There are more than 200 types of zoonotic diseases in the world and leptospirosis is the most important. Leptospirosis occurs mostly in areas with a tropical climate and abundant rainfall. There are no specific statistics of this disease worldwide, and records are underestimated for several reasons. Hence, the World Health Organization (WHO) named leptospirosis as a neglected tropical disease in the world and more research is needed in this field. When paddy season in the north of Iran begins, the disease spread and in severe cases leads to death. Leptospirosis is recognized globally as a multi-faceted disease and failure to recognize or treat it onetime can lead to death of patients. The main cause of the spread of this disease is a bacterium present in the body of domestic and wild animals, especially mice and dogs (as reservoirs of disease) and transmitted through the urine or feces to the environment. As a result, the bacteria can be transmitted to the human body through injuries to the skin or contact with contaminated soil and water. The environment and occupation are very effective in the spread of the disease, which is recognized as a work-related illness and can be dangerous in both urban and rural areas. The emergence of this disease can be due to reasons such as agriculture, livestock, butchers, recreational activities and water sports, poverty, travel to tropical areas, and any activity that leads to contact with water, soil or contaminated environment. This disease is more prevalent in fishermen and farmers, especially sugar cane farmers and workers, and it is very important to cause problems such as inability to work properly in the time and season needed planting and harvesting, as well as medical costs and even mortality. Compared to other provinces, Guilan province has the highest rate of leptospirosis recently. Therefore, the study and modeling of this disease in the province is of great importance. In this paper, the disease statistics in the rural area during 2009-2011 were assessed as the dependant variable and five variables considered as independent variables for modeling spatial distribution. Considering the important effects of bandwidth and weighting function on modeling results, the efficiency of fixed and adaptive kernels, Bi-Square and Gaussian weighting functions investigated. Two criteria were utilized to evaluate the results include MSE and Definition Coefficient. The results showed that the adaptive kernel and Bi-Square performed better than the fixed kernel and Gaussian, respectively. In terms of bandwidth selection criteria, AIC, CV and BIC played more meaningful role consecutively. Among the environmental variables, Temperature, Humidity and Evaporation illustrated positive relationship with disease and, elevation and slope showed negative relationship. The maps of the distribution of the disease indicated that the central regions of Guilan province are more prone to this disease than the other areas, and management and control of the disease in these areas is very important. Finally, all results were assessed by validation criteria and decision makers can use this helpful information for prevention programs and allocation of budget to the risky areas.

Air pollution is one of the most problems that people are facing today in metropolitan areas. Suspended particulates, carbon monoxide, sulfur dioxide, ozone and nitrogen dioxide are the five major pollutants of air that pose many problems to human health. The goal of this study is to propose a spatial approach for estimation and analyzing the spatial and temporal distribution of ozone based on GIS analysis and multi perceptron neural network.
In the first step, by considering the accuracy of different interpolation methods, IDW method was selected as the best interpolation method for mapping the concentration of ozone in Tehran. according to the daily data of these pollutants, the daily, monthly, and annual mean concentrations maps were prepared for years 2015, 2016 and 2017. According to the results, it can be said that the highest concentrations of ozone are found in the southwest and parts of the central part of the city.
Finally, a neural network was developed to predict the amount of ozone pollutants according to meteorological parameters.
According to the data of ozone pollutants in year 2018, the accuracy of neural network for hot and cold days of year were about 68% and 77% respectively. Therefore, it can be said that the meteorological parameters of temperature, wind speed and direction, and precipitation are significantly related to the concentration of O3 pollutant.