The effects of external costs on the system selection for treatment and disposal of municipal solid wastes: a deterministic case study for a pre-assessment

Journal of Material Cycles and Waste Management - Selecting a system for treatment and disposal of municipal solid wastes (i.e., selection of the capacity, location and type of the processes and...     

Determination of noise induced hearing loss in mining: an application of hierarchical loglinear modelling

The levels of noise arise from mining industry seem to be higher when compared to other industries. For this reason, noise exposure and noise-induced hearing loss (NIHL) are prevalent in mining. Assessment of noise emission levels that arise from various mining operations is required to prevent and minimize the NIHL. Because the studies for preventing occupational hearing loss among miners are inadequate, a quarry and stone crushing-screening plant was selected to generate site-specific data. The noise levels of the environments in which workers work were measured and also a hearing test centre applied hearing tests to the workers. According to the hearing test results, it was determined that the part of workers have hearing loss. The main factors affecting the NIHL were assumed as experience, noise level, miners’ age and occupation, and by taking into account the sub factors of the main factors, multi way contingency tables were prepared. Then hierarchical loglinear analysis method was implemented to categorized data; thus, the probabilities might effect NIHL was investigated. At the end of this study, it was found that the most risky occupation group was the drivers, and additionally, these workers were mostly exposed to 70–79 dB(A) noise level. When the important interactions are evaluated, it is determined that 4–11 years experienced crusher workers have high probability of NIHL because of high exposure to 90–99 dB(A) noise level. Moreover, the most important interactions which may affect the NIHL were identified and the precautions to reduce hearing loss were presented.     

An online air pollution forecasting system using neural networks

In this work, an online air pollution forecasting system for Greater Istanbul Area is developed. The system predicts three air pollution indicator (SO(2), PM(10) and CO) levels for the next three days (+1, +2, and +3 days) using neural networks. AirPolTool, a user-friendly website (http://airpol.fatih.edu.tr), publishes +1, +2, and +3 days predictions of air pollutants updated twice a day. Experiments presented in this paper show that quite accurate predictions of air pollutant indicator levels are possible with a simple neural network. It is shown that further optimizations of the model can be achieved using different input parameters and different experimental setups. Firstly, +1, +2, and +3 days' pollution levels are predicted independently using same training data, then +2 and +3 days are predicted cumulatively using previously days predicted values. Better prediction results are obtained in the cumulative method. Secondly, the size of training data base used in the model is optimized. The best modeling performance with minimum error rate is achieved using 3-15 past days in the training data set. Finally, the effect of the day of week as an input parameter is investigated. Better forecasts with higher accuracy are observed using the day of week as an input parameter.