Treatment performance of multistage active biological process (MSABP) reactor for saline sauerkraut wastewater: acclimatization, optimization and improvement.

The wastewater with a high concentration of organics and salt is a major contaminant in the production of sauerkraut. In this study, a multistage active biological process (MSABP) system was constructed to treat sauerkraut wastewater. The key process parameters of the MSABP system were analyzed and optimized by response surface methodology. The optimization results indicated that the most optimal removal efficiencies and removal loading rates of chemical oxygen demand (COD) and NH4+-N were 87.9%, 95.5%, 2.11 kg·m-3·d-1 and 0.12 kg·m-3·d-1, respectively, with hydraulic retention time (HRT) of 2.5 d and pH of 7.3. Meanwhile, this system could also be improved for the further treatment of COD and total nitrogen by effluent recycle and ozone oxidation. The COD and total nitrogen removal efficiencies of the modified MSABP system were 99.9% and 60.2%, respectively. In addition, the modified system could also reduce the potential harm from high concentrations of NO2--N.

Spatiotemporal impact of COVID-19 on Taiwan air quality in the absence of a lockdown: Influence of urban public transportation use and meteorological conditions.

The unprecedented outbreak of COVID-19 significantly improved the atmospheric environment for lockdown-imposed regions; however, scant evidence exists on its impacts on regions without lockdown. A novel research framework is proposed to evaluate the long-term monthly spatiotemporal impact of COVID-19 on Taiwan air quality through different statistical analyses, including geostatistical analysis, change detection analysis and identification of nonattainment pollutant occurrence between the average mean air pollutant concentrations from 2018-2019 and 2020, considering both meteorological and public transportation impacts. Contrary to lockdown-imposed regions, insignificant or worsened air quality conditions were observed at the beginning of COVID-19, but a delayed improvement occurred after April in Taiwan. The annual mean concentrations of PM10, PM2.5, SO2, NO2, CO and O3 in 2020 were reduced by 24%, 18%, 15%, 9.6%, 7.4% and 1.3%, respectively (relative to 2018-2019), and the overall occurrence frequency of nonattainment air pollutants declined by over 30%. Backward stepwise regression models for each air pollutant were successfully constructed utilizing 12 meteorological parameters (R2 > 0.8 except for SO2) to simulate the meteorological normalized business-as-usual concentration. The hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model simulated the fate of air pollutants (e.g., local emissions or transboundary pollution) for anomalous months. The changes in different public transportation usage volumes (e.g., roadway, railway, air, and waterway) moderately reduced air pollution, particularly CO and NO2. Reduced public transportation use had a more significant impact than meteorology on air quality improvement in Taiwan, highlighting the importance of proper public transportation management for air pollution control and paving a new path for sustainable air quality management even in the absence of a lockdown.

Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia.

Rivers in Malaysia are classified based on water quality index (WQI) that comprises of six parameters, namely, ammoniacal nitrogen (AN), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). Due to its tropical climate, the impact of seasonal monsoons on river quality is significant, with the increased occurrence of extreme precipitation events; however, there has been little discussion on the application of artificial intelligence models for monsoonal river classification. In light of these, this study had applied artificial neural network (ANN) and support vector machine (SVM) models for monsoonal (dry and wet seasons) river classification using three of the water quality parameters to minimise the cost of river monitoring and associated errors in WQI computation. A structured trial-and-error approach was applied on input parameter selection and hyperparameter optimisation for both models. Accuracy, sensitivity, and precision were selected as the performance criteria. For dry season, BOD-DO-pH was selected as the optimum input combination by both ANN and SVM models, with testing accuracy of 88.7% and 82.1%, respectively. As for wet season, the optimum input combinations of ANN and SVM models were BOD-pH-SS and BOD-DO-pH with testing accuracy of 89.5% and 88.0%, respectively. As a result, both optimised ANN and SVM models have proven their prediction capacities for river classification, which may be deployed as effective and reliable tools in tropical regions. Notably, better learning and higher capacity of the ANN model for dataset characteristics extraction generated better predictability and generalisability than SVM model under imbalanced dataset.

Comparison among different ASEAN water quality indices for the assessment of the spatial variation of surface water quality in the Selangor river basin, Malaysia.

The assessment of surface water quality is often laborious, expensive and tedious, as well as impractical, especially for the developing and middle-income countries in the ASEAN region. The application of the water quality index (WQI), which depends on several independent key parameters, has great potential and is a useful tool in this region. Therefore, this study aims to find out the spatial variability of various water quality parameters in geographical information system (GIS) environment and perform a comparative study among the ASEAN WQI systems. At present, there are four ASEAN countries which have implemented the WQI system to evaluate their surface water quality, which are (i) Own WQI system-Malaysia, Thailand and Vietnam-and (ii) Adopted WQI system: Indonesia. A spatial distribution of 12 water quality parameters in the Selangor river basin, Malaysia, was plotted and then applied into the different ASEAN WQI systems. The WQI values obtained from the different WQI systems have an appreciable difference, even for the same water samples due to the disparity in the parameter selection and the standards among them. WQI systems which consider all biophysicochemical parameters provide a consistent evaluation (Very Poor), but the system which either considers physicochemical or biochemical parameters gives a relatively lenient evaluation (Fair-Poor). The Selangor river basin is stressed and impacted by all physical, biological and chemical parameters caused by both the aridity of the climate and anthropogenic activities. Therefore, it is crucial to include all these aspects into the evaluation and corresponding actions should be taken.

Comparative study of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) for modeling of Cu (II) adsorption from aqueous solution using biochar derived from rambutan (Nephelium lappaceum) peel.

Presence of copper within water bodies deteriorates human health and degrades natural environment. This heavy metal in water is treated using a promising biochar derived from rambutan (Nephelium lappaceum) peel through slow pyrolysis. This research compares the efficacies of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models and evaluates their capability in estimating the adsorption efficiency of biochar for the removal of Cu (II) ions based on 480 experimental sets obtained in a laboratory batch study. The effects of operational parameters such as contact time, operating temperature, biochar dosage, and initial Cu (II) ion concentration on removing Cu (II) ions were investigated. Eleven different training algorithms in ANN and 8 different membership functions in ANFIS were compared statistically and evaluated in terms of estimation errors, which are root mean squared error (RMSE), mean absolute error (MAE), and accuracy. The effects of number of hidden neuron in ANN model and fuzzy set combination in ANFIS were studied. In this study, ANFIS model with Gaussian membership function and fuzzy set combination of [4 5 2 3] was found to be the best method, with accuracy of 90.24% and 87.06% for training and testing dataset, respectively. Contribution of this study is that ANN, ANFIS, and MLR modeling techniques were used for the first time to study the adsorption of Cu (II) ions from aqueous solutions using rambutan peel biochar.

Parametric optimization of polycaprolactone synthesis catalysed by Candida antarctica lipase B using response surface methodology.

A statistical approach with D-optimal design was used to optimize the process parameters for polycaprolactone (PCL) synthesis. The variables selected were temperature (50°C-110°C), time (1-7 h), mixing speed (50-500 rpm) and monomer/solvent ratio (1:1-1:6). Molecular weight was chosen as response and was determined using matrix-assisted laser desorption/ionization time of flight (MALDI TOF). Using the D-optimal method in design of experiments, the interactions between parameters and responses were analysed and validated. The results show a good agreement with a minimum error between the actual and predicted values.

Evaluating the necessity of post-processing techniques on d4PDF data for extreme climate assessment.

The occurrence and severity of extreme precipitation events have been increasing globally. Although numerous projections have been proposed and developed for evaluating the climate change impacts, most models suffer from significant bias error due to the coarse resolution of the climate datasets, which affects the accuracy of the climate change assessment. Therefore, in this study, post-processing techniques (interpolation and bias correction methods) were adopted on the database for Policy Decision Making for Future Climate Change (d4PDF) model for extreme climatic flood events simulation in the Chao Phraya River Basin, Thailand, under + 4-K future climate simulation. Due to the limited number of the rain gages, the gradient plus inverse distance squared interpolation method (combination of multiple linear regression and distance weighting methods) was applied in this study. In the bias correction methods, the additional setting of monthly and seasonal periods was adjusted. The proposed bias correction approach deployed gamma distribution combined with generalized Pareto distribution setting with the seasonal period for the rainy season datasets, whereas only the gamma setting was applied with the monthly period during the dry season. The outcomes revealed that the proposed method could react to extreme rainfall events, expand dry days during dry season, and intensify rainfall amount during rainy season. The post-processing d4PDF trends of six sea surface temperature (SST) patterns (consists of 90 ensemble members) of two periods (near future: 2051-2070 and far future: 2091-2110) recorded the highest and lowest amounts of annual rainfalls of 4,450 mm/year in mid-stream of Nan River and 710 mm/year in the lower CPRB, respectively. Notably, the significant variances noted in the rainfall patterns among ensembles, demanding further investigation in future climate change, impact studies. The findings of the study provided novel insights on the importance of proper post-processing techniques for improving the robustness of d4PDF in climate change impacts assessment.

Water quality index modeling using random forest and improved SMO algorithm for support vector machine in Saf-Saf river basin.

The water quality index is one of the prominent general indicators to assess and classify surface water quality, which plays a critical role in river water resources practices. This research constructs a hybrid artificial intelligence model namely sequential minimal optimization-support vector machine (SMO-SVM) along with random forest (RF) as a benchmark model for predicting water quality values at the Wadi Saf-Saf river basin in Algeria. The fifteen input water quality datasets such as biochemical oxygen demand (BOD), oxygen saturation (OS), the potential for hydrogen (pH), chemical oxygen demand (COD), chloride (Cl-), dissolved oxygen (DO), electrical conductivity (EC), total dissolved solids (TDS), nitrate-nitrogen (NO3-N), nitrite-nitrogen (NO2-N), phosphate (PO43-), ammonium (NH4+), temperature (T), turbidity (NTU), and suspended solids (SS) were employed for constructing the predictive models. Different input data combinations are evaluated in terms of predictive performance, using a set of statistical metrics and graphical representation. Results show that less than 40% of samples were observed to be poor quality water during the dry season in downstream northeastern part of the basin. The findings also show that the RF model mostly generates more precise water quality index predictions than the SMO-SVM model for both training and testing stages. Although thirteen input parameters attain the optimal predictive performance (R2 testing = 0.82, RMSE testing = 5.17), a couple of five input parameters, e.g., only pH, EC, TDS, T, and saturation, gives the second optimal predictive precision (R2 test = 0.81, RMSE testing = 5.55). The sensitivity analysis results indicate a greater sensitivity by the all input variables chosen except NO2- of the predictive outcomes to the earlier influencing water quality parameters. Overall, the RF model reveals an improvement on earlier tools for predicting water quality index, according to predictive performance and reducing in the number of input variables.