کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6315723 | 1619161 | 2016 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Near roadway air pollution across a spatially extensive road and cycling network
ترجمه فارسی عنوان
نزدیک به آلودگی هوا در خیابان در یک شبکه گسترده جاده ای و دوچرخه سواری
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم محیط زیست
شیمی زیست محیطی
چکیده انگلیسی
This study investigates the variability in near-road concentrations of ultra-fine particles (UFP). Our results are based on a mobile data collection campaign conducted in 2012 in Montreal, Canada using instrumented bicycles and covering approximately 475Â km of unique roadways. The spatial extent of the data collected included a diverse array of roads and land use patterns. Average concentrations of UFP per roadway segment varied greatly across the study area (1411-192,340 particles/cm3) as well as across the different visits to the same segment. Mixed effects linear regression models were estimated for UFP (R2Â =Â 43.80%), incorporating a wide range of predictors including land-use, built environment, road characteristics, and meteorology. Temperature and wind speed had a large negative effect on near-road concentrations of UFP. Both the day of the week and time of day had a significant effect with Tuesdays and afternoon periods positively associated with UFP. Since UFP are largely associated with traffic emissions and considering the wide spatial extent of our data collection campaign, it was impossible to collect traffic volume data. For this purpose, we used simulated data for traffic volumes and speeds across the region and observed a positive effect for volumes and negative effect for speed. Finally, proximity to truck routes was also associated with higher UFP concentrations.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Environmental Pollution - Volume 212, May 2016, Pages 498-507
Journal: Environmental Pollution - Volume 212, May 2016, Pages 498-507
نویسندگان
William Farrell, Scott Weichenthal, Mark Goldberg, Marie-France Valois, Maryam Shekarrizfard, Marianne Hatzopoulou,