Detection and modeling of some micro pollutants in leachate

One of the causes of environmental pollution is micro-pollutants. These micro-pollutants can remain in nature for years, causing toxic effects by mixing into the ecosystem. Due to the resistant structures of micro-pollutants in nature, they must be disposed of. Heavy metals found in leachate from sanitary landfills pose a serious threat to living organisms and natural life. The pollution concentration of leachate can vary depending on factors such as environmental conditions and the composition of the stored waste. Waste brought to sanitary landfills along with domestic waste may contain heavy metals. Within the scope of this study, samples were collected from the leachate of the solid waste regular storage area in Niğde province over a period of 52 weeks, and the pollutant parameters, meteorological data, and heavy metals (As, Ni, Cr, Cu, Cd, Mn, Co, Pb, Zn, Fe) were identified using polynomial linear regression and trend analysis. using the 'Trend Analysis for Windows, Delphi 7.0' program, and an assessment was made at a 95% confidence level in terms of environmental and public health. Prediction models for heavy metals, which are inorganic micro-pollutants, were developed using the Matlab2023b program with multi-layer artificial neural networks. The heavy metals found in the seepage water were used as 14 pollutant parameters, with 7 to 20 hidden neurons and a single output neuron, resulting in a prediction model with an R2 value of 0.98 or higher. In this thesis study, it was observed that all contaminant concentrations in the leachate water varied seasonally. As a result of the analyses, it was determined that the concentration values were below those reported in the literature for this DDT area, which is in the young-middle age range.