#TITLE_ALTERNATIVE#

• Main Author: LEVIANA (NIM 15304005), NADYA
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/11021
• Institution: Institut Teknologi Bandung

Nitrogen dioxide (NO2) and Nitric Oxide (NO) are pollutants that emitted from motor vehicles. Quantitatively, the amount of those pollutants is expressed as oxides nitrogen (NOx=NO+NO2). From those pollutants, NO2 causes health impact more than NO, so that it used to evaluate air quality. NO2 is generated in the atmosphere through oxidation and photolytic reactions between NO and O3. Due to its proven health impacts, it is important to predict and to evaluate NO2 concentration particularly near a major source, e.g roadways.<p>CALINE4 model that developed by Caltrans is a dispersion model that is used to predict the concentration of NO2 from road source. Unfortunately, the freeware version of CALINE4 can not predict NO2 directly. In order to predict NO2 concentration from CALINE4, empirical relationship between NO2 and NOx from roadside monitoring data were developed. The empirical function found then used to convert the model outputs into NO2 before the validation of model. Modeling data were taken from roadside air monitoring data at TransJakarta Busway Coridor I and North Bandung. To find the empirical functions, data were divided into 3 scenarios based on location, those are the pooled-location, Jakarta location and Bandung location with time variation (pooled and splitted time). The initial objective is to find the general empirical function of NO2 and NOx relationship (the general empirical function found is (NO2)1/2 = 3,365 Log10NOx + (-0,544)), however from the statistical analysis it is found that the empirical functions are better when the locations are modeled separately. The results show that based on correlation analysis the best function is found from Bandung data set. For CALINE4 modeling, data were divided into 2 scenarios based on location, those are Jakarta and Bandung locations with the variation of time (morning, noon, evening) and emission factor (EF) variation (from India and Indonesia). The model validation done after the model outputs were converted into NO2 using the empirical function. The validation results shows that the data with EF from Indonesia is more accurate based on the error analysis. The error is particularly produced from the magnitude of Indian's EF that are (6-7 for Jakarta and 2-3 for Bandung) times higher that the Indonesians. Error value of EF from India is about 105,94 %-201,25% for Jakarta and about 94,7%-226,8% for Bandung. Error value of EF from Indonesia is about 6,75%-64,65% for Jakarta and about 21,43%-35,84% for Bandung. EF from Indonesia is plotted on the factor of 1 and 2 lines while EF from India plotted on the factor of 2 and 3 lines. Modeling results with Indonesian's FE seems like having a better accuration level, but actually it could not be used for evaluating the results of a microscale strategy on reducing the emission, e.g emission from motor vehicle, because it has not included the impact of motor velocity changes. By converting NOx concentration from model outputs using the empirical function between NOx dan NO2, the validation results are better than the preceding research (Melissa, 2007).<p>Modeling results applied for TransJakarta Coridor I in order to evaluate the effect of Busway implementation there. The isopleths results shows that there are there are a decreasing of NO2 concentration after the implementation of Busway, but there are no effect to the spreading location of NO2 caused of Busway implementation (applied for data with emission factor from Indonesia and India). It should be noted that the NO2 concentration value from modeling have not quantitavely valid. The model results could be used to see the spreading pattern of the pollutants, but the accuration level of the prediction of concentration value has to be fixed first by improving the model inputs. It can be concluded that freeware version of CALINE4 model can be used for predicting the roadside NO2 concentration (with the accuration level noted above) by using the empirical function of NOx dan NO2 relationship to convert the NOx concentration as the model outputs into NO2 concentration. <br />