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Amongst the various pollutants in the air, particulate matters (PM) have significant adverse effects on human health. The current research is based on existing epidemiological literature for quantitative estimation of the current health impacts related to particulate matters in some selected principal Iranian megacities. In order to find the influence of air pollution on human health, we used the AirQ software tool presented by the World Health Organization (WHO) European Centre for Environment and Health (ECEH), Bilthoven Division. The adverse health outcomes used in the study consist of mortality (all causes excluding accidental causes), due to cardiovascular (CVD) and respiratory (RES) diseases, and morbidity (hospital admissions for CVD and RES causes). For this purpose, hourly PM10 data were taken from the monitoring stations in eight study cities during 2011 and 2012. Results showed annual average concentrations of PM10 and PM2.5 in all megacities exceeded national and international air quality standards and even reached levels nearly ten times higher than WHO guidelines in some cities. Considering the short-term effects, PM2.5 had the maximum effects on the health of the 19,048,000 residents of the eight Iranian cities, causing total mortality of 5,670 out of 87,907 during a one-year time-period. Hence, reducing concentrations and controlling air pollution, particularly the presence of particles, is urgent in these metropolises.

Background: Air pollution is an important issue and public concern throughout the world. Sulfur dioxide is one of the pollutants that can lead to many adverse effects on human health, animal and plant life.

Background and Aims The COVID‐19 pandemic poses an extraordinary threat to global public health. We designed an ecological study to explore the association between socioeconomic factors and the COVID‐19 outcomes in 184 countries, using the geographic map and multilevel regression models. Methods We conducted a cross‐sectional ecological study in 184 countries. We performed regression analysis to assess the association of various socioeconomic variables with COVID‐19 outcomes in 184 countries, using ordinary least squares and multilevel modeling analysis. We performed two‐level analyses with countries at Level 1 and geographical regions at Level 2 in multilevel modeling analysis, using the same set of predictor variables used in ordinary least squares. Results There was a significant relationship between COVID‐19 cases rate (Log) per 100,000 inhabitants‐day at risk with human development index (HDI), percentage of the urban population, unemployment, and cardiovascular disease prevalence. The results displayed that the variances are varied between Level 1 (country level) and Level 2 (World Health Organization [WHO] regions), meaning that the geographic distribution represented a proportion of the changes in the COVID‐19 outcomes. Conclusion The study suggests that in addition to the socioeconomic status affects the COVID‐19 outcomes, countries' geographical location makes a part of changes in outcomes of diseases. Therefore, health policy‐makers could overcome morbidity and mortality in COVID‐19 by controlling the socioeconomics factors.

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