Seasonal variation in ambient PM mass and number concentrations (case study: Tehran, Iran)

Halek, F.; Kianpour-rad, M.; Kavousirahim, Ali

doi:10.1007/s10661-009-1192-2

Cited by 28 publications

(19 citation statements)

References 19 publications

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“…Farah Halek et al (2010) reported PM 2.5 concentrations at four stations in the northwest of Tehran and found higher accumulation of particles during warmer seasons compared to cold seasons and the total mean PM 2.5 concentration was reported as 210.5 µg.m -3 while it was 100 µg.m -3 in the present study. This difference could be due to lower sample size and different study areas that some of them may not be representative of the whole city (22). AditiKulshresta et al (2009) studied seasonal variations of particle concentration in Agra, India, and reported 104.9 µg.m -3 PM 2.5 concentrations in urban areas, which was equal to the mean concentration of the pollutant in the present study.…”

Section: Resultscontrasting

confidence: 54%

Application of Poisson Hidden Markov Model to Predict Number of PM2.5 Exceedance Days in Tehran During 2016-2017

Sarvi

Nadali

Khodadost

et al. 2017

Avicenna J Environ Health Eng

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PM2.5 is an important indicator of air pollution. This pollutant can result in lung and respiratory problems in people. The aim of the present study was to predict number of PM2.5 exceedance days using Hidden Markov Model considering Poisson distribution as an indicator for people susceptible to that particular level of air quality. In this study, evaluations were made for number of PM2.5 exceedance days in Tehran, Iran, from Oct. 2010 to Dec. 2015. The Poisson hidden Markov model was applied considering various hidden states to make a two-year forecast for number of PM2.5 exceedance days.We estimated the Poisson Hidden Markov’s parameters (transition matrix, probability, and lambda) by using maximum likelihood method. By applying the Akaike Information Criteria, the hidden Markov model with three states was used to make the prediction. The results of forecasting mean, median, mode, and interval for the three states of Poisson hidden Markov model are reported. The results showed that the number of exceedance days in a month for the next two years using the third state of the model would be 5 to 16 days. The predicted mode and mean for the third months afterward at the third state were 11 and 11. These predictions showed that number of exceedance days (predicted mean of 6.87 to 11.39 days) is relatively high for sensitive individuals according to the PM2.5 Air Quality Index. Thus, it is essential to monitor levels of suspended particulate air pollution in Tehran.

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Section: Resultscontrasting

confidence: 54%

Application of Poisson Hidden Markov Model to Predict Number of PM2.5 Exceedance Days in Tehran During 2016-2017

Sarvi

Nadali

Khodadost

et al. 2017

Avicenna J Environ Health Eng

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“…Its inhabitants are generally exposed to high levels of urban background PM due to the large number of anthropogenic sources and the unique geographic characteristics of a city surrounded by a high altitude mountain chain on its boundary, downstream of the prevailing wind Halek et al, 2010;Sabetghadam et al, 2012) Previous researches have indicated suspended particles to be one of the causes of the most serious air pollution problems in Tehran (Nabi Bidhendi and Halek, 2007). Limited measurements of particulate matter with respect to its chemical composition are available from Tehran.…”

Section: Introductionmentioning

confidence: 99%

Dry deposition, seasonal variation and source interpretation of ionic species at Abali, Firouzkouh and Varamin, Tehran Province, Iran

Javid

Bahramifar

Younesi

et al. 2015

Atmospheric Research

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“…Temperature inversion is another factor contributing to air pollution, particularly in cold seasons [60]. During these seasons, PM as the major air pollutant of Tehran's air is increased [61]. The annual average of PM 10 [54].…”

Section: Iranmentioning

confidence: 99%

“…This study has shown that, in cold seasons, the PM 10 mass concentration increased almost 2 folds and PM 2.5 and PM 1.0 nearly 3 times. The mean number concentration of these particles was found to be almost 4.8 times in the same season [61]. This could be attributed to the Tehran's geographical position being edged by mountains from the north, and to the fact that the wind is cold and slow, which makes air pollutants become trapped over Tehran [61][62][63].…”

Section: Iranmentioning

confidence: 99%

Outdoor particulate matter (PM) and associated cardiovascular diseases in the Middle East

Nasser¹,

Salameh²,

Nasser³

et al. 2015

Int J Occup Med Environ Health

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Air pollution is a widespread environmental concern. Considerable epidemiological evidence indicates air pollution, particularly particulate matter (PM), as a major risk factor for cardiovascular diseases (CVD) in the developed countries. The main objective of our review is to assess the levels and sources of PM across the Middle East area and to search evidence for the relationship between PM exposure and CVD. An extensive review of the published literature pertaining to the subject (2000-2013) was conducted using PubMed, Medline and Google Scholar databases. We reveal that low utilization of public transport, ageing vehicle fleet and the increasing number of personal cars in the developing countries all contribute to the traffic congestion and aggravate the pollution problem. The annual average values of PM pollutants in the Middle East region are much higher than the World Health Organization 2006 guidelines (PM 2.5 = 10 μg/m 3 , PM 10 = 20 μg/m 3 ). We uncover evidence on the association between PM and CVD in 4 Middle East countries: Iran, Kingdom of Saudi Arabia, Qatar and the United Arab Emirates. The findings are in light of the international figures. Ambient PM pollution is considered a potential risk factor for platelet activation and atherosclerosis and has been found to be linked with an increased risk for mortality and hospital admissions due to CVD. This review highlights the importance of developing a strategy to improve air quality and reduce outdoor air pollution in the developing countries, particularly in the Middle East. Future studies should weigh the potential impact of PM on the overall burden of cardiac diseases.

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Seasonal variation in ambient PM mass and number concentrations (case study: Tehran, Iran)

Cited by 28 publications

References 19 publications

Application of Poisson Hidden Markov Model to Predict Number of PM2.5 Exceedance Days in Tehran During 2016-2017

Application of Poisson Hidden Markov Model to Predict Number of PM2.5 Exceedance Days in Tehran During 2016-2017

Dry deposition, seasonal variation and source interpretation of ionic species at Abali, Firouzkouh and Varamin, Tehran Province, Iran

Outdoor particulate matter (PM) and associated cardiovascular diseases in the Middle East

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