Amir Hadian; Mina Moradizadeh
Abstract
Air pollution is one of the most important crises that most countries are facing today due to the progress of industry and technology. The country of Iran and especially the city of Tehran is not exempt from this phenomenon. Air pollution measurement stations at city level, in spite of the high accuracy ...
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Air pollution is one of the most important crises that most countries are facing today due to the progress of industry and technology. The country of Iran and especially the city of Tehran is not exempt from this phenomenon. Air pollution measurement stations at city level, in spite of the high accuracy of the measurement of pollutants, are not generalizable due to time and place limits and point measurement. A complementary and sometimes alternative solution is the use of remote sensing and satellite data, which is a suitable method for monitoring air pollution due to the optimal cost and wide coverage. Nitrogen dioxide (NO2) and ozone (O3) pollutants are among the most important indicators of air pollution. In this research, an effort will be made to model the distribution of their concentration in the city of Tehran with the same spatial resolution (almost one kilometer) and higher accuracy than satellite data. For this purpose, the concentration distribution of these two pollutants has been modeled by using an innovative method based on the kriging interpolation method and simultaneous use of pollution measurement station data and high spatial resolution of Sentinel 5P satellite data. In order to evaluate the results, air pollution measurement station data were used, and the average monthly error of this model has decreased from 16.8 to 1.73% for NO2 pollutants and from 21.9 to 2.53% for O3 pollutants compared to the data of the Sentinel 5P satellite. Also, the root mean square error (RMSE) of this model for NO2 and O3 pollutants is equal to 2.79 ppb and 0.86 ppb, respectively, which shows the proper performance of this model in modeling the concentration distribution of pollutants.
Mina Moradizadeh; Mohamad Reza Talari
Abstract
Atmospheric water vapor is a key parameter in modeling the energy balance on the earth's surface and plays a major role in keeping the temperature of the earth's atmosphere balanced. Retrieving of this parameter, as the most influential atmospheric parameter on the sensors received radiance, ...
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Atmospheric water vapor is a key parameter in modeling the energy balance on the earth's surface and plays a major role in keeping the temperature of the earth's atmosphere balanced. Retrieving of this parameter, as the most influential atmospheric parameter on the sensors received radiance, is of great importance. Since the atmospheric water vapor content in the near of surface is more and its temporal and spatial changes are more intense, the measurements of ground meteorological stations, despite their high accuracy, are not generalizable due to temporal and spatial limitations and point measurements. Therefore, it seems necessary to provide practical satellite-based methods to accurate and continuous retrieval of this parameter with appropriate spatial distribution. Therefore, retrieving the near surface water vapor content with accuracy and appropriate spatial resolution is very important, and the purpose of this research is to provide four innovative and accurate methods to estimate the mass mixing ratio of near surface water vapor in Isfahan province in 1 km resolution. Different sensors measure water vapor with different resolution and sensitivities to this parameter. Thus, providing methods based on the integration of different sensor's and ground observations data is essential to simultaneously improve the spatial resolution and accuracy of water vapor retrievals. In this research, the combination of MODIS and AIRS data and ground station observations have been used. Also, the band ratio method, IDW interpolation and scaling have been used along with the proposed methods. Correcting the bias of AIRS-derieved water vapor during the scaling stage and interpolation error is on the agenda. Validation results of proposed methods show that the method based on the generalization of accurate ground-basedwater vapor observations and removing interpolation error, through integration with MODIS-derieved water vapor values, has the best performance (R2=0.55, RMSE=1.05 Gr/Kr).
Rasoul Atashi Deligani; Mina Moradizadeh; Behnam Tashayo
Abstract
Ground surface ozone is one of the most dangerous pollutants that has significant harmful effects on the residents of urban areas. The purpose of this study is to identify the factors affecting ozone concentration and modeling its changes using satellite data and different machine learning methods in ...
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Ground surface ozone is one of the most dangerous pollutants that has significant harmful effects on the residents of urban areas. The purpose of this study is to identify the factors affecting ozone concentration and modeling its changes using satellite data and different machine learning methods in Tehran. For this purpose, pollutant concentration and meteorological data were used along with the satellite product of land surface temperature (LST) in the period from 2015 to 2021. After calculating the correlation between ozone concentration and independent parameters, ozone concentration modeling was done in five different modes in terms of input parameters and learning method and applying data refinement. In the first and second mode, modeling was done using pollutant concentration and meteorological data through multivariate linear regression method. The only difference between these two modes is the filtering of the input data using the WTEST method in the second mode. In the third mode, the LST product was added to the input data, and in the fourth and fifth mode, ozone modeling was done using multilayer neural network and recurrent neural network, respectively. The comparison of the five modes showed that the modeling of the first to fifth stages with adjusted coefficient of determination of 0.5, 0.64, 0.69, 0.74 and 0.8 were able to recover the ozone concentration, respectively. It was also found that among different pollutants, nitrogen monoxide, nitrogen dioxide and nitrox have the greatest impact on ozone concentration, just as temperature, humidity and wind speed are the most influential among meteorological data. Although the use of WTEST statistics led to the identification and elimination of inconsistencies and errors in the observations of pollution measurement stations, the neural network learning method showed better performance in modeling than multivariate regression due to its less sensitivity to noise. As a notable result, adding the LST product to the input data brought a 5% increase in accuracy in estimating ozone concentration.
Mina Moradizadeh
Abstract
Atmospheric column water vapor, which is the total atmospheric precipitable water vapor contained in a vertical air column, is one of the most important factors in all surface-atmosphere interactions (such as energy fluxes between the earth and the atmosphere) and plays a key role in wide variety of ...
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Atmospheric column water vapor, which is the total atmospheric precipitable water vapor contained in a vertical air column, is one of the most important factors in all surface-atmosphere interactions (such as energy fluxes between the earth and the atmosphere) and plays a key role in wide variety of environmental studies, ecological and agricultural applications. However, measuring this parameter at meteorological stations requires the use of radiosonde instruments, which being pointwise and costly are limitations of these observations. Therefore, remote sensing is used as an alternative to estimate this important atmospheric parameter. Compared to other atmospheric parameters, atmospheric water vapor which attenuates remotely sensed radiance is of great importance. Although this atmospheric parameter is measured by AIRS (Atmospheric Infrared Sounder) sensor, its low resolution (about 40 km) is not acceptable for many applications. Therefore, developing an algorithm to downscale the AIRS-derived column water vapor is the main goal of this study, so that its spatial resolution can be improved. To do this, using the ratio method, the AIRS-derived column water vapor is fused with the MODIS (Moderate Resolution Imaging spectroradiometer) data. Then, due to the major influence of this parameter on Land Surface Temperature (LST) estimation, the role of improved resolution atmospheric column water vapor in the estimation of LST is investigated as a secondary goal. In order to validate the estimated parameters and evaluate their accuracy, independent datasets were used. Results of the implementation indicate that proposed downscaling method has high potential to enhance the spatial resolution of AIRS-derived atmospheric column water vapor, without significant degradation of the RMSE. It was also found that the atmospheric column water vapor when moving into higher spatial resolution can dramatically increase the accuracy of the LST estimation.
