PURPOSES : This study analyzes the characteristics of nitrogen oxide concentration by applying titanium dioxide to existing roads in urban areas, using correlation analysis and a generalized linear model. METHODS : To analyze the characteristics of nitrogen oxide concentration with/without applying titanium dioxide to the urban road segment, data acquisition was conducted for nitrogen oxide concentration, weather information, and traffic information, etc., and a correlation analysis was conducted for each factor, with/without applying titanium dioxide to the roads. In addition, nitrogen oxide concentration generation models with/without the application of titanium dioxide to the roads were estimated using a generalized linear model. RESULTS : The results demonstrate that relative humidity and temperature were found to be slightly correlated with the nitrogen oxide concentration, both with and without the application of titanium dioxide to the roads; however, wind speed, solar radiation, and traffic volume were found to have somewhat low correlation according to the results of a correlation analysis. Moreover, relative humidity, temperature, solar radiation, and traffic volume were significant when titanium dioxide was applied to the roads, based on the estimated model from a generalized linear model, and the wind speed, solar radiation, and traffic volume were significant for the absence of titanium dioxide on the roads. CONCLUSIONS : Analytical results indicated that the characteristics of nitrogen oxide concentration vary depending on the application of titanium dioxide to the roads. In particular, when titanium dioxide was applied to the roads, the relative humidity and temperature were analyzed; according to both analyses, i.e., correlation analysis and a generalized linear model, the nitrogen oxide concentration was affected.

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.