Analysis of temporal and spatial trends of greenhouse gas emission sources with conditional bivariate probability functions.

摘要

The primary purpose of this study is to develop a method for estimating the location of a single point source of a greenhouse gas (GHG) leak. The case study is the University of Utah campus. Specifically, we hypothesized that sewer access covers ("manholes") are significant sources of GHG emissions on campus, and we used these known point source locations to test the ability of standard GHG measurement instruments to develop methods for identifying a priori unknown locations. We sited gas analyzers at specific distances and directions from sewer access covers and then evaluated whether tailored probability density functions would "point" towards the sites. All GHG data were measured by a Picarro(c) cavity ring-down spectrometer (CRDS) gas analyzer and contemporaneous wind speed and direction were measured with a 3-dimensional (3D) anemometer. Since this study focused on locating leakage sources, we assigned the concentration threshold for leakage concentration to be the 99th percentile concentration of each dataset, a common approach in leakage detection studies. The leakage concentration data along with corresponding wind speed and direction were used to create conditional bivariate probability function (CBPF) plots. All CBPF plots were constructed for varying time spans throughout the day at each collection site to discern the probable location of GHG leakage sources. The results revealed that all 99th percentile concentrations were associated with lower wind speeds (<1.5 m/s). Higher GHG concentrations associated with high wind speeds were most likely diluted before the signal could reach the receptor. Furthermore, the study site is characterized by hilly terrain, large buildings, and a moderate amount of large vegetation (trees) that likely tend to disperse what would otherwise be detectable GHG signals. Differences in CBPF plots at different receptor (tower) locations confirm that the distance between the tower and leakage sources is a critical issue. To our knowledge, this study is the first to apply CBPF's with the intent of quantifying trends in spatio-temporal GHG distributions from point leakage sources and determining probable locations of those sources.