Abstract
A detailed study on particulate matter from vehicle emission was carried out at roadsides in Hong Kong downtown area. The study aims to explore the variations of particulate matter at roadsides in morning and afternoon. Data of concentrations of different size groups of particulate matter were collected and analyzed. It was found that the particulate levels generally vary periodically with traffic signal changes. During the green traffic light period, concentrations of the particulate matter increase to peak point and then diffuse to a relatively stable level in the red traffic light period. Such stable level is regarded as background level, to which pedestrians are exposed when they walk-by and cross the zebra zones. To analyze and further explore the collected data, a statistical distribution model, i. e., goodness-of-fit test, was employed. It was noticed that the lognormal distribution best fits the particulate matter data in both morning and afternoon. In addition, the non-parametric test was also used to assess the differences between morning and afternoon data. The results show that both data sets statistically differ from each other at 5% significance level. It can be deduced that the change of traffic volume, humidity and wind speed between morning and afternoon may cause this difference. © 2011 Springer-Verlag.
| Original language | English |
|---|---|
| Pages (from-to) | 177-187 |
| Journal | Stochastic Environmental Research and Risk Assessment |
| Volume | 26 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - Feb 2012 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 11 Sustainable Cities and Communities
Research Keywords
- Goodness-of-fit test
- Lognormal distribution
- Non-parametric test
- Particulate matter
- Vehicle emissions
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