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.

A simplified method for determining potential heavy metal leached from sediments of stormwater and combined sewer systems - importance for public health.

INTRODUCTION AND OBJECTIVE: The identification and understanding of interactions between contaminants present in sediments from stormwater and combined sewer systems is a prerequisite for their proper management, and provides a basis for developing effective strategies to minimize their negative impact on humans and the environment. The studypresents the method described in PN-EN 12457-2:2006 as a possible technique for studying the mobility of heavy metals in sediments from stormwater and combined sewer systems. MATERIAL AND METHODS: The presented PN-EN 12457-2:2006 method is a relatively simple technique for preparing extracts for the determination of heavy metals in sediments from stormwater and combined sewer systems, consisting of one-step leaching, which is quick to perform. In addition, it allows determination of the characteristics of the samples to be analyzed, and indicates procedures and tests for evaluating hazardous substances released from solid waste. RESULTS: The results of the concentrations of leached heavy metals: chromium, copper, nickel, lead and zinc, obtained in the study, corresponded to the concentrations of the exchange fraction of sludge when using the recommended method with sequential extraction (Student's t-test, p=0.263). In the literature review conducted, no papers were found on the application of the leaching method to prepare extracts for the determination of heavy metals in sediments from stormwater and combined sewer systems. CONCLUSIONS: The PN-EN 12457-2:2006 method is capable of providing important data on the potential risks to humans and the environment from the presence of contaminants in sewage sludge.

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.