Environment
A. Yousefi Kebriya; M. Nadi
Abstract
The Air Quality Index is a numerical tool used to quantify air pollution levels and classify pollution severity. It plays a vital role in ensuring healthcare system stability by understanding air pollution's dynamic behavior and shifts in pollution intensity. To analyze the probabilistic transition between ...
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The Air Quality Index is a numerical tool used to quantify air pollution levels and classify pollution severity. It plays a vital role in ensuring healthcare system stability by understanding air pollution's dynamic behavior and shifts in pollution intensity. To analyze the probabilistic transition between pollution severity levels, a Markov Chain model was utilized. This study examined six air pollution states (Clean, Healthy, Unhealthy for Sensitive Groups, Unhealthy, Very Unhealthy, Hazardous) across 12 stations in Tehran's northern, southern, eastern, western, and central regions from 2018 to 2022. Results revealed that the western and southern areas exhibited the highest pollution levels, with over 44% and 34% of instances corresponding to unhealthy indices, respectively. In contrast, northern Tehran consistently maintained cleaner air quality. Probability transition matrices highlighted the greatest stability continuity in healthy air quality across all regions. Transitioning between clean/healthy air to very unhealthy/hazardous air was minimal, with hazardous air quality almost absent in most stations, except for the west and south (25% stability). The probability of continued unhealthy air quality in these areas reached 60%, indicating heightened pollution. The findings of transition probability matrices indicated that the western and southern regions had the highest likelihood of sustained pollution, while the northern region consistently maintained cleaner air. The probability of continuous clean air in the west was below 30%, while transitioning from very unhealthy/hazardous air to unhealthy air reached 50%. Conversely, the northern Tehran region exhibited over 40% stability for unhealthy air quality and over 50% for clean and healthy air.
Environment
Z. Poormolaie; M. Mohammadi; M. Ghafoori; E. Khayyami
Abstract
The aim of this study was to determine the air quality index (AQI) and to investigate its relationship with meteorological parameters in Mashhad for 2014. In this study, moment concentrations of air pollutants in Mashhad for 2014 were prepared and the amount of AQI was calculated and air quality was ...
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The aim of this study was to determine the air quality index (AQI) and to investigate its relationship with meteorological parameters in Mashhad for 2014. In this study, moment concentrations of air pollutants in Mashhad for 2014 were prepared and the amount of AQI was calculated and air quality was determined. Then, data analysis was performed using Kruskal-Wallis and Mann-Whitney tests at a significant level of 5% in SPSSV.23 software. Finally, data related to meteorological parameters were prepared during 2014 and ARIMA time series model and R software (3.3.0) were used to investigate its relationship with air index pollutants in non-delayed and one day late modes. The results showed that air quality of Mashhad was in a very bad condition in terms of maintaining the health of community members, especially sensitive groups, as the concentration of pollutants in this city was higher than Iranian standard (100) in 245 days of the study period. The PM2.5 was the most important pollutant during the study. It was also found that among the climatic parameters, temperature and pressure have the greatest direct effect on the concentration of air pollutants. Moreover, results showed the immediate effect of temperature on the concentration of air pollutants, although other atmospheric elements are able to significantly affect the outcome over time and with a time delay (one day in this study). The results indicated that quality of model computation depends on changes in atmospheric parameters, so that a quantitative measurement for each pollutant can be achieved based on meteorological data.
