AGIS method based on spatial autocorrelation analysis used to identification and ranking of thoron (220Rn)concentration. Spatial radiation patterns are analyzed using Moran's I statistic. Getis-Ord Gi* is utilized to locate clusters of high and low measurements and create a map of thoron hot spots. One hundred schools in the center of Najaf City were examined for thoron using CR-39 detectors (produced from Track Analysis Systems Ltd., UK)  for this research. Average thoron levels were 2.99 Bq/m3, with a range of 9.00 Bq/m3 to 0.22 Bq/m3. The radiation levels found in this investigation were significantly lower than the UNSCEAR 2000 safety standards of 40 Bq/m3. Moran's, I have used it to analyze the clustering of districts across a research region and to measure the spatial distribution of data. Getis-Ord Gi* statistics were used to identify cold and hot spots within the research area. Thoron concentrations were shown to have insignificant spatially random distribution patterns, as demonstrated by Global Moran's I. (Moran’s I =0.28, p-value=0.24).

Defined as any substance in the air that may harm humans, animals, vegetation, and materials, air pollution poses a great danger to human health. It has turned into a worldwide problem as well as a huge environmental risk. Recent years have witnessed the increase of air pollution in many cities around the world. Similarly, it has become a big problem in Iran. Although ground-level monitoring can provide accurate PM2.5 measurements, it has limited spatial coverage and resolution. As a result, Satellite Remote Sensing (RS) has emerged as an approach to estimate ground-level ambient air pollution, making it possible to monitor atmospheric particulate matters continuously and have a spatial coverage of them. Recent studies show a high correlation between ground level PM2.5, estimated by RS on the one hand, and measurements, collected at regulatory monitoring sites on the other. As such, the present study addresses the relation between air pollution and satellite images. For so doing, it derives RS estimates, using satellite measurements from Landsat satellite images. Monitoring data is the daily concentration of PM2.5 contaminants, obtained from air pollution stations. The relation between the concentration of pollutants and the values of various bands of Landsat satellite images is examined through 19 regression models. Among them, the Ensembles Bagged Trees has the lowest Root-Mean-Square Error (RMSE), equal to 21.88. Results show that this model can be used to estimate PM2.5 contaminants, based on Landsat satellite images.

For the first time, sulfur dioxide concentration was monitored between 2005 and 2016 over Iran which is among the countries with a high SO2 emission rate in the world. To that end, SO2 column concentration at Planetary Boundary Layer (PBL) from Ozone Monitoring Instrument (OMI) was analyzed. OMI is a sensor onboard the Aura satellite which can measure daily SO2 concentration on the global scale. From OMI maps, 19 notable SO2 hotspots were detected over Iran. The results indicate that the most elevated level of SO2 among these 19 hotspots belong to Khark Island and Asaluye in Bushehr province, southwest of Iran. Annual trend analysis shows that SO2 concentration has been slightly augmented during 2005-2016 over this country. Distribution analysis of SO2 concentration over Iran showed that the most polluted provinces are Bushehr, Khuzestan and Ilam lied in the southwest of Iran. On the contrary, the lowest level of SO2 has observed over northwest of Iran at West and East Azerbaijan and Ardabil provinces. The correlation coefficient between total energy production in Iran and SO2 concentration from 2005 to 2016 is as high as ~0.7. Hence, it can be derived that energy production, most notably production of crude oil, plays a pivotal role in SO2 concentration over Iran.

Air pollution is considered a global concern due to its impacts on human life and the urban environment. Therefore, precise modeling techniques are necessary to predict air quality in congested areas such as megacities. Recently, machine learning algorithms such as Neural Networks show significant possibilities in air quality studies. This paper proposes a model to estimate air quality in a congested urban area in Baghdad city using Artificial Neural Network (ANN) algorithm and Geospatial Information System (GIS) techniques. Carbon Monoxide (CO) gas is selected as the main air pollutant. The model parameters involve; CO samples, traffic flow, weather data, and land use information collected in the field. The proposed model is implemented in Matlab environment and the results are processed after entering ArcGIS software. Using its spatial analysis tools, the outputs are presented as a map. The final findings indicate the highest value of CO emissions that reached 34 ppm during the daytime. The most polluted areas are located near congested roads and industrial locations in comparison with residential areas. The proposed model is validated by using actual values that are collected from the field, where the model's accuracy is 79%. The proposed model showed feasibility and applicability in a congested urban area due to the integration between the machine learning algorithm and GIS modeling. Therefore, the proposed model in this research can be used as a supportive model for decision making of city managers.

Evaluation of surface water quality is a complex process undertakingmultiple parameters. Converting great amount of parameters into a simpler expressionand enabling easy interpretation of data are the main purposes of water quality indices.The main aim of this study is to plan effective water resources management system forKarun River by combination of CCMEWQI and Geographic Information System (GIS).The investigation was carried out to set a management plan through exploratory andspatial analysis of physicochemical water parameters of collected samples from 10stations over one year period. Since all indices were obtained from index, river zoningwas conducted by GIS. Moreover, trace metals concentrations (As, Cr, Cd, Fe, Zn, Mn,and Al) ranged in safer limit. The highest values of F1 belonged to aquatic life and thelowest ones belonged to irrigation. Aquatic life and drinking uses received the maximumvalues of F2. The lowest values were devoted to livestock and then recreation uses. It wasinferred from index that the quality of the Karun River is principally impacted by highturbidity, TDS, NO3, SO4, and PO4 due to high suspended sediment loads. The maincause is incremental agricultural, industrial, and residential effluents. Amongst stations,station one only received the priority for drinking water supply and recreation.

The development of suitability species models look for the availability to growth in a study area. These models can be used for different targets. In this research, a suitability model of Eucalyptus has been developed to soils contaminated by trace elements management. Guadiamar Green Corridor has been selected due to the huge data available regarding trace elements, forestry species and so on. Logistic regression (LR) and Random Forest (RF), as popular machine learning model, were applied in a geodatabase from Guadiamar Green Corridor with more of 20 years of data. This database is composed by soil physical and chemical variables, climate (temperature min and max, annual precipitation), forestry species. The results show the poor performance of LR and RF applied directly over the unbalanced training set. However, when Up-sampling or SMOTE are applied, both procedures improve its sensitivity, however, RF show more improve that LR. The methodology applied can help to determine the potential distribution of Eucalyptus in similar Mediterranean areas and extended to different areas according to Soil, Climate and Trace Elements data. Finally, the models developed under this research work can be used to reduce human and environmental health by trace elements taking into account local conditions but also climate change scenarios.

During the past years, air quality has become an important global issue, due to its impact on people's lives and the environment, and has caused severe problems for humans. As a prevention to effectively control air pollution, forecasting models have been developed as a base for decision-makers and urban managers during the past decades. In general, these methods can be divided into three classes: statistical methods, machine learning methods and hybrid methods. This study's primary intent is to supply an overview of air pollution prediction techniques in urban areas and their advantages and disadvantages. A comparison has also been made between the methods in terms of error assessment and the use of geospatial information systems (GIS). In addition, several approaches were applied to actual data, and the findings were compared to those acquired from previous published literatures. The results showed that forecasting using machine learning and hybrid methods has provided better results. It has also been demonstrated that GIS can improve the results of the forecasting methods.

The purpose of this study is to use both agent-based modeling as a new method in modeling dynamic phenomena and GIS to show the effects of carbon monoxide (CO) on individuals in the city of Tehran. After collecting the latest information about the severity of carbon monoxide pollutants on different days, one of the days with a very high severity of this pollutant has been selected for investigation and the interpolation map of its data has been developed via IDW method in ArcGIS software environment, which is then re-classified with the NetLogo software environment used to run the agent-based model. At this stage, the agents are randomly produced in four different age groups in the environment and begin moving with the onset of the running process in the environment. Also, the symptoms, caused by the pollution effects on the agents, appear in form of changes in color and are based on carboxyhemoglobin (COHb) levels (percentage) of each. The results indicate that among the considered older age groups, the members of the age group above 65, have had been mostly affected by pollution and the effect of pollution on the agents of the age group of 13 to 30 years old has been less than the other groups.