Fariborz Bahrami; Aslan Egdernezhad
Abstract
Background and Purpose: Due to the complexities in the nature of ground water systems, it sounds like a demanding job to model either the time or the location of ground water. However, artificial neural networks have a high capability to model both complicated and non-linear models. Besides, Geostatistic ...
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Background and Purpose: Due to the complexities in the nature of ground water systems, it sounds like a demanding job to model either the time or the location of ground water. However, artificial neural networks have a high capability to model both complicated and non-linear models. Besides, Geostatistic Methods are, to a good extent, accurate in modelling ground water.Material and Methods: The aim of this study is to simulate groundwater quality parameters (SAR, TDS and EC) of Dezful Andimeshk plain using ANN-PSO and geostatistical models. For this purpose, information from 61 observation wells in Dezful-Andimeshk plain has been used. Neural network model inputs including qualitative parameters SO42- ، pH ، HCO32-، Na+، Mg2+، Ca2+، TDS، SAR and EC were considered.Results: The results of simulation with intelligent model showed that the highest accuracy of ANN-PSO model in simulation is related to EC, SAR and TDS parameters, respectively. The results of interpolation by geostatistical method showed that the highest accuracy of Kriging model in simulation is related to EC, TDS and SAR parameters, respectively. The general results obtained from the simulation of groundwater quality parameters showed that the ANN-PSO model is more accurate in simulating the groundwater quality parameters of the plain in Andimeshk than the Kriging model. So that the value of R2 for simulating SAR, TDS and EC parameters using ANN-PSO model in the test phase is 0.92, 0.918 and 0.955 respectively and using kriging model is 0.902. 0.915 and 0.931 were estimated.Conclusion: The results of this study also showed that the combination of intelligent models with optimization algorithms is used as a useful tool to simulate groundwater quality parameters.
Maryam Charmzan; Reza Esmaili; Mitra Mohammadi; Vahid Moradnezadhesare
Abstract
AbstractBackground and Aim:Air pollution is one of the most important environmental problems in the last century that threatens human health and particulate matter is one of the deadliest types of air pollution.This study was done to choose the best interpolation algorithm in the spatial distribution ...
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AbstractBackground and Aim:Air pollution is one of the most important environmental problems in the last century that threatens human health and particulate matter is one of the deadliest types of air pollution.This study was done to choose the best interpolation algorithm in the spatial distribution of PM2.5 suspended particles in Mashhad in 1395by different spatial models.Material and Methods: PM2.5 particulate concentrations was collected from 21 active air quality measuring stations in different parts of Mashhad and IDW, Ordinary Kriging (OK) and Universal Kriging (UK) interpolation models were evaluated to spatially investigate the air pollution situation in Mashhad. The root mean square error (RMSE) was used to compare the models and select the best model, and the Standardized RMSE was used to choose the most optimal conditions for running the OK and UK models.Results: The results showed that the highest seasonal average of PM2.5 pollutants in 1395 was related to autumn (40.84 µg/m3) and the lowest was related to spring (27.78 µg/m3). Also, the east to north area of Mashhad is in a more unfavorable situation in pollution concentration than the western areas of the city. Comparison of models using RMSE index also showed that OK model due to having the lowest amount of RMSE for seasonal average and annual concentration of suspended particles PM2.5 has a lower error in the predicted values than the measurement, so it has better conditions for intermediation.Conclusion: This research eventually led to the production of maps of PM2.5 Pollutants situation in the whole city of Mashhad, which is very useful in order to identify high-risk areas in the city and use useful measures to reduce air pollution in those areas.
Shamim Ramezani Azghandi; Azita Farashi; Mohsen Najjari; Mahshid Hosseini
Abstract
Abstract Background and Aim: Rodents are the largest order of mammals, with a large population on the earth, the source of many economic losses and health problems. Rodents are the reservoirs of some zoonotic diseases. Among these diseases, we can mention leishmaniasis. This study aimed to ...
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Abstract Background and Aim: Rodents are the largest order of mammals, with a large population on the earth, the source of many economic losses and health problems. Rodents are the reservoirs of some zoonotic diseases. Among these diseases, we can mention leishmaniasis. This study aimed to model the habitat suitability of Iranian Jird as a reservoir of cutaneous leishmaniasis in Iran. Materials and methods:For this purpose, 17 habitat variables including two topographic variables, seven climatic variables, and eight land use/land cover variables as habitat variables along with species presence points were used in MaxEnt modeling. Species distribution models are useful tools in identifying the areas for the presence of wildlife species and therefore are of great importance in species conservation and habitat management. Among these species distribution models, we can mention the MaxEnt model. Results:According to the results of MaxEnt modeling, the suitable habitats of Iranian Jird species cover an area of 430,900 square kilometers, accounting for 30% of Iran. Also, three geological variables, distance from the road, and land use were identified as effective variables in the habitat suitability modeling of this species of rural leishmaniasis reservoir. Another result of this study was the preparation of the habitat suitability map of Iranian Jird in both continuous and categorical forms, which showed the highest distribution in Golestan, North Khorasan, and Mazandaran provinces. Conclusion: Finally, according to the results of this modeling and the effect of biological and anthropological variables as effective variables in the habitat suitability modeling of this reservoir and the possible psychological and economic effects of leishmaniasis and lack of effective vaccines and the presence of rodents in the pathogenic cycle, the identification of reservoirs and their suitable habitats are necessary for better management of this disease. Keyword: Habitat; MaxEnt; Modeling; Leishmaniasis; Rodent; Reservoir
Reza Mazaheri Jajaie
Abstract
Population growth and the increase in urban migration in the past decades have led to an increase in population density and size of major cities. Unfortunately, this kind of pollution has mostly gone under-noticed. The purpose of this research was to model the correlation between noise pollution level ...
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Population growth and the increase in urban migration in the past decades have led to an increase in population density and size of major cities. Unfortunately, this kind of pollution has mostly gone under-noticed. The purpose of this research was to model the correlation between noise pollution level and landscape metrics of urban structures and vegetation. To do so, 67 stations were selected in different parts of Isfahan and noise parameters were measured at peak traffic hours (16 to 19) during the fall season. Sampling stations were located through a systematic-random method based on the amount of construction, green spaces and structural diversity. There were 27 types of landscapes and three stations were randomly selected in each. In most stations, the noise level was above the permitted level(Residential 45-55, Residential-Commercial 50-60). The advanced regression method of random forest was used for the analysis. Through this method, the most effective metrics identified in different buffers were IJI index, FRAC_MN index, CLUMPY index, CONTIG_MN index, SHAPE_MN index, ENN_MN index. Also, checking of the first six metrics in each of the buffers and land uses showed the importance of the metrics is different. Identification of important metrics in each buffer and land use helps better design urban blocks and their arrangement.
Mahsa Moein; Soolmaz Shamsaei; Zahra Khebri
Abstract
Background and amain: The most important global environmental problem, especially in large cities, is air pollution. This is regarded as a serious threat to human, society and environment health. This paper aims to investigate the physical factors of stack affecting the concentrations of the pollutants ...
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Background and amain: The most important global environmental problem, especially in large cities, is air pollution. This is regarded as a serious threat to human, society and environment health. This paper aims to investigate the physical factors of stack affecting the concentrations of the pollutants through the AERMOD model. Materials and Methods: In this research, modeling was carried out for four factories Orchin, Khayyam, Noavaran and Meybod Tile Company during the first six months of 2015. The study area comprised 20 × 20 km2., being centered on the Khayyam factory. Meybod meteorological data were used in the form of a three-hour mean status to perform the sub-model of AERMET. A Digital Elevation Model (DEM; SRTM 50 m) was used to perform the sub-model of AERMAP. In order to conduct the statistical analyses, the SPSS software program (Version 22) was used. Results: The results of the statistical analyses showed that the abovementioned factories had significant differences in terms of dispersion of particles: The Noavaran Factory and Meybod Tile Company had the maximum and minimum concentrations respectively. Finally, the model was verified by measuring 23 points in different months with an environmental device. According to the obtained results, the correlation results, the results of the model, and the samples areas were confirmed with P-value=0.002. Conclusion: According to the results of Freidman ranking, the physical factors of the factories affecting the concentration of the particles in order of priority were the stack diameter, the exit rate of suspended particles, the exit speed of the particles, the height of the stack, the temperature of the stack, and the receiver’s height.