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The current study primarily sought to select the best index for elaborating on dust activities, analyze the temporal and spatial changes of the index’s trend, and examine the relationship of the index with environmental factors in Iranian dry regions. To this end, the study examined the data collected on dust concentration, dust storm index, the number of dusty days (NDD), the pollution caused by dust storms, and the frequency of all dust events over a period of 18 years (2001-2018) using the MODIS-aerosols optical depth (AOD) product. Moreover, Pearson's correlation coefficient was used to analyze the correlation between the indices and AOD data sets. On the other hand, the trend of the best index annual changes in twenty-eight Iranian urban areas was analyzed using the Mann-Kendall method. Also, the most important environmental factors controlling dust activities in high-risk areas were identified using the random forest model. The results of the study indicated a strong correlation between NDD and AOD in Iranian dry regions (r= 0.7; p-value= 0.001). It was also found that the trend of NDD’s annual changes significantly increased in Torbat Heydarieh, Nehbandan, and Anar (Z>+1.96). However, the trend significantly decreased in Chabahar and Iranshahr (Z>│-1.96│. Generally, the results indicated an insignificant decreasing trend of annual NDD changes across the entire Iranian arid regions from 2001 to 2018 (Z= -0.45). on the other hand, the random forest model suggested that air pressure and wind speed exerted the greatest influence on dust activities that occurred in Iran’s high-risk area throughout the study period. Therefore, it could be argued that the findings of this study can help better monitor dust events and reduce their environmental risks in Iranian dry areas. [ABSTRACT FROM AUTHOR]

This study was carried out to determine whether the changes in dust concentration (DC) in Iran were attributed to changes in aridity index (AI) from 1990 to 2018. Long-term precipitation and potential evapotranspiration were used to compute the United Nations Environment Program AI and horizontal visibility data were used to calculate the DC in the study area. The DC spatial distribution maps were prepared to better identify areas affected by sand-dust events. Pearson's correlation analysis was performed to explore the association between DC and AI in Iran and study stations during the monitoring period. Spatiotemporal variations in DC showed that more than 60 percent of Iran's area experienced severe wind erosion events in 2001, 2008, and 1990 when AI was less than 0.2. The results also showed that approximately 45 percent of Iran's area has suffered from the average DC over the study period (565 µg/m3). The incremental changes in DC were observed in almost half of the entire area of Iran during the second period (2000–2008) and one third of this area during the third period (2009–2018). Areas affected by decreasing changes in AI during the second and third periods were 90 percent and 73 percent of the area affected during the previous period, respectively. Overall, there was a significant negative correlation between AI and DC in Iran over the monitoring period (r = −0.51, p value < 0.01). Insignificant positive correlations were observed in humid regions, whereas negative correlations were mostly detected in arid and semiarid regions of Iran. [ABSTRACT FROM AUTHOR]

With intense human activity causing constant environmental change, there is a greater need than ever to have accurate and frequently updated soil information. Traditional soil maps are large‐scale polygon maps of soil type and implicitly assume no variation in soil properties within polygons. DSM approaches can increase the accuracy of modelling. Therefore, the current study was conducted to evaluate the applicability of a classic adaptive neuro‐fuzzy inference system (ANFIS) and three hybridized ANFIS models (i.e. particle swarm optimization—PSO, the genetic model—GA and the artificial bee colony model—ABC) for predicting eight soil properties in western Iran. The ANFIS+ABC and ANFIS+PSO approaches showed the best performance in predicting soil properties such as soil organic carbon (SOC) and total nitrogen (N). The ABC+ANFIS‐hybrid model increased R2 values by 0.32, 0.24, 0.23 and 0.37 and decreased RMSEs by 11%, 32.7 mg kg−1, 0.08 mmhos cm−1 and 0.3% when predicting SOC, K, EC and SP, respectively, compared with the ANFIS model alone. Also, the use of the PSO+ANFIS‐hybrid model led to an increase in R2 values of 0.44, 0.29, 0.34 and 0.36 and a decrease in RMSEs of 0.02, 0.29 ppm, 0.34% and 0.36% for predicting N, P, pH and TNV, respectively, compared with the ANFIS model alone. In general, the ANFIS+ABC and ANFIS+PSO improved the prediction accuracies for SOC and N by 32% and 24%, respectively, compared with the classic ANFIS model. As a whole, our findings demonstrated the ability of hybrid models for improving the accuracy of the ANFIS approach for predicting soil features in western Iran, which supports our main hypothesis. [ABSTRACT FROM AUTHOR]

Wind erosion is a serious environmental issue caused by the interaction between various climatic and terrestrial factors. Using the ridge regression (RR) method, the present study aimed to investigate the relationship between these parameters and dust storm index (DSI) in arid regions of Central Iran during 2000–2014. The removal area of soil particles in the dustiest season was further detected via sand and dust roses. The results showed that the highest activity of wind erosion occurred in spring (DSI > 1). The RR analysis indicated that only the air temperature changes had a significant positive effect on DSI variations during the first period (β = +0.38; p <.05). For the second period, decreased precipitation and increased wind velocity were identified as the most important factors influencing the DSI variations. The estimated regression coefficients concerning precipitation and wind speed at 95% confidence level were (−1.02) and (+0.51), respectively. Based on the R2 value, 27% of the DSI changes could be explained by the air temperature variations in the first period. However, approximately 50% of the DSI variations were explained by the rainfall and wind speed changes in the second period. These results illustrate the effective application of RR to elucidate the relationship of climatic and terrestrial factors with DSI. It was further observed that the removal area of soil particles in the northeastern, southeastern, and western parts of Central Iran was easterly, northerly, and westerly, respectively. [ABSTRACT FROM AUTHOR]