Comparison of leaching behaviour of heavy metals from sediments sampled in sewer systems - environmental and public health aspect.

INTRODUCTION AND OBJECTIVE: The article analyzes the content of heavy metals and standard physical as well as chemical pollution indicators in different types of sediments from stormwater, combined sewer and sanitary sewer systems. MATERIAL AND METHODS: Nickel, lead, chromium, copper, zinc and cadmium, as well as standard physical and chemical pollution indicators, were determined in sewage sediments. Aqueous extracts of sediments samples, taken from storm water sewer inlet sediments traps, storm sewers, sanitary sewers and combined sewers, were prepared in accordance with PN-EN 12457-2:2006. After mineralization, the concentrations of the metals: nickel, lead, chromium, copper, zinc and cadmium in the extracts were determined using the inductively coupled plasma emission spectroscopy technique. RESULTS: The results were analyzed with a non-metric multidimensional scaling algorithm. The heavy metal content was variable depending on the sediments collection site. The heavy metals nickel, lead, chromium, copper, zinc and cadmium were found in the sediments from stormwater inlets, storm sewer and sanitary sewer channels, with variability in the concentration of individual metals. The sediments from the flushing of sanitary sewers and combined sewers did not contain cadmium. CONCLUSIONS: The content of heavy metals in sediments varied depending on the sampling location and type of sewer system, indicating the need for detailed monitoring to identify the sources of emissions. Sediments from stormwater sewers have higher concentrations of heavy metals, with those from sewer inlets showing zinc concentrations exceeding regulatory limits, highlighting the variability and potential environmental impact of different sewer systems.

Rapid Method of Wastewater Classification by Electronic Nose for Performance Evaluation of Bioreactors with Activated Sludge.

Currently, e-noses are used for measuring odorous compounds at wastewater treatment plants. These devices mimic the mammalian olfactory sense, comprising an array of multiple non-specific gas sensors. An array of sensors creates a unique set of signals called a "gas fingerprint", which enables it to differentiate between the analyzed samples of gas mixtures. However, appropriate advanced analyses of multidimensional data need to be conducted for this purpose. The failures of the wastewater treatment process are directly connected to the odor nuisance of bioreactors and are reflected in the level of pollution indicators. Thus, it can be assumed that using the appropriately selected methods of data analysis from a gas sensors array, it will be possible to distinguish and classify the operating states of bioreactors (i.e., phases of normal operation), as well as the occurrence of malfunction. This work focuses on developing a complete protocol for analyzing and interpreting multidimensional data from a gas sensor array measuring the properties of the air headspace in a bioreactor. These methods include dimensionality reduction and visualization in two-dimensional space using the principal component analysis (PCA) method, application of data clustering using an unsupervised method by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, and at the last stage, application of extra trees as a supervised machine learning method to achieve the best possible accuracy and precision in data classification.

Environmental Engineering Applications of Electronic Nose Systems Based on MOX Gas Sensors.

Nowadays, the electronic nose (e-nose) has gained a huge amount of attention due to its ability to detect and differentiate mixtures of various gases and odors using a limited number of sensors. Its applications in the environmental fields include analysis of the parameters for environmental control, process control, and confirming the efficiency of the odor-control systems. The e-nose has been developed by mimicking the olfactory system of mammals. This paper investigates e-noses and their sensors for the detection of environmental contaminants. Among different types of gas chemical sensors, metal oxide semiconductor sensors (MOXs) can be used for the detection of volatile compounds in air at ppm and sub-ppm levels. In this regard, the advantages and disadvantages of MOX sensors and the solutions to solve the problems arising upon these sensors' applications are addressed, and the research works in the field of environmental contamination monitoring are overviewed. These studies have revealed the suitability of e-noses for most of the reported applications, especially when the tools were specifically developed for that application, e.g., in the facilities of water and wastewater management systems. As a general rule, the literature review discusses the aspects related to various applications as well as the development of effective solutions. However, the main limitation in the expansion of the use of e-noses as an environmental monitoring tool is their complexity and lack of specific standards, which can be corrected through appropriate data processing methods applications.

Application of Machine Learning Methods for an Analysis of E-Nose Multidimensional Signals in Wastewater Treatment.

The work represents a successful attempt to combine a gas sensors array with instrumentation (hardware), and machine learning methods as the basis for creating numerical codes (software), together constituting an electronic nose, to correct the classification of the various stages of the wastewater treatment process. To evaluate the multidimensional measurement derived from the gas sensors array, dimensionality reduction was performed using the t-SNE method, which (unlike the commonly used PCA method) preserves the local structure of the data by minimizing the Kullback-Leibler divergence between the two distributions with respect to the location of points on the map. The k-median method was used to evaluate the discretization potential of the collected multidimensional data. It showed that observations from different stages of the wastewater treatment process have varying chemical fingerprints. In the final stage of data analysis, a supervised machine learning method, in the form of a random forest, was used to classify observations based on the measurements from the sensors array. The quality of the resulting model was assessed based on several measures commonly used in classification tasks. All the measures used confirmed that the classification model perfectly assigned classes to the observations from the test set, which also confirmed the absence of model overfitting.