Emission Data Uncertainty in Urban Air Quality Modeling—Case Study

Abstract

Air pollution models are often used to support decisions in air quality management. Due to the complexity of the forecasting system and difficulty in acquiring precise enough input data, an environmental prognosis of air quality with an analytical model of the air pollution dispersion is burdened with a substantial share of uncertainty, especially as regards urban areas. To ignore the uncertainty in the modeling would lead to incorrect policy decisions, with further negative environmental and health consequences. This paper presents a case study which shows how emission uncertainty of air pollutants generated by the industry, traffic, and the municipal sector relates to concentrations measured at receptor points. The computational experiment was implemented in the Warsaw metropolitan area, Poland. The main source of this adverse environmental impact is the transportation system, including the transit traffic. The Monte Carlo technique was used for assessing the key uncertainty factors. Several types of pollution species that are characteristic for the urban atmospheric environment (e.g., PM10, PM2.5, NOx, SO2, Pb) were included in the analysis. The results show significant spatial variability of the modeled uncertainty. The reason of this variability is discussed in detail. It depends not only on the category of the emission source but also on the contributing emission sources and their quantity.