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Utilizing carbon-based counter electrodes (CEs) in dye-sensitized solar cells (DSSCs) have received much attention in recent times, owing to their low cost, good electrochemical activity, natural abundance and eco-friendly nature. Herein, we have facilely prepared quince leaves derived porous carbon (QLPC) using fallen quince leaves (QLs) and it was used as a cost-effective CE for the fabrication of DSSCs. By means of alkali treatment and pyrolysis process (at different temperatures of 700, 800 and 900 °C), the QLs powder undergoes chemical activation and carbonization, which results in a honeycomb-like QLPC with abundant micro/mesopores and large surface area. Simple and straightforward coating of QLPC samples onto fluorine doped tin oxide glass substrates led to improved electrocatalytic activity and good tri-iodide reduction in DSSCs. When the DSSCs were illuminated under 1 sun condition (AM 1.5; 100 mW cm −2 ), the device assembled with QLPC-based CE (prepared at 800 °C) showed a higher current density of ∼14.99 mA/cm 2 and power conversion efficiency of ∼5.52% among the other QLPC-based CEs, which are comparable with the platinum-based CE in DSSCs. This facile process for the preparation of biomass derived carbon-based CE provides an alternative to the noble metal-free CE in DSSCs.

Honeycomb-like activated porous carbon (HPC) powder with meso/macropore structures was successfully prepared via chemical activation and pyrolysis processes under inert gas atmosphere. Herein, the naturally available pine cone flowers with abundant carbon contents were employed as a biomass to synthesize the HPC sample. The structure and morphology of the HPC powder were characterized by various physicochemical techniques. Moreover, the as-prepared HPC sample was uniformly coated on fluorine doped tin oxide glass using a smooth brush and used as a cost-effective counter electrode (CE) in dye-sensitized solar cells (DSSCs). Under AM1.5G illumination, the fabricated DSSCs with HPC-based CE exhibited a high short-circuit current density (JSC) of 13.51 mA/cm2 and an excellent power conversion efficiency (PCE) of 4.98%, which is attributed to the high specific surface area and hierarchical porous property of the HPC sample. The obtained results were improved compared to the commercially available activated carbon-based CE in DSSCs (JSC = 12.11 mA/cm2 and PCE = 4.45%). This facile fabrication of highly porous activated carbon-based materials from the biomass can be utilized as a high-performance electrode material in DSSCs and energy storage device applications.

When stormwater runoff flows into a constructed wetland, the water level changes inside, and the volume of the wetland changes accordingly. In this study, the relationship between pollutant removal and water depth flowrate changes was analyzed with velocity phase analysis, using the concepts of horizontal velocity and vertical velocity. A constructed wetland located in an agricultural area was used for the velocity phase analysis. Analysis results revealed that all four ponds and Phases I and III accounted for over 90% of the pollutant load. In particular, Phase III, where vertical and horizontal velocities were reduced, was responsible for approximately twice the load on average. The reason for the highest load in Phase III was due to the inflow of contaminants into the constructed wetland through the first flush effect occurred at the time when the flow rate decreased due to the difference in runoff strength. Therefore, it is necessary to maximize Phases I and III in the velocity phase when designing multi-stage wetlands to make the flow of water more natural. Furthermore, considering pollutant load levels, a design review is needed to expand the size of downstream wetlands, so that they can optimize water quality management functions.

Chlorophyll-a (Chl-a) is a direct indicator used to evaluate the ecological state of a waterbody, such as algal blooms that degrade the water quality in lakes, reservoirs and estuaries. In this study, artificial neural network (ANN) and support vector machine (SVM) were used to predict Chl-a concentration for the early warning in the Juam Reservoir and Yeongsan Reservoir, which are located in an upstream region (freshwater reservoir) and downstream region (estuarine reservoir), respectively. Weekly water quality data and meteorological data for a 7-year period were used to train and validate both the ANN and SVM models. The Latin-hypercube one-factor-at-a-time (LH-OAT) method and a pattern search algorithm were applied to perform sensitivity analyses for the input variables and to optimize the parameters of the two models, respectively. Results revealed that the two models well-reproduced the temporal variation of Chl-a based on the weekly input variables. In particular, the SVM model showed better performance than the ANN model, displaying a higher prediction accuracy in the validation step. The Williams-Kloot test and sensitivity analysis demonstrated that the SVM model was superior for predicting Chl-a in terms of prediction accuracy and description of the cause-and-effect relationship between Chl-a concentration and environmental variables in both the Juam Reservoir and Yeongsan Reservoir. Furthermore, a 7-day interval was determined as an efficient early warning interval in the two reservoirs. As such, this study suggested an effective early-warning prediction method for Chl-a concentration and improved the eutrophication management scheme for reservoirs.

This study reveals land-use factors that explain stream water quality during wet and dry weather conditions in a large river basin using two different linear models-multiple linear regression (MLR) models and constrained least squares (CLS) models. Six land-use types and three topographical parameters (size, slope, and permeability) of the watershed were incorporated into the models as explanatory variables. The suggested models were then demonstrated using a digitized elevation map in conjunction with the land-use and the measured concentration data for Escherichia coli (EC), Enterococci bacteria (ENT), and six heavy metal species collected monthly during 2007-2008 at 50 monitoring sites in the Yeongsan Watershed, Korea. The results showed that the MLR models can be a powerful tool for predicting the average concentrations of pollutants in stream water (the Nash-Sutcliffe (NS) model efficiency coefficients ranged from 0.67 to 0.95). On the other hand, the CLS models, with moderately good prediction performance (the NS coefficients ranged 0.28-0.85), were more suitable for quantifying contributions of respective land-uses to the stream water quality. The CLS models suggested that industrial and urban land-uses are major contributors to the stream concentrations of EC and ENT, whereas agricultural, industrial, and mining areas were significant sources of many heavy metal species. In addition, the slope, size, and permeability of the watershed were found to be important factors determining the extent of the contribution from each land-use type to the stream water quality. The models proposed in this paper can be considered useful tools for developing land cover guidelines and for prioritizing locations for implementing management practices to maintain stream water quality standard in a large river basin.

Gwangju Creek (GJC) in Korea, which drains a highly urbanized watershed, has suffered from substantial fecal contamination, thereby limiting the beneficial use of the water in addition to threatening public health. In this study, to quantitatively estimate the sinks and sources of fecal indicator bacteria (FIB) in GJC under varying meteorological conditions, two FIB (i.e., Escherichia coli and enterococci bacteria) were monitored hourly for 24 h periods during both wet and dry weather conditions at four sites along GJC, and the collected data was subsequently used to develop a spatiotemporal FIB prediction model. The monitoring data revealed that storm washoff and irradiational die-off by sunlight are the two key processes controlling FIB populations in wet and dry weather, respectively. FIB populations significantly increased during precipitation, with greater concentrations occurring at higher rainfall intensity. During dry weather, FIB populations decreased in the presence of sunlight in daytime but quickly recovered at nighttime due to continuous point-source inputs. In this way, the contributions of the key processes (i.e., irradiational die-off by sunlight, settling, storm washoff, and resuspension) to the FIB levels in GJC under different meteorological conditions were quantitatively estimated using the developed model. The modeling results showed that the die-off by sunlight is the major sink of FIB during the daytime in dry weather with a minor contribution from the settling process. During wet weather, storm washoff and resuspension are equally important processes that are responsible for the substantial increase of FIB populations.

Statistical regression models involve linear equations, which often lead to significant prediction errors due to poor statistical stability and accuracy. This concern arises from multicollinearity in the models, which may drastically affect model performance in terms of a trade-off scenario for effective water resource management logistics. In this paper, we propose a new methodology for improving the statistical stability and accuracy of regression models, and then show how to cope with pitfalls in the models and determine optimal parameters with a decreased number of predictive variables. Here, a comparison of the predictive performance was made using four types of multiple linear regression (MLR) and principal component regression (PCR) models in the prediction of chlorophyll-a (chl-a) concentration in the Yeongsan (YS) Reservoir, Korea, an estuarine reservoir that historically suffers from high levels of nutrient input. During a 3-year water quality monitoring period, results showed that PCRs could be a compact solution for improving the accuracy of the models, as in each case MLR could not accurately produce reliable predictions due to a persistent collinearity problem. Furthermore, based on R2 (goodness of fit) and F-overall number (confidence of regression), and the number of explanatory variables (R-F-N) curve, it was revealed that PCR-F(7) was the best model among the four regression models in predicting chl-a, having the fewest explanatory variables (seven) and the lowest uncertainty. Seven PCs were identified as significant variables, related to eight water quality parameters: pH, 5-day biochemical oxygen demand, total coliform, fecal indicator bacteria, chemical oxygen demand, ammonia–nitrogen, total nitrogen, and dissolved oxygen. Overall, the results not only demonstrated that the models employed successfully simulated chl-a in a reservoir in both the test and validation periods, but also suggested that the optimal parameters should cautiously be considered in the design of regression models.