The present study deals with the application of the self-organizing map (SOM) technique in the exploration of spatiotemporal dynamics of spring and stream water samples collected in the Chochołowski Stream Basin located in the Tatra Mountains (Poland). The SOM-based classification helped to uncover relationships between physical and chemical parameters of water samples and factors determining the quality of water in the studied high-mountain area. In the upper part of the Chochołowski Stream Basin, located on the top of the crystalline core of the Tatras, concentrations of the majority of ionic substances were the lowest due to limited leaching. Significantly higher concentration of ionic substances was detected in spring and stream samples draining sedimentary rocks. The influence of karst-type springs on the quality of stream water was also demonstrated.

Concentrations of the elements Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn were measured in the leaves and bark of Robinia pseudoacacia and the soil in which it grew, in the town of Oleśnica (SW Poland) and at a control site. We selected this town because emission from motor vehicles is practically the only source of air pollution, and it seemed interesting to evaluate its influence on soil and plants. The self-organizing feature map (SOFM) yielded distinct groups of soils and R. pseudoacacia leaves and bark, depending on traffic intensity. Only the map classifying bark samples identified an additional group of highly polluted sites along the main highway from Wrocław to Warszawa. The bark of R. pseudoacacia seems to be a better bioindicator of long-term cumulative traffic pollution in the investigated area, while leaves are good indicators of short-term seasonal accumulation trends. Once trained, SOFM could be used in the future to recognize types of pollution.

Cell phone use and radio-frequency electromagnetic radiation (RF-EMF) are rapidly increasing and may be associated with lower semen quality, yet results from epidemiological studies are inconclusive. Information on electronic devices use was collected through standard questionnaires from 1454 men aged 22–45 years old. Semen volume, sperm concentration, total sperm count, total motility, progressive motility, and normal morphology in repeated specimens were determined by trained clinical technicians. Percent changes [95% confidence intervals (CIs)] were estimated as (10ᵝ−1) × 100 for electronic devices use associated with repeated sperm quality parameters in the linear mixed-effect models. After adjusting for multiple confounders, we found significant inverse associations of total duration of electronic devices use with sperm progressive motility and total motility, duration of cell phone and computer use with sperm concentration, progressive motility, and total motility (all P < 0.05). No significant association was found between cell phone/computer use alone and sperm quality parameters. Moreover, per hour increase of time spent on cell phone talking was associated with decreased sperm concentration and total count by an average of −8.0% (95% CI: −15.2%, −0.2%) and −12.7% (95% CI: −21.3%, −3.1%), respectively. Besides, daily calling time was associated with lower sperm progressive motility and total motility among those who used headsets during a call (P for interaction <0.05). In conclusion, our study suggested that more time spent on electronic devices use had a modest reduction effect on semen quality. Daily calling time was significantly associated with lower sperm concentration and total count, and using headsets during a call appeared to aggravate the negative association between daily calling time and sperm motility. Additional studies are needed to confirm these findings.

This study investigated the occurrence of 8 polybrominated diphenyl ether (PBDE) congeners from homes (n = 20), offices (n = 20), air conditioners (n = 6), and computers (n = 6). High detection frequencies for most of the congeners were observed, indicating continued widespread use of Penta-, Octa- and Deca-BDE mixtures. The median concentrations of ∑PBDEs were 119 and 194 pg m−3 for home air and office air, respectively. Regarding dust, the median concentrations of ∑PBDEs were 239 and 437 ng g−1 for home and office dust, respectively. The ratios of the median concentrations of BDE-209 to ∑PBDEs were approximately 0.95 and 0.87 for home dust and office dust, respectively. The median concentrations of ∑PBDEs were 359 ng g−1 and 350 ng g−1 for dust on air conditioner filters and the back cabinet of the computer, respectively. The ratios of the median concentrations of BDE-209 to ∑PBDEs were approximately 0.58 and 0.46 for air conditioner and computer samples. Running air conditioners contributed to ΣPBDEs in office air through direct and indirect pathways. The daily intake of PBDEs was estimated to be 2630 pg (kg bw)−1 day−1 for toddlers in homes and 319 pg (kg bw)−1 day−1 for adults in homes and offices.

There is strong interest in developing tools to link chemical concentrations of contaminants to the potential for observing sediment toxicity that can be used in initial screening-level sediment quality assessments. This paper presents new approaches for predicting toxicity in sediments, based on 10-day survival tests with marine amphipods, from sediment chemistry, by means of the application of Partial Least Squares-Discriminant Analysis (PLS-DA) and Counter-propagation Artificial Neural Networks (CP-ANNs) to large historical databases of chemical and toxicity data. The exploration of the internal structure of the developed models revealed inherent limitations of predicting toxicity from common chemical analyses of bulk contaminant concentrations. However, the results obtained in the validation of these models combined relevant values of non-error classification rate, sensitivity and specificity of, respectively, 76, 87 and 73% with PLS-DA and 92, 75 and 97% with CP-ANNs, outperforming the results reported for previous approaches. Models for predicting toxicity based on amphipod tests, derived using PLS-DA and CP-ANN, can be useful aids for screening-level sediment quality assessment.

The application of advanced oxidation process (AOP) in the treatment of wastewater contaminated with oil was investigated in this study. The AOP investigated is the homogeneous photo-Fenton (UV/H₂O₂/Fe⁺²) process. The reaction is influenced by the input concentration of hydrogen peroxide H₂O₂, amount of the iron catalyst Fe⁺², pH, temperature, irradiation time, and concentration of oil in the wastewater. The removal efficiency for the used system at the optimal operational parameters (H₂O₂ = 400 mg/L, Fe⁺² = 40 mg/L, pH = 3, irradiation time = 150 min, and temperature = 30 °C) for 1,000 mg/L oil load was found to be 72 %. The study examined the implementation of artificial neural network (ANN) for the prediction and simulation of oil degradation in aqueous solution by photo-Fenton process. The multilayered feed-forward networks were trained by using a backpropagation algorithm; a three-layer network with 22 neurons in the hidden layer gave optimal results. The results show that the ANN model can predict the experimental results with high correlation coefficient (R² = 0.9949). The sensitivity analysis showed that all studied variables (H₂O₂, Fe⁺², pH, irradiation time, temperature, and oil concentration) have strong effect on the oil degradation. The pH was found to be the most influential parameter with relative importance of 20.6 %.

Recent progress in developing artificial neural network (ANN) metamodels has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture, and quantification of model uncertainties remain key challenges to their practical use. This study has three main objectives: to select an ensemble of input parameters for ANN metamodels consisting of meteorological variables that are predictable by conventional weather forecast models and variables that properly describe the complex nature of pollutant source conditions in a major city, to optimize the ANN models to achieve the most accurate hourly prediction for a case study (city of Tehran), and to examine a methodology to analyze uncertainties based on ANN and Monte Carlo simulations (MCS). In the current study, the ANNs were constructed to predict criteria pollutants of nitrogen oxides (NOx), nitrogen dioxide (NO2), nitrogen monoxide (NO), ozone (O3), carbon monoxide (CO), and particulate matter with aerodynamic diameter of less than 10 μm (PM10) in Tehran based on the data collected at a monitoring station in the densely populated central area of the city. The best combination of input variables was comprehensively investigated taking into account the predictability of meteorological input variables and the study of model performance, correlation coefficients, and spectral analysis. Among numerous meteorological variables, wind speed, air temperature, relative humidity and wind direction were chosen as input variables for the ANN models. The complex nature of pollutant source conditions was reflected through the use of hour of the day and month of the year as input variables and the development of different models for each day of the week. After that, ANN models were constructed and validated, and a methodology of computing prediction intervals (PI) and probability of exceeding air quality thresholds was developed by combining ANNs and MCSs based on Latin Hypercube Sampling (LHS). The results showed that proper ANN models can be used as reliable metamodels for the prediction of hourly air pollutants in urban environments. High correlations were obtained with R (2) of more than 0.82 between modeled and observed hourly pollutant levels for CO, NOx, NO2, NO, and PM10. However, predicted O3 levels were less accurate. The combined use of ANNs and MCSs seems very promising in analyzing air pollution prediction uncertainties. Replacing deterministic predictions with probabilistic PIs can enhance the reliability of ANN models and provide a means of quantifying prediction uncertainties.

In the present investigation, batch experiments were undertaken in the laboratory for different initial phenol concentration ranging from 10 to 40 mg/L using various types of fine-grained soils namely types A, B, C, D, and E based on physical compositions. The batch kinetic data were statistically analyzed with a three-layered feed-forward artificial neural network (ANN) model for predicting the phenol removal efficiency from the water environment. The input parameters considered were the adsorbent dose, initial phenol concentration, contact time, and percentage of clay and silt content in soils. The response output of the ANN model was considered as the phenol removal efficiency. The predicted results of phenol removal efficiency were compared with the experimental values as obtained from batch tests and also tests for goodness of fitting in ANN model with experimental results. The estimated values of coefficient of correlation (R = 0.99) and mean squared error (MSE = 0.006) reveals a reasonable closeness of experimental and predicted values. Out of five different types of soil, type E exhibited the highest removal efficiency (31.6 %) corresponding to 20 mg/L of initial phenol concentration. A sensitivity analysis was also carried out on the ANN model to ascertain the degree of effectiveness of various input variables.

We discuss the accuracy and performance of the adaptive neuro-fuzzy inference system (ANFIS) in training and prediction of dissolved oxygen (DO) concentrations. The model was used to analyze historical data generated through continuous monitoring of water quality parameters at several stations on the Johor River to predict DO concentrations. Four water quality parameters were selected for ANFIS modeling, including temperature, pH, nitrate (NO₃) concentration, and ammoniacal nitrogen concentration (NH₃-NL). Sensitivity analysis was performed to evaluate the effects of the input parameters. The inputs with the greatest effect were those related to oxygen content (NO₃) or oxygen demand (NH₃-NL). Temperature was the parameter with the least effect, whereas pH provided the lowest contribution to the proposed model. To evaluate the performance of the model, three statistical indices were used: the coefficient of determination (R ²), the mean absolute prediction error, and the correlation coefficient. The performance of the ANFIS model was compared with an artificial neural network model. The ANFIS model was capable of providing greater accuracy, particularly in the case of extreme events.

In this paper, bootstrapped wavelet neural network (BWNN) was developed for predicting monthly ammonia nitrogen (NH⁴⁺–N) and dissolved oxygen (DO) in Harbin region, northeast of China. The Morlet wavelet basis function (WBF) was employed as a nonlinear activation function of traditional three-layer artificial neural network (ANN) structure. Prediction intervals (PI) were constructed according to the calculated uncertainties from the model structure and data noise. Performance of BWNN model was also compared with four different models: traditional ANN, WNN, bootstrapped ANN, and autoregressive integrated moving average model. The results showed that BWNN could handle the severely fluctuating and non-seasonal time series data of water quality, and it produced better performance than the other four models. The uncertainty from data noise was smaller than that from the model structure for NH⁴⁺–N; conversely, the uncertainty from data noise was larger for DO series. Besides, total uncertainties in the low-flow period were the biggest due to complicated processes during the freeze-up period of the Songhua River. Further, a data missing–refilling scheme was designed, and better performances of BWNNs for structural data missing (SD) were observed than incidental data missing (ID). For both ID and SD, temporal method was satisfactory for filling NH⁴⁺–N series, whereas spatial imputation was fit for DO series. This filling BWNN forecasting method was applied to other areas suffering “real” data missing, and the results demonstrated its efficiency. Thus, the methods introduced here will help managers to obtain informed decisions.