Medical meteorology;the relationship between meteorological parameters [humidity, rainfall, wind, and temperature] and brucellosis in Zanjan Province

Background: Brucellosis [Malta fever] is a major contagious zoonotic disease, with economic and public health importance

Methods: To assess the effect of meteorological [temperature, rainfall, humidity, and wind] and climate parameters on incidence of brucellosis, brucellosis distribution and meteorological zoning maps of Zanjan Province were prepared using Inverse Distance Weighting [IDW] and Kriging technique in Arc GIS medium. Zoning maps of mean temperature, rainfall, humidity, and wind were compared to brucellosis distribution maps

Results: Correlation test showed no relationship between the mean number of patients with brucellosis and any of the four meteorological parameters

Conclusion: It seems that in Zanjan province there is no correlation between brucellosis and meteorological parameters

Using ANN and EPR models to predict carbon monoxide concentrations in urban area of Tabriz

Background: Forecasting of air pollutants has become a popular topic of environmental research today. For this purpose, the artificial neural network [AAN] technique is widely used as a reliable method for forecasting air pollutants in urban areas. On the other hand, the evolutionary polynomial regression [EPR] model has recently been used as a forecasting tool in some environmental issues. In this research, we compared the ability of these models to forecast carbon monoxide [CO] concentrations in the urban area of Tabriz city

Methods: The dataset of CO concentrations measured at the fixed stations operated by the East Azerbaijan Environmental Office along with meteorological data obtained from the East Azerbaijan Meteorological Bureau from March 2007 to March 2013, were used as input for the ANN and EPR models

Results: Based on the results, the performance of ANN is more reliable in comparison with EPR. Using the ANN model, the correlation coefficient values at all monitoring stations were calculated above 0.85. Conversely, the R2 values for these stations were obtained <0.41 using the EPR model

Conclusion: The EPR model could not overcome the nonlinearities of input data. However, the ANN model displayed more accurate results compared to the EPR. Hence, the ANN models are robust tools for predicting air pollutant concentrations