No abstract

BackgroundDeserts are the main sources of emitted dust, and are highly responsive to wind erosion. Low content of soil moisture and lack of vegetation cover lead to fine particle’s release. One of the semi-arid bare lands in Iran, located in the South-West of Iran in Khoozestan province, was selected to investigate Sand and Dust storm potential.MethodsThis paper focused on the metrological parameters of the sampling site, their changes and the relationship between these changes and dust storm occurrence, estimation of Reconaissance Drought Index, the Atterberg limits of soil samples and their relation with soil erosion ability, the chemical composition, size distribution of soil and airborne dust samples, and estimation of vertical mass flux by COMSALT through considering the effect of saffman force and interparticle cohesion forces during warm period (April–September) in 2010. The chemical compositions are measured with X-ray fluorescence, Atomic absorption spectrophotometer and X-ray diffraction. The particle size distribution analysis was conducted by using Laser particle size and sieve techniques.ResultsThere was a strong negative correlation between dust storm occurrence and annual and seasonal rainfall and relative humidity. Positive strong correlation between annual and seasonal maximum temperature and dust storm frequency was seen. Estimation of RDIst in the studied period showed an extremely dry condition. Using the results of particle size distribution and soil consistency, the weak structure of soil was represented. X-ray diffraction analyses of soil and dust samples showed that soil mineralogy was dominated mainly by Quartz and calcite. X-ray fluorescence analyses of samples indicated that the most important major oxide compositions of the soil and airborne dust samples were SiO2, Al2O3, CaO, MgO, Na2O, and Fe2O3, demonstrating similar percentages for soil and dust samples. Estimation of Enrichment Factors for all studied trace elements in soil samples showed Br, Cl, Mo, S, Zn, and Hg with EF values higher than 10.ConclusionThe findings, showed the possible correlation between the degree of anthropogenic soil pollutants, and the remains of Iraq-Iran war. The results expressed sand and dust storm emission potential in this area, was illustrated with measured vertical mass fluxes by COMSALT.

Environmental pollution has mainly been attributed to urbanization and industrial developments across the globe. Air pollution has been marked as one of the major problems of metropolitan areas around the world, especially in Tehran, the capital of Iran, where its administrators and residents have long been struggling with air pollution damage such as the health issues of its citizens. As far as the study area of this research is concerned, a considerable proportion of Tehran air pollution is attributed to PM10 and PM2.5 pollutants. Therefore, the present study was conducted to determine the prediction models to determine air pollutions based on PM10 and PM2.5 pollution concentrations in Tehran. To predict the air-pollution, the data related to day of week, month of year, topography, meteorology, and pollutant rate of two nearest neighbors as the input parameters and machine learning methods were used. These methods include a regression support vector machine, geographically weighted regression, artificial neural network and auto-regressive nonlinear neural network with an external input as the machine learning method for the air pollution prediction. A prediction model was then proposed to improve the afore-mentioned methods, by which the error percentage has been reduced and improved by 57%, 47%, 47% and 94%, respectively. The most reliable algorithm for the prediction of air pollution was autoregressive nonlinear neural network with external input using the proposed prediction model, where its one-day prediction error reached 1.79 µg/m3. Finally, using genetic algorithm, data for day of week, month of year, topography, wind direction, maximum temperature and pollutant rate of the two nearest neighbors were identified as the most effective parameters in the prediction of air pollution.

Globally, NO2 and PM2.5 declined while O3 increased during strict lockdown periods.

This study investigates the changes of short-lived climate pollutants and other air pollutants during the COVID-19 pandemic in Tehran, Iran. Concentrations of air pollutants were obtained from 21 monitoring stations for the period from 5 January 2019 to 5 August 2019, representing normal conditions unaffected by COVID-19, and the period 5 January 2020 to 5 August 2020, i.e., during the COVID-19 crisis. We concentrated our analysis on three time windows (23 February 2020 to 15 March 2020, 18 March 2020 to 3 April 2020, and 5 April 2020 to 17 April 2020) during the lockdown when different sets of measures were taken to limit the spread of COVID-19. In comparison to the period not affected by COVID-19 measures, mean concentrations of pollutants were increased during the first lockdown period; when the number of COVID-19 patients increased sharply compared to the other periods, the mean surface concentrations of NO 2 , SO 2 , and CO were decreased and concentrations of other pollutants (i.e., O 3 , PM 10 , and PM 2.5 ) were increased during the second lockdown period compared to the corresponding period in 2019. In the third period, the mean concentrations were decreased compared to the corresponding period in 2019. For the full period, decreases in mean concentrations of O 3 , NO 2 , SO 2 , CO, and PM 10 and increases in PM 2.5 were observed during the COVID-19 crisis, compared to 2019. Overall, the strongest reductions, 12% and 6%, respectively, were observed for CO and NO 2 , pointing to reduced emissions from traffic as a result of lockdown measures. The concentrations of other pollutants changed little, suggesting that the lockdown measures did not result in strong changes in the emissions from stationary sources.