Characterizing driving patterns for Zhuhai for traffic emissions estimation.

Zhuhai, a relatively less developed city on the western coast of the Pearl River Delta (PRD) of China, is planning to undergo major development in coming years. A Hong Kong-Zhuhai-Macao Bridge project has been approved by the Central Government of China. The project will have great impact on the driving pattern and vehicular emissions to the city. This baseline study collected speed-time data of two instrumented private cars in morning and evening periods, as well as a daytime nonpeak period of >10 consecutive days in the spring and winter of 2003. The authors used the microwave speed sensor and global positioning system installed in the instrumented cars and used car-chasing technique to perform the data collection. They used the statistical package SPSS to assess the consistency, as well as to evaluate the variability of the data. Nine parameters, namely, average speed, average running speed, average acceleration rate, average deceleration rate, mean length of a driving period, time proportions of driving modes, average number of acceleration-deceleration changes, root mean square acceleration, and positive acceleration kinetic energy are calculated to represent the driving characteristics. A driving cycle for private cars was developed. If emission tests were conducted using the Zhuhai driving cycle, the level of vehicle emissions measured is likely to be in between that of the Federal Test Procedure (FTP) cycle and the Melbourne Peak cycle.

A modal approach to vehicular emissions and fuel consumption model development.

This study reports on the analysis of emissions and fuel consumption from motor vehicles using a modal approach. The four standard driving modes are idling, accelerating, cruising, and decelerating. On-road data were collected using instrumented test vehicles traveling many times through the urban areas of Hong Kong. A model was developed for estimating vehicular fuel consumption and emissions as a function of instantaneous speed and driving mode. Piecewise interpolation functions were proposed for each nonidling driving mode. Idling emission and fuel consumption rates were estimated as negative exponential functions of idling time. Preliminary modeling results showed good agreements for the test vehicles and indicated that the on-road measurements are feasible for the development of modal emission and fuel consumption models.

The effect of wet film thickness on VOC emissions from a finishing varnish.

Finishing varnishes, a typical type of oil-based varnishes, are widely used to shine metal, wood trim and cabinet surfaces in Hong Kong. The influence of wet film thickness on volatile organic compound (VOC) emissions from a finishing varnish was studied in an environmental test chamber. The varnish was applied on an aluminium foil with three different wet film thickness (35.2, 69.9 and 107.3 microm). The experimental conditions were 25.0 degrees C, 50.0% relative humidity (RH) with an air exchange rate of 0.5 h(-1). The concentrations of the major VOCs were monitored for the first 10 h. The air samples were collected by canisters and analysed by gas chromatography/mass selective detector (GC/MSD). Six major VOCs including toluene, chlorobenzene, ethylbenzene, m,p-xylene, o-xylene and 1,3,5-trimethylbenzene were identified and quantified. Marked differences were observed for three different film thicknesses. VOC concentrations increased rapidly during the first few hours and then decreased as the emission rates declined. The thicker the wet film, the higher the VOC emissions. A model expression included an exponentially decreasing emission rate of varnish film. The concentration and time data measured in the chamber were used to determine the parameters of empirical emission rate model. The present work confirmed that the film thickness of varnish influenced markedly the concentrations and emissions of VOCs.

An algorithm for assessing the risk of traffic accident.

INTRODUCTION: This study is aimed at developing an algorithm to estimate the number of traffic accidents and assess the risk of traffic accidents in a study area. METHOD: The algorithm involves a combination of mapping technique (Geographical Information System (GIS) techniques) and statistical methods (cluster analysis and regression analysis). Geographical Information System is used to locate accidents on a digital map and realize their distribution. Cluster analysis is used to group the homogeneous data together. Regression analysis is performed to realize the relation between the number of accident events and the potential causal factors. Negative binomial regression model is found to be an appropriate mathematical form to mimic this relation. Accident risk of the area, derived from historical accident records and causal factors, is also determined in the algorithm. The risk is computed using the Empirical Bayes (EB) approach. A case study of Hong Kong is presented to illustrate the effectiveness of the proposed algorithm. RESULTS: The results show that the algorithm improves accident risk estimation when comparing to the estimated risk based on only the historical accident records. The algorithm is found to be more efficient, especially in the case of fatality and pedestrian-related accident analysis. IMPACT ON INDUSTRY: The output of the proposed algorithm can help authorities effectively identify areas with high accident risk. In addition, it can serve as a reference for town planners considering road safety.

Perception of policy-makers on policy-making criteria: the case of vehicle emissions control.

In this paper, we analyzed data addressing people's perceptions of the importance of selection criteria for vehicle-related emissions control policies and measures based on a three-round survey organized during three professional air quality control international conferences in 2006 through 2010 based on the Delphi survey approach. More than 300 participants were solicited to answer a ranking questionnaire. The results from the simple tabulation, figures and a rigorous statistical model revealed the divergence in people's perceptions of the importance of criteria guiding emissions control policies and selection of measures, and we attribute these differences in opinion to differences in people's working backgrounds and the economic and political conditions in their countries. Our multinomial logit model estimation pushed our investigation further and provided a more direct illustration of the potential determining role of each of these background factors. The estimations found that economic and political differences among countries seem to result in more divergence of opinion about the importance of the criteria. Furthermore, some criteria, particularly less classical ones such as ability to administer changes and time to reach effectiveness, showed more divergence in people's opinions than classical criteria, such as cost, effectiveness etc.