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Background: Assessing the changes in public attitudes toward pandemics is crucial in adapting public health strategies to changing circumstances. This study aims to assess the community's attitude toward the COVID-19 pandemic using the Social Profile Ratio (SPR) method among different occupational groups in Ardabil province. Methods: Employing a cross-sectional survey design, this research assessed the attitudes of 230 respondents toward the COVID-19 pandemic in June 2020. Then, the standard SPR method, along with the Index of Resolution (IR), was utilized to analyze the SPR scores, considering different aspects (prevention, control, treatment, and impacts) of the pandemic. Results: According to the SPR values, the occupational groups had different points of view. The highest life expectancy (30 years) was attributed to employees, students, collegians, and instructors. Notably, employees and university professors had the highest SPR and IR values (9.343 and 1) for the prevention indicator. Regarding the control indicator, the unemployed and university professors had SPR and IR values of 5.36 and 0.733, respectively. The highest SPR value of 44.11 in terms of treatment was attributed to employees, while students had the highest SPR value of 10.03 in terms of impact indicator. Conclusion: The study identified employees, university professors, and the unemployed as the most effective groups across prevention, control, treatment, and impact indicators. [ABSTRACT FROM AUTHOR]

Land degradation is a cause of many social, economic, and environmental problems. Therefore identification and monitoring of high-risk areas for land degradation are necessary. Despite the importance of land degradation, the topic receives often relatively little attention. The present study aims to create a land degradation map in terms of soil erosion caused by wind and water erosion of semi-dry land. We focus on the Lut watershed in Iran encompassing the Lut Desert that is influenced by both monsoon rainfalls and dust storms. Dust sources are identified using MODIS satellite images with the help of four different indices to quantify uncertainty. The dust source maps are assessed with three machine learning algorithms encompassing artificial neural network (ANN), random forest (RF), and flexible discriminant analysis (FDA) to map dust sources paired with soil erosion susceptibility due to water. We assess the accuracy of the maps from the machine learning results with the metric Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC). The maps for water and aeolian soil erosion are used to identify different classes of land degradation risks. The results show that 43 % of the watershed is prone to land degradation in terms of both aeolian and water erosion. Most regions (45 %) have a risk of water erosion and some regions (7 %) a risk of aeolian erosion. Only a small fraction (4 %) of the total area of the region had a low to very low susceptibility for land degradation. The results of this study underline the risk of land degradation for an inhabited region in Iran. Future work should focus on land degradation associated with soil erosion from water and storms in larger regions to evaluate the risks also elsewhere. [ABSTRACT FROM AUTHOR]