Modeling spatial distribution of Tehran air pollutants using geostatistical methods incorporate uncertainty maps

modeling spatial distribution of tehran air pollutants using geostatistical methods incorporate uncertainty maps

the estimation of pollution fields, especially in densely populated areas, is an important application in the field of environmental science due to the significant effects of air pollution on public health. in this paper, we investigate the spatial distribution of three air pollutants in tehran’s atmosphere: carbon monoxide (co), nitrogen dioxide (no2), and atmospheric particulate matters less ...

Geostatistical modeling of topography using auxiliary maps

spatial distribution of nitrate ion in andimeshk groundwaters using the geostatistical methods

according to the importance of groundwater resources in providing of human needs, lack of control and inappropriate management of these resources can lead to the problems such as nitrate pollutions arising from the use of chemical fertilizers in agricultural lands. with generalized point data to the area using the geostatistics methods can be determine the pollution content in areas without sam...

Evaluation of Seasonal and Spatial Variations of Air Quality Index and Ambient Air Pollutants in Isfahan Using Geographic Information System

Introduction: Due to more than 1.7 million population, many vehicles and large industries around Isfahan, it has become one of the most polluted cities in Iran. The aim of this study was a spatial analysis of the concentrations of air pollutants and the air quality index (AQI). Materials and Methods: In this descriptive and evaluative study, the air quality data of 7 monitoring stations in 201...

Statistical trend analysis and forecast modeling of air pollutants

The study provides a statistical trend analysis of different air pollutants using Mann-Kendall and Sen’s slope estimator approach on past pollutants statistics from air quality index station of Varanasi, India. Further, using autoregressive integrated moving average model, future values of air pollutant levels are predicted. Carbon monoxide, nitrogen dioxide, sulphur dioxide, particu...