Performance evaluation of vehicles emissions prediction models
Abstract Road traffic is a dominant source of urban air pollution. Therefore, it is necessary to quantify emission levels as accurately as possible to evaluate their impacts on the public health and the environment. Several models were developed to predict these emissions. These models can be groupe...
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Gespeichert in:
| Autor*in: |
Abo-Qudais, Saad [verfasserIn] Qdais, Hani Abu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2005 |
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| Übergeordnetes Werk: |
Enthalten in: Clean Products and Processes - Springer-Verlag, 2001, 7(2005), 4 vom: 20. Juli, Seite 279-284 |
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| Übergeordnetes Werk: |
volume:7 ; year:2005 ; number:4 ; day:20 ; month:07 ; pages:279-284 |
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10.1007/s10098-005-0279-x |
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| 520 | |a Abstract Road traffic is a dominant source of urban air pollution. Therefore, it is necessary to quantify emission levels as accurately as possible to evaluate their impacts on the public health and the environment. Several models were developed to predict these emissions. These models can be grouped into three categories, namely, emission factors models, average speed models, and modal models. The prediction capability of most developed models is relatively poor. Therefore, there is a pressing need to improve the predictability of the existing models or to develop new ones with better accuracy. The main focus of this paper is to review different traffic emissions modeling efforts and to describe the effect of different factors on emission levels and modeling accuracy, so as to get reliable emission estimates. In addition, different models were evaluated for the prediction capability of certain emissions such as carbon monoxide (CO), nitrogen monoxide (NO), nitrogen dioxide ($ NO_{2} $) and hydrocarbons (HC). These models are mainly based on traffic volume, composition, and flow. The predicted values by one of the models were compared to measured values based on field surveys. The result of comparison indicated that there is a significant difference between the measured and predicted values. These differences ranged from 17% for $ NO_{2} $ to 72% in the case of CO, which suggests that the $ NO_{2} $ model has better predictability. This deviation in prediction may be attributed to the fact that prediction models ignored some of the parameters affecting vehicle emissions such as the type of fuel used, air–fuel ratio, engine compression ratio, spark timing, surrounding environment, wind effect, regional characteristics and high pollutants emitters effect. | ||
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10.1007/s10098-005-0279-x doi (DE-627)SPR00871388X (SPR)s10098-005-0279-x-e DE-627 ger DE-627 rakwb eng Abo-Qudais, Saad verfasserin aut Performance evaluation of vehicles emissions prediction models 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Road traffic is a dominant source of urban air pollution. Therefore, it is necessary to quantify emission levels as accurately as possible to evaluate their impacts on the public health and the environment. Several models were developed to predict these emissions. These models can be grouped into three categories, namely, emission factors models, average speed models, and modal models. The prediction capability of most developed models is relatively poor. Therefore, there is a pressing need to improve the predictability of the existing models or to develop new ones with better accuracy. The main focus of this paper is to review different traffic emissions modeling efforts and to describe the effect of different factors on emission levels and modeling accuracy, so as to get reliable emission estimates. In addition, different models were evaluated for the prediction capability of certain emissions such as carbon monoxide (CO), nitrogen monoxide (NO), nitrogen dioxide ($ NO_{2} $) and hydrocarbons (HC). These models are mainly based on traffic volume, composition, and flow. The predicted values by one of the models were compared to measured values based on field surveys. The result of comparison indicated that there is a significant difference between the measured and predicted values. These differences ranged from 17% for $ NO_{2} $ to 72% in the case of CO, which suggests that the $ NO_{2} $ model has better predictability. This deviation in prediction may be attributed to the fact that prediction models ignored some of the parameters affecting vehicle emissions such as the type of fuel used, air–fuel ratio, engine compression ratio, spark timing, surrounding environment, wind effect, regional characteristics and high pollutants emitters effect. Emission Rate (dpeaa)DE-He213 Traffic Volume (dpeaa)DE-He213 Emission Level (dpeaa)DE-He213 Street Canyon (dpeaa)DE-He213 Vehicle Emission (dpeaa)DE-He213 Qdais, Hani Abu verfasserin aut Enthalten in Clean Products and Processes Springer-Verlag, 2001 7(2005), 4 vom: 20. Juli, Seite 279-284 (DE-627)SPR008711836 nnns volume:7 year:2005 number:4 day:20 month:07 pages:279-284 https://dx.doi.org/10.1007/s10098-005-0279-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 7 2005 4 20 07 279-284 |
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10.1007/s10098-005-0279-x doi (DE-627)SPR00871388X (SPR)s10098-005-0279-x-e DE-627 ger DE-627 rakwb eng Abo-Qudais, Saad verfasserin aut Performance evaluation of vehicles emissions prediction models 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Road traffic is a dominant source of urban air pollution. Therefore, it is necessary to quantify emission levels as accurately as possible to evaluate their impacts on the public health and the environment. Several models were developed to predict these emissions. These models can be grouped into three categories, namely, emission factors models, average speed models, and modal models. The prediction capability of most developed models is relatively poor. Therefore, there is a pressing need to improve the predictability of the existing models or to develop new ones with better accuracy. The main focus of this paper is to review different traffic emissions modeling efforts and to describe the effect of different factors on emission levels and modeling accuracy, so as to get reliable emission estimates. In addition, different models were evaluated for the prediction capability of certain emissions such as carbon monoxide (CO), nitrogen monoxide (NO), nitrogen dioxide ($ NO_{2} $) and hydrocarbons (HC). These models are mainly based on traffic volume, composition, and flow. The predicted values by one of the models were compared to measured values based on field surveys. The result of comparison indicated that there is a significant difference between the measured and predicted values. These differences ranged from 17% for $ NO_{2} $ to 72% in the case of CO, which suggests that the $ NO_{2} $ model has better predictability. This deviation in prediction may be attributed to the fact that prediction models ignored some of the parameters affecting vehicle emissions such as the type of fuel used, air–fuel ratio, engine compression ratio, spark timing, surrounding environment, wind effect, regional characteristics and high pollutants emitters effect. Emission Rate (dpeaa)DE-He213 Traffic Volume (dpeaa)DE-He213 Emission Level (dpeaa)DE-He213 Street Canyon (dpeaa)DE-He213 Vehicle Emission (dpeaa)DE-He213 Qdais, Hani Abu verfasserin aut Enthalten in Clean Products and Processes Springer-Verlag, 2001 7(2005), 4 vom: 20. Juli, Seite 279-284 (DE-627)SPR008711836 nnns volume:7 year:2005 number:4 day:20 month:07 pages:279-284 https://dx.doi.org/10.1007/s10098-005-0279-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 7 2005 4 20 07 279-284 |
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10.1007/s10098-005-0279-x doi (DE-627)SPR00871388X (SPR)s10098-005-0279-x-e DE-627 ger DE-627 rakwb eng Abo-Qudais, Saad verfasserin aut Performance evaluation of vehicles emissions prediction models 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Road traffic is a dominant source of urban air pollution. Therefore, it is necessary to quantify emission levels as accurately as possible to evaluate their impacts on the public health and the environment. Several models were developed to predict these emissions. These models can be grouped into three categories, namely, emission factors models, average speed models, and modal models. The prediction capability of most developed models is relatively poor. Therefore, there is a pressing need to improve the predictability of the existing models or to develop new ones with better accuracy. The main focus of this paper is to review different traffic emissions modeling efforts and to describe the effect of different factors on emission levels and modeling accuracy, so as to get reliable emission estimates. In addition, different models were evaluated for the prediction capability of certain emissions such as carbon monoxide (CO), nitrogen monoxide (NO), nitrogen dioxide ($ NO_{2} $) and hydrocarbons (HC). These models are mainly based on traffic volume, composition, and flow. The predicted values by one of the models were compared to measured values based on field surveys. The result of comparison indicated that there is a significant difference between the measured and predicted values. These differences ranged from 17% for $ NO_{2} $ to 72% in the case of CO, which suggests that the $ NO_{2} $ model has better predictability. This deviation in prediction may be attributed to the fact that prediction models ignored some of the parameters affecting vehicle emissions such as the type of fuel used, air–fuel ratio, engine compression ratio, spark timing, surrounding environment, wind effect, regional characteristics and high pollutants emitters effect. Emission Rate (dpeaa)DE-He213 Traffic Volume (dpeaa)DE-He213 Emission Level (dpeaa)DE-He213 Street Canyon (dpeaa)DE-He213 Vehicle Emission (dpeaa)DE-He213 Qdais, Hani Abu verfasserin aut Enthalten in Clean Products and Processes Springer-Verlag, 2001 7(2005), 4 vom: 20. Juli, Seite 279-284 (DE-627)SPR008711836 nnns volume:7 year:2005 number:4 day:20 month:07 pages:279-284 https://dx.doi.org/10.1007/s10098-005-0279-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 7 2005 4 20 07 279-284 |
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10.1007/s10098-005-0279-x doi (DE-627)SPR00871388X (SPR)s10098-005-0279-x-e DE-627 ger DE-627 rakwb eng Abo-Qudais, Saad verfasserin aut Performance evaluation of vehicles emissions prediction models 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Road traffic is a dominant source of urban air pollution. Therefore, it is necessary to quantify emission levels as accurately as possible to evaluate their impacts on the public health and the environment. Several models were developed to predict these emissions. These models can be grouped into three categories, namely, emission factors models, average speed models, and modal models. The prediction capability of most developed models is relatively poor. Therefore, there is a pressing need to improve the predictability of the existing models or to develop new ones with better accuracy. The main focus of this paper is to review different traffic emissions modeling efforts and to describe the effect of different factors on emission levels and modeling accuracy, so as to get reliable emission estimates. In addition, different models were evaluated for the prediction capability of certain emissions such as carbon monoxide (CO), nitrogen monoxide (NO), nitrogen dioxide ($ NO_{2} $) and hydrocarbons (HC). These models are mainly based on traffic volume, composition, and flow. The predicted values by one of the models were compared to measured values based on field surveys. The result of comparison indicated that there is a significant difference between the measured and predicted values. These differences ranged from 17% for $ NO_{2} $ to 72% in the case of CO, which suggests that the $ NO_{2} $ model has better predictability. This deviation in prediction may be attributed to the fact that prediction models ignored some of the parameters affecting vehicle emissions such as the type of fuel used, air–fuel ratio, engine compression ratio, spark timing, surrounding environment, wind effect, regional characteristics and high pollutants emitters effect. Emission Rate (dpeaa)DE-He213 Traffic Volume (dpeaa)DE-He213 Emission Level (dpeaa)DE-He213 Street Canyon (dpeaa)DE-He213 Vehicle Emission (dpeaa)DE-He213 Qdais, Hani Abu verfasserin aut Enthalten in Clean Products and Processes Springer-Verlag, 2001 7(2005), 4 vom: 20. Juli, Seite 279-284 (DE-627)SPR008711836 nnns volume:7 year:2005 number:4 day:20 month:07 pages:279-284 https://dx.doi.org/10.1007/s10098-005-0279-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 7 2005 4 20 07 279-284 |
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10.1007/s10098-005-0279-x doi (DE-627)SPR00871388X (SPR)s10098-005-0279-x-e DE-627 ger DE-627 rakwb eng Abo-Qudais, Saad verfasserin aut Performance evaluation of vehicles emissions prediction models 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Road traffic is a dominant source of urban air pollution. Therefore, it is necessary to quantify emission levels as accurately as possible to evaluate their impacts on the public health and the environment. Several models were developed to predict these emissions. These models can be grouped into three categories, namely, emission factors models, average speed models, and modal models. The prediction capability of most developed models is relatively poor. Therefore, there is a pressing need to improve the predictability of the existing models or to develop new ones with better accuracy. The main focus of this paper is to review different traffic emissions modeling efforts and to describe the effect of different factors on emission levels and modeling accuracy, so as to get reliable emission estimates. In addition, different models were evaluated for the prediction capability of certain emissions such as carbon monoxide (CO), nitrogen monoxide (NO), nitrogen dioxide ($ NO_{2} $) and hydrocarbons (HC). These models are mainly based on traffic volume, composition, and flow. The predicted values by one of the models were compared to measured values based on field surveys. The result of comparison indicated that there is a significant difference between the measured and predicted values. These differences ranged from 17% for $ NO_{2} $ to 72% in the case of CO, which suggests that the $ NO_{2} $ model has better predictability. This deviation in prediction may be attributed to the fact that prediction models ignored some of the parameters affecting vehicle emissions such as the type of fuel used, air–fuel ratio, engine compression ratio, spark timing, surrounding environment, wind effect, regional characteristics and high pollutants emitters effect. Emission Rate (dpeaa)DE-He213 Traffic Volume (dpeaa)DE-He213 Emission Level (dpeaa)DE-He213 Street Canyon (dpeaa)DE-He213 Vehicle Emission (dpeaa)DE-He213 Qdais, Hani Abu verfasserin aut Enthalten in Clean Products and Processes Springer-Verlag, 2001 7(2005), 4 vom: 20. Juli, Seite 279-284 (DE-627)SPR008711836 nnns volume:7 year:2005 number:4 day:20 month:07 pages:279-284 https://dx.doi.org/10.1007/s10098-005-0279-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 7 2005 4 20 07 279-284 |
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Abstract Road traffic is a dominant source of urban air pollution. Therefore, it is necessary to quantify emission levels as accurately as possible to evaluate their impacts on the public health and the environment. Several models were developed to predict these emissions. These models can be grouped into three categories, namely, emission factors models, average speed models, and modal models. The prediction capability of most developed models is relatively poor. Therefore, there is a pressing need to improve the predictability of the existing models or to develop new ones with better accuracy. The main focus of this paper is to review different traffic emissions modeling efforts and to describe the effect of different factors on emission levels and modeling accuracy, so as to get reliable emission estimates. In addition, different models were evaluated for the prediction capability of certain emissions such as carbon monoxide (CO), nitrogen monoxide (NO), nitrogen dioxide ($ NO_{2} $) and hydrocarbons (HC). These models are mainly based on traffic volume, composition, and flow. The predicted values by one of the models were compared to measured values based on field surveys. The result of comparison indicated that there is a significant difference between the measured and predicted values. These differences ranged from 17% for $ NO_{2} $ to 72% in the case of CO, which suggests that the $ NO_{2} $ model has better predictability. This deviation in prediction may be attributed to the fact that prediction models ignored some of the parameters affecting vehicle emissions such as the type of fuel used, air–fuel ratio, engine compression ratio, spark timing, surrounding environment, wind effect, regional characteristics and high pollutants emitters effect. |
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Abstract Road traffic is a dominant source of urban air pollution. Therefore, it is necessary to quantify emission levels as accurately as possible to evaluate their impacts on the public health and the environment. Several models were developed to predict these emissions. These models can be grouped into three categories, namely, emission factors models, average speed models, and modal models. The prediction capability of most developed models is relatively poor. Therefore, there is a pressing need to improve the predictability of the existing models or to develop new ones with better accuracy. The main focus of this paper is to review different traffic emissions modeling efforts and to describe the effect of different factors on emission levels and modeling accuracy, so as to get reliable emission estimates. In addition, different models were evaluated for the prediction capability of certain emissions such as carbon monoxide (CO), nitrogen monoxide (NO), nitrogen dioxide ($ NO_{2} $) and hydrocarbons (HC). These models are mainly based on traffic volume, composition, and flow. The predicted values by one of the models were compared to measured values based on field surveys. The result of comparison indicated that there is a significant difference between the measured and predicted values. These differences ranged from 17% for $ NO_{2} $ to 72% in the case of CO, which suggests that the $ NO_{2} $ model has better predictability. This deviation in prediction may be attributed to the fact that prediction models ignored some of the parameters affecting vehicle emissions such as the type of fuel used, air–fuel ratio, engine compression ratio, spark timing, surrounding environment, wind effect, regional characteristics and high pollutants emitters effect. |
| abstract_unstemmed |
Abstract Road traffic is a dominant source of urban air pollution. Therefore, it is necessary to quantify emission levels as accurately as possible to evaluate their impacts on the public health and the environment. Several models were developed to predict these emissions. These models can be grouped into three categories, namely, emission factors models, average speed models, and modal models. The prediction capability of most developed models is relatively poor. Therefore, there is a pressing need to improve the predictability of the existing models or to develop new ones with better accuracy. The main focus of this paper is to review different traffic emissions modeling efforts and to describe the effect of different factors on emission levels and modeling accuracy, so as to get reliable emission estimates. In addition, different models were evaluated for the prediction capability of certain emissions such as carbon monoxide (CO), nitrogen monoxide (NO), nitrogen dioxide ($ NO_{2} $) and hydrocarbons (HC). These models are mainly based on traffic volume, composition, and flow. The predicted values by one of the models were compared to measured values based on field surveys. The result of comparison indicated that there is a significant difference between the measured and predicted values. These differences ranged from 17% for $ NO_{2} $ to 72% in the case of CO, which suggests that the $ NO_{2} $ model has better predictability. This deviation in prediction may be attributed to the fact that prediction models ignored some of the parameters affecting vehicle emissions such as the type of fuel used, air–fuel ratio, engine compression ratio, spark timing, surrounding environment, wind effect, regional characteristics and high pollutants emitters effect. |
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Performance evaluation of vehicles emissions prediction models |
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Qdais, Hani Abu |
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2024-07-03T22:46:02.494Z |
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7.399703 |