Assessment and prediction of the impact of road transport on ambient concentrations of particulate matter PM10

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@article{8051ea16c8794492ab671c93906e3a92,
title = "Assessment and prediction of the impact of road transport on ambient concentrations of particulate matter PM10",
abstract = "The main challenge facing the air quality management authorities in most cities is meeting the air quality limits and objectives in areas where road traffic is high. The difficulty and uncertainties associated with the estimation and prediction of the road traffic contribution to the overall air quality levels is the major contributing factor. In this paper, particulate matter (PM10) data from 10 monitoring sites in London was investigated with a view to estimating and developing Artificial Neural Network models (ANN) for predicting the impact of the road traffic on the levels of PM10 concentration in London. Twin studies in conjunction with bivariate polar plots were used to identify and estimate the contribution of road traffic and other sources of PM10 at the monitoring sites. The road traffic was found to have contributed between 24% and 62% of the hourly average roadside PM10 concentrations. The ANN models performed well in predicting the road contributions with their R-values ranging between 0.6 and 0.9, FAC2 between 0.6 and 0.95, and the normalised mean bias between 0.01 and 0.11. The hourly emission rates of the vehicles were found to be the most contributing input variables to the outputs of the ANN models followed by background PM10, gaseous pollutants and meteorological variables respectively.",
keywords = "Artificial neural network, Bivariate polar plot, Particulate matter, Road traffic contribution",
author = "A. Suleiman and Tight, {M. R.} and Andrew Quinn",
year = "2016",
month = dec,
day = "1",
doi = "10.1016/j.trd.2016.10.010",
language = "English",
volume = "49",
pages = "301--312",
journal = "Transportation Research Part D: Transport and Environment",
issn = "1361-9209",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Assessment and prediction of the impact of road transport on ambient concentrations of particulate matter PM10

AU - Suleiman, A.

AU - Tight, M. R.

AU - Quinn, Andrew

PY - 2016/12/1

Y1 - 2016/12/1

N2 - The main challenge facing the air quality management authorities in most cities is meeting the air quality limits and objectives in areas where road traffic is high. The difficulty and uncertainties associated with the estimation and prediction of the road traffic contribution to the overall air quality levels is the major contributing factor. In this paper, particulate matter (PM10) data from 10 monitoring sites in London was investigated with a view to estimating and developing Artificial Neural Network models (ANN) for predicting the impact of the road traffic on the levels of PM10 concentration in London. Twin studies in conjunction with bivariate polar plots were used to identify and estimate the contribution of road traffic and other sources of PM10 at the monitoring sites. The road traffic was found to have contributed between 24% and 62% of the hourly average roadside PM10 concentrations. The ANN models performed well in predicting the road contributions with their R-values ranging between 0.6 and 0.9, FAC2 between 0.6 and 0.95, and the normalised mean bias between 0.01 and 0.11. The hourly emission rates of the vehicles were found to be the most contributing input variables to the outputs of the ANN models followed by background PM10, gaseous pollutants and meteorological variables respectively.

AB - The main challenge facing the air quality management authorities in most cities is meeting the air quality limits and objectives in areas where road traffic is high. The difficulty and uncertainties associated with the estimation and prediction of the road traffic contribution to the overall air quality levels is the major contributing factor. In this paper, particulate matter (PM10) data from 10 monitoring sites in London was investigated with a view to estimating and developing Artificial Neural Network models (ANN) for predicting the impact of the road traffic on the levels of PM10 concentration in London. Twin studies in conjunction with bivariate polar plots were used to identify and estimate the contribution of road traffic and other sources of PM10 at the monitoring sites. The road traffic was found to have contributed between 24% and 62% of the hourly average roadside PM10 concentrations. The ANN models performed well in predicting the road contributions with their R-values ranging between 0.6 and 0.9, FAC2 between 0.6 and 0.95, and the normalised mean bias between 0.01 and 0.11. The hourly emission rates of the vehicles were found to be the most contributing input variables to the outputs of the ANN models followed by background PM10, gaseous pollutants and meteorological variables respectively.

KW - Artificial neural network

KW - Bivariate polar plot

KW - Particulate matter

KW - Road traffic contribution

UR - http://www.scopus.com/inward/record.url?scp=84994672126&partnerID=8YFLogxK

U2 - 10.1016/j.trd.2016.10.010

DO - 10.1016/j.trd.2016.10.010

M3 - Article

AN - SCOPUS:84994672126

VL - 49

SP - 301

EP - 312

JO - Transportation Research Part D: Transport and Environment

JF - Transportation Research Part D: Transport and Environment

SN - 1361-9209

ER -