Multivariate Time Series Model for Pollutant Transport Using Environmental Monitoring Data

SYNOPTIC ABSTRACT

An important objective of environmental monitoring study is to understand and characterize the effects of local transport of air pollutants through the diurnal or semidiurnal pattern of pollutant concentrations. Due to the complexity of spatial and temporal dynamics of pollutant data, this problem has not received serious attention in statistics. The problem involves developing a statistical model for a hypothesis test for no-transport and estimating the incoming direction and speed of the transport that crosses a set of monitoring stations at a given time period. This paper discusses a nonlinear time series model that can best be described as a complex-valued random coefficient regression, with the design matrix depending nonlinearly on the direction and speed parameters. Using observed pollutant data from a set of local monitoring stations, maximum likelihood estimation for the transport parameters and a hypothesis test for no-transport are considered under stochastic correlated signal assumption. Data used in this study are based on hourly PM_2.5 records from three monitoring stations in southeast Texas for 2002. Three known and one unknown PM_2.5 transport events are considered to test the proposed model. 95% confidence bands of the estimated directions are given. The proposed model has potential to be a useful tool in air pollution source attribution studies.

Key Words:

• Nonlinear time series
• Maximum Likelihood Estimation
• Source Attribution
• Environmental Monitoring
• Particulate Matter