جستجوی مقالات مرتبط با کلیدواژه « تحلیل مکانی » در نشریات گروه « محیط زیست »

Water is the most important component for the sustainable production of agricultural products. Droughts and the increase in plants' water requirement show the necessity of proper and accurate plant irrigation planning. Lack of water is one of the important economic and social factors in most developing countries, especially countries located in arid and semi-arid regions of the world. One of the most important agricultural products in the world is wheat. This product is always a part of macro-agricultural policies, and the analysis of the productivity situation and related challenges plays an important role in agricultural planning. Considering the high cultivated area of this plant and in order for irrigated cultivation to be a sustainable cultivation, it is necessary that its management, modification and methods of water consumption in agricultural lands should be given more attention by agricultural experts and managers. In order to be able to generalize the parameters measured at a point to the regional scale and to determine the water requirement at the regional scale, methods that, in addition to the quantity of data, also measure the location should be used. Therefore, this information should be converted from a point mode to a regional mode and establish the spatial integrity of the point data.

In the current research, the indicators of irrigation planning in wheat production (water requirement, irrigation depth and irrigation interval) in Darab Plain lands in Fars Province have been investigated.In order to create an optimal management, an irrigation database including soil, climate and plant information layers was formed in the GIS, and this information and data was obtained from the Agricultural Jahad Organization. The desired spatial variables are the water requirement of the wheat crop and the net depth of irrigation water. The weather data used to determine the water requirement, such as air temperature and soil parameters, are spatial data and are dependent on spatial characteristics. Then, based on the information provided in the GIS, the location map of the farms was prepared in the first stage. According to the size of the area, after the field visit and information about the condition of the farms, 30 farms that had a pressurized irrigation plan were selected. Due to considering proper dispersion and non-overlapping of information, a number of points were removed, and due to the fact that in order to use the kriging model, it is necessary for the points to have proper distribution, so finally 15 points (farms) were selected. The whole plain is scattered. Based on the selected points, the condition of the soil type was determined. The required information on water requirement, net and gross depth of water irrigation and soil texture were formed in the Geographic Information Systems. So that in the points without data, an estimate of the desired data was made and the required maps were prepared. In order to estimate the irrigation interval in each of the studied lands, using spatial analysis in the GIS and irrigation water depth maps and wheat water requirements, the irrigation interval map was prepared separately for each month.The purpose of the research is spatial analysis and the studied data is of the spatial data type, and geostatistics models were used to prepare spatial distribution maps of water requirement and net depth of water irrigation, and interpolation of data were done using Kriging method.

First, the condition of the soil texture in the study area was investigated. Based on the soil texture in the selected areas and with the help of the Thyssen method, the soil texture map of the study area was prepared. The texture of the soil in the northeast, east, southwest and west is loamy and in the northwest, center, south and southeast it is clay loam. The north of the studied area is sandy loam. The growing season of wheat cultivation in the study area is from December to June. The maps prepared for water requirement and irrigation depth showed that in most months, the highest amount of water requirement is related to the north and northeast parts and the lowest amount is related to the northwest and west parts. Also, the water requirement maps showed that during the wheat growth period in the study area (November to June), the month of May has the highest monthly water requirement with an average of 86.45 mm/month. The irrigation depth maps also showed that the irrigation depth in the center of the plain has the highest value and the northern parts of the plain have the lowest value. Also, the irrigation depth maps showed that the irrigation depth in the whole area varies from 8.6 cm to 13.8 cm. According to the water requirement maps, since the changes of water requirement in the study area are large and also the net irrigation depth map shows a high depth, as a result, the irrigation interval is affected by both. The lowest of wheat irrigation interval in the study area (1 day) is in the months of April and May for lands in the north, northeast and southeast, which increases from the east to the center, and in the northwest and southwest, the maximum irrigation interval is 3 days. Considering that the minimum water requirement of wheat in the growth period is in January and the range of its changes in different parts of the study area is between 6.6 and 18.05 mm/month. Therefore, the longest irrigation interval (5 to 18 days) is related to this month. According to the obtained results, the estimation of water requirement, net irrigation depth and irrigation interval based on regional changes and spatial analysis is a suitable method in irrigation planning. The use of spatial analysis and advanced geostatistics models can be effective in estimating irrigation parameters and productivity indicators and help different land managements and determine the amount of water requirement and the appropriate irrigation interval.

Human physical and mental health greatly depends on the climatic conditions of its bio-location. Identifying environmental factors creating or exacerbating diseases can be useful in optimizing decision making for prevention and control. The purpose of this study is to determine the spatial resolution of respiratory diseases and its relation with environmental factors in order to understand spatial distribution, cluster discovery and spatial prediction modeling.

The population of patients with respiratory diseases referred to the medical centers and the study area of ​​Kurdistan province between 2007 and 1396. Regarding the dispersion of patients from spatial and moron standard deviations, we used spatial regression method to determine the spatial and morphometric variability of the samples using independent variables of dust, height, direction of inclination and temperature.

The results showed that the area of ​​the ellipsoid is three times the standard deviation of the northwest of the southeast, indicating that more than 99% of these diseases are spreading in this direction. Moran index 0.82 also indicates spatial autocorrelation and disease numbers at a significant level of 99%. In spatial modeling to predict the spatial dispersion of a positive symptom disease, the coefficients obtained for dust and temperature with the disease indicate a direct relationship and the negative coefficients between elevation and slope indicate an indirect relationship with the disease. Modeling also showed that dust is the most important parameter in predicting the disease.

The value of R2 = 0.88 indicates that the extracted model is able to fully predict the dependent variable, respiratory disease, in Kurdistan province, taking into account independent environmental variables. Using the prediction map, the regions with respiratory disease can be better identified in order to improve the decision-making process for allocating and distributing spatial services.

Recently, pollutant particles, in particular suspended particles with a diameter of less than 2.5 microns, have been the most common pollutant in Tehran. One of the most important processes to solve the problem of suspended particles is to obtain appropriate information on the distribution of these pollutants at different time periods. The goal of this study is to propose a spatial approach for modeling and analyzing the spatial and temporal distribution of suspended particles based on GIS analyzes.

At first step, PM 2.5 and PM 10 pollutants was collected from Tehran Air Quality Control Company. Then, the accuracy of different interpolation methods was evaluated. Finally, the IDW method was selected as the best interpolation method for plotting the concentration of these pollutants in Tehran

Daily, Monthly average and annual average concentrations map were prepared according to the daily data of these pollutants in the years 93 and 94. As a result, spatial and temporal analysis of distribution of PM 2.5 and PM 10 contaminants in Tehran was made
Discussion and conculation: According to the performed study, it can be said that the highest concentration of PM10 pollutants is found in the southwest of the city and the highest concentration of PM 2.5 pollutants is found in Shahr-e-Ray and central part of the city.