Criteria air pollutants, such as nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), particulate matter (PM), and ozone (O3), are characterized by temporal and locational hot spots in urban areas, frequently violating pollution standards, and, as a result, threatening the health and well-being of the population. Several factors, such as the intensity and duration of emissions, the chemical reactions among pollutants, the uptake and assimilation of pollutants by urban vegetation, and the meteorological factors that induce chemical reactions and atmospheric dispersion, have been considered as explanatory variables in air quality models. Among them, emissions from motor vehicles turn out to be a key determinant of the spatial and temporal patterns of ambient pollution concentrations.
The purpose of this research is to formulate and estimate (1) metropolitan-wide time-series air quality models and (2) land-use regression (LUR) air quality panel models, in order to explain spatio-temporal variations in pollution concentrations. Using the Seoul Metropolitan Area as a case study, traffic counts, vehicle-kilometers-traveled (VKT), land uses, and meteorological factors, such as solar radiation, temperature, humidity, wind speed and wind direction, are used as explanatory variables. An extensive understanding of atmospheric pollutants chemistry is reflected in the formulation of these models. Differences in concentrations measured at air quality monitoring stations (AQMs) across the week (weekdays vs. Sunday) and geographical locations (roadside vs. background), are also investigated using dummy variables and the product of these variables with the original variables.
The results of the time-series models and panel regression models indicate that traffic counts and VKT are significant in explaining the concentrations of both directly emitted pollutants, such as NO2, CO, SO2, and PM, and O3, a secondary pollutant. The concentrations of the directly emitted pollutants are positively correlated with vehicle flows. In the case of O3, however, vehicle emissions have a negative impact on O3 concentrations. Since solar radiation, temperature, humidity, and wind speed influence both chemical reactions and physical dispersion, these factors are closely correlated with pollution concentrations. In particular, solar radiation plays a critical role regarding NO2 and O3 concentrations. Ultraviolet (UV) radiation causes the photodissociation of NO2, providing favorable conditions for the generation of O3 in the troposphere. The estimated models confirm that solar radiation have a positive effect in the O3 models, and a negative one in the NO2 models.
Reduced traffic flows on Sunday increase the ratio of volatile organic compounds (VOC) to NOx and, consequently, lead to favorable conditions for O3 generation. Less O3 titration and less HNO3 formation occur on Sunday as compared to weekdays, resulting in higher O3 concentrations on Sunday. For directly emitted pollutants, reduced traffic flows on Sunday induce a decrease in concentrations. The locations of the AQMs turn out to be critical. Traffic flows closer to AQMs have larger impacts on pollution concentrations. The product terms between VKT and roadside dummy variable display the expected results: for directly emitted pollutants, the coefficients are significant and positive, suggesting that the impacts of roadside VKT are greater than those of background VKT. In the case of O3, the estimated coefficients are negative, indicating that the negative impacts of VKT on O3 concentrations are increased at roadside areas. Nitric oxide emissions from commercial and residential areas have a negative impact on O3 concentrations. Plants have an O3 assimilation capacity, but also emit biogenic VOC during the growing seasons, generating simultaneous negative and positive impacts. The overall vegetative areas impact on O3 concentrations is positive. For directly emitted pollutants, however, vegetative areas have a negative impact. Since residential, commercial, and industrial areas generate anthropogenic emissions, the coefficients of these land uses are positive.