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|Title: ||Investigation on characteristics of polluting particulate matter at urban traffic intersections in Hong Kong|
|Other Titles: ||Xianggang dao lu jiao cha kou de wu ran ke li wu zhi shi ce yu yan jiu|
|Authors: ||He, Hongdi (何紅弟)|
|Department: ||Department of Building and Construction|
|Degree: ||Doctor of Philosophy|
|Issue Date: ||2010|
|Publisher: ||City University of Hong Kong|
|Subjects: ||Motor vehicles -- Motors -- Exhaust gas -- Environmental aspects -- China -- Hong Kong.|
Particles -- Environmental aspects -- China -- Hong Kong.
Transportation -- Environmental aspects -- China -- Hong Kong.
Air pollution -- China -- Hong Kong.
|Notes: ||CityU Call Number: TD886.5 .H4 2010|
xix, 167 leaves : ill. (some col.) 30 cm.
Thesis (Ph.D.)--City University of Hong Kong, 2010.
Includes bibliographical references (leaves -167)
|Abstract: ||The aim of this thesis is to investigate the variation in polluting particulate matter
(PM) at urban traffic intersections. At the intersections, vehicles stop frequently
with idling engines during the red-light periods and speed up rapidly during the
green-light periods, which generally produce severe pollution than that in other
situations. Meanwhile, the pedestrians are just exposed to high levels of particulate
pollutants as they walk-by or cross the zebra areas. The inhaled particulates may
affect the functions of hearts and lungs of human beings and cause adverse health
effects. With these considerations, a detailed investigation on characteristics of PM
variation at urban traffic intersections in Hong Kong is carried out and reported in
this thesis. The main contents of the thesis are as follows.
I. Measurements at a typical traffic intersection in Hong Kong
In the experiments, particles with the diameters larger than 0.3μm were detected and
classified into six categories. The traffic conditions were recorded by a digital
camera while the meteorological conditions, such as wind speed, temperature and
relative humidity, were simultaneously measured. To acquire the representative
datasets, the measurements were performed for one and half hours every time and
lasted for one week in spring and winter seasons, respectively. Within multi-day
measurements, the data about the PM levels, traffic conditions, and meteorological
conditions were collected as the original database.
II. Statistical analysis of PM variation
Based on the measurements, statistical analysis was conducted to explore the
characteristics of PM at intersections. The results show that the majority of
measured particles vary periodically with traffic signal intervals. In particular, the
variation in particles within the range of 0.5-5μm appears to correspond with the
traffic signal periods, implying close correlation between them. The
auto-correlation function was used to reveal the periodic behaviour and the results
indicate that the variation in PM in afternoon exhibits a more pronounced periodic
behaviour than that in morning, especially for particles within the range of 0.5~5μm.
The stable variations in measured data in the red-light periods were also selected to
seek an appropriate statistical distribution via the goodness-of-fit test. Lognormal
distribution was recognized as the most suitable one for particles smaller than 5μm.
III. Impacts of traffic conditions and meteorological conditions on PM variation
The multivariate statistical techniques of cluster analysis and principal component
analysis (PCA) were applied to identify the relationships among PM concentrations,
traffic conditions, and meteorological conditions in two respects. One was to
clarify the relationships between the instantaneous PM concentrations and
corresponding meteorological variables such as temperature, relative humidity, and
wind speed on two days in different seasons. The other was to explore the parallel
relationships among the average PM concentrations during green-light periods with traffic conditions and meteorological variables over one week in different seasons.
Through analyses, a strong relationship between particles within the range of
0.5-5μm and the diesel vehicle count was identified, and these particles were thus
concluded to be mainly related to the number of diesel vehicles. Meteorological
conditions, particularly wind speed, were found to have a significant influence on the
number concentrations of particles larger than 5μm. The results also revealed that
the number concentrations of ultrafine particles within the 0.3-0.5μm range were
strongly dependent on the relative humidity.
IV. Prediction of PM concentrations using multilayer perception (MLP) model
with assistance of PCA
In view of the nonlinear relationships among the PM concentrations, traffic
conditions, and meteorological conditions, the PM concentrations were predicted
using the nonlinear approach of MLP model with three algorithms, i.e., the
Levenberg-Marquardt (LM) algorithm, particle swarm optimization (PSO) algorithm,
and PSO algorithm with chaotic mapping (CPSO). Through comparing the
obtained results, the CPSO algorithm was shown to be more effective than the others
in both predictive capability and precision. It might be due to the fact that the
MLP-CPSO model combines the merits of PSO and chaos, with the former
overcoming the over-fitting problem and the latter avoiding the model becoming
trapped in local optima. In addition, PCA was employed to generate principal
components (PCs), rather than the original data, as the input variables to reduce the model complexity and eliminate data collinearity. The performance of the models
using PCs as the input variables was shown better than that of those using the
original data. The MLP-PC model trained with the CPSO algorithm again
outperformed the model trained with the other two algorithms.
V. Prediction of PM concentrations using semi-empirical box model with
instantaneous velocity and acceleration
Apart from the indirect estimating approach of the MLP model, a direct approach of
semi-empirical box model was applied to predict the PM concentrations from vehicle
emission. In order to minimize the influence of meteorological conditions, the
application of the model was firstly carried out in a sunny day and then further
testified between two sunny days. From the evaluation and predication results, the
semi-empirical model with instantaneous vehicle velocity and acceleration
performed well in all cases, although certain values were overestimated or
underestimated. The motive of using instantaneous vehicle velocity and
acceleration in semi-empirical box model was to attempt to describe the traffic flow
and calculate the vehicle emission with a more realistic representation from
microscopic point of view. Through application of the improved model in two
situations, the attempt was proved to be significant and informative. It was shown
to be a practical alternative approach for evaluating the PM dispersion at urban
traffic intersections with a reasonable accuracy.|
|Online Catalog Link: ||http://lib.cityu.edu.hk/record=b3947673|
|Appears in Collections:||BC - Doctor of Philosophy |
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