Monitoring and prediction of air polution from traffic in the urban environment
Traffic-related air pollution is now a major concern. The Rio Earth Summit and the Government's commitment to Agenda 21 has led to Local Authorities taking responsibility to manage the growing number of vehicles and to reduce the impact of traffic on the environment. There is an urgent need to...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
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1996
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| Online Access: | https://eprints.nottingham.ac.uk/11740/ |
| _version_ | 1848791349138030592 |
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| author | Reynolds, Shirley Anne |
| author_facet | Reynolds, Shirley Anne |
| author_sort | Reynolds, Shirley Anne |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Traffic-related air pollution is now a major concern. The Rio Earth Summit and the Government's commitment to Agenda 21 has led to Local Authorities taking responsibility to manage the growing number of vehicles and to reduce the impact of traffic on the environment. There is an urgent need to effectively monitor urban air quality at reasonable cost and to develop long and short term air pollution prediction models.
The aim of the research described was to investigate relationships between traffic characteristics and kerbside air pollution concentrations. Initially, the only pollution monitoring equipment available was basic and required constant supervision. The traffic data was made available from the demand-responsive traffic signal control systems in Leicestershire and Nottinghamshire. However, it was found that the surveys were too short to produce statistically significant results, and no useful conclusions could be drawn.
Subsequently, an automatic, remote kerbside monitoring system was developed specifically for this research. The data collected was analysed using multiple regression techniques in an attempt to obtain an empirical relationship which could be used to predict roadside pollution concentrations from traffic and meteorological data. However, the residual series were found to be autocorrelated, which meant that the statistical tests were invalid. It was then found to be possible to fit an accurate model to the data using time series analysis, but that it could not predict levels even in the short-term.
Finally, a semi-empirical model was developed by estimating the proportion of vehicles passing a point in each operating mode (cruising, accelerating, decelerating and idling) and using real data to derive the coefficients. Unfortunately, it was again not possible to define a reliable predictive relationship. However, suggestions have been made about how this research could be progressed to achieve its aim. |
| first_indexed | 2025-11-14T18:27:06Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-11740 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:27:06Z |
| publishDate | 1996 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-117402025-02-28T11:15:20Z https://eprints.nottingham.ac.uk/11740/ Monitoring and prediction of air polution from traffic in the urban environment Reynolds, Shirley Anne Traffic-related air pollution is now a major concern. The Rio Earth Summit and the Government's commitment to Agenda 21 has led to Local Authorities taking responsibility to manage the growing number of vehicles and to reduce the impact of traffic on the environment. There is an urgent need to effectively monitor urban air quality at reasonable cost and to develop long and short term air pollution prediction models. The aim of the research described was to investigate relationships between traffic characteristics and kerbside air pollution concentrations. Initially, the only pollution monitoring equipment available was basic and required constant supervision. The traffic data was made available from the demand-responsive traffic signal control systems in Leicestershire and Nottinghamshire. However, it was found that the surveys were too short to produce statistically significant results, and no useful conclusions could be drawn. Subsequently, an automatic, remote kerbside monitoring system was developed specifically for this research. The data collected was analysed using multiple regression techniques in an attempt to obtain an empirical relationship which could be used to predict roadside pollution concentrations from traffic and meteorological data. However, the residual series were found to be autocorrelated, which meant that the statistical tests were invalid. It was then found to be possible to fit an accurate model to the data using time series analysis, but that it could not predict levels even in the short-term. Finally, a semi-empirical model was developed by estimating the proportion of vehicles passing a point in each operating mode (cruising, accelerating, decelerating and idling) and using real data to derive the coefficients. Unfortunately, it was again not possible to define a reliable predictive relationship. However, suggestions have been made about how this research could be progressed to achieve its aim. 1996 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en arr https://eprints.nottingham.ac.uk/11740/1/319950.pdf Reynolds, Shirley Anne (1996) Monitoring and prediction of air polution from traffic in the urban environment. PhD thesis, University of Nottingham. Air pollution automobiles motors exhaust |
| spellingShingle | Air pollution automobiles motors exhaust Reynolds, Shirley Anne Monitoring and prediction of air polution from traffic in the urban environment |
| title | Monitoring and prediction of air polution from traffic in the urban environment |
| title_full | Monitoring and prediction of air polution from traffic in the urban environment |
| title_fullStr | Monitoring and prediction of air polution from traffic in the urban environment |
| title_full_unstemmed | Monitoring and prediction of air polution from traffic in the urban environment |
| title_short | Monitoring and prediction of air polution from traffic in the urban environment |
| title_sort | monitoring and prediction of air polution from traffic in the urban environment |
| topic | Air pollution automobiles motors exhaust |
| url | https://eprints.nottingham.ac.uk/11740/ |