جستجوی مقالات مرتبط با کلیدواژه « aerosol optical depth » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

Dust storm is one of the desertification and land gegradation consequences especially in arid and hyper-arid regions of the world. Fine particles released from the Earth's surface can be released several kilometers and subsequently over long distances, sometimes even across continents. These particles play a key role in the Earth system by affecting the radiation balance as well as forest and ocean ecosystems. Weather is often considered as the main factor influencing the occurrence of dust storms. Dust storms have increased in recent years mainly due to continued droughts and global warming. Temperature is an important variable in climatilogy in terms of its direct effect on weather variables such as pressure, wind movement, cloud formation and precipitation. Based on previous studies, the increase in temperature along with the decrease in rainfall and the increase in wind speed provide the conditions for the creation of dust storms. In general, the review of the studies conducted in the field of evaluating dust changes and the effect of temperature parameters on it shows that it is possible to conduct these studies from different perspectives and methods. Therefore, it is necessary to find a comprehensive view on dust and factors affecting it by reviewing and evaluating the results of other studies so that the phenomenon can be controlled and managed. The number of dusty days in Khuzestan province is significant and has increased in recent years. The reason for that is its proximity to the great deserts of the neighboring countries. Therefore, this province is one of the challenging areas of the country in the field of dust management and control. The purpose of this study is to evaluate the trend of dust changes and the effect of temperature on this phenomenon. The results of this study help to better understand the phenomenon of dust in Khuzestan province.

In this research, three data groups were used including the monthly average temperature of 13 meteorological stations in the study area, the monthly average land surface temperature (LST), and the monthly average aerosol optical depth (AOD). First, the trend of dust changes was evaluated using the Mann-Kendall test. Then, the relationship between temperature parameters and dust was investigated using the correlation between the AOD in a buffer of 5 km around the selected stationwith temperature parameters including monthly average temperature and average monthly LST in buffers of 5, 10 and 20 km around the selected station.

The evaluation of the average changes in the buffer of 5 km around the selected stations showed that in the months of February, July, December and April, no increasing trend was observed in any of the stations and even in the month of April, there was a decreasing trend in two stations. The trend of AOD changes was increasing with a probability of more than 90% in the months of August, September and November in seven, March in five, October in four, January in three, May in two and June in one station. The correlation results of temperature parameters and AOD showed that a very strong correlation between average LST and AOD has occurred only in the three months of May, June and July in different buffers. There are the highest number of strong correlations in the months of May to August between temperature parameters and AOD. Also, the highest number of very weak correlations occurred in April, March, November, and December. The highest number of very weak and weak correlations are related to air temperature (85 and 47 numbers, respectively). This shows that LST is more important than temperature in evaluating changes in dust intensity in the study area.

According to the results of this research, it can be said that air temperature has a weak correlation with dust, and most of the strong and very strong correlations occurred with the average LST in different buffers, which shows that LST is more important than temperature in evaluating dust intensity changes in the study area. In general, evaluation and monitoring of dust changes can be effective in identifying new dust sources as well as evaluating wind erosion, dust control, and management activities. Therefore, it can be suggested that an online and up-to-date system based on remote sensing and artificial intelligence be designed and presented to evaluate and monitor dust changes. In this case, the management of the dust phenomenon in Iran will be more targeted and the dust sources can be identified faster. On the other hand, one should pay attention to the factors affecting these changes and evaluate their effect on dust.

Dust is a phenomenon that mainly occurs in arid and semi-arid areas as a result of high wind speed and turbulence on the surface of the soil without vegetation and prone to erosion. Various factors such as wind speed, vegetation cover, soil characteristics, climatic factors, etc. are involved in the creation of wind erosion and the resulting dust, all of which are related to each other and lead to an increase or decrease in wind erosion and dust storms. The problems caused by dust storms are due to the lack of sufficient information about the prevailing conditions in the region, the way these conditions change, and the lack of knowledge of sensitive and prone areas to dust storms. To deal with this phenomenon and provide appropriate management solutions, it is necessary to know the areas prone to dust and the effective factors in the occurrence of this phenomenon. In this regard, remote sensing and modeling can be very effective in investigating the dust phenomenon. Numerous studies have also been conducted to investigate dust storms and dust sources and to model areas sensitive to this phenomenon using remote sensing data and machine learning.Isfahan province is considered one of the most important geographical regions of the country, which is susceptible to successive drought, desertification, and dust storms due to its special geographical location, low rainfall, and proximity to the desert. So, it is necessary to carry out studies that will lead us to a correct understanding of dust-prone areas in this province. Therefore, In the current research, zoning of dust-prone areas in Isfahan province was done using meteorological codes related to dust, Aerosol optical depth values of MODIS sensor of Terra satellite (2001-2022), and machine learning algorithms including RF, BRT, SVM, and CART.

Study area Isfahan Province with an area of nearly 107017 km2 (6.4% of Iran area) is located between 30° 43′ to 34° 30′ N and 49° 38′ to 55° 31′ E in central Iran (Fig. 1). The mean annual precipitation of this province is between 40 mm and more than 800 mm and its mean annual temperature varies from 10 °C to 20 °C (Iran Meteorological Organization). According to the Torrent White method, the climate of Isfahan province is dry in 58.73% of its area (eastern, northeastern, and sub-central parts of the province), semi-arid in 28% of its area (central and northern parts of the province), and humid and semi-humid in 13.27% of its area (western and southern parts of the province).

First, using AOD values, the occurrence and non-occurrence points of dust were determined. Ten various factors including land use, temperature, rainfall, erosive wind speed, slope, altitude, albedo, EVI, NDSI, and NDMI were determined as predictive factors. n the next step, the correlation between the predictive factors was calculated using the variance inflation factor (VIF). Using machine learning algorithms, spatial modeling of susceptible areas to dust was done and the importance of predictive factors in zoning was determined using the jackknife test. Finally, using the value of the area under the ROC curve (ROC-AUC), the validation of the model was done.

The zoning map of dust-prone areas in Isfahan province showed that the low-altitude and flat parts of the north, parts of the northeast, southeast of the province, and the central areas towards the southwest and west of Isfahan province are vulnerable areas against the occurrence of dust.The highest percentage of areas prone to dust in the RF model was in the very low class with a value of 21.36%, in the BRT model was in the medium class with a value of 22.66%, and in the SVM and CART models, it was in very high and low classes with values of 23.92% and 37.6%, respectively.The results of validation illustrated that the RF model with AUC = 0.86 was the most efficient, followed by BRT, CART, and SVM models with AUC values of 0.82, 0.79, and 0.77 respectively. According to the results of the Jackknife test, in RF, BRT, and CART models, rainfall had the most effect in modeling while in the SVM model, temperature and then rainfall had the most effect in the modeling.

Based on the results, the most vulnerable areas to dust are assigned to salt land, barren lands, and rangeland with poor quality. These areas, which are mainly located in the northern, central, and parts of the eastern sides of the province, have the lowest amount of surface soil moisture, the lowest amount of rainfall, and the highest temperature, and as a result, they face the lack of vegetation or weak vegetation. Therefore, these areas are exposed to the occurrence of dust, and with winds blowing at a speed exceeding the threshold speed of wind erosion, dust storms will happen.According to the validation results, the RF model has the best performance among the applied models, followed by the BRT, CART, and SVM models. Random forest algorithm is one of the advanced decision tree models used for classification and regression. This algorithm has a much more accurate performance compared to other simple regression trees or parametric statistical methods and is defined based on a large number of decision trees.The results of the jackknife test introduced rainfall as the most important factor in RF, BRT, and CART models. In the RF model, as the best model, after the rainfall factor, the temperature and altitude factors are more important than other factors. Considering the low amount of rainfall in dust-prone areas, it can be said that low rainfall, soil dryness, and, as a result, the reduction of vegetation, increase the conditions for creating wind erosion and dust.

The impact of dust storms on the ecosystem, human health, and economy is significant in the affected areas. In arid and semi-arid regions, dust is a natural occurrence that covers about one third of the earth's surface. This phenomenon is caused by wind erosion and factors such as the size of soil particles and their adhesion force, surface roughness, weather conditions such as long-term droughts cause it to intensify. To understand the impact of the dust phenomenon on humans and the environment, it is essential to evaluate the spatial and temporal distribution of dust and its changes and effects. Numerous studies have been carried out in the field of evaluating dust changes using different methods. For example, using CMIP5 models, synoptic fog systems are predicted to increase during the 21st century in the Middle East. In addition, using AOD obtained from MODIS and MERRA-2 sensors, researchers showed a significant upward trend in dust changes from 2000 to 2010. A significant upward trend was shown in Iran's winter AOD values during the period 2000 to 2010, and a decreasing trend during the period 2010 to 2018. A point can be spotted by examining various studies in the Middle East and Iran that evaluate the spatio-temporal changes of dust. Statistical tests of time series study, such as Mann-Kendall spatially and pixel by pixel, have been used in limited research to evaluate the trend of dust changes. In Iran, there is a research gap in not using spatial and pixel-by-pixel statistical tests to evaluate the trend of dust changes, as stated. This research aimed to provide a solution and address the problem by analyzing the spatial and temporal changes of dust using the AOD index in the eastern half of the country.

 In this research, in order to evaluate the temporal-spatial changes of dust, the AOD data of the blue band (470 nm) of the MCD19A2 product of the MODIS sensor was used. AOD parameter is known as one of the most key factors in studying the climatic effects of aerosols and atmospheric pollution. In order to extract AOD data, monthly data from 2001 to 2022 were obtained in the Google Earth Engine system by averaging the daily AOD data. Over a 22-year period, the average of each month was calculated. The months that had the highest average AOD values were chosen and their changes were evaluated. In this research, the Mann-Kendall test was used to evaluate the change process. Menkendall's ZM coefficient was calculated for months in the Earth Trend Modeler (ETM) of the TerrSet software to achieve this. In the next step, the intensity of monthly AOD changes per time unit was calculated for 22 years in selected months. To simulate the process of changes, linear regression analysis can be utilized for this purpose. This method is used to determine the linear relationship between all the data of a dependent variable and the corresponding data of the independent index. If the slope is higher than zero, the dependent variable will change in the same direction as the independent variable. The dependent variable changes in the opposite direction of the independent variable if the slope is smaller than zero. The steeper the slope of changes, the greater the impact of the independent variable on the dependent variable. The Earth Trend Modeler (ETM) of TerrSet software also carried out this step.

Based on the evaluation of the monthly average AOD changes in the studied area, the trend and intensity of AOD changes from 2001 to 2022 were assessed in April, May, June, and July. In most areas of the studied area, AOD is increasing with a probability of more than 70%, and the intensity of changes is mostly high and very high in April. It can be concluded that AOD is experiencing a strong increase in April. This is despite the fact that in May, June and July, respectively, a considerable part of the western half, northern half and eastern half is increasing with different intensities with a probability of more than 70%. It can be concluded that the trend and intensity of AOD changes in the above-mentioned months follow a different spatial pattern. The dispersion of dust production centers inside and outside Iran, and the local and regional synoptic conditions governing dust production centers is the cause of changes in the spatio-temporal patterns of dust storms. The unprincipled extraction of water resources by humans, land degradation, soil moisture reduction, and the loss of vegetation due to climate change all affect these factors in turn. The results showed that the monitoring of monthly average AOD changes can help to identify new hotspots and evaluate the results of wind and dust erosion control and management activities. Therefore, it can be suggested that a system based on remote sensing must be designed and presented to monitor dust changes, so that the management of the dust phenomenon in Iran becomes more. We need to pay attention to the factors that influence these changes and evaluate their impact on the dust phenomenon.  On the other hand, by modeling the environmental factors affecting on the trend of dust changes in each region by using methods such as dust evaluation, it is possible to determine the role of each factor and the most important factor affecting the trend of dust changes in each region.

Due to the prevailing arid and semi-arid climate, Iran has always suffered from the production and spread of airborne substances, especially dust. This phenomenon is very destructive due to its direct impact on the environment and human health. Therefore, this study aimed to investigate the variation trend of aerosol optical depth (AOD), normalized difference vegetation index (NDVI), rainfall, temperature, wind speed, and correlation between AOD and climatic parameters in Iran. Data on AOD and NDVI were collected from Modis sensor images and other data from the ECMWF climate site over 2000-2019 with a spatial resolution of 10 km. The Mann-Kendall trend analysis was used to investigate the trend of changes in these parameters, and the linear correlation model was used to check the correlation between AOD and climatic data. The results of the variations trend of AOD, NDVI, and climate data showed that the trend of these parameters was different in different regions of Iran so that NDVI and rainfall had decreased by 85.13% and 67.57%, respectively, while AOD, wind speed, and the temperature had increased by 71.43%, 71.86%, and 99.37% across the country, respectively. The correlation of AOD, NDVI, rainfall, temperature and wind speed revealed that AOD had a negative correlation with NDVI and rainfall in 50.94% and 51.31% of the country, respectively, while its correlation was positive with temperature and wind speed over 68.42% and 50.36% of the country, respectively. Therefore, the increase in airborne suspended particles strongly depends on the trend of variations in vegetation cover and climatic factors, including rainfall, temperature, and wind speed, which can be well studied using satellite and climatic data with appropriate spatial and temporal resolution.

Wind erosion is one of the problems in arid and semi-arid regions that is associated with the process of detachment and transportation of soil particles by wind. The Wind Erosion Risk Index is a framework for modelling wind erosion that examines the sensitivity of land to wind erosion based on a set of surface and climate thresholds. The purpose of the study is to assess the risk of wind erosion in various geobiofaces at Abarkouh plain, Yazd province, using the WEHI model from 2003 to 2017. Three factors including frequency of erosive winds, percentage of bare land and soil surface moisture were used to run the model. Wind erosion risk map was produced under three categories: low, moderate and severe. To evaluate the effectiveness of the model, aerosol optical depth (AOD) data were used that confirmed the accuracy and significance of the model (α=0.01). The results showed that 107,369 ha of lands in the severe wind erosion category was increased. Wind erosion is widespread in the study area, particularly in the geobiofaces of the salt-lake and saline lands. The risk of wind erosion is reduced only on near-dense farmlands. This may expose the land upstream of the study area to the hazards of salt storms given biological, economic and social threats.

Dust phenomenon is one of the natural disasters that is considered as a serious environmental hazard, especially in arid and semi-arid regions due to the great damage it causes every year. The present study aimed to investigate the relationship between 14 climatic variables with the maximum monthly aerosol optical depth (AOD) due to the dust events in Hormozgan province. First, by coding in the Google Earth Engine (GEE) environment, a satellite image was retrieved from the MODIS aerosol products for each day, and while preparing the AOD time series, the average maximum monthly dust values for a 17-year period (2000-2017) was extracted. Also, monthly climate and water balance products of University of Idaho including actual and reference evapotranspiration, minimum and maximum temperature, precipitation accumulation, soil moisture, Palmer drought severity index, climate water deficit, downward surface shortwave radiation, vapor pressure, vapor pressure deficit, and wind speed, as well as land surface temperature (LST) and vegetation index (EVI) were extracted and, while sampling these images, the relationship between the average maximum monthly dust values with them were computed using the ordinary least squares (OLS) and geographic weighted regression (GWR) methods. Then, the global Moran's I statistics was employed to analyze the spatial autocorrelation and distribution of dust over the province. The results showed that the GWR model with the root mean square error of 0.14, the sum of residual squares of 11.3, the coefficient of determination of 0.82, and the corrected Akaike information criterion of -570.19, performed better than the OLS method. The evaluation of the coefficients of the GWR model showed that the variables of vegetation cover, soil moisture and precipitation had the greatest effect on the amount of dust, respectively. Also from the perspective of spatial autocorrelation, a cluster pattern was observed for dust distribution over the province.

Specific climatic and geographical conditions of Yazd province have always exposed this region to a severe storm, and air pollution. Low visibility is the primary impact of air pollution due to atmospheric phenomena. Increasing dust density, along with a severe decrease in visibility, has harsh effects on the health of living things, socio-economic sections, and transportation systems. Therefore, identifying hazardous areas considering low visibility during dust events is important to manage and conserve the health of area residents. This study aims to evaluate the trend of changes in horizontal visibility of areas affected by dust storms in Yazd province, Iran. For this purpose, the quantitative model of horizontal visibility based on the relationship between Aerosol Optical Depth (AOD) of MODIS sensor derived from Deep Blue algorithm and horizontal visibility data derived from meteorological stations in the study area was introduced the horizontal visibility of dust events from 2015 to 2017 was mapped using geostatistics Kriging method. Results showed a significant relationship between aerosol optical depth and horizontal visibility data at the 99% level. The AOD value ranges from 0.1 in areas with no dust to 1.9 in areas with severe dust at six studied events, and the visibilities coincide with AOD values from 64 m in dust centers to 19951 m beyond these centers were estimated. Therefore, AOD image measures with more than 0.8 have horizontal visibility less than 1000 m. Determining of horizontal visibility extent without spatial limitation and identification of areas affected dust events are advantages of the quantitative model of horizontal visibility and its mapping. According to land cover/use map in Yazd province, mineral and constructing sites, high-density dirt roads, sand dunes, poor rangelands are the most effective factors in increasing of dust density and low horizontal visibility in pathways of dust storms of this province.

In recent years, frequency and intensity of dust storms have been increased because of human destructive activities and caused significant loss in different aspects of hygienic and health, environmental and socio-economic sections. Therefore, detection and trace of dust storms in shortest time is the first effective step in preparation and implementation of strategic and operational plans in regions where are affected by dust storms. Using of remote sensing techniques in running of detector algorithms of dust storm plumes via satellite images are efficient methods to detect dust storm events, especially in large areas presented by researchers who study in this field. In this research has been tried to compare the most important algorithms to detect dust plumes and introduce the most suitable algorithm for the Yazd province suffered dust storm events and their loss.  To detect dust storms, the events with wind speed of more than 10 m/s and horizontal visibility of less than 1000 m were identified using meteorological synoptic stations data in the study area during 2010-2015. In the following, six common algorithms for detection of dust plume including Ackerman, Miller, Roskovensky and Liou, Thermal Infrared dust index (TDI), Thermal Infrared Integrated Dust Index (TIIDI) and Normalized Difference Dust Index (NDDI) were examined via satellite data of MODIS sensor in four dust storm events. In order to evaluate the accuracy assessment of dust maps, relation between the produced maps and horizontal visibility records of the meteorological stations located in the study area, as well as, Aerosol Optical Depth (AOD) of dust based on deep blue algorithms were analyzed.   Results showed that Roskovensky and Liou, Thermal Infrared dust index (TDI) and Thermal Infrared Integrated Dust Index (TIIDI) had better accuracy and precision to detect dust storm events based on aerosol optical depth (AOD) maps and horizontal visibility records of the study area. According to the results, TDI and TIIDI algorithms had better performance to detect dust plume in 03/02/2011 event. Correlation of TDI and TIIDI maps with AOD were 0.65 and 0.49, respectively, which were significant at 1% level, while, Roskovenskey and Liou algorithm presented more suitable result to detect dust storm in 10/02/2015 event. Correlation of Roskovensky and Liou map with AOD and horizontal visibility records were 0.68 and 0.76, respectively, which were significantly at 1% and 5% levels, respectively. In 13/04/205 and 20/07/2015 events, TIIDI and TDI algorithms showed better performance to detect dust plumes. Correlation of TIIDI with AOD was 0.81 which was significant at 5% level. Also, the correlation between TDI and horizontal visibility records was 0.71 which was significant at 1% level.Discussion and Some dust storm events are analyzed by remote sensing techniques via methods such as indices and algorithms related to dust detection, interpretation of false color composite and brightness temperature difference of features and phenomena in satellite images. These methods are very efficient to detect dust especially, the dust plumes which their visual detection are difficult because of low density. However, the results of indices of dust detection are different, because of various chemical composite of dust particles and resulted in different spectral and thermal properties in different regions. According to the results, none of the algorithms could detect dust plumes in all events. They were just able to detect dust plumes in one or two events. However, the algorithms that used thermal bands or combination of thermal and reflective bands in their equations had been more effective to detect dust storm. To have better dust storm detection, using threshold ranges according spectral and thermal properties are required in each region and even in each event. Using quantities data of aerosol optical depth of dust as well as horizontal visibility records of meteorological stations are suitable tools to adjust to the real situation and analyze accuracy assessment of dust maps made by different algorithms.