Generic foresight model in changing hygiene habits with the pandemic: use of wet wipes in next generations.

The vast use of wet wipes has now become a habitude, particularly following the altered perception of cleanliness during the pandemic and the encouragement towards using WW (wet wipe) to ensure parent's and children's hygiene. This study primarily aims to create a projection of the WW waste that will emerge in Turkey as a result of the promoted consumption by children who are predicted to retain the WW usage practices of their parents. In line with this habit adopted by children, the number of daily WW usage which is currently around 210 million is expected to rise to over 250 million between the years 2040 and 2060, depending on how the children are guided by their parent's existing habits. In this study, related calculations were made with FT-IR spectroscopy, taking into account the functional bond structure and percentage distribution of polymers in WWs. In this way, it is detected that 360 T, 568 T, and 623 T polymer materials would be thrown into the environment per day in 2021, 2040 and 2060, respectively. The damage of chemicals in WW content, employed at various concentrations, to the ecosystem structure is predicted and measures to be taken are outlined.

Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique.

Artificial neural networks (ANNs) are being used increasingly to predict and forecast water resources' variables. The feed-forward neural network modeling technique is the most widely used ANN type in water resources applications. The main purpose of the study is to investigate the abilities of an artificial neural networks' (ANNs) model to improve the accuracy of the biological oxygen demand (BOD) estimation. Many of the water quality variables (chemical oxygen demand, temperature, dissolved oxygen, water flow, chlorophyll a and nutrients, ammonia, nitrite, nitrate) that affect biological oxygen demand concentrations were collected at 11 sampling sites in the Melen River Basin during 2001-2002. To develop an ANN model for estimating BOD, the available data set was partitioned into a training set and a test set according to station. In order to reach an optimum amount of hidden layer nodes, nodes 2, 3, 5, 10 were tested. Within this range, the ANN architecture having 8 inputs and 1 hidden layer with 3 nodes gives the best choice. Comparison of results reveals that the ANN model gives reasonable estimates for the BOD prediction.