Spatial traffic noise pollution assessment - A case study.

OBJECTIVES Spatial assessment of traffic noise pollution intensity will provide urban planners with approximate estimation of citizens exposure to impermissible sound levels. They could identify critical noise pollution areas wherein noise barriers should be embedded. The present study aims at using the Geographic Information System (GIS) to assess spatial changes in traffic noise pollution in Tehran, the capital of Iran, and the largest city in the Middle East. MATERIAL AND METHODS For this purpose, while measuring equivalent sound levels at different time periods of a day and different days of a week in District 14 of Tehran, wherein there are highways and busy streets, the geographic coordination of the measurement points was recorded at the stations. The obtained results indicated that the equivalent sound level did not show a statistically significant difference between weekdays, and morning, afternoon and evening hours as well as time intervals of 10 min, 15 min and 30 min. Then, 91 stations were selected in the target area and equivalent sound level was measured for each station on 3 occasions of the morning (7:00-9:00 a.m.), afternoon (12.00-3:00 p.m.) and evening (5:00-8:00 p.m.) on Saturdays to Wednesdays. RESULTS As the results suggest, the maximum equivalent sound level (Leq) was reported from Basij Highway, which is a very important connecting thoroughfare in the district, and was equal to 84.2 dB(A), while the minimum equivalent sound level (Leq), measured in the Fajr Hospital, was equal to 59.9 dB(A). CONCLUSIONS The average equivalent sound level was higher than the national standard limit at all stations. The use of sound walls in Highways Basij and Mahallati as well as widening the Streets 17th Shahrivar, Pirouzi and Khavaran, benchmarked on a map, were recommended as the most effective mitigation measures. Additionally, the research findings confirm the outstanding applicability of the Geographic Information System in handling noise pollution data towards depicting noise pollution intensity caused by traffic.