Meteorological correlates and AirQ+ health risk assessment of ambient fine particulate matter in Tehran, Iran

ABSTRACT There are few studies in the world that have been evaluated the mortality due to exposure to particulate matter smaller than 2.5 &mgr;m by AirQ+ software. Therefore, the study aimed to correlate between fine particulate matter (PM2.5) and meteorological variables and estimate all‐cause annual mortality and mortality from cerebrovascular disease (stroke), ischemic heart disease (IHD), acute lower respiratory infection (ALRI), lung cancer (LC), chronic obstructive pulmonary disease (COPD) attributed to long‐term exposure to ambient PM2.5 in Tehran from March 2017 to March 2018 using the WHO AirQ+ software. Data related to air quality, meteorological condition, population and the baseline incidence rates of health endpoints in Tehran were gathered from government agencies. The association between the PM2.5 concentrations and meteorological variables in the period of study were assessed by correlation analysis. The results of correlation analysis showed a weak positive correlation between PM2.5 concentrations and average monthly temperature (r = 0.42, P < 0.05) and average monthly humidity (r = 0.37, P < 0.05) in Tehran. The quantitative risk assessment related to all‐cause annual mortality, the mortality of IHD, stroke, COPD, LC and ALRI were estimated 6710, 3797, 1145, 172, 135 and 27 cases, respectively. The results of regression association analysis between PM2.5 and the number of recorded deaths was showed that with an increase of one microgram per cubic meter of PM2.5, it is expected that about 27 cases will be added to air pollution mortality in Tehran. HighlightsAir pollution is the most important environmental risk factor.Correlations between fine particulate matter (PM2.5) and meteorological parameters in Tehran, Iran was calculated.Linear association between the PM2.5 and the change in all‐cause mortality was calculated.Health risk assessment of PM2.5 in Tehran was estimated by using AirQ+ model.

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