Due to the complexity of air pollution action, artificial intelligence models specifically, neural networks are utilized to simulate air pollution. So far, numerous artificial neural network models have been used to estimate the concentration of atmospheric PMs. These models have had different accuracies that scholars are constantly exceed their efficiency using numerous parameters. The current research aims to compare Elman and Jordan recurrent networks for error distribution and validation to estimate atmospheric particular matters concentration in Ahvaz city. The used parameters are relative humidity, air pressure, and temperature and aerosol optical depth. The latter one is extracted from MODIS sensor images and air pollution monitoring stations. The results show that Jordan model with RMSE of 219.9 milligram per cubic meter has more accuracy rather than Elman model with RMSE of 228.5. The value of R2 index that shows the linear relation between the estimated from the model and observed values for Jordan is equal to 0.5 that implies 50% estimation accuracy. The value is because of MODIS spatial resolution, inadequacy in numbers as well as spatial distribution of meteorological station inside the study area. According to the results of the current research, it seems that air pollution monitoring stations have to increase in terms of numbers and suitable spatial distribution. Also, other ancillary data like volunteer geographic air pollution data entry using mobile connected cheap sensors as portable stations may be used to implement more accurate simulation for air pollution.
 

Complexity of multi dimension developments and infrastructures intensifies the land related challenges to adopt legal laws, restrictions and responsibilities.  Although, multi dimension estates have been registering for many years, as the complexity of the estates are increased, disadvantages of the 2D cadaster is more appeared. Hence, the 3D cadaster has been a necessity for sustainable development. Visualizing is one of the important components of 3D cadaster. In the current research, for efficient and effective visualization of land ownerships and their related 3D information, firstly, the needed essentials of cadaster visualization systems have been classified into 3 groups including: cadaster, visualization and independent properties. Then, the trends of 3D visualization developments for older plugin and WebGL based technologies have been considered. Finally, a number of the most important systems according to the needed criteria for web-based 3D cadaster were evaluated and consequently, Cesium virtual environment has been selected as the best for the development purpose. To develop a system for 3D cadaster visualization, 2D building properties was converted to 3D using different software and then, land law properties were added and subsequently, Building Information Model (BIM) was provided. HTML5, JavaScript and CSS languages along with WebGL library and Cesium API were utilized. The implemented service is able to display WFS-based standard vector layers as well as WMS image of OGC standard. The system has the possibilities of 3D visualization like web-based 3D cadaster visualization and land law properties over the web. This enables the user to make the printed output of BIM along with descriptive information of the buildings.