Study of the Water Quality Index and Polycyclic Aromatic Hydrocarbon for a River Receiving Treated Landfill Leachate

Assessing and forecasting water quality in the Danube River by using neural network approaches

The health and quality of the Danube River ecosystems is strongly affected by the nutrients loads (N and P), degree of contamination with hazardous substances or with oxygen depleting substances, microbiological contamination and changes in river flow patterns and sediment transport regimes. Water quality index (WQI) is an important dynamic attribute in the characterization of the Danube River ecosystems health and quality. The WQ index scores do not reflect the actual condition of water quality. We proposed a new forecast scheme for water quality based on the following qualitative classes very good (0-25), good (26-50), poor (51-75), very poor (76-100) and extremely polluted/non-potable (>100). Water quality forecasting by using Artificial Intelligence (AI) is a meaningful method of protecting public health because of its possibility to provide early warning regarding harmful water pollutants. The main objective of the present study is to forecast the WQI time series data based on water physical, chemical and flow status parameters and associated WQ index scores. The Cascade-forward network (CFN) models, along with the Radial Basis Function Network (RBF) as a benchmark model, were developed using data from 2011 to 2017 and WQI forecasts were produced for the period 2018-2019 at all sites. The nineteen input water quality features represent the initial dataset. Moreover, the Random Forest (RF) algorithm refines the initial dataset by selecting eight features considered the most relevant. Both datasets are employed for constructing the predictive models. According to the results of appraisal, the CFN models produced better outcomes (MSE = 0.083/0,319 and R-value 0.940/0.911 in quarter I/quarter IV) than the RBF models. In addition, results show that both the CFN and RBF models could be effective for predicting time series data for water quality when the eight most relevant features are used as input variables. Also, the CFNs provide the most accurate short-term forecasting curves which reproduce the WQI for the first and fourth quarters (the cold season). The second and third quarters presented a slightly lower accuracy. The reported results clearly demonstrate that CFNs successfully forecast the short-term WQI as they may learn historic patterns and determine the nonlinear relationships between the input and output variables.

Comparative Effects of Graphitic Carbon Nitride Precursors on the Photocatalytic Degradation of Pyrene

Pyrene is a ubiquitous, persistent, and mutagenic pollutant that belongs to the polycyclic aromatic hydrocarbons. Graphitic carbon nitride (g-C3N4) has emerged as a promising photocatalyst for degradation of various pollutants in water and wastewater treatment due to its unique band structure and excellent physiochemical stability. This paper presents the comparative study of composites g-C3N4 from various combinations of precursors using thermal polycondensation at 600 °C. Comparative experiments revealed that the preparation conditions of both precursors and the mass ratios of precursor influenced the overall performance of photocatalyst during photocatalytic degradation of pyrene. Experimental results indicated that the best performance of composites g-C3N4t photocatalyst was prepared from a wet mixture of dicyandiamide and guanidine carbonate precursors at a mass ratio of 1:1 with 43.9 % pyrene degradation under visible light irradiation for 240 mins. The reusability of the best g-C3N4 composites for the photocatalytic degradation of pyrene was also investigated. It was found that the prepared photocatalyst was stable up to five cycles of photocatalysis. Meanwhile, holes (h+) and hydroxyl radicals (·OH) were identified as the primary and secondary dominant reactive species in the photocatalytic degradation through scavenging trapping experiments.

A New Scenario-Based Approach for Water Quality and Environmental Impact Assessment Due to Mining Activities

Water quality assessment and its monitoring are necessary for areas of mining activities. In Malaysia, the mining industry is the backbone of the manufacturing and construction sectors. This study used spatio-temporal water quality modeling along a reach with mining activities during high and low discharges at Sungai (river) Lebir and Sungai Aring, situated in Gua Musang, Kelantan, Peninsular Malaysia. The objective was to assess the spatio-temporal environmental impact of mining activities during the wet and dry seasons. Data were collected at different locations along the reach. Point and non-point sources were near the mining site. Overland flow calculation at the mining site was found with the widely used SCS (Soil Conservation Service) curve number method. Several scenarios were analyzed, such as baseline, worst-case, and with-mitigation. The study revealed that baseline values of all parameters were either in a natural condition or slightly polluted, except for aluminum. All parameters were estimated at a high concentration from the mining site to downstream during the worst case of the wet season. Whereas, during the worst case of the dry season, no significant differences were observed compared to baseline values. In the with-mitigation scenario, parameter concentrations were improved and similar to baseline values. Overall, the scenario selection was helpful in the environmental impact assessment. Furthermore, this study will be significant in pre- and post-mining assessment and environmental clearance.

Forecasting Water Quality Index in Groundwater Using Artificial Neural Network

Groundwater quality monitoring in the vicinity of drilling sites is crucial for the protection of water resources. Selected physicochemical parameters of waters were marked in the study. The water was collected from 19 wells located close to a shale gas extraction site. The water quality index was determined from the obtained parameters. A secondary objective of the study was to test the capacity of the artificial neural network (ANN) methods to model the water quality index in groundwater. The number of ANN input parameters was optimized and limited to seven, which was derived using a multiple regression model. Subsequently, using the stepwise regression method, models with ever fewer variables were tested. The best parameters were obtained for a network with five input neurons (electrical conductivity, pH as well as calcium, magnesium and sodium ions), in addition to five neurons in the hidden layer. The results showed that the use of the parameters is a convenient approach to modeling water quality index with satisfactory and appropriate accuracy. Artificial neural network methods exhibited the capacity to predict water quality index at the desirable level of accuracy (RMSE = 0.651258, R = 0.9992 and R2 = 0.9984). Neural network models can thus be used to directly predict the quality of groundwater, particularly in industrial areas. This proposed method, using advanced artificial intelligence, can aid in water treatment and management. The novelty of these studies is the use of the ANN network to forecast WQI groundwater in an area in eastern Poland that was not previously studied—in Lublin.