A large number of organic pollutants (OPs) emitted from vehicles and traffic-related activities exhibit environmental persistence and a tendency to bioaccumulate, and may have detrimental long-term effects on aquatic life. The aim of the study was to establish a list of significant sources of OPs occurring in road runoff, identify the OPs emitted from these sources, select a number of priority pollutants (PP), and estimate the quantity of PPs emitted in a road environment case study using substance flow analysis (SFA). The priority pollutants included in the SFA were selected from a list of approximately 1100 compounds found after comprehensive screening, including literature and database searches, expert judgments, the Ranking and Identification of Chemical Hazards method, and chemical analysis of sediments. The results showed the following priority order: polycyclic aromatic hydrocarbons (PAHs)>alkanes C-C>alkylphenols>phthalates>aldehydes>phenolic antioxidants>bisphenol A>oxygenated-PAHs>naphtha C-C>amides>amines. Among these, PAHs were chosen for a SFA, which was performed for a highway case study area in Gothenburg (Sweden). The SFA showed that the main sources of PAHs emitted in the area were vehicle exhaust gases, followed by tyre wear, motor lubricant oils, road surface wear, and brake linings. Only 2-6% of the total 5.8-29kg annually emitted PAHs/ha ended up in the stormwater sewer system. The measured PAH loads were found in much smaller amounts than the calculated loads and the outflow to stormwater contained much more of the hazardous PAHs than the total loads emitted in the catchment area.
Various human activities have been the main causes of surface water pollution. The uneven distribution of industrial enterprises in the territories of the main river basins of Ukraine do not always allow the real state of the water quality to be assessed. This article has three purposes: (1) the modification of the Ukrainian method for assessing the WQI, taking into account the level of negative impact of the most dangerous chemical elements, (2) the modeling of WQI assessment using fuzzy logic and (3) the creation of an artificial neural network model for the prediction of the WQI. The fuzzy logic model used four input variables and calculated one output variable (WQI). In the final stage of the study, six ANN models were analyzed, which differed from each other in various loss function optimizers and activation functions. The optimal results were shown using an ANN with the softmax activation function and Adam’s loss function optimizer (MAPE = 9.6%; R2 = 0.964). A comparison of the MAPE and R2 indicators of the created ANN model with other models for assessing water quality showed that the level of agreement between the forecast and target data is satisfactory. The novelty of this study is in the proposal to modify the WQI assessment methodology which is used in Ukraine. At the same time, the phased and joint use of mathematical tools such as the fuzzy logic method and the ANN allow one to effectively evaluate and predict WQI values, respectively.
The paper presents a method of application of an ANN (Artificial Neural Network) to predict the permeability coefficient k in sandy soils: FSa, MSa, CSa. To develop an ANN the results of permeability coefficients from pumping and consolidation tests were applied. The proposed ANN with an architecture 6-8-1 predicts the value of permeability coefficient k based on the following parameters: soil type, relative density ID, void ratio e and effective soil diameter d10. The mean relative error and single maximum value of the relative error for the proposed ANN are following: Mean RE = ±4%, Max RE = 7.59%. The use of the ANN to predict the soil permeability coefficient allows the reduction of the costs and time needed to conduct laboratory or field tests to determine this parameter.
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