Locating nearby sources of air pollution using air quality data and wind direction

Regulatory agencies count on self-reported emission inventories of air pollutants to make regulatory decisions. Incorrect emissions used in air quality models may lead to poor control strategies. Thus, an independent method is required to evaluate these emission inventories. This research work brings in a statistical method to determine the locations of nearby sources based on wind direction and measured pollutant concentrations. This statistical method then can be applied to the source contributions estimated by receptor models to indicate the directions and strengths of nearby sources. These predicted locations and strengths of nearby sources provide the fundamental link between the emissions inventory and observed concentrations.; Nonparametric regression is the statistical method introduced in this work to estimate the wind direction that gives a local maximum in the average concentration of an air pollutant. Nonparametric regression can distinguish real concentration peaks from random noise and determine the precise direction of a nearby source with much better accuracy than other traditional methods such as pollution roses. A test of the nonparametric regression method was carried out using measured cyclohexane concentrations at two monitoring sites near a heavy petrochemical region in Houston, Texas. The source location determined by triangulation demonstrates that the nonparametric regression method can estimate the direction of the dominant cyclohexane source precisely and locate that dominant source to within 500m.; The nonparametric regression method can be applied to the source contributions estimated by receptor models as well as individual species concentrations. Unmix multivariate receptor modeling and analysis software is used to determine the sources, compositions, and contributions from monitoring measurements. The most satisfactory Unmix models of 1997 Houston VOC measurements are presented. Nonparametric regression of source contributions on wind direction determines the direction and source impacts of nearby sources to the monitoring site. These observationally-based results are compared with the emission inventory to determine any inconsistencies within the self-reported emission inventories. These inconsistencies will guide the necessary correction to the emission inventories in the future. The nonparametric regression method is a powerful technique that is going to provide significant accomplishments in air quality studies and atmospheric science.