Low-cost, reliable, and highly efficient removal of COD and total nitrogen from sewage using a sponge-filled trickling filter.

Development of low-cost and reliable reactors demanding minimal supervision is a need-of-the-hour for sewage treatment in rural areas. This study explores the performance of a multi-stage sponge-filled trickling filter (SPTF) for sewage treatment, employing polyethylene (PE) and polyurethane (PU) media. Chemical oxygen demand (COD) and nitrogen transformation were evaluated at hydraulic loading rates (HLRs) ranging from 2 to 6 m/d using synthetic sewage as influent. At influent COD of ∼350 mg/L, PU-SPTF and PE-SPTF achieved a COD removal of 97% across all HLRs with most of the removal occurring in the first segments. Operation of PE-SPTF at an HLR of 6 m/d caused substantial wash-out of biomass, while PU-SPTF retained biomass and achieved effluent COD < 10 mg/L even at HLR of 8-10 m/d. The maximum Total Nitrogen removal by PE-SPTF and PU-SPTF reactors was 93.56 ± 1.36 and 92.24 ± 0.66%, respectively, at an HLR of 6 m/d. Simultaneous removal of ammonia and nitrate was observed at all the HLRs in the first segment of both SPTFs indicating ANAMMOX activity. COD removal data, media depth, and HLRs were fitted (R2 > 0.99) to a first-order kinetic relationship. For a comparable COD removal, CO2 emission by PU-SPTF was 3.5% of that of an activated sludge system.

Improving the identification of pollution source areas with catchment-resolution sensitivity analysis.

The significant impacts of total nitrogen (TN) and total phosphorus (TP) on riverine ecosystems underscores the critical need to identify the primary nutrient source areas in watersheds. This study aims to unravel the influences of terrain and land use types on mean monthly TN (TNM) and mean monthly TP (TPM) export across varying catchment resolutions in the Qiantang River Watershed of China. The findings of this study illuminated the critical role of topography in understanding nutrient dynamics, wielding a profound influence over water flow patterns and nutrient dispersion. Both land slope and Stream Power Index (SPI) displayed substantial negative correlations (r < -0.6) with TNM and TPM concentrations, whereas the Topographic Wetness Index (TWI) showed positive correlations with the nutrient indexes. In addition to terrain characteristics, impervious land surfaces had a positive correlation with nutrient concentrations, while grassland and forest areas exhibited negative correlations. Results further underscored the substantial influence of catchment resolution on correlations between watershed properties and riverine nutrient concentrations. It was imperative to choose an effective catchment resolution in watershed delineation - not too coarse, nor too fine - to accurately capture the topographic and land use impacts on nutrient dynamics. With the most appropriate catchment size (Catchment 700 km2), the critical pollution source areas for TN and TP pollution were identified, and thus could be used to guide future pollution reduction efforts. The study not only highlights the importance of identifying an appropriate catchment size for water pollution, but also emphasizes the necessity of effectively extracting critical pollution source areas to mitigate water nutrient pollution and increase the ecological integrity of the Qiantang River Watershed.

Comprehensive Study of Total Nitrogen Content and Microfluidic Profiles in Additive-Enriched Plant-Based Drinks.

The growing consumption of plant-based milk substitutes raises important questions about their composition. The various additives used by manufacturers, including those employed as flavor enhancers, protein additives, and stabilizers, may contain both protein and non-protein nitrogen components. In our study, we examined not only popular milk alternatives but also other milk substitutes made from specific plants. We present a reproducible and rapid method for the simultaneous qualitative and quantitative determination of the total nitrogen content in milk alternatives, focusing on applicability. Using the microchip gel electrophoretic method, we determined that the total nitrogen content differed from the protein content indicated on the packaging. Our results, along with statistical evaluations, supported the hypothesis that different brands of products, derived from the same plant source, resulted in different microfluidic profiles, likely due to the presence of additives. As expected, the microfluidic profiles of additive-free products differed from those of fortified products made from the same plant-based milk replacer. Total nitrogen content provides crucial information for individuals with kidney disease, as is essential to reduce the burden on the kidneys to slow deterioration, alleviate symptoms and avoid complications.

Multi-machine learning methods to predict spatial variation characteristics of total nitrogen at watershed scale: Evidences from the largest watershed (Yangtze River Watershed), Asian.

Nitrogen pollution has emerged as a significant threat to the health of global river systems, garnering considerable attention. However, numerous challenges persist in understanding the characteristics and predicting the spatial changes of total nitrogen (TN) at the catchment scale. We leveraged data from 530 monitoring sections to calculate a land-use composite index and perform statistical analyses to explore the primary factors influencing nitrogen enrichment in the Yangtze River Watershed. We developed three machine learning models to forecast future TN concentrations at monitoring points. Our results showed that agricultural activities and rainfall were the primary drivers of monthly variations in TN concentrations. The upstream region of the watershed exhibited larger variations in TN concentrations (0.097 to 11.099 mg/L), significantly higher than the middle and downstream areas (0.348 to 6.844 mg/L). Microbial-mediated organic matter decomposition in sediment and changes in land-use were identified as key contributors to regional differences in nitrogen enrichment. Potential nitrogen sources include sediment release, urban sewage, and agricultural fertilization. Random Forest model achieved a prediction accuracy of 77.6 %, surpassing the BP and LSTM models. We identified 37 high-risk areas of nitrogen enrichment, concentrated in the Chengdu-Chongqing, Yunnan-Central urban cluster, and the Chaohu Lake sub-watershed. Increased urban land-use and industrial inputs primarily influenced nitrogen enrichment in the upstream area, while agricultural inputs were the main drivers in the middle and downstream regions. Our multi-machine learning models identified the relationship between TN and influencing factors, providing a reliable method for assessing nitrogen enrichment risk in the watershed.

Easily overlooked petiole traits are key factors that affect soil carbon sequestration in plantations in karst areas.

Vegetation restoration in karst areas has shifted from expanding planting areas to the collective enhancement of various ecological functions, especially carbon sequestration. Identifying and regulating key plant functional traits involved in the carbon cycle is an effective approach to increase carbon sequestration. However, reports on the significant contribution of petiole traits to the carbon cycle are scarce. Eucalyptus globulus and Bauhinia purpurea plantations in Liujiang river basin were investigated in this study. Petiole traits, understory characteristics, and soil organic carbon have been measured. The aim is to explore key effect of petiole traits for increasing soil carbon sequestration and to provide scientific evidence for the high-quality development of plantations in karst areas. The results indicate that in Eucalyptus globulus plantations, when the understory vegetation coverage is below 50 %, petioles tend to elongate rather than thicken, leading to an increase in specific petiole length. In Bauhinia purpurea plantations, petioles consistently tend to increase diameter. However, when specific leaf area decreases, specific petiole length increases. In both plantations, an increase in specific petiole length accelerates leaf shedding. It leads to increased litter accumulation so that soil carbon content increases. In Eucalyptus globulus plantations, to enhance soil carbon sequestration as an ecological goal, it is recommended to keep the soil total nitrogen below 1.20 mg/g, to control understory vegetation coverage below 50 %, and to limit the extension of Bidens pilosa. In Bauhinia purpurea plantations, within 100 m of altitude, the soil total nitrogen can be controlled below 1.00 mg/g to increase soil organic carbon from large leaf shedding due to the increase of specific petiole length. At lower altitudes, increasing soil total nitrogen can enhance understory vegetation coverage, allowing soil organic carbon to originate from both leaf shedding and understory vegetation residues.

Effects of Exocellobiohydrolase CBHA on Fermentation of Tobacco Leaves.

The quality of tobacco is directly affected by macromolecular content, fermentation is an effective method to improve biochemical properties. In this study, we utilized CBHA (cellobiohydrolase A) glycosylase, which was expressed by Pichia pastoris, as an additive for fermentation. The contents of main chemical components of tobacco leaves after fermentation were determined, and the changes of microbial community structure and abundance in tobacco leaves during fermentation were analyzed. The relationship between chemical composition and changes in microbial composition was investigated, and the function of bacteria and fungi in fermentation was predicted to identify possible metabolic pathways. After 48 h of CBHA fermentation, the contents of starch, cellulose and total nitrogen in tobacco leaf decreased by 17.60%, 28.91% and 16.05%, respectively. The microbial community structure changed significantly, with Aspergillus abundance decreasing significantly, while Filobasidum, Cladosporium, Bullera, Komagataella, etc., increased in CBHA treated group. Soluble sugar was most affected by microbial community in tobacco leaves, which was negatively correlated with starch, cellulose and total nitrogen. During the fermentation process, the relative abundance of metabolism-related functional genes increased, and the expressions of cellulase and endopeptidase also increased. The results showed that the changes of bacterial community and dominant microbial community on tobacco leaves affected the content of chemical components in tobacco leaves, and adding CBHA for fermentation had a positive effect on improving the quality of tobacco leaves.

Development of near-infrared spectroscopy (NIRS) for estimating organic matter, total carbon, and total nitrogen in agricultural soil.

The analysis of soil organic matter (OM), total carbon (TC), and total nitrogen (TN) using traditional methods is quite time-consuming and involves the use of hazardous chemical reagents. Absorbance spectroscopy, especially near-infrared (NIR), is becoming more popular for soil analysis. This method requires little sample preparation, no chemicals, and a single spectral analysis to evaluate soil properties. Thus, this research aimed to develop an NIR spectroscopy method for the analysis of OM, TC, and TN in agricultural soils. These findings can provide a good concept of using PLS regression with NIR techniques. The method is as follows:•Topsoil (0-20 cm) samples were collected from various agricultural fields. OM, TC, and TN were analyzed using traditional methods and NIR spectroscopy.•NIR spectra were obtained using an FT-NIR spectrometer, original spectral including with Savitzky-Golay smoothing, standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing method were used to create a predicted model through Partial Least Squares (PLS) regression with 65 % calibration, and the rest 35 % for validation.•The results showed significant relationships between measured soil properties (SOM and TC) and NIR absorbance spectra in agricultural soil (R 2 of calibration and validation higher than 0.80).

Universal high-frequency monitoring methods of river water quality in China based on machine learning.

The in-situ high-frequency monitoring of total nitrogen (TN) and total phosphorus (TP) in rivers is a challenge and key to instant water quality judgment and early warning. Based on the physical and chemical association between TN/TP and sensor-measurable predictors, we proposed a novel "indirect" measurement method for TN and TP in rivers. This method combines the timeliness of multi-sensor and the accuracy of intelligent algorithms, utilizing 188,629 data sets from 131 water monitoring stations across China. Under 5 algorithms and 4 predictor group scenarios, the results showed that: (1) extra tree regression (ETR) with 6 predictors exhibited the best precision, and the mean determination coefficient (R2) of TN and TP inversion across 131 stations reached 0.78 ± 0.25 and 0.79 ± 0.22 respectively; (2) among 6 potential predictors, the importance degrees of temperature, electrical conductivity, NH4-N, and turbidity were greater than that of pH and DO, and >80 % of stations exhibited acceptable prediction accuracy (R2 > 0.6) when the number of predictors (P) ranged from 4 to 6, which showed good tolerability to predictor variations; (3) the accurate classification rates of water quality standard (ACRws) of all stations based on TN and TP reached 90.41 ± 6.96 % and 92.33 ± 6.41 %; (4) in 9 regions/basins of China, this method showed universal application potential with no significant prediction difference. Compared with laboratory test, water quality automatic monitoring station, and remote sensing inversion, the proposed method offers high-frequency, high-precision, regional adaptability, low cost, and stable operation under rainy, cloudy, and nighttime conditions. The new method may provide important technological support for timely pollutant tracing, pre-warning, and emergency control for river pollution.

[N2O Emission Factors from Wastewater Treatment Plants Based on Literature Statistics and Model Fitting].

The emission of nitrous oxide （N2O） during wastewater treatment cannot be ignored. The analysis of statistical data from literature based on 126 empirical studies revealed that the geographical factors of wastewater treatment plants （WWTPs） had a significant impact on N2O emission factors. However, the N2O emission factors of WWTPs in all regions of the world were generally lower than the Intergovernmental Panel on Climate Change （IPCC） recommended values. In China, the N2O emission factors （in N2O-N/Ninfluent） of WWTPs were approximately 0.000 35-0.065 20 kg·kg-1. Meanwhile, the N2O emission factors of different wastewater treatment processes were also significantly different, especially since the sequencing batch reactor （SBR） process had higher emissions. The use of uniform default emission factors for accounting was prone to overestimate N2O emissions, and it is recommended that countries conduct actual monitoring or modeling studies to develop categorical emission factors suitable for local conditions. In addition, the N2O emission factor based on total nitrogen （TN） removal was weakly negatively correlated with TN removal in 126 empirical data, which was more in line with bioprocessing stoichiometry and could provide an accurate accounting method for N2O. To this end, a digital twin model was developed to dynamically simulate a case anaerobic-anoxic-aerobic （AAO） WWTP to comprehensively quantify the dynamic emission behavior of N2O, which demonstrated that N2O emissions had significant seasonal and daily variability and were only equivalent to 11% of the calculated value of the emission factor based on the IPCC recommendation. Comparing the scatter linear fitting and categorical mean exponential fitting methods, it was found that the latter could more accurately reflect the negative correlation between the N2O emission factors and the TN removal rate, and an exponential regression equation between the average N2O emission factor based on the amount of TN removed and the TN removal rate was further developed to predict the N2O emission. The dynamic simulation and categorical index fitting methods provided in this study are important references for the accurate accounting of N2O emissions in similar WWTPs and provide help for understanding and responding to the N2O emission problems.

Prediction Accuracy of Soil Chemical Parameters by Field- and Laboratory-Obtained vis-NIR Spectra after External Parameter Orthogonalization.

One challenge in predicting soil parameters using in situ visible and near infrared spectroscopy is the distortion of the spectra due to soil moisture. External parameter orthogonalization (EPO) is a mathematical method to remove unwanted variability from spectra. We created two different EPO correction matrices based on the difference between spectra collected in situ and, respectively, spectra collected from the same soil samples after drying and sieving and after drying, sieving and finely grinding. Spectra from 134 soil samples recorded with two different spectrometers were split into calibration and validation sets and the two EPO corrections were applied. Clay, organic carbon and total nitrogen content were predicted by partial least squares regression for uncorrected and EPO-corrected spectra using models based on the same type of spectra ("within domain") as well as using laboratory-based models to predict in situ collected spectra ("cross-domain"). Our results show that the within-domain prediction of clay is improved with EPO corrections only for the research grade spectrometer, with no improvement for the other parameters. For the cross-domain predictions, there was a positive effect from both EPO corrections on all parameters. Overall, we also found that in situ collected spectra provided an equally successful prediction as laboratory-based spectra.

Chlorella vulgaris-mediated bioremediation of food and beverage wastewater from industries in Mexico: Results and perspectives towards sustainability and circular economy.

The food and beverage industries in Mexico generate substantial effluents, including nejayote, cheese-whey, and tequila vinasses, which pose significant environmental challenges due to their extreme physicochemical characteristics and excessive organic load. This study aimed to assess the potential of Chlorella vulgaris in bioremediating these complex wastewaters while also producing added-value compounds. A UV mutagenesis treatment (40 min) enhanced C. vulgaris adaptability to grow in the effluent conditions. Robust growth was observed in all three effluents, with nejayote identified as the optimal medium. Physicochemical measurements conducted pre- and post-cultivation revealed notable reductions of pollutants in nejayote, including complete removal of nitrogen and phosphates, and an 85 % reduction in COD. Tequila vinasses exhibited promise with a 66 % reduction in nitrogen and a 70 % reduction in COD, while cheese-whey showed a 17 % reduction in phosphates. Regarding valuable compounds, nejayote yielded the highest pigment (1.62 mg·g-1) and phenolic compound (3.67 mg·g-1) content, while tequila vinasses had the highest protein content (16.83 %). The main highlight of this study is that C. vulgaris successfully grew in 100 % of the three effluents (without additional water or nutrients), demonstrating its potential for sustainable bioremediation and added-value compound production. When grown in 100 % of the effluents, they become a sustainable option since they don't require an input of fresh water and therefore do not contribute to water scarcity. These findings offer a practical solution for addressing environmental challenges in the food and beverage industries within a circular economy framework.

Deployment strategy of multiple miniaturized near-infrared spectrometers based on spectral transfer for characterizing soil organic matter and nitrogen.

Developing timely, convenient, and low-cost methods for high-frequency characterization of soil nutrients is necessary for implementing precise soil nutrient management. With the current availability of numerous calibration models of laboratory benchtop near-infrared (NIR) spectrometers for rapid soil nutrient characterization and the appearance of low-cost, convenient miniaturized NIR spectrometers, this study proposes an efficient deployment strategy to address model failure due to inter-device variation based on spectral transfer. The strategy involves using Direct Standardization (DS) to migrate the spectra from multiple miniaturized NIR spectrometers with a laboratory benchtop NIR spectrometer and then directly applying the existing calibration models of the laboratory benchtop instrument to the transferred spectra for soil nutrient analysis. The results indicated that the DS method successfully transferred the spectra of miniaturized devices to be consistent with the spectra of the laboratory benchtop instrument. The soil organic matter (SOM) predictions using the transferred spectra and the calibration models of the laboratory benchtop instrument were even more accurate than those using the respective models developed for each miniaturized devices, with root mean square error (RMSE) of 0.177 %, 0.177 %, and 0.150 %, respectively, while the performances of total nitrogen (TN) predictions were comparable to those using the respective models, with RMSE of 0.013 %, 0.012 %, and 0.010 %, respectively. Bland-Altman plots demonstrated good consistency between the strategy proposed in this study and the strategy of developing respective models for each miniaturized device, with no difference in predictions for the independent validation set compared to the laboratory benchtop instrument. This study proved the feasibility of deployment strategy of multiple miniaturized NIR spectrometers based on spectral transfer, offering a new solution for high-frequency on-site soil nutrient characterization.

Enzymatic Activity of Soil on the Occurrence of the Endangered Beetle Cheilotoma musciformis (Coleoptera: Chrysomelidae) in Xerothermic Grasslands.

This work attempts to find the reasons for the rather limited range of occurrence of Cheilotoma musciformis in Poland, based on soil properties, which affects both the plant cover and the entomofauna. The aim of the study was to assess the influence of soil enzyme activity on the occurrence of Ch. musciformis in xerothermic grasslands in Southern Poland. The sites inhabited by the beetle were most often extensively grazed by farm animals or had recently been cleared of bushes. The control plots were in wasteland. The soils of most sites with Ch. musciformis were characterized by significantly higher activity of the tested enzymes and higher content of total organic carbon and total nitrogen, as well as lower pHKCl compared to the control sites. The higher enzymatic activity of soils in sites with the beetle than in the control sites may indicate the dependence of the occurrence of this beetle on the presence of patches of extensively grazed xerothermic grasslands. Grazing influences the behavior of preferred host plant species. Therefore, when planning active protection of xerothermic grasslands inhabited by Ch. musciformis, changes in the biochemical properties of the soil and vegetation structure should be taken into account.

Assessing spatiotemporal risks of nonpoint source pollution via soil erosion: a coastal case in the Yellow River Delta, China.

Nonpoint source pollution (NPSP) has always been the dominant threat to regional waters. Based on empirical models of the revised universal soil loss equation and the phosphorus index, an NPSP risk assessment model denoted as SL-NPSRI was developed. The surface soil pollutant loss was estimated by simulating the rain-runoff topographic process, and the influence of path attenuation was quantified. A case study in the Yellow River Delta and corresponding field surveys of soil pollutants and water quality showed that the established model can be applied to evaluate the spatial heterogeneity of NPSP. NPSP usually occurs during high-intensity rainfall periods and in larger estuaries. Summer rainfall increased pollutant transport into the sea from late July to mid-August and caused estuarine dilution. Higher NPSP risks often correspond to coastal areas with lower vegetation coverage, higher soil erodibility, and higher soil pollutant concentrations. Agricultural NPSP originating from cropland significantly increase the pollutant fluxes. Therefore, area-specific land use management and vegetation coverage improvement, and temporal-specific strategies can be explored for NPSP control during source-transport hydrological processes. This research provides a novel insight for coastal NPSP simulations by comprehensively analyzing the soil erosion process and its associated pollutant loss effects, which can be useful for targeted spatiotemporal solutions.

Purification of inorganic nitrogen from the mariculture tail water by anaerobic/anoxic/oxic (A2O) process.

This study aims to address the suboptimal performance of conventional denitrifying strains in treating mariculture tail water (MTW) containing inorganic nitrogen (IN). The concentration of inorganic nitrogen in the mariculture tail water is about 5-20 mg·L-1. A biofilm treatment process was developed and evaluated using an anoxic-anoxic-aerobic biofilter composite system inoculated with the denitrifying strain Meyerozyma guilliermondii Y8. The removal effect of total nitrogen (TN), IN, and Chemical Oxygen Demand (CODMn) from MTW was investigated. The results indicate that the A2O composite biological filter has excellent pollutant removal efficiency within 25 days of operation, after the acclimation of the denitrifying microorganisms. The initial concentrations of TN, IN, and CODMn ranged between 10.24 and 12.89 mg·L-1, 7.84-10.49 mg·L-1, and 9.44-11.52 mg·L-1, respectively, and the removal rates of these indexes reached 38-68 %, 45-70 %, and 55-70 %, respectively. The experiments with different hydraulic retention times (HRT = 6 h, 8 h, 10 h) demonstrated that longer HRT was more conducive to the removal of inorganic nitrogen. Moreover, scanning electron microscopy observations revealed that the target strain successfully grew and attached to the filler in large quantities. The findings of this study provide practical guidance for the development of efficient biofilm processes for the treatment of MTW.

Advanced electrolysis sulfur-based biofiltration for simultaneous total nitrogen removal and estrogen toxicity reduction from low carbon-to-nitrogen ratio wastewater.

A sulfur-based biofilter enhanced by phosphate modified activated carbon as particle electrodes was constructed to simultaneously remove total nitrogen (TN) and estrogen from low carbon-to-nitrogen ratio (C/N) wastewater containing 1 mg/L 17-alpha-ethinylestradiol (EE2). Results showed that the enhanced biofilter achieved outstanding performance in EE2 removal (93.2 %) and TN reduction (effluent < 5 mg/L), demonstrating robustness against C/N fluctuations. It was noteworthy that it successfully reduced both acute toxicity (59.5 %) and estrogenic activity (88.6 %). Comprehensive characterization investigations and microbial community structure analysis revealed that enhanced electron transfer and increased microbial abundance likely contributed to improved biofilter performance. Core microorganisms, such as Pseudomonas and Chryseobacterium were identified as key contributors to synergistic estrogen degradation and denitrification. This study presented a feasible and promising strategy of combined process with three-dimensional electrodes and sulfur-based biofilter, highlighting substantial potential for advanced purification and safe reuse of wastewater.

Safety, quality, and microbial community analysis of salt-fermented shrimp (Saeu-jeot) in South Korea.

This study thoroughly explores the quality and safety aspects of saeu-jeot, a popular salt-fermented shrimp in South Korea, with a specific focus on products fermented in underground tunnels. In this study, an extensive analysis of key quality factors (pH, salinity, total nitrogen [TN], and amino nitrogen [AN]), along with detailed investigation into chemical hazards (volatile basic nitrogen [VBN] and biogenic amines [BAs]), and microbiological hazards (total aerobic bacteria [TAB], fecal indicator bacteria, halophilic bacteria, and foodborne pathogens) were performed. The results indicate that the shrimp grade did not dramatically affect the quality and safety of the saeu-jeot. However, given the prevalent small-scale production of saeu-jeot, the study strongly underscores the pressing need for the establishment of a standardized manufacturing process. The absence of grade-dependent variations in quality highlights the critical importance of implementing standardized procedures to ensure the consistent quality and safety of saeu-jeot, particularly in the context of its frequent small-scale production. These findings provide crucial insights for the industry to enhance practices and meet quality and safety standards effectively.

Development of a basin-scale total nitrogen prediction model by integrating clustering and regression methods.

Nutrient runoff into rivers caused by human activity has led to global eutrophication issues. The Nakdong River in South Korea is currently facing significant challenges related to eutrophication and harmful algal blooms, underscoring the critical importance of managing total nitrogen (T-N) levels. However, traditional methods of indoor analysis, which depend on sampling, are labor-intensive and face limitations in collecting high-frequency data. Despite advancements in sensor allowing for the measurement of various parameters, sensors still cannot directly measure T-N, necessitating surrogate regression methods. Therefore, we conducted T-N predictions using a water quality dataset collected from 2018 to 2022 at 157 observatories within the Nakdong River basin. To account for the water quality characteristics of each location, we employed a clustering technique to divide the basin and compared a Gaussian mixture model with K-means clustering. Moreover, optimal regressor for each cluster was selected by comparing multiple linear regression (MLR), random forest, and XGBoost. The results showed that forming four clusters via K-means clustering was the most suitable approach and MLR was reasonably accurate for all clusters. Subsequently, recursive feature elimination cross-validation was used to identify suitable parameters for T-N prediction, thus leading to the construction of high-accuracy T-N prediction models. Clustering was useful not only for improving the regressors but also for spatially analyzing the water quality characteristics of the Nakdong River. The MLR model can reveal causal relationships and thus is useful for decision-making. The results of this study revealed that the combination of a simple linear regression model and clustering method can be applied to a wide watershed. The clustering-based regression model showed potential for accurately predicting T-N at the basin level and is expected to contribute to nationwide water quality management through future applications in various fields.

Hybrid supervised hierarchical control of a biological wastewater treatment plant.

In wastewater treatment intensification, hierarchical control structures are developed to improve the plant's performance. This paper proposes two novel hybrid supervised hierarchical control structures for specifying the dissolved oxygen concentration in the last aerobic reactor of the wastewater treatment plant (WWTP) based on the nitrification rate and the ammonia level in this reactor. These structures combine the optimum disturbance rejection PI control (OPI), adaptive neuro-fuzzy inference system (ANFIS), and genetic algorithms (GA) to reduce energy consumption and operational costs, improve effluent quality, and reduce the number and percentage of times the established maximum concentration of pollutants in the effluent of the WWTP is violated. The proposed control strategy is implemented and evaluated using benchmark simulation model no. 1 (BSM1). The OPI-ANFIS-GA configuration significantly enhances effluent quality in dry, rainy, and stormy weather conditions, reducing total nitrogen violations by 50.17%, 63.35%, and 47.35%, respectively. Then, 6.79% and 7.12% of aeration energy and 1.44% and 1.46% of operational costs are reduced in dry and rain weather conditions. The OPI-ANFIS configuration enhanced significant energy savings and a cost reduction in storm weather conditions. Both configurations led to a 49.89% decrease in total suspended sludge (TSS) during stormy weather conditions. The proposed controller significantly improves the performance of the WWTP in all weather scenarios compared to the default controller and similar controllers found in the literature.

A hybrid deep learning approach to improve real-time effluent quality prediction in wastewater treatment plant.

Wastewater treatment plant (WWTP) operation is usually intricate due to large variations in influent characteristics and nonlinear sewage treatment processes. Effective modeling of WWTP effluent water quality can provide valuable decision-making support to facilitate their operations and management. In this study, we developed a novel hybrid deep learning model by combining the temporal convolutional network (TCN) model with the long short-term memory (LSTM) network model to improve the simulation of hourly total nitrogen (TN) concentration in WWTP effluent. The developed model was tested in a WWTP in Jiangsu Province, China, where the prediction results of the hybrid TCN-LSTM model were compared with those of single deep learning models (TCN and LSTM) and traditional machine learning model (feedforward neural network, FFNN). The hybrid TCN-LSTM model could achieve 33.1 % higher accuracy as compared to the single TCN or LSTM model, and its performance could improve by 63.6 % comparing to the traditional FFNN model. The developed hybrid model also exhibited a higher power prediction of WWTP effluent TN for the next multiple time steps within eight hours, as compared to the standalone TCN, LSTM, and FFNN models. Finally, employing model interpretation approach of Shapley additive explanation to identify the key parameters influencing the behavior of WWTP effluent water quality, it was found that removing variables that did not contribute to the model output could further improve modeling efficiency while optimizing monitoring and management strategies.