Dust is one of the most important effective factor on solar radiation forcing and reflection on earth's atmosphere, and in this point, it has a significant impact on local climate. Detection of AEROSOLs on desert zones, despite the sea and oceans (dark surfaces), is difficult because of reflectometric interference spectroscopy of bright surfaces. Representing a simple and low costs method for detecting dusts and predicting their effects is essential. One of the most important indexes for dust and smoke detection is the AOT (AEROSOL OPTICAL THICKNESS), which provided in large-scale (10x10 km) which is not suitable for local dust scales detection. The purpose of this study is using visible and mid-infrared spectrum of OLI sensor for detection dust of deserts. In this study, by using of mid-wave infrared (2. 1 μ m), red and blue wavelengths the AOT was calculated. The results indicated that ratio between the red and mid-wave infrared wavelengths is 0. 95 and blue wavelengths and mid-wave infrared is 1. 05 respectively. The comparison results of AOT index by radiometer showed that the correlation between computational method for data and the direct measurement for the red and blue wavelengths were 0. 83 and 0. 95 with root-mean-square deviation (RMSE) were 0. 91 and 9. 4 respectively. Therefore, it can be said that this method for estimating the AEROSOL OPTICAL THICKNESS at 0. 65 μ m (AOT 0. 65μ m) is enough accuracy and is not suitable to measure AEROSOL OPTICAL THICKNESS at 0. 47 μ m (AOT 0. 47μ m).

Dust, as an AEROSOL, significantly impacts air quality in arid and semi arid regions. Dust events in Iran, as a result of the semiarid climate, and its location in the global dust belt neighboring the deserts of Arabian countries, occur frequently. In recent years, the intensity of dust phenomena has increased, most especially in west and southwest Iran. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) AEROSOLs OPTICAL THICKNESS (AOT) product is applied in order to estimate dust intensity. Since AEROSOL OPTICAL THICKNESS at 550nm has a close relationship with the brightness temperature of MODIS bands 31 and 32, and Normalized Difference Dust Index (NDDI), the calculated AOT is proposed through quantitative analysis of MODIS data for major dust events over west and southwest Iran during years 2000-2009. The proposed calculated AOT matches MODIS AOT very well, with a squared correlation coefficient of 0.740. Moreover, based on MODIS measurements, sequential separation of the dust cloud from the bright underlying surface and water cloud is accomplished through the Brightness Temperature Difference (BTD) of suspended particle matter in 11 and 12 micrometer wavelengths of MODIS, NDDI and refined cloud threshold utilization. By statistical analysis of MODIS measurements, thresholds are determined over west and south west regions of Iran. Validations with ground meteorological observations over the region revealed good agreement of the proposed method in separating dust from the bright surface and cloud, which obviate the deficiency of remote-sensing data products of dust particles near the source as a result of bright surface radiance contributions. In addition, there is considerable correlation between MODIS AOT at 550 nm and Ahwaz station PM10, while there is a negative correlation between calculated AOT and horizontal visibility. The statistical analysis and case studies reveal the accuracy of this method in extracting dust from underlying AEROSOLs, and the usefulness of this technique in dust enhancement optimization.

The dependencye of AEROSOL OPTICAL depth on wavelength as well as the fit of the humidity, temperature and pressure approximation under atmoshperic condition at Biskra city of Algeria has been investigated. Our work consists of measuring and modeling solar radiation on the horizontal area to create a mathematical model of global solar radiation which depends on the AEROSOL OPTICAL depth data between two wavelengths: 550 and 1250 nm. Simultaneous measurements of global solar radiation were carried out and recorded on the horizontal zone on an urban site (Biskra, Algeria) to characterize the radiative effect of atmospheric AEROSOLs from January to December 2013. In addition, the effect of meteorological parameters such as: humidity, ambient temperature, and time durations were studied. This relationship constitutes an alternative tool to estimate AOD at the routine lighting measurements available at many radiometric stations around the world. Finally, a comparative study was established between the theoretical results and the experimental data which leads at an excellent correlation by a low relative error which is limited by the interval 2 and 15%.

Background and Objective The AEROSOL OPTICAL Depth index is one of the most commonly used indicators for assessing air pollution in various regions, especially arid and semi-arid areas. The arid and semi-arid regions are the main sources of dust particles. Due to locating in the arid and semi-arid region, Iran faces dust storms several times over the year, which have caused irreparable environmental and socio-economic damages to different parts of the country. The southeastern of Iran is one of these regions that is affected by dust storms in the first half of the year (early spring to late summer) due to 120-day winds, and large amounts of sand and dust particles enter the atmosphere each year. Therefore, it is important to study the temporal and spatial changes of suspended particles in the atmosphere, of which dust is a major part of AEROSOLs in these regions. In fact, knowing the temporal and spatial changes of suspended particles can be helpful in providing appropriate solutions to reduce the damages caused by these particles...

Nowadays, dust storm in one of the most important natural hazards which is considered as a national concern in scientific communities. This paper considers the capabilities of some classical and intelligent methods for dust detection from satellite imagery around the Middle East region. In the study of dust detection, MODIS images have been a good candidate due to their suitable spectral and temporal resolution. In this study, physical-based and intelligent methods including decision tree, ANN (Artificial Neural Network) and SVM (Support Vector Machine) have been applied to detect dust storms. Among the mentioned approaches, in this paper, SVM method has been implemented for the first time in domain of dust detection studies. Finally, AOD (AEROSOL OPTICAL Depth) images, which are one the referenced standard products of OMI (Ozone Monitoring Instrument) sensor, have been used to asses the accuracy of all the implemented methods. Since the SVM method can distinguish dust storm over lands and oceans simultaneously, therefore the accuracy of SVM method is achieved better than the other applied approaches. As a conclusion, this paper shows that SVM can be a powerful tool for production of dust images with remarkable accuracy in comparison with AOT (AEROSOL OPTICAL THICKNESS) product of NASA.

This research seeks to investigate the consistency of satellite data and the information obtained from the ground meteorological stations in Iran. In this study, the AEROSOL OPTICAL Depth (AOD) data of Moderate Resolution Imaging Spectroradiometer (MODIS) deep blue algorithm of Terra satellite from 2000-2018 was used. The data of 390 meteorological stations during2000-2018 were used to evaluate and validate the satellite data. The AEROSOL OPTICAL depth (AOD) was studied and compared with the current weather codes of meteorological stations (codes 00 to 99). The frequency percentage and spatiotemporal matching methods were further used. Based on the results, the AOD at 550 nm data of the Terra satellite MODIS sensor had a significant relationship with the meteorological codes of 00 to 99 in Iran. This topic is useful in the study of meteorological phenomena. The present study evaluated the large values of AEROSOL OPTICAL depth (AOD) of meteorological phenomena in the boundary layer. The highest frequency percentage of the AEROSOL OPTICAL depth (AOD) between 0 and 3. 5 belonged to the present weather codes No. 5 and 6. The amount of AEROSOL OPTICAL depth (AOD) was directly related to meteorological phenomena (short-or long-term) such as natural, industrial, and urban pollution, smoke, humidity changes, lightning, thunderstorms, and heavy rainfall. The amount of AEROSOL OPTICAL depth (AOD) varied depending on the season, place, and meteorological phenomena in Iran.

AEROSOLs affect the earth's atmospheric radiative fluxes via direct, semi-direct, and indirect mechanisms. AEROSOLs also are one of the main sources of uncertainty in climate models. In the Middle East, in addition to climate effects, various problems such as reduced visibility, human health hazards, and air pollution are caused by AEROSOLs. Studying the OPTICAL and physical properties of AEROSOLs on local and global scales helps reduce the uncertainties in climate forcing. In this study, AEROSOL OPTICAL properties, including AEROSOL OPTICAL Depth (AOD), Angstrom Exponent (AE), ASYmmetry parameter (ASY), Single Scattering Albedo (SSA), and phase function were analyzed. These properties were investigated over five sites in the Middle East during 2013 using the AEROSOL Robotic NETwork (AERONET) data. The results revealed an inverse relationship between AOD and AE in all sites. A high AOD value and a low AE value were detected in spring and summer in all studied sites, suggestive of coarse mode dust particles. ASY initially decreased due to the dominance of absorbing type AEROSOLs in the visible spectrum with the increase in wavelength. Afterwards, ASY increased with the increase in wavelength in the infrared region due to the dominance of the coarse mode particles. In most sites, SSA increased, particularly in spring and summer, with the increase in the wavelength because of the dominance of desert dust. In spring and summer, the phase function was high over all sites. High phase functions associated with small scattering angles were caused by the coarse mode particles.

Introduction Tropospheric AEROSOL particles play an important role in the Earth's radiative energy balance directly by scattering and absorbing solar radiation and indirectly by modulating the microphysical and radiative properties of clouds. AEROSOL OPTICAL depth (AOD) based on satellite remote sensing data is a quantitative estimate of the amount of AEROSOL in the atmosphere and can be used as an indicator of AEROSOL particle concentration. In general, the review of previous studies indicates the high importance of AEROSOL products based on satellite remote sensing data in modeling the spatial-temporal patterns of dust storms and in particular the identification of dust sources. The advantages of using satellite AOD to identifying dust events are possible in arid areas with relatively little cloud cover. The presence of clouds in the sky also severely limits AOD terrestrial and satellite measurements. Thus, AOD datasets sometimes have a gap due to factors such as cloudiness. Since the possibility of monitoring and measuring AEROSOLs in cloudy conditions is limited, the use of proxy datasets to fill the gap will be an advantage. In this regard, several studies based on the analysis of satellite data have emphasized the association between climatic parameters and dust events (specifically AOD) in different regions. Therefore, considering the relationship between climatic parameters and AOD, these parameters can be used as a proxy data set to estimate AOD values for areas without data or with cloud cover. Also, using the predicted values of climatic parameters, AOD values can be predicted. Accordingly, in order to achieve reliable AOD prediction results, it is necessary to use a generalizable approach that can model the complex relationships between large data sets and satisfactorily solve the mentioned problems. For this purpose, one of the efficient data mining algorithms called M5P was considered to analyze and extract the relationships between climatic parameters and AOD to obtain predictive models. The M5P algorithm is a combination of tree and regression models with capabilities such as high prediction accuracy and ease of interpreting results. Materials and methods In this study, in order to derive AOD predictive models based on climatic parameters, M5P data mining algorithm based on tree structure and multivariate linear regression analysis were used. Accordingly, a spatial database of remote sensing time series data related to 4 climatic parameters (as independent variables) including surface air temperature (SAT), precipitation (P), surface relative humidity (SRH) and wind speed (WS), and AOD (as dependent variable) was generated. WEKA software was used to implement the M5P model. After analyzing the relationships between independent and dependent variables through the tree model structure and linear multivariate regression, AOD predictive rules were extracted. Statistical indicators were used to validate the linear predictive models. Results and discussion After pre-processing the time series data of climatic parameters and AOD as training data set, the input independent and dependent variables of the M5P were defined. Implementation steps of the M5P algorithm, including homogenization of independent input data sets by forming decision-making trees based on a series of "if-then" rules, multivariate linear regression analysis in homogeneous classes, and finally validation of the model results was performed in WEKA software. Thus, a total of four linear models (LM) or predictive rules for estimating AOD based on the values of climatic parameters were extracted. Finally, by placing the values of climatic parameters in the obtained linear models, the AOD value can be estimated based on the thresholds defined by the M5P algorithm. The obtained linear models are able to predict AOD values in different conditions (based on climatic parameters). Validation of the results of the M5P algorithm based on correlation analysis between input variables and evaluation of prediction errors through MAE and RMSE statistics shows the acceptable performance and accuracy of linear models in relation to AOD prediction. Given the dynamics of AEROSOL particles (especially dust) and their ability to transportability by the wind even at very far distances from their source of emission, it is likely that the amount of measured AOD for a pixel by a satellite sensor, does not exactly belong to the same area on earth. Therefore, in relation to the prediction error of the models, it should be noted that this may be due to the ability of the AEROSOL particles to be carried by the wind. Due to the strong correlation between AOD and climatic parameters, possible discrepancies may be due to the mentioned reason. Because a dust storm arising from a source may have no relation with the values of the climatic parameters at the destination. Conclusion In general, in this study, the capability of M5P data mining algorithm in order to AOD prediction was evaluated. Using the M5P algorithm based on inductive learning and using remote sensing time series data, through the formation of decision trees based on the set of "if-then" rules, four linear predictive models based on climatic parameters were extracted. Predictive models were extracted and validated using a data set for Ahvaz city. AOD, as an indicator of the state of the atmospheric AEROSOL, has great importance for dust storms studies. Access to AOD data is restricted in some parts of the world and in some seasons due to some limitations such as cloud cover. On the other hand, it is important to be aware of future spatial-temporal patterns of dust storms in order to adopt crisis management measures. Using the obtained predictor linear models in this study, it is possible to make an acceptable estimation of AOD in some areas, there are restrictions on access to AOD. Also, by entering the predicted values of climatic parameters, it is possible to estimate the future spatial-temporal patterns of AOD. Dust storms generally occur as a function of a wide range of environmental conditions, including atmospheric properties, as well as surface parameters such as vegetation, soil moisture, and soil texture. With this background, only considering the atmospheric conditions and their impacts on the spatial-temporal patterns of AOD may sometimes not produce the desired results. Therefore, it is recommended in future studies in this field, in addition to climatic parameters, which are mostly indicators of the atmospheric condition, ground surface parameters should also be used in modeling. By doing so expected to increase the accuracy of linear models for predicting AOD.