الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Transport is a vital part of modern life and there are a very massive number of vehicles running in the street emitting huge amounts of air pollutants, which it is closely associated with both the environment and human health road transport (traffic), Is the major contributor to air pollution.
Alexandria is the second-largest city in Egypt. It is one of the major economic centers in Egypt. Its population density is 1,870 pop/Km2 according to the annual report of population estimates of the Central Agency for Mobilization and Statistics in January 2016.
Air quality health index (AQHI) was first developed by Government of Canada. It is a scale designed to help people to understand what the air quality around them means to their health and to give them daily air quality indications of air quality using a scale from 1 to 10+ (from Low to serious). It gives a health messages to both general and at risk population. The Hong Kong equation was used in the present study to calculate the added health risk (AR %) which was then classified into AQHI categories.
AR% = ([e (0.0004462559 × C (NO2) – 1] + [e (0.0001393235 × C (SO2) – 1] + [e (0.0005116328 × C (O3) – 1] + [e (0.0002821751× C (PM10) – 1]) *100
It was developed based on the Canadian experience but comprises the main four pollutants ”Particulate Matter (PM10), Nitrogen Dioxide (NO2), ground-level Ozone (O3), and Sulfur Dioxide (SO2).
Many countries used AQHI to monitor the air pollution and protect people’s health. Unfortunately, it is not used in Egypt. Therefore, it is essential to assess traffic air quality health index (AQHI) to test its applicability on one of the Alexandria streets.
Aim was to assess traffic air quality health index on the selected streets in Alexandria, Egypt. This aim was achieved by assessing the three-hour traffic PM10, O3, SO2, and NO2 concentrations all over the year, and evaluating air quality health index (AQHI) according to the Hong Kong equation, then comparing the AQHI all over the year to cover different seasonal conditions and all human activities and recommending the uses, benefits, and limitations of applying AQHI.
The study has been carried out at Ibrahim Sherif Street, which was selected for conducting this study due to its feasibility as the Occupational Health and Air Pollution Research Unit - High Institute of Public Health, Alexandria University is located on it. Moreover, it is one of the heaviest traffic streets in Alexandria. It is the pathway leading to ElMehwar Highway connecting the west of Alexandria, where most of the industries are focused with the rest of Alexandria city. It connects Smouha area with Abo Qir Street (Mustafa Kamel area).
This was a time-series study, which was conducted during the period from January 1, 2016 to December 31, 2016. Three-hour air sampling PM10, O3, SO2, and NO2 was collected weekly every other day (156 samples for each pollutant) all over the year to covering the seasonal variation and activities. Samples have been collected during the morning rush hours.
Summery
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The used methods for sampling and analysis are the standard methods approved by United States Environment Protection. Ozone (O3) was sampled and analyzed using the ”potassium iodide spectrophotometric method”. Sampling and analysis of Nitrogen Dioxide (NO2) was conducted using the ”Modified Jacob and Hochheiser Method”. Sampling and analysis of Sulfur Dioxide (SO2) was done by using the standard method ”Improved West and Gaeke Method”. Respirable particulate matter (PM10) was be collected using the standard PM10 high volume sampler, and then analyzed gravimetrically.
The highest concentrations for PM10 was during August [1429.857(159.295)] were of the maximum T & UV index, and moderate RH, and the minimum was during January [504.486 (200.648)] were of high RH, lowest Temp & UV index. The study results proofed that there is a highly significant monthly variation in PM10 concentrations. There is strong direct and significant correlation between the PM10 concentrations and UV-Index while it is moderate direct with temperature and it is inverse weak with RH%.
The highest concentrations for NO2 was during March [578.123 (739.997)] and the minimum was during July [277.499 (75.156).] The study results proofed that there were highly significant monthly variations in NO2 concentrations. There was statistically insignificant correlation between the NO2 concentrations and meteorological data. The main factor affecting the NO2 concentrations was the traffic density.
The highest concentrations for O3 was during July [96.259 (19.818)] where of the maximum T & UV index, and moderate RH, and the minimum concentrations was during February [29.0195 (34.68182)] where of high RH, lowest Temp & UV index. The study results proofed that there were highly significant monthly variations in O3 concentrations. There was strong direct and significant correlation between the O3 concentrations and UV-Index, and moderate direct with temperature, while it was an inverse weak correlation with RH%.
The highest concentrations for SO2 was during February [4.339 (.850)] and the minimum concentrations was during October [2.553 (.766)]. The study results proofed that there is a highly significant monthly variation in SO2 concentrations. The very low concentrations of SO2 reduce the strength of correlation with meteorological parameters.
The highest values for % AR was during April [72.875 (23.357)] and the minimum values was during January [32.156 (10.044)]. Spring was the season of the highest %AR [70.968 (19.674)], and winter was the lowest [40.616 (19.012)]. The study results proofed that there was highly significant monthly and seasonally variations in %AR.
The massive pollutant’s concentrations and the very high added health-risk values caused the Air Quality Health Index (AQHI) to be in the serious category (10+) for all sampling days.
The key benefit of using an AQHI is the ability to communicated data with the public, both easily and conveniently. They are easily scaled, and color coded. The AQHI is formulated to reflect the combined effects of air pollutants as a mixture rather than a single pollutant, and the index value has an interpretation that directly related to the risk of mortality. Its limitation may be due to the lake of knowledge regarding how multiple pollutants interact and impact on human health. It also fails to recognize the public-health importance of chronic exposure to ambient air pollution.