The main greenhouse gases such as carbon dioxide (CO2) and methane (CH4) have a direct effect on the radiative balance of the atmosphere. They are the main driver of climate change, because their global average concentration during the industrial period has increased by about 47% and 156% for CO2 and CH4, respectively, as a result of human activities (IPCC, 2021).

The importance of these gases, henceforth called greenhouse gases (GHGs), led to the creation of global monitoring networks to monitor their trends and variability. Terrestrial remote sensing networks such as the Atmospheric Composition Change Detection Network (NDACC) and the Total Carbon Column Observation Network (TCCON) are used and welcomed by researchers due to the long time span of accurate column observations (De Mazière et al., 2018). ; Wunch et al., 2011).

Observational grid measurements of Fourier transform infrared (FTIR) spectrometers use direct sunlight to measure the absorption of rare gases along the line of sight and provide detailed information on the total column abundance or vertical profile of greenhouse gases and other species. These observations are used by scientists around the world to detect changes in atmospheric composition, to improve our understanding of the carbon cycle, or to provide validation for space-based measurements. Recently, low-cost mobile FTIR spectrometers have been used in the Collaborative Carbon Column Observing Network (COCCON) to validate fluxes in urban areas (Hase et al., 2015; Vogel et al., 2019; Makarova et al., 2021).

In addition to FTIR observations, surface observations of these gases are performed for better management of springs and wells on a smaller scale. Both types of observations contain valuable information about the emission and atmospheric distribution of these species and complement each other. The need to estimate greenhouse gas emissions using independent approaches has encouraged scientists to develop policies based on different measurements (Dayalu et al., 2009).

One of the important factors of global warming, the concentration of CO2 and its changes has aroused sensitivities all over the world. Establishing a clear understanding of the spatial and temporal distribution of CO2 concentrations at a regional scale is a critical technical challenge for climate change research. Modeling with the spatial resolution of the CO2 atmospheric concentration was done using the Weather Research and Forecasting-Chemistry (WRF-GHG) model and from the X CO2 data retrieved from the GOSAT satellite observations, as accuracy control information and Evaluation of the simulated data was used in CO2 column concentration. WRF-GHG is a model for simulating atmospheric chemical transport, designed for regional studies of CO2 concentration. The studied area, the Middle East region was designed as the first domain and Iran as the second domain with a resolution of 30 and 10 km, respectively. The performance of the simulations in predicting the concentration of greenhouse gas carbon dioxide (CO2), for the study period of February and August 2010, showed that the spatial and temporal variability of meteorological variables is well represented by the model with a correlation coefficient of 93%. -76%, 39%-47%, and 52-80% for temperature, wind, and relative humidity have been simulated.

The most important sources of CO2 emissions include man-made emissions, biomass emissions, fire emissions, and ocean emissions. Among the emission sources of CO2 column concentration, human resources have the largest share in the emission of this pollutant with the amount of (38.33 and 23.70) percent in February and August. Biogenic emissions, after human emissions, with (24.08 and 46.64) percentages of absorption and (31.81 and 28.64) in February and August have the largest share in the production and absorption of this pollutant. Fire emissions with (5.76 and 2.04) percent and ocean with a value of (3.23-10 x 6) percent for CO2 pollutants are in third and fourth place respectively in the share of total CO2 emissions. From the analysis of the behavior of monthly average values from the advection field, in both February and August, it was determined that the maximum values of advection occurred at night to early morning. The evaluation results of the RMSE error indicate that the simulation has performed better in the cold season than in the hot season in the aforementioned pollutant simulations. CO2 column concentration was underestimated in winter (0.79 ppmv) and overestimated in summer (0.45 ppmv). This study showed that the WRF-GHG model is able to represent well many important features of observations (such as temperature, wind field, and humidity) in Southwest Asia (Middle East-Iran region) and the use of the model for future studies in It assures the region.