PURPOSES : The objective of this study is to determine the characteristics of nitrogen oxide (NOx) concentration in an exclusive median bus lane station. The hourly variations of NOx concentration and the effect of traffic volume on NOx concentration were analyzed using NOx measurement data and the number of vehicles at the station. METHODS : Data were collected using the chemiluminescence method for NOx concentration. Atmospheric information and traffic volume of buses were collected from the Korea Meteorological Administration and Bus Information System, respectively. RESULTS : As a result, the NO2 concentration in the test section was found to have a strong correlation with those in the atmospheric measurement station located near the test-bed. In addition, the average NOx concentrations in the test section were significantly higher than those of the other monitoring stations due to bus emissions. The average NOx concentration in the exit section was higher than that in the entry section owing to the vehicle’s frequent stops and fuel consumption due to acceleration. During the measurement period, the average NOx concentration was measured as approximately 33 % higher in the exit section than in the entry section. In addition, the NOx concentration at the bus station was found to increase as the bus dwell time increased, rather than the number of bus passages. CONCLUSIONS : This study provides clear characteristics of the NOx correlations with traffic information in an exclusive median bus lane station. It was shown that the NOx concentration at the bus station increased as the number of passes and bus dwell time increased. According to the coefficient of determination, the dwell time is more closely correlated to the NOx concentration at the bus station than the number of bus passes, indicating that it is a better parameter for predicting NOx concentration at bus stations.

PURPOSES : This study analyzes the characteristics of generated fine particulate matter (PM2.5) and nitrogen oxide (NOX) at roadsides using a statistical method, namely, a generalized linear model (GLM). The study also investigates the applicability and capability of a machine learning methods such as a generalized regression neural network (GRNN) for predicting PM2.5 and NOX generations. METHODS : To analyze the characteristics of PM2.5 and NOX generations at roadsides, data acquisition was conducted in a specific segment of roads, and PM2.5 and NOX prediction models were estimated using GLM. In addition, to investigate the applicability and capability of a machine learning methods, PM2.5 and NOX prediction models were estimated using a GRNN and were compared with models employing previously estimated GLMs using r-square, mean absolute deviation (MAD), mean absolute percentage error (MAPE), and root mean square error (RMSE) as parameters. RESULTS : Results revealed that relative humidity, wind speed, and traffic volume were significant for both PM2.5 and NOX prediction models based on estimated models from a GLM. In addition, to compare the applicability and capability of the GLM and GRNN models (i.e., PM2.5 and NOX prediction models), the GRNN model of PM2.5 and NOX prediction was found to yield better statistical significance for r-square, MAD, MAPE, and RMSE as compared with the same parameters used in the GLM. CONCLUSIONS : Analytical results indicated that a higher relative humidity and traffic volume could lead to higher PM2.5 and NOX concentrations. By contrast, lower wind speed could affect higher PM2.5 and NOX concentrations at roadsides. In addition, based on a comparison of two statistical methods (i.e., GLM and GRNN models used to estimate PM2.5 and NOX), GRNN model yielded better statistical significance as compared with GLM.

PURPOSES : The objective of this study is to figure out the trend and characteristics of fine particulate matter (PM2.5) and nitrogen oxide (NOx) concentration in underpass sections. The effect of traffic and meteorological condition on PM2.5 / NOx concentration was analyzed using field monitoring data. METHODS : Based on the literature review, PM2.5 and NOx concentration data were monitored using DustTrak II aerosol monitoring system and Serinus 40 oxides of nitrogen analyzer, respectively. Meteorological and traffic information was collected using automatic weather system and traffic volume counter, respectively. RESULTS : PM2.5 has a positive and negative correlation with relative humidity and wind speed, respectively. Meanwhile, NOx was found to have no correlation with meteorological conditions. The NO/NO2 ratio tends to change with traffic volume, indicating higher correlation between NO and traffic volume; the observed NO2 is mostly a secondary material produced by NO oxidation. CONCLUSIONS : Our study provides clear characteristics of NOx and PM2.5 and correlations with meteorological and traffic information in the underpass sections. It is found from this study that the increase in wind speed causes reduction in the concentration of PM2.5 owing to the diffusion and dispersion phenomena. On the other hand, the meteorological conditions were found to barely have correlations with NOx concentrations in this study. The traffic volume could significantly affect the NOx concentration and NO / NO2 ratio, which is directly correlated to the emissions from vehicles.