remediate heavy metal-polluted water using designer biochars derived from biowastes, particularly animal carcasses.

● The performance and costs of 20 municipal WWTPs were analyzed.

Climate affects the natural landscape, the economic productivity of societies, and the lifestyles of its inhabitants. It also influences municipal wastewater treatment. Biological processes are widely employed in municipal wastewater treatment plants (WWTPs), and the prolonged cold conditions brought by the winter months each year pose obstacles to meeting the national standards in relatively cold regions. Therefore, both a systematic analysis of existing technical bottlenecks as well as promising novel technologies are urgently needed for these cold regions. Taking North-east China as a case, this review studied and analyzed the main challenges affecting 20 municipal WWTPs. Moreover, we outlined the currently employed strategies and research issues pertaining to low temperature conditions. Low temperatures have been found to reduce the metabolism of microbes by 58% or more, thereby leading to chemical oxygen demand (COD) and NH₄⁺-N levels that have frequently exceeded the national standard during the winter months. Furthermore, the extracellular matrix tends to lead to activated sludge bulking issues. Widely employed strategies to combat these issues include increasing the aeration intensity, reflux volume, and flocculant addition; however, these strategies increase electricity consumption by > 42% in the winter months. Internationally, the processes of anaerobic ammonium oxidation (anammox), granular sludge, and aerobic denitrification have become the focus of research for overcoming low temperature. These have inspired us to review and propose directions for the further development of novel technologies suitable for cold regions, thereby overcoming the issues inherent in traditional processes that have failed to meet the presently reformed WWTP requirements.

● Simultaneous NH₄⁺/NO₃^– removal was achieved in the FeS denitrification system

An autotrophic denitrifying bioreactor with iron sulfide (FeS) as the electron donor was operated to remove ammonium (NH₄⁺) and nitrate (NO₃⁻) synergistically from wastewater for more than 298 d. The concentration of FeS greatly affected the removal of NH₄⁺/NO₃⁻. Additionally, a low hydraulic retention time worsened the removal efficiency of NH₄⁺/NO₃⁻. When the hydraulic retention time was 12 h, the optimal removal was achieved with NH₄⁺ and NO₃⁻ removal percentages both above 88%, and the corresponding nitrogen removal loading rates of NH₄⁺ and NO₃⁻ were 49.1 and 44.0 mg/(L·d), respectively. The removal of NH₄⁺ mainly occurred in the bottom section of the bioreactor through sulfate/ferric reducing anaerobic ammonium oxidation (Sulfammox/Feammox), nitrification, and anaerobic ammonium oxidation (Anammox) by functional microbes such as Nitrospira, Nitrosomonas, and Candidatus Kuenenia. Meanwhile, NO₃⁻ was mainly removed in the middle and upper sections of the bioreactor through autotrophic denitrification by Ferritrophicum, Thiobacillus, Rhodanobacter, and Pseudomonas, which possessed complete denitrification-related genes with high relative abundances.

● Medium poly Al salts dominated the PAC residual salts with a rational dosage.

With the widespread introduction of pre-coagulation prior to the biological unit in various industrial wastewater treatments, it is noteworthy that long-term accumulation of residual coagulants has certains effect on both micro and macro characteristics of activated sludge (AS). In this study, the morphology distributions of residual aluminum salts (RAS) and their effects on the removal efficiency of AS were investigated under different PAC concentrations. The results showed that the dominance of medium polymeric RAS, formed under an appropriate PAC dose of 20 mg/L enhanced the hydrophobicity, flocculation, and sedimentation performances of AS, as well as the enzymatic activity in cells in the sludge system, improving the main pollutants removal efficiency of the treatment system. Comparatively the species composition with monomer and dimer / high polymer RAS as the overwhelming parts under an over-dosed PAC concentration of 55 mg/L resulted in excessive secretion of EPS with loose flocs structure and conspicuous inhibition of cellular activity, leading to the deterioration of physico-chemical and biological properties of AS. Based on these findings, this study can shed light on the role of the RAS hydrolyzed species distributions, closely relevant to Al dosage, in affecting the comprehensive properties of AS and provide a theoretical reference for coagulants dosage precise control in the pretreatment of industrial wastewater.

● A novel framework integrating quantile regression with machine learning is proposed.

The identification of factors that may be forcing ecological observations to approach the upper boundary provides insight into potential mechanisms affecting driver-response relationships, and can help inform ecosystem management, but has rarely been explored. In this study, we propose a novel framework integrating quantile regression with interpretable machine learning. In the first stage of the framework, we estimate the upper boundary of a driver-response relationship using quantile regression. Next, we calculate “potentials” of the response variable depending on the driver, which are defined as vertical distances from the estimated upper boundary of the relationship to observations in the driver-response variable scatter plot. Finally, we identify key factors impacting the potential using a machine learning model. We illustrate the necessary steps to implement the framework using the total phosphorus (TP)-Chlorophyll a (CHL) relationship in lakes across the continental US. We found that the nitrogen to phosphorus ratio (N׃P), annual average precipitation, total nitrogen (TN), and summer average air temperature were key factors impacting the potential of CHL depending on TP. We further revealed important implications of our findings for lake eutrophication management. The important role of N׃P and TN on the potential highlights the co-limitation of phosphorus and nitrogen and indicates the need for dual nutrient criteria. Future wetter and/or warmer climate scenarios can decrease the potential which may reduce the efficacy of lake eutrophication management. The novel framework advances the application of quantile regression to identify factors driving observations to approach the upper boundary of driver-response relationships.

● MSWNet was proposed to classify municipal solid waste.

An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions.