Develop dynamic model for predicting traffic CO emissions in urban areas

Abstract

The greater the use of energy in the transportation sectors, the higher the emission of carbon monoxide (CO), and hence inevitable harm to environment and human health. In this concern, measuring and predicting of CO emission from transportation sector—especially large cities—is important as it constitute 90 % of all CO emission. Many urban cities in developing world have not properly experienced such measurements or predictions. In this paper, for the first time, field measurements of traffic characteristics data and corresponding CO concentration have been performed for developing a model for predicting CO emissions from transportation sector for New Borg El Arab (NBC), Egypt. The performance of Swiss-German Handbook Emission Factors for Road Transport (HBEFA v3.1) model has been assessed for predicting the CO concentration at roadside in the study area. Results indicated that HBEFA v3.1 underestimate emission figures. The developed CO dynamic emission model involves the traffic flow characteristics with roadside CO concentrations. Acceptable representation of measured CO concentration has been shown by the developed dynamic CO emission model which introduces R ² = 0.77, mean biases and frictional biases of −0.27 mg m⁻³ and 0.09, respectively. A comparison between predicted CO concentrations using HBEFA v3.1 and the promoted dynamic model indicate that HBEFA v3.1 estimates CO emission concentrations in the study area with a mean error and frictional biases 159.26 and 233.33 %, respectively, higher than those of the developed model.

Appendix

$$ \mathrm{R}\mathrm{e} lative\; error\; in\; the\; average\; concentration=\frac{\overline{C_p}-\overline{C_m}}{\overline{C_m}}\ast 100 $$

$$ Global\; mean\; biases\;(MB)=\overline{C_p}-{C}_m $$

$$ Frictional\; Biases\;(FB)=2\left(\frac{\overline{C_m}-\overline{C_p}}{\overline{C_m}+\overline{C_p}}\right) $$

$$ Geometrical\; mean\;(MG)\; bias= \exp \left(\overline{1n{C}_m}-\overline{1n{C}_p}\right) $$

$$ Root\; mean\; square\; error\;(RMSE)=\sqrt{\frac{{\left({C}_p-{C}_m\right)}^2}{N-1}} $$

$$ Normalized\; mean\; square\; error\;(NMSE)=\frac{\overline{{\left({C}_p-{C}_m\right)}^2}}{\overline{C_p}\ast \overline{C_m}} $$

where C _p is the predicted (modeled) CO concentration (mg m⁻³), C _m is the measured (monitored) CO concentration (mg m⁻³), and N is the number of observations.