Accuracy assessment of moderate resolution image spectroradiometer products for dust storms in semiarid environment.

Dust storms are strongly and negatively associated with the annual cycle of rainfall and coincide with the west and southwesterly winds in west and south west of Iran. Accuracy assessment of particulate matter products of moderate resolution image spectroradiometer was studied in this research. Moderate resolution image spectroradiometer products consist of aerosol optical thickness, its corresponding image red, green and blue and moderate resolution image spectroradiometer/ terra calibrated radiances 5 minutes L1B swath 1 km, which shows the environmental information at terrestrial, atmospheric and ocean phenomenology. Daily aerosol optical thickness data retrieved from moderate resolution image spectroradiometer from May 2009 to May 2010 were compared with the amount of particulate matter measured at ground in Sanandaj, Iran, using non-linear correlation coefficient. Results showed that the moderate resolution image spectroradiometer image / terra calibrated radiances 5 minutes L1B swath 1 km is able to detect dust storms distribution and their blowing direction over study area clearly. The air quality conditions obtained in with dust storm period were unhealthy and correlation coefficients between moderate resolution image spectroradiometer aerosol optical thickness and particulate matter concentration in this period were higher than without dust storm period. The moderate resolution image spectroradiometer aerosol optical thickness values lower than 0.1 were acquired uncertainty level. Comparison of moderate resolution image spectroradiometer images/ terra calibrated radiances 5 minutes L1B swath 1 km and image red, green and blue showed that moderate resolution image spectroradiometer has limitation in retrieval of aerosol optical thickness from the dust storm with high concentration of particulate matter. This study reveals that the algorithm which is applied to refine the aerosol optical thickness is not able to recognize the amount of particulate matter in low and very high concentrations sensitively. No study has previously been conducted to investigate the accuracy of the moderate resolution image spectroradiometer particulate matter products

Ancient Migratory Events in the Middle East: New Clues from the Y-Chromosome Variation of Modern Iranians

Knowledge of high resolution Y-chromosome haplogroup diversification within Iran provides important geographic context regarding the spread and compartmentalization of male lineages in the Middle East and southwestern Asia. At present, the Iranian population is characterized by an extraordinary mix of different ethnic groups speaking a variety of Indo-Iranian, Semitic and Turkic languages. Despite these features, only few studies have investigated the multiethnic components of the Iranian gene pool. In this survey 938 Iranian male DNAs belonging to 15 ethnic groups from 14 Iranian provinces were analyzed for 84 Y-chromosome biallelic markers and 10 STRs. The results show an autochthonous but non-homogeneous ancient background mainly composed by J2a sub-clades with different external contributions. The phylogeography of the main haplogroups allowed identifying post-glacial and Neolithic expansions toward western Eurasia but also recent movements towards the Iranian region from western Eurasia (R1b-L23), Central Asia (Q-M25), Asia Minor (J2a-M92) and southern Mesopotamia (J1-Page08). In spite of the presence of important geographic barriers (Zagros and Alborz mountain ranges, and the Dasht-e Kavir and Dash-e Lut deserts) which may have limited gene flow, AMOVA analysis revealed that language, in addition to geography, has played an important role in shaping the nowadays Iranian gene pool. Overall, this study provides a portrait of the Y-chromosomal variation in Iran, useful for depicting a more comprehensive history of the peoples of this area as well as for reconstructing ancient migration routes. In addition, our results evidence the important role of the Iranian plateau as source and recipient of gene flow between culturally and genetically distinct population

Air quality data series estimation based on machine learning approaches for urban environments

Air pollution is one of the main environmental problems in residential areas. In many cases, the effects of air pollution on human health can be prevented by forecasting the air quality in the next day. In order to predict the 1 day in advance air quality index (AQI) of Orumiyeh city, the hybrid single decomposition (HSD) and hybrid two-phase decomposition (HTPD) models were used. In the first step, the AQI data were decomposed by complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and was hybridized with general regression neural network (GRNN) and extreme learning machine (ELM) as HSD models. In the second step, using variational mode decomposition (VMD) technique the results of the first intrinsic mode functions (IMFs) of CEEMDAN model were decomposed into nine VMs and were predicted by GRNN and ELM models to obtain IMF1. Finally, in the third step, GRNN and ELM were used again to predict the IMFS as HTPD models. Results showed that in predicting AQI series data by HSD models both CEEMDAN-ELM and CEEMDAN-GRNN models were similarly accurate. Among all the models used, the accuracy of CEEMDAN-VMD-GRNN as the HTPD model was the highest in the training phase (R2 = 0.98, RMSE = 4.13 and MAE = 2.99) and in the testing phase (R2 = 0.74, RMSE = 5.45 and MAE = 3.87). It can be concluded that HTPD models have more accurate results to predict AQI data compared with HSD models. © 2020, Springer Nature B.V

Evaluation of linear, nonlinear, and hybrid models for predicting PM2.5 based on a GTWR model and MODIS AOD data

Ground monitoring station data of PM2.5 are not available for each day and all places in urban areas. In this research, taking Tehran as an example, a geographically and temporally weighted regression (GTWR) model was utilized to investigate the spatial and temporal variability relationship between PM2.5 concentrations measured at ground monitoring stations and satellite aerosol optical depth (AOD) data. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor produced AOD values with 3-km spatial resolution. Using meteorological variables and land use information as additional predictors utilized in the GTWR model, the AOD was converted to PM2.5 at ground level for warm (October to March) and cold seasons (April to September) from 2011 to 2017. To improve the accuracy of the correlation coefficient between converted PM2.5 from the GTWR model and PM2.5 concentrations measured at ground monitoring station, the results of a linear model (LR), a nonlinear model (artificial neural network (ANN)), and hybrid models including general regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) were compared. The results of the linear, nonlinear, and hybrid models for the cold season display higher accuracy compared with the results for the warm season. Among the used models, the GRNN model has higher accuracy compared with the other models. This study reveals that AOD conversion to particulate matter by the GTWR model and its simulation to PM2.5 at ground level using a hybrid model such as the GRNN can be used to study air quality. © 2019, Springer Nature B.V

Estimating solar radiation using NOAA/AVHRR and ground measurement data

Solar radiation (SR) data are commonly used in different areas of renewable energy research. Researchers are often compelled to predict SR at ground stations for areas with no proper equipment. The objective of this study was to test the accuracy of the artificial neural network (ANN) and multiple linear regression (MLR) models for estimating monthly average SR over Kurdistan Province, Iran. Input data of the models were two data series with similar longitude, latitude, altitude, and month (number of months) data, but there were differences between the monthly mean temperatures in the first data series obtained from AVHRR sensor of NOAA satellite (DS1) and in the second data series measured at ground stations (DS2). In order to retrieve land surface temperature (LST) from AVHRR sensor, emissivity of the area was considered and for that purpose normalized vegetation difference index (NDVI) calculated from channels 1 and 2 of AVHRR sensor was utilized. The acquired results showed that the ANN model with DS1 data input with R2 = 0.96, RMSE = 1.04, MAE = 1.1 in the training phase and R2 = 0.96, RMSE = 1.06, MAE = 1.15 in the testing phase achieved more satisfactory performance compared with MLR model. It can be concluded that ANN model with remote sensing data has the potential to predict SR in locations with no ground measurement stations. © 2017 Elsevier B.V

Urban heat evolution in a tropical area utilizing Landsat imagery

Cloud cover is the main limitation of using remote sensing to study Land Use and Land Cover (LULC) change, and Land Surface Temperature (LST) in tropical area like Malaysia. In order to study LULC change and its effect on LST, the Landsat images were utilized within Geographical Information System (GIS) with the aim of removing the effect of cloud cover and image's gaps on the Digital Number (DN) of the pixels. 5356 points according to pixels coordinate which represent the 960. m to 960. m area were created in GIS environment and matched with thermal bands of the study area in remote sensing environment. The DNs of these points were processed to extract LST and imported in GIS environment to derive the temperature maps. Temperature was found to be generally higher in 2010 than in 2000. The comparison of the highest temperature area in the temperature maps with ground stations data showed that the topographical characteristics of the area, and the wind speed, and direction influence the occurrence of Urban Heat Island (UHI) effect. This study concludes that integration of remote sensing data and GIS is a useful tool in urban LST detection in tropical area. © 2015 Elsevier B.V

Development of the models to estimate particulate matter from thermal infrared band of Landsat Enhanced Thematic Mapper

Particulate matter concentration and assessment of its movement pattern is crucial in air pollution studies. However, no study has been conducted to determine the PM10 concentration using atmospheric correction of thermal band by temperature of nearest dark pixels group (TNDPG) of this band. For that purpose, 16 Landsat Enhanced Thematic Mapper plus ETM+ images for Sanandaj and Tehran in Iran were utilized to determine the amount of PM10 concentration in the air. Thermal infrared (band 6) of all images was also used to determine the ground station temperature (GST b6) and temperature of nearest dark pixels group. Based on atmospheric correction of images using temperature retrieval from Landsat ETM+, three empirical models were established. Non-linear correlation coefficient with polynomial equation was used to analyze the correlations between particulate matter concentration and the ground station temperature for the three models. Similar analyses were also undertaken for three stations in Klang Valley, Malaysia, using 11 Landsat ETM+ images to show the effectiveness of the model in different region. The data analysis indicated a good correlation coefficient R = 0.89 and R = 0.91 between the trend of the result of temperature of nearest dark pixels group b6 - (GST b6 - GST) model and the trend of PM10 concentration in Iran and Malaysia, respectively. This study reveals the applicability of the thermal band of Landsat TM and ETM+ to determine the PM10 concentration over large areas. © 2013 CEERS, IAU

Development of the models to estimate particulate matter from thermal infrared band of Landsat Enhanced Thematic Mapper

Particulate matter concentration and assess- ment of its movement pattern is crucial in air pollution studies. However, no study has been conducted to deter- mine the PM 10concentration using atmospheric correction of thermal band by temperature of nearest dark pixels group (TNDPG) of this band. For that purpose, 16 Landsat Enhanced Thematic Mapper plus ETM+ images for San- andaj and Tehran in Iran were utilized to determine the amount of PM 10 concentration in the air. Thermal infrared (band 6) of all images was also used to determine the ground station temperature (GST b6) and temperature of nearest dark pixels group. Based on atmospheric correction of images using temperature retrieval from Landsat ETM+ , three empirical models were established. Non- linear correlation coefficient with polynomial equation was used to analyze the correlations between particulate matter concentration and the ground station temperature for the three models. Similar analyses were also undertaken for three stations in Klang Valley, Malaysia, using 11 Landsat ETM+ images to show the effectiveness of the model in different region. The data analysis indicated a good cor- relation coefficient R = 0.89 and R = 0.91 between the trend of the result of temperature of nearest dark pixels group b6 - (GST b6 - GST) model and the trend of PM 10 concentration in Iran and Malaysia, respectively. This study reveals the applicability of the thermal band of Landsat TM and ETM+ to determine the PM 10 concentration over large areas