Analyzing Nitrogen Dioxide (NO2) Dispersion Patterns Using the Distribution Lag Model (DLM) in Urban Environments

Abstract

Recent research into deteriorating air quality and its related dangers has revealed a notable connection between the expanding pace of urban development and the rising presence of automobiles on our roadways. Additionally, it is noteworthy that the escalating emissions of pollutants from vehicular operations, including nitrogen dioxide, carry detrimental effects on both individuals and the environment. The objective of this investigation is to scrutinize the influence of certain identified factors on the spread of nitrogen dioxide utilizing the Distribution Lag Model (DLM). Data collection was conducted at seven chosen locations, namely the University of Benin Main Gate, Ekosodin junction, Agen Junction, Super D junction, Nitel junction, Okhunmwun junction, and Oluku Market junction. The monitoring of pollutants from vehicular emissions, such as nitrogen dioxide, was carried out in both morning and evening periods over a span of 35 days, from the 7th of July to the 12th of August 2020. This was facilitated using the Aeroqual multi-parameter environmental monitor (series 500) and radiation alert meters. Additionally, other parameters of interest, including maximum temperature and wind speed, were measured using infra-red thermometers and the Sky master thermo anemometer (SM-28). Diagnostic statistics, such as autocorrelation tests, heteroscedasticity assessments, variance inflation factor analysis, and tests of reliability, were conducted to evaluate the quality of the data for regression analysis. The distribution lag model was then employed to explore potential collinearity among the regressor variables and to assess the significant effects of each independent variable on the dependent variable. The study findings indicated elevated levels of nitrogen dioxide (NO₂) in the vicinity of Ugbowo main gate and the Okhunmwun community, particularly during peak traffic hours (4-6 pm) when vehicular activity is highest. Moreover, the results from the distribution lag model revealed a potential correlation between sampling distance and wind speed. Consequently, it was deduced that both sampling distance and wind speed significantly influenced the variation in NO₂ concentration within the study area. Furthermore, based on a computed p-value of 0.0340, it was concluded that temperature has a noteworthy impact on NO₂ dispersion, reaching statistical significance at the 5% confidence level.