Spatiotemporal variations of internal dust events in urban environments of Iran, Southwest Asia.

This article investigates the Dust Storm Index (DSI) and its trend using the Mann-Kendall test, across urban areas of Iran on the monthly, seasonally, and annually scales from 2000 to 2018. The results showed that cities located in the humid region, especially Khoram Abad and Avaj, had the lowest DSI values, and the cities located in arid regions, particularly Zabol, Sarakhs, and Zahedan, had the highest DSI values during the study period. On a monthly basis, the positive trends were observed in most cities of Iran in March, October, and August, while the negative trends were mainly observed in Feb, May, and June.

Heavy metal mapping, source identification, and ecological risk assessment in the International Hamoun wetland, Sistan region, Iran.

This study is aimed at assessing the ecological risk of heavy metals (HMs) in the International Hamoun wetland, southeastern Iran. Twenty sediment samples were collected from the wetland surface for geochemical analysis of 23 HMs. The inverse distance weighting (IDW) technique was used to map the HMs.

Monitoring the water surface of wetlands in Iran and their relationship with air pollution in nearby cities.

Understanding the impact of wetland water area (WWA) fluctuations on air pollution in nearby cities is of great environmental importance. This study is the first effort for investigating the WWA changes in Iran and their impacts on air pollution in the surrounding cities during different seasons. Three-hourly data related to wind speed, wind direction, and horizontal visibility recorded in meteorological stations around Iranian wetlands were used to identify cities located in the direction of dusty winds blown from shrinking wetlands in Iran.

Predicting the dust events frequency around a degraded ecosystem and determining the contribution of their controlling factors using gradient boosting-based approaches and game theory.

This study was aimed to evaluate the performance of gradient boosting machine (GBM) and extreme gradient boosting (XGB) models with linear, tree, and DART boosters to predict monthly dust events frequency (MDEF) around a degraded wetland in southwestern Iran. The monthly required data for a long-term period from 1988 to 2018 were obtained through ground stations and satellite imageries. The best predictors were selected among the eighteen climatic, terrestrial, and hydrological variables based on the multicollinearity (MC) test and the Boruta algorithm.

Decoupling pollution-agricultural growth and predicting climate change impacts on decoupling index using Bayesian network in different climatic regions.

Applying the principles of healthy products through agriculture practices has become an important issue due to significant environmental impacts of agrochemicals application. The agrochemicals have been recognized as an essential component of modern agriculture, but they are also an important source of environmental pollution that threatens the human's health and are main sources of carbon emissions. Pesticides and fertilizers application are important in the process of Iran's food production.

Dust storm index anomaly for sand-dust events monitoring in western Iran and its association with the NDVI and LST anomalies.

Sand-dust events (SDE) are an increasing concern in many arid and semi-arid regions of the world, which have severely damaged air quality and human health in recent years. This study was conducted to monitor the SDE in western Iran using the dust storm index anomaly (DSIA) during 2000-2018. The spatio-temporal change detection and statistical analysis were used to understand the impacts of normalized difference vegetation cover anomaly (NDVIA) and land surface temperature anomaly (LSTA) on the SDE activities.

Using multivariate adaptive regression splines and extremely randomized trees algorithms to predict dust events frequency around an international wetland and prioritize its drivers.

This study aimed to evaluate the performance of multivariate adaptive regression splines (MARS) and extremely randomized trees (ERT) models for predicting the internal and external dust events frequencies (DEF) across the northeastern and southwestern regions of the Gavkhouni International Wetland. These models were also evaluated to model the internal DEF (IDEF) across the northwestern, southeastern, northern, and western regions around the wetland. Furthermore, the main factors controlling DEF and IDEF were identified based on the importance value (IV) of predictors in the best model.

Vegetation response to changes in temperature, rainfall, and dust in arid environments.

Climate change is responsible for changes in the world's vegetation. This study was aimed to investigate the effect of long-term variations in the air temperature, precipitation, and dust concentration on normalized difference vegetation index (NDVI) variations in the spring, summer, and growing season over arid regions of Iran. The results showed that the precipitation had a positive association with the NDVI in the spring and growing seasons (β > + 0.

Accuracy, uncertainty, and interpretability assessments of ANFIS models to predict dust concentration in semi-arid regions.

Accurate prediction of the dust concentration (DC) is necessary to reduce its undesirable environmental effects in different geographical areas. Although the adaptive neuro-fuzzy inference system (ANFIS) is a powerful model for predicting dust events, no attempt has been made to investigate its uncertainty and interpretability. In this study, therefore, the uncertainty of the ANFIS model was quantified using uncertainty estimation based on local errors and clustering methods.