The first case of COVID-19 in Iran was reported on 19 February 2020, 1 month before the Nowruz holidays coincided with the global pandemic, leading to quarantine and lockdown. Many studies have shown that environmental pollutants were drastically reduced with the spread of this disease and the decline in industrial activities. Among these pollutants, nitrogen dioxide (NO2) and carbon monoxide (CO) are widely caused by anthropogenic and industrial activities. In this study, the changes in these pollutants in Iran and its four metropolises (i.e., Tehran, Mashhad, Isfahan, and Tabriz) in three periods from 11 March to 8 April 2019, 2020, and 2021 were investigated. To this end, timeseries of the Sentinel-5P TROPOMI and in situ data within the Google Earth Engine (GEE) cloud-based platform were employed. It was observed that the results of the NO2 derived from Sentinel-5P were in agreement with the in situ data acquired from ground-based stations (average correlation coefficient = 0.7). Moreover, the results showed that the concentration of NO2 and CO pollutants in 2020 (the first year of the COVID-19 pandemic) was 5% lower than in 2019, indicating the observance of quarantine rules, as well as people’s initial fear of the coronavirus. Contrarily, these pollutants in 2021 (the second year of the COVID-19 pandemic) were higher than those in 2020 by 5%, which could have been due to high vehicle traffic and a lack of serious policy- and law-making by the government to ban urban and interurban traffic. These findings are essential criteria that might be used to guide future manufacturing logistics, traffic planning and management, and environmental sustainability policies and plans. Furthermore, using the COVID-19 scenario and free satellite-derived data, it is now possible to investigate how harmful gas emissions influence air quality. These findings may also be helpful in making future strategic decisions on how to cope with the virus spread and lessen its negative social and economic consequences.

The unexpected collapse of land surface due to subsidence is one of the most significant geohazards that threatens human life and infrastructure. Kabudrahang and Famenin are two Iranian plains experiencing several sinkholes due to the characteristics of the underground soil layers and extreme groundwater depletion. In this study, space-based Synthetic Aperture Radar images are used to investigate the ground displacement behavior to examine the feasibility of Sentinel-1 data in detecting precursory deformation proceeding before the sinkhole formation. The selected sinkhole occurred in August 2018 in the vicinity of Kerdabad village in Hamedan province with a 40 m diameter and depth of ~40 m. Time series of the European constellation Sentinel-1 data, spanning from January 2015 to August 2018, is analyzed, and the results revealed a 3 cm annual subsidence (–3cm/year) along with the line-of-sight direction. Time-series analysis demonstrated that the driving mechanism of the sinkhole formation had a gradual process. Displacement of persistent scatterers (PSs) near the cave area had an acceleration by approaching the sinkhole formation date. In contrast, other areas that are far from the cave area show linear subsidence behavior over time. Additionally, the one-kilometer deformation profile over the cave area indicates a high subsidence rate precisely at the location where the sinkhole was formed later on 20 August 2018.

Paintings evoke certain emotions in the viewers. Colors, shape, texture, and many other factors affect the feeling conveyed by paintings, but colors seem to have a stronger effect due to a century-long study of color-emotion association in various fields of psychology, art, and color science. There are many color-emotion theories and most of them have been implemented, however, the Luscher Color Test is untouched amongst them. Based on several reasons, discussed in detail inside the paper, we believe this theory can cover problems in this domain of emotion extraction. The main motivation for choosing the Luscher test was that this method is designed for personality and mood analysis and it can better study abstract paintings. In this paper, a set of paintings from Iranian-Islamic cultural heritage is chosen as a dataset. We have proposed the L-EEP method based on Culture Technology (CT) concept to extract emotions from paintings with image processing techniques and psychology knowledge. This method extracts specific colors from paintings and by performing the Luscher test automatically, is able to determine eight emotions. For this matter, paintings are assessed in two moods: 1. The full extent of the painting 2. Cropped interest area of the painting that attracts more attention. Then, the color palette which is extracted colors ordered based on their coverage extent enters search engine. The search engine performs the searching process in the 3D knowledge base of Luscher color-emotion layers to extract relative values of emotions in both scenarios. For the evaluation of the results, three steps were taken. First, we compared the output results of ancient Persian painting with literature and text of their background stories. Then a viewer evaluation is done to compare the results with human viewpoint. Finally, a set of modern abstract paintings peer-rated in the IAPS standard system to further examine the proposed method. The results of the three forms of evaluation indicate the applicability of the L-EEP approach.

The first cases of Covid-19 in Iran were reported shortly after the disease outbreak in Wuhan, China. The end of the Persian year and the beginning of the Nowruz holidays in the following year (March 2020) coincided with its global pandemic, which led to quarantine and lockdown in the country. Many studies have shown that with the spread of this disease and the decline of industrial activities, environmental pollutants were drastically reduced. Among these pollutants, Nitrogen Dioxide (NO2) and Carbon Monoxide (CO) are widely caused by anthropogenic and industrial activities. In this study, the changes of these pollutants in Iran and its four metropolises (i.e., Tehran, Mashhad, Isfahan, and Tabriz) in three time periods from March 11 to April 8 of 2019, 2020, and 2021 were investigated. To this end, time-series of the Sentinel-5P TROPOMI and in-situ data within the Google Earth Engine (GEE) cloud-based platform were employed. It was observed that the results obtained from the satellite data were in agreement with the in-situ data (average correlation coefficient = 0.7). Moreover, the results showed that the concentration of NO2 and CO pollutants in 2020 (the first year of the Covid-19 pandemic) was 5% lower than in 2019, indicating the observance of quarantine rules as well as people’s initial fear of the Coronavirus. Contrarily, these pollutants in 2021 (the second year of the Covid-19 pandemic) were higher than those in 2020 by 5%, which could be due to high vehicle traffic and the lack of serious policy and law-making by the government to ban urban and interurban traffic. Furthermore, the increase of the NO2 and CO in 2021 was followed by an increase in the deaths caused by Covid-19 and triggering the fourth peak in the Covid-19 cases, signifying a link between exposure to air pollution and Covid-19 mortality in Iran.

No abstract