Exploring the Influence of Local Urban and Industrial Carbon-Based Pollutant Sources on Total Column Concentration Enhancements in Houston, Texas during TRACER

Carbon-based pollutants play important roles in climate change, urban air quality, and atmospheric chemistry. Greenhouse gases methane (CH₄), and carbon dioxide (CO₂), along with carbon monoxide (CO), while produced by both anthropogenic and natural sources, play key roles in emission monitoring, mitigation, and impact assessment efforts. The success of these efforts is largely driven by the understanding of local urban and industrial sources. Uncertainties surrounding these sources can have profound effects, highlighted by recent fugitive emissions studies. To further understand these sources, carbon-based pollutants were observed during the 2022 GeoCarb-TRACER Campaign in Houston, Texas, the fourth most populated city in the United States, the largest U.S. city without zoning laws, and a world leader in both petrochemical and plastics manufacturing.

The GeoCarb-TRACER Campaign was as a part of the greater TRacking Aerosol Convection interactions ExpeRiment (TRACER), organized by the U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility. During the GeoCarb-TRACER Campaign, Bruker EM27/SUN spectrometers were deployed at various urban and background sites around the Greater Houston area from late May through mid-September of 2022 during seven intense operational periods (IOPs). The portable Fourier transform spectrometers employed during the campaign capture high-resolution (0.5 cm^-1) spectra in the near- and shortwave-infrared wavelength range. These spectra are commonly analyzed to retrieve column-averaged abundances of species present in the atmosphere, primarily CO₂, CO, CH₄, and water vapor. Up to three EM27/SUN spectrometers were deployed simultaneously alongside instruments gathering boundary layer, aerosol, and near-surface meteorological information.

Data collected during the campaign were analyzed to understand the relationships between total column pollutant enhancements, surface meteorological data, and local pollutant sources. Co-emission and unsupervised machine learning analysis techniques were utilized to understand these complex relationships, building the foundation for data-driven source attribution. Preliminary results suggest that EM27/SUN data can be used to identify periods of time with specific local pollutant sources, such as anomalous fugitive emission releases from nearby petrochemical and related facilities. This presentation will explore this case, along with the other notable connections between local sources, expected carbon-based pollutant enhancements, and the complex meteorology that influences pollutant transport in the Houston area.