Globally, the COVID-19 pandemic is a top-level public health concern. This paper attempts to identify the COVID-19 pandemic in Qom and Mazandaran provinces, Iran using spatial analysis approaches. This study was based on secondary data of confirmed cases and deaths from February 3, 2020, to late October 2021, in two Qom and Mazandaran provinces from hospitals and the website of the National Institute of Health. In this paper, three geographical models in ArcGIS 10.8.1 were utilized to analyze and evaluate COVID-19, including geographic weight regression (GWR), ordinary least squares (OLS), and spatial autocorrelation (Moran I). The results from this study indicate that the rate of scattering of confirmed cases for Qom province for the period was 44.25%, while the rate of dispersal of the deaths was 4.34%. Based on the GWR and OLS model, Moran’s statistics demonstrated that confirmed cases, deaths, and recovered followed a clustering pattern during the study period. Moran’s Z-score for all three indicators of confirmed cases, deaths, and recovered was confirmed to be greater than 2.5 (95% confidence level) for both GWR and OLS models. The spatial distribution of indicators of confirmed cases, deaths, and recovered based on the GWR model has been more scattered in the northwestern and southwestern cities of Qom province. Whereas the spatial distribution of the recoveries of the COVID-19 pandemic in Qom province was 61.7%, the central regions of this province had the highest spread of recoveries. The spatial spread of the COVID-19 pandemic from February 3, 2020, to October 2021 in Mazandaran province was 35.57%, of which 2.61% died, according to information published by the COVID-19 pandemic headquarters. Most confirmed cases and deaths are scattered in the north of this province. The ordinary least squares model results showed that the spatial dispersion of recovered people from the COVID-19 pandemic is more significant in the central and southern regions of Mazandaran province. The Z-score for the deaths Index is more significant than 14.314. The results obtained from this study and the information published by the National Headquarters for the fight against the COVID-19 pandemic showed that tourism and pilgrimages are possible factors for the spatial distribution of the COVID-19 pandemic in Qom and Mazandaran provinces. The spatial information obtained from these modeling approaches could provide general insights to authorities and researchers for further targeted investigations and policies in similar circumcises.

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Due to the gradual drying of parts of Urmia Lake, several centers of salt dust which is created and influence the agriculture, economy, public health and causes migrations within the region of the Urmia Lake. Hence, knowing the temporal and spatial distribution of this phenomenon is very important to quantify these effects. In the present study, using Sentinel-5 and MODIS products for 2020 in the Google Earth system, have shown despite month-to-month fluctuations, has an increasing trend and the incremental changes of fine dust are more considerable in May and June and their dispersal are greater in the northern and northwestern cities in the basin of the Urmia Lake. The distribution of fine dust in the cities of Tabriz, Shabestar, Urmia, Mahabad, Khoy, Salmas, and Tabak, shows heavy concentrations of the dusts, and exhibits destructive impacts on the economy (60.80%) in December and also has adverse effects on the health index. And most of the referrals of people suffering from diseases caused by fine dust in December is (47.50%). The two indicators of agriculture and migration are closely related and the most effects of salt dust for these two indicators showed (15%) in November and (40.51%) in July, respectively. According to the results, it can be said that these dust particles have the greatest impact on the indices (migration, economy, agriculture, and health) of urban regions of the basin of Urmia Lake from 2019 to 2020. The results of this study can directly contribute to the decision-making process by the local authorities to understand the environmental problems across urban and rural areas of Urmia lakes which is at considerable risk.

Vegetation, precipitation, and surface temperature are three important elements of the environment. By increasing the concerns about climate change and global warming, monitoring vegetation dynamics are considered to be crucial. In this study, the cross-relationship between vegetation, surface temperature, and precipitation, and their fluctuations over the past 21 years are evaluated. Day time LST from Terra sensor of MODIS, nir and red bands of Landsat 7 ETM+ and Landsat 8 OLI, and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) are used in this research. Data were evaluated and processed using the google earth engine cloud processing platform. According to the results, it was concluded that the correlations between the annual average of normalized difference vegetation index and precipitation are not significant. Evaluation of the cross-seasonal correlations exhibited the availability of the strong and significant correlation with a value of r2 = 0.82 between vegetation thickness and precipitation, during the spring and summer, especially from April to August. Moreover, surface temperature exposed an inverse correlation with precipitation and NDVI with the values of r2= 0.776 and r2= 0.68 respectively, these relationships are highly significant. According to the results of this study, vegetation declined sharply in particular years, and this decrease occurred due to insufficient rainfalls.