Current status of municipal wastewater treatment plants in North-east China: implications for reforming and upgrading

● The performance and costs of 20 municipal WWTPs were analyzed. ● Effluent COD and NH₄⁺-N effluent exceed the limits more frequently in winter. ● Nitrification and refractory pollutant removal are limited at low temperatures. ● To meet the national standards, electricity cost must increase by > 42% in winter. ● Anammox, granular sludge, and aerobic denitrification are promising technologies. 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.     

Long-term exposure to air pollution and cerebrovascular disease: findings from Beijing Health Management Cohort study

● This study explored the long-term association by double robust additive models. ● Individual exposure concentrations were assessed by integrating GAM, LUR and BPNN. ● PM_2.5, SO₂ and NO₂ are positively associated with cerebrovascular disease. ● CO could reduce the risk of cerebrovascular disease with the highest robustness. ● The elderly, women and people with normal BMI are at higher risk for air pollution. The relationship between air pollution and cerebrovascular disease has become a popular topic, yet research findings are highly heterogeneous. This study aims to investigate this association based on detailed individual health data and a precise evaluation of their exposure levels. The integrated models of generalized additive model, land use regression model and back propagation neural network were used to evaluate the exposure concentrations. And doubly robust additive model was conducted to explore the association between cerebrovascular disease and air pollution after adjusted for demographic characteristics, physical examination, disease information, geographic and socioeconomic status. A total of 25097 subjects were included in the Beijing Health Management Cohort from 2013 to 2018. With a 1 μg/m³ increase in the concentrations of PM_2.5, SO₂ and NO₂, the incidence risk of cerebrovascular disease increased by 1.02 (95% CI: 1.008–1.034), 1.06 (95% CI: 1.034–1.095) and 1.02 (95% CI: 1.010–1.029) respectively. Whereas CO exposure could decrease the risk, with an odds ratio of 0.38 (95% CI: 0.212–0.626). In the subgroup analysis, individuals under the age of 50 with normal BMI were at higher risk caused by PM_2.5, and SO₂ was considered more hazardous to women. Meanwhile, the protective effect of CO on women and those with normal BMI was stronger. Successful reduction of long-term exposure to PM_2.5, SO₂ and NO₂ would lead to substantial benefits for decrease the risk of cerebrovascular disease especially for the health of the susceptible individuals.     

Electrocatalytic reduction of nitrate using Pd-Cu modified carbon nanotube membranes

● Pd-Cu modified CNT membranes were prepared successfully by electrodeposition method. ● The deposition voltage and deposition time were optimized for Pd-Cu co-deposition. ● NO₃⁻-N was removed efficiently from water by Pd-Cu modified CNT membranes. ● The presence of dissolved oxygen did not affect the nitrate reduction performance. ● Mass transfer rate was promoted significantly with the increase in membrane flux. Excessive nitrate in water is harmful to the ecological environment and human health. Electrocatalytic reduction is a promising technology for nitrate removal. Herein, a Pd-Cu modified carbon nanotube membrane was fabricated with an electrodeposition method and used to reduce nitrate in a flow-through electrochemical reactor. The optimal potential and duration for codeposition of Pd and Cu were −0.7 V and 5 min, respectively, according to linear scan voltammetry results. The membrane obtained with a Pd:Cu ratio of 1:1 exhibited a relatively high nitrate removal efficiency and N₂ selectivity. Nitrate was almost completely reduced (~99 %) by the membrane at potentials lower than −1.2 V. However, −0.8 V was the optimal potential for nitrate reduction in terms of both nitrate removal efficiency and product selectivity. The nitrate removal efficiency was 56.2 %, and the N₂ selectivity was 23.8 % for the Pd:Cu=1:1 membrane operated at −0.8 V. Nitrate removal was enhanced under acidic conditions, while N₂ selectivity was decreased. The concentrations of Cl⁻ ions and dissolved oxygen showed little effect on nitrate reduction. The mass transfer rate constant was greatly improved by 6.6 times from 1.14 × 10⁻³ m/h at a membrane flux of 1 L/(m²·h) to 8.71 × 10⁻³ m/h at a membrane flux of 15 L/(m²·h), which resulted in a significant increase in the nitrate removal rate from 13.6 to 133.5 mg/(m²·h). These findings show that the Pd-Cu modified CNT membrane is an efficient material for nitrate reduction.     

Phylogenetic diversity of NO reductases,new tools for nor monitoring,and insights into N2O production in natural and engineered environments

● 548 representative nor genes were collected to create complete phylogenetic trees. ● The distribution of nor and nod genes were detected in 18 different phyla. ● The most conserved amino acids in NOR were located adjacent to the active site. ● nor-universal and Clade-specific primers were designed, suggested, and tested. Nitric oxide reductases (NORs) have a central role in denitrification, detoxification of nitric oxide (NO) in host-pathogen interactions, and NO-mediated cell-cell signaling. In this study, we focus on the phylogeny and detection of qNOR and cNOR genes because of their nucleotide sequence similarity and evolutionary relatedness to cytochrome oxidases, their key role in denitrification, and their abundance in natural, agricultural, and wastewater ecosystems. We also include nitric oxide dismutase (NOD) due to its similarity to qNOR. Using 548 nor sequences from publicly accessible databases and sequenced isolates from N₂O-producing bioreactors, we constructed phylogenetic trees for 289 qnor/nod genes and 259 cnorB genes. These trees contain evidence of horizontal gene transfer and gene duplication, with 13.4% of the sequenced strains containing two or more nor genes. By aligning amino acid sequences for qnor + cnor, qnor, and cnor, we identified four highly conserved regions for NOR and NOD, including two highly conserved histidine residues at the active site for qNOR and cNOR. Extending this approach, we identified conserved sequences for: 1) all nor (nor-universal); 2) all qnor (qnor-universal) and all cnor (cnor-universal); 3) qnor of Comamonadaceae; 4) Clade-specific sequences; and 5) nod of Candidatus Methylomirabilis oxyfera. Examples of primer performance were confirmed experimentally.     

Performance and mechanism of carbamazepine removal by FeS-S2O82– process: experimental investigation and DFT calculations

● Synergistic removal of carbamazepine (CBZ) was obtained in the FeS-S₂O₈^2– process. ● SO₄^•− and •OH were identified as the main radicals in the FeS-S₂O₈^2– process. ● Heterogeneous oxidation would be dominant first, followed by homogeneous reaction. ● Degradation pathway of CBZ was well elucidated by experiments and DFT calculations. As persulfate (S₂O₈^2–) is being increasingly used as an alternative oxidizing agent, developing low-cost and eco-friendly catalysts for efficient S₂O₈^2– activation is potentially useful for the treatment of wastewater containing refractory organic pollutant. In this study, the degradative features and mechanisms of carbamazepine (CBZ) were systematically investigated in a novel FeS- S₂O₈^2– process under near-neutral conditions. The results exhibited that CBZ can be effectively eliminated by the FeS-S₂O₈^2– process and the optimal conditions were: 250 mg/L FeS, 0.5 mmol/L S₂O₈^2–, and pH = 6.0. The existence of Cl⁻ (1 and 50 mmol/L) has little influence on the CBZ elimination, while both HCO₃⁻ and HPO₄²⁻ (1 and 50 mmol/L) significantly suppressed the CBZ removal in the FeS-S₂O₈^2– process. CBZ could be degraded via a radical mechanism in the FeS-S₂O₈^2– process, the working radical species (i.e., SO₄^•− and •OH) were efficiently formed via the promoted decomposition of S₂O₈^2– by the surface Fe²⁺ on the FeS and the dissolved ferrous ions in solution. Based on the identified oxidized products and Fukui index calculations, a possible degradation pathway of CBZ was speculated. More importantly, a two-stage oxidation mechanism of CBZ elimination was speculated in the FeS-S₂O₈^2– process, the activation of S₂O₈^2– by the surface-active Fe^(II) of FeS dominated in the initial 5 min, while homogeneous oxidation reactions played more essential parts than others in the following reaction stage (5–60 min). Overall, this study demonstrated that the FeS-S₂O₈^2– process is capable of removing CBZ from water efficiently.     

Bioaerosol emissions variations in large-scale landfill region and their health risk impacts

● The airborne bacteria in landfills were 4–50 times higher than fungi. ● Bioaerosols released from the working area would pose risk to on-site workers. ● The safe distance for the working area should be set as 80 m. Landfills are widely complained about due to the long-term odor and landfill gas emissions for local residents, while the bioaerosols are always neglected as another threat to on-site workers. In this study, bioaerosols samples were collected from the typical operation scenes in the large-scale modern landfill, and the emission levels of airborne bacteria, pathogenic species, and fungi were quantified and co-related. The corresponding exposure risks were assessed based on the average daily dose via inhalation and skin contact. It was found that the levels of culturable bacteria and fungi in all landfill samples were around 33–22778 CFU/m³ and 8–450 CFU/m³, and the active-working landfill area and the covered area were the maximum and minimum emission sources, respectively, meaning that the bioaerosols were mainly released from the areas related with the fresh waste operation. Acinetobacter sp., Massilia sp., Methylobacterium-Methylorubrum sp. and Noviherbaspirillum sp. were the main bacterial populations, with a percentage of 42.56%, 89.82%, 70.24% and 30.20% respectively in total bioaerosols measured. With regards to the health risk, the health risks via inhalation were the main potential risks, with four orders of magnitude higher than that of skin contact. Active-working area showed the critical point for non-carcinogenic risks, with a hazard quotient of 1.68, where 80 m protection distance is recommended for on-site worker protection, plus more careful protection measures.     

Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning

● A method based on ATR-FTIR and ML was developed to predict CHNS contents in waste. ● Feature selection methods were used to improve models’ prediction accuracy. ● The best model predicted C, H, and N contents with accuracy R ² ≥ 0.93, 0.87, 0.97. ● Some suitable models showed insensitivity to spectral noise. ● Under moisture interference, the models still had good prediction performance. Elemental composition is a key parameter in solid waste treatment and disposal. This study has proposed a method based on infrared spectroscopy and machine learning algorithms that can rapidly predict the elemental composition (C, H, N, S) of solid waste. Both noise and moisture spectral interference that may occur in practical application are investigated. By comparing two feature selection methods and five machine learning algorithms, the most suitable models are selected. Moreover, the impacts of noise and moisture on the models are discussed, with paper, plastic, textiles, wood, and leather as examples of recyclable waste components. The results show that the combination of the feature selection and K-nearest neighbor (KNN) approaches exhibits the best prediction performance and generalization ability. Particularly, the coefficient of determination (R²) of the validation set, cross validation and test set are higher than 0.93, 0.89, and 0.97 for predicting the C, H, and N contents, respectively. Further, KNN is less sensitive to noise. Under moisture interference, the combination of feature selection and support vector regression or partial least-squares regression shows satisfactory results. Therefore, the elemental compositions of solid waste are quickly and accurately predicted under noise and moisture disturbances using infrared spectroscopy and machine learning algorithms.     

Enhancement of extracellular Cr(VI) reduction for anammox recovery using hydrazine: performance,pathways, and mechanism

● N₂H₄ addition enhanced and recovered anammox performance under Cr(VI) stress. ● N₂H₄ accelerated electron transfer of Cr(VI) reduction for detoxification. ● N₂H₄ enhanced anammox metabolism for activity recovery from Cr(VI) inhibition. ● Extracellular Cr(VI) reduction to less toxic Cr(III) was the dominant mechanism. The hexavalent chromium (Cr(VI)) would frequently impose inhibition to anaerobic ammonium oxidation (anammox) process, hindering the efficiency of nitrogen removal in wastewater treatment. Hydrazine (N₂H₄), which is an intermediate product of anammox, participates in intracellular metabolism and extracellular Cr(VI) reduction. However, the roles of N₂H₄-induced intracellular metabolism and extracellular reduction in nitrogen removal under Cr(VI) stress remain unclear. The addition of 3.67 mg/L of N₂H₄ increased the anammox activity by 17%. As an intermediate, N₂H₄ enhanced anammox metabolism by increasing the heme c content and electron transfer system activity. As a reductant, N₂H₄ accelerated the reduction of c-Cyts-mediated extracellular Cr(VI) to the less toxic Cr(III). Extracellular Cr(III) accounts for 74% of the total Cr in a Cr(VI)-stressed anammox consortia. These findings highlight that N₂H₄-induced extracellular Cr(VI) reduction is the dominant mechanism for the survival of anammox consortia. We also found that N₂H₄ increased the production of extracellular polymeric substances to sequester excessive Cr(VI) and produced Cr(III). Taken together, the study findings suggest a potential strategy for enhancing nitrogen removal from ammonium-rich wastewater contaminated with Cr(VI).     

Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model

● Data acquisition and pre-processing for wastewater treatment were summarized. ● A PSO-SVR model for predicting COD_eff in wastewater was proposed. ● The COD_eff prediction performances of the three models in the paper were compared. ● The COD_eff prediction effects of different models in other studies were discussed. The mining-beneficiation wastewater treatment is highly complex and nonlinear. Various factors like influent quality, flow rate, pH and chemical dose, tend to restrict the effluent effectiveness of mining-beneficiation wastewater treatment. Chemical oxygen demand (COD) is a crucial indicator to measure the quality of mining-beneficiation wastewater. Predicting COD concentration accurately of mining-beneficiation wastewater after treatment is essential for achieving stable and compliant discharge. This reduces environmental risk and significantly improves the discharge quality of wastewater. This paper presents a novel AI algorithm PSO-SVR, to predict water quality. Hyperparameter optimization of our proposed model PSO-SVR, uses particle swarm optimization to improve support vector regression for COD prediction. The generalization capacity tested on out-of-distribution (OOD) data for our PSO-SVR model is strong, with the following performance metrics of root means square error (RMSE) is 1.51, mean absolute error (MAE) is 1.26, and the coefficient of determination (R²) is 0.85. We compare the performance of PSO-SVR model with back propagation neural network (BPNN) and radial basis function neural network (RBFNN) and shows it edges over in terms of the performance metrics of RMSE, MAE and R², and is the best model for COD prediction of mining-beneficiation wastewater. This is because of the less overfitting tendency of PSO-SVR compared with neural network architectures. Our proposed PSO-SVR model is optimum for the prediction of COD in copper-molybdenum mining-beneficiation wastewater treatment. In addition, PSO-SVR can be used to predict COD on a wide variety of wastewater through the process of transfer learning.     

Fluorescence detection of phosphate in an aqueous environment by an aluminum-based metal-organic framework with amido functionalized ligands

● A novel Al-MOF was successfully synthesized by a facile solvothermal method. ● Al-MOF showed superior performance for phosphate detection. ● High selectivity and anti-interference for detection were demonstrated. ● The high coordination between Al-O and PO₄³⁻ was the key in fluorescence sensing. The on-site monitoring of phosphate is important for environmental management. Conventional phosphate detection methods are not appropriate to on-site monitoring owing to the use of complicated detection procedures, and the consequent high cost and maintenance requirements of the detection apparatus. Here, a highly sensitive fluorescence-based method for phosphate detection with a wide detection range was developed based on a luminescent aluminum-based metal-organic framework (Al-MOF). The Al-MOF was prepared by introducing amine functional groups to conventional MIL to enhance phosphate binding, and exhibited excellent fluorescence properties that originated from the ligand-to-metal charge transfer (LMCT). The detection limit was as low as 3.25 μmol/L (0.10 mg/L) and the detection range was as wide as 3–350 μmol/L (0.10–10.85 mg/L). Moreover, Al-MOF displayed specific recognition toward phosphate over most anions and metal cations, even for a high concentration of the co-existent ions. The mechanism of phosphate detection was analyzed through the characterization of the combination of Al-MOF and phosphate, and the results indicated the high affinity between Al-O and phosphate inhibited that the LMCT process and recovered the intrinsic fluorescence of NH₂-H₂BDC. The recovery of the developed detection method reached a satisfactory range of 85.1%–111.0%, and the feasibility of on-site phosphate detection was verified using a prototype sensor for tap water and lake water samples. It was demonstrated that the prepared Al-MOF is highly promising for on-site detection of phosphate in an aqueous environment.     

Tank-dependence of the functionality and network differentiation of activated sludge community in a full-scale anaerobic/anoxic/aerobic municipal sewage treatment plant

● Environmental parameters affected functional bacteria and network associations. ● The structure and interactions of AS networks changed greatly within tanks. ● Anoxic co-occurrence network was more unstable and easily influenced. ● Composition of functional bacteria had a seasonal succession pattern. ● Tetrasphaera was the major PAO in spring and winter leading a better P removal. Understanding the structures and dynamics of bacterial communities in activated sludge (AS) in full-scale wastewater treatment plants (WWTPs) is of both engineering and ecological significance. Previous investigations have mainly focused on the AS communities of WWTP aeration tanks, and the differences and interactions between the communities in anaerobic and anoxic tanks of the AS system remain poorly understood. Here, we investigated the structures of bacterial communities and their inter-connections in three tanks (anaerobic, anoxic, and aerobic) and influent from a full-scale WWTP with conventional anaerobic/anoxic/aerobic (A/A/O) process over a year to explore their functionality and network differentiation. High-throughput sequencing showed that community compositions did not differ appreciably between the different tanks, likely due to the continuous sludge community interchange between tanks. However, network analysis showed significant differences in inter-species relationships, OTU topological roles, and keystone populations in the different AS communities. Moreover, the anoxic network is expected to be more unstable and easily affected by environmental disturbance. Tank-associated environmental factors, including dissolved oxygen, pH, and nutrients, were found to affect the relative abundance of functional genera (i.e., AOB, NOB, PAOs, and denitrifiers), suggesting that these groups were more susceptible to environmental variables than other bacteria. Therefore, this work could assist in improving our understanding of tank-associated microbial ecology, particularly the response of functional bacteria to seasonal variations in WWTPs employing A/A/O process.     

Electroconductive RGO-MXene membranes with wettability-regulated channels: improved water permeability and electro-enhanced rejection performance

● Electroconductive RGO-MXene membranes were fabricated. ● Wettable membrane channels were established between RGO and MXene nanosheets. ● Hydrophilic MXene reduces the resistance of water entering the membrane channels. ● Water permeance of RGO-MXene membrane is 16.8 times higher than that of RGO membrane. ● Electro-assistance can enhance the dye rejection performance of RGO-MXene membrane. Reduced graphene oxide (RGO) membranes are theoretically more conducive to the rapid transport of water molecules in their channels compared with graphene oxide (GO) membranes, as they have fewer oxygen-containing functional groups and more non-oxidized regions. However, the weak hydrophilicity of RGO membranes inhibits water entry into their channels, resulting in their low water permeability. In this work, we constructed wettable RGO-MXene channels by intercalating hydrophilic MXene nanosheets into the RGO membrane for improving the water permeance. The RGO-MXene composite membrane exhibits high pure water permeance of 62.1 L/(m²·h·bar), approximately 16.8 times that of the RGO membrane (3.7 L/(m²·h·bar)). Wettability test results and molecular dynamics simulations suggest that the improved water permeance results from the enhanced wettability of RGO-MXene membrane and increased rate of water molecules entering the RGO-MXene channels. Benefiting from good conductivity, the RGO-MXene membrane with electro-assistance exhibits significantly increased rejection rates for negatively charged dyes (from 56.0% at 0 V to 91.4% at 2.0 V for Orange G) without decreasing the permeate flux, which could be attributed to enhanced electrostatic repulsion under electro-assistance.     

Distribution,enrichment mechanism and risk assessment for fluoride in groundwater: a case study of Mihe-Weihe River Basin,China

● High fluorine is mainly HCO₃·Cl-Na and HCO₃-Na type. ● F⁻ decreases with the increase of depth to water table. ● High fluoride is mainly affected by fluorine-containing minerals and weak alkaline. ● Fluorine pollution is mainly in the north near Laizhou Bay (wet season > dry season). ● Groundwater samples have a high F⁻ health risk (children > adults). 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.     

Porous silica synthesis out of coal fly ash with no residue generation and complete silicon separation

● Both amorphous and crystalline silicon are completely separated from coal fly ash. ● Porous silica is synthesized out of coal fly ash. ● No residues is produced during the whole synthesis process. ● The one-step method to synthesize silica don’t need long-time reaction and aging. Ordered mesoporous silica materials exhibit enormous potential in industrial production. Since coal fly ash (CFA) is abundant in Si, it has become a green and promising way to utilize CFA by synthesizing porous silica materials. However, the stable crystalline structure of CFA limits the extraction of Si, and the residue is generated during the process of extracting Si. In this work, we proposed a no-residue method to synthesize ordered mesoporous silica out of CFA. Sodium carbonate (Na₂CO₃) was used to reconstruct the crystals of the CFA, and the calcined mixture then directly reacted with the precipitators. This method combined the process of Si extraction and porous material synthesis. In this method, no residue was generated and the silicon in both amorphous and crystalline phases of CFA was fully utilized. By this method, the extraction efficiency of Si was increased from 31.75% to nearly 100%. The as-synthesized mesoporous silica had a highly-ordered pore structure with a space group of la-3d, a surface area of 663.87 m²/g, a pore volume of 0.41 cm³/g, and an average pore diameter of 2.73 nm. The mechanism of crystalline transformation and material structure formation were systematically studied. This method provides a new idea to dispose of CFA and synthesize porous silica materials.     

Is atmospheric oxidation capacity better in indicating tropospheric O3 formation?

● This study summarizes and evaluates different approaches that indicate O₃ formation. ● Isopleth and sensitivity methods are useful but have many prerequisites. ● AOC is a better indicator of photochemical reactions leading to O₃ formation. Tropospheric ozone (O₃) concentration is increasing in China along with dramatic changes in precursor emissions and meteorological conditions, adversely affecting human health and ecosystems. O₃ is formed from the complex nonlinear photochemical reactions from nitrogen oxides (NO_x = NO + NO₂) and volatile organic compounds (VOCs). Although the mechanism of O₃ formation is rather clear, describing and analyzing its changes and formation potential at fine spatial and temporal resolution is still a challenge today. In this study, we briefly summarized and evaluated different approaches that indicate O₃ formation regimes. We identify that atmospheric oxidation capacity (AOC) is a better indicator of photochemical reactions leading to the formation of O₃ and other secondary pollutants. Results show that AOC has a prominent positive relationship to O₃ in the major city clusters in China, with a goodness of fit (R²) up to 0.6. This outcome provides a novel perspective in characterizing O₃ formation and has significant implications for formulating control strategies of secondary pollutants.     

Photosensitivity sources of dissolved organic matter from wastewater treatment plants and their mediation effect on 17α-ethinylestradiol photodegradation

● EE2 photodegradation behavior in the presence of four WWTPs’ DOM was explored. ● The ³DOM* played a major role in the EE2 photodegradation mediated by WWTPs’ DOM. ● The A²/O process DOM contained more aromatic and oxygen-containing substances. ● Possible photosensitivity sources of DOM in the A²/O process were proposed. 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.     

Nitrate removal to its fate in wetland mesocosm filled with sponge iron: Impact of influent COD/N ratio

• CW-Fe allowed a high-performance of NO₃^‒-N removal at the COD/N ratio of 0. • Higher COD/N resulted in lower chem-denitrification and higher bio-denitrification. • The application of s-Fe⁰ contributed to TIN removal in wetland mesocosm. • s-Fe⁰ changed the main denitrifiers in wetland mesocosm. Sponge iron (s-Fe⁰) is a porous metal with the potential to be an electron donor for denitrification. This study aims to evaluate the feasibility of using s-Fe⁰ as the substrate of wetland mesocosms. Here, wetland mesocosms with the addition of s-Fe⁰ particles (CW-Fe) and a blank control group (CW-CK) were established. The NO₃^‒-N reduction property and water quality parameters (pH, DO, and ORP) were examined at three COD/N ratios (0, 5, and 10). Results showed that the NO₃^‒-N removal efficiencies were significantly increased by 6.6 to 58.9% in the presence of s-Fe⁰. NH₄⁺-N was mainly produced by chemical denitrification, and approximately 50% of the NO₃^‒-N was reduced to NH₄⁺-N, at the COD/ratio of 0. An increase of the influent COD/N ratio resulted in lower chemical denitrification and higher bio-denitrification. Although chemical denitrification mediated by s-Fe⁰ led to an accumulation of NH₄⁺-N at COD/N ratios of 0 and 5, the TIN removal efficiencies increased by 4.5%‒12.4%. Moreover, the effluent pH, DO, and ORP values showed a significant negative correlation with total Fe and Fe (II) (P<0.01). High-throughput sequencing analysis indicated that Trichococcus (77.2%) was the most abundant microorganism in the CW-Fe mesocosm, while Thauera, Zoogloea, and Herbaspirillum were the primary denitrifying bacteria. The denitrifiers, Simplicispira, Dechloromonas, and Denitratisoma, were the dominant bacteria for CW-CK. This study provides a valuable method and an improved understanding of NO₃^‒-N reduction characteristics of s-Fe⁰ in a wetland mesocosm.     

Microplastics in municipal wastewater treatment plants: a case study of Denizli/Turkey

● High amounts of microplastics are released to receiving media from WWTPs. ● The effect of classical treatment processes on MP removal is important. ● MP load in the effluent of WWTPs is important for developing treatment technology. ● Additional physical treatment could help further reduce MP discharge. Plastic particles smaller than 5 mm are microplastics. They are among the significant pollutants that recently attracted attention. Great quantities of microplastics enter the sewage system daily and reach wastewater treatment plants (WWTPs). As a result, WWTPs are potential microplastic sources. Hence, they create a pathway for microplastics to reach aquatic environments with treated wastewater discharge. Studies on microplastic characterization in WWTPs have gained momentum in academia. This study investigates the abundance, size, shape, color, polymer type, and removal efficiencies of microplastics in a municipal wastewater treatment plant (WWTP) in Denizli/Turkey. The results showed that the dominant microplastic shape in wastewater samples was fibers (41.78%–60.77%) in the 100–500 µm (58.57%–80.07%) size range. Most of the microplastics were transparent-white (32.86%–58.93%). The dominant polymer types were polyethylene (54.05%) and polyethylene vinyl acetate (37.84%) in raw wastewater. Furthermore, the microplastic removal efficiencies of the Denizli Central WWTP as a whole and for individual treatment units were evaluated. Although the microplastic pollution removal efficiency of the Denizli Central WWTP was over 95%, the microplastic concentration discharged daily into the receiving environment was considerably high (1.28 × 10¹⁰ MP/d). Thus, Denizli Central WWTP effluents result in a high volume of emissions in terms of microplastic pollution with a significant daily discharge to the Çürüksu Stream.     

Prediction of high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificial neural networks

● Reducting the sampling frequency can enhance the modelling process. ● The pyrolysis of HDPE was investigated at three different heating rates. ● The average E_a and k₀ were calculated by Friedman, KAS, FWO, and CR methods. ● ANN was employed to predict the HDPE weight loss with the optimal MSE and R². Pyrolysis is considered an attractive option and a promising way to dispose waste plastics. The thermogravimetric experiments of high-density polyethylene (HDPE) were conducted from 105 °C to 900 °C at different heating rates (10 °C/min, 20 °C/min, and 30 °C/min) to investigate their thermal pyrolysis behavior. We investigated four methods including three model-free methods and one model-fitting method to estimate dynamic parameters. Additionally, an artificial neural network model was developed by providing the heating rates and temperatures to predict the weight loss (wt.%) of HDPE, and optimized via assessing mean squared error and determination coefficient on the test set. The optimal MSE (2.6297 × 10⁻²) and R² value (R² > 0.999) were obtained. Activation energy and pre-exponential factor obtained from four different models achieves the acceptable value between experimental and predicted results. The relative error of the model increased from 2.4 % to 6.8 % when the sampling frequency changed from 50 s to 60 s, but showed no significant difference when the sampling frequency was below 50 s. This result provides a promising approach to simplify the further modelling work and to reduce the required data storage space. This study revealed the possibility of simulating the HDPE pyrolysis process via machine learning with no significant accuracy loss of the kinetic parameters. It is hoped that this work could potentially benefit to the development of pyrolysis process modelling of HDPE and the other plastics.