A statistical modeling approach for air quality data based on physical dispersion processes and its application to ozone modeling

Abstract

For many complex environmental processes such as air pollution, the underlying physical mechanism usually provides valuable insights into the statistical modeling. In this paper, we propose a statistical air quality model motivated by a commonly used physical dispersion model, called the scalar transport equation. The emission of a pollutant is modeled by covariates such as land use, traffic pattern and meteorological conditions, while the transport and decay of a pollutant are modeled through a convolution approach which takes into account the dynamic wind field. This approach naturally establishes a nonstationary random field with a space–time nonseparable and anisotropic covariance structure. Note that, due to the extremely complex interactions between the pollutant and environmental conditions, the space–time covariance structure of pollutant concentration data is often dynamic and can hardly be specified or envisioned directly. The relationship between the proposed spatial-temporal model and the physics model is also shown, and the approach is applied to model the hourly ozone concentration data in Singapore.