Conference Agenda

Forests have traditionally been viewed as carbon sinks, sequestering up to twice as much carbon dioxide as they emit. More recently, however, trees have been found to release appreciable quantities of methane (CH₄), a greenhouse gas with a warming potential 25-times that of CO₂. Despite the significance of this flux for accurate accounting of forest carbon budgets, the mechanism of CH₄ production remains unclear, precluding the development of carbon-conscious forest management decisions. As the majority of CH₄ produced globally is the metabolic byproduct of a group of archaea known as methanogens, understanding the microbial processes occurring within trees—where gas fluxes are emanating from—is of clear importance for evaluating the source of tree-borne CH₄. In response, we sampled over 150 trees across 16 species within a forest in the Northeastern U.S.A. to investigate the causal mechanisms behind tree CH₄ emission. In addition to 16S rRNA and ITS amplicon profiling of tree stem communities, internal gas concentrations, CH₄ flux rates, and soil characteristics were quantified. Our results suggest that the origin of CH₄ emitted from trees is microbial production. Tree wood samples consistently contained genes (mcrA) associated with microbial CH₄ production, and the interior of trees hosted 10-1000x more methanogenic archaea than did the surrounding soil on a per unit-mass comparison. In at least a subset of high-methane emitting trees, the internal fungal and bacterial microbiomes were divergent from those of low-emitting trees.

Reducing emissions of methane may be the fastest way to slow down climate change in the near future, where 30% emissions reductions by 2030 could save 0.5 °C warming by 2050. Concentrated point sources such as oil and gas facilities present an opportunity to reduce primary anthropogenic methane emissions. However, the number of oil and gas facilities is vast in the United States (about 1 million producing wells), raising the cost of emission monitoring and lengthening the detection/fixation cycle. In this work, we applied Machine Learning to predict integrity issues of oil and gas wells (e.g., fluid flow or sustained pressure inside well annulus) which could lead to fugitive methane emissions. This would enable the prioritization of methane monitoring efforts on a high-risk subset of wells, thus bringing down the cost and enhance efficiency. Physical parameters of the well construction, including casing and cement, were manually extracted from 1,300 well completion reports in Bradford County, PA to serve as predictors. In addition, geospatial relationship with the neighboring wells (with or without integrity issues) were also used as predictors. Over 60% precision and recall were obtained using only the physical predictors, while including geospatial predictors further improved the performance. Geospatial correlations between faulty wells and between those wells and topographical and geological features were also analyzed to explore contributing factors of integrity failures. Overall, this work could provide a useful tool to enhance the efficiency of methane emission detection and mitigation effort within the oil and gas industry.

The presence of per- and polyfluoroalkyl substances (PFASs) in landfill leachate is well established and likely represents a source of surface water contamination since PFAS are not attenuated in traditional wastewater treatment systems. In contrast to leachate, there has been little work on the release of PFASs from landfill gas (LFG). The overall objective of this research is to estimate the mass of (PFAS) that are present in landfill gas (LFG) in the U.S.

LFG samples were analyzed for PFAS by pulling samples through a sorbent tube, followed by thermal desorption gas chromatography coupled with mass spectrometry (TD-GC-MS). A total of 47 target, semi-quantitative, and suspect volatile PFAS from nine classes were analyzed including fluorotelomer alcohols (4:2-, 6:2-, 8:2-, and 10:2- FTOHs), C8 perfluorinated sulfonamides (N-MeFOSA, N-EtFOSA), and C8 perfluorinated sulfonamidoethanols (N-MeFOSE, N-EtFOSE). Data from 8 landfills showed that PFAS in the LFG sample is dominated by FTOHs with concentrations one to two orders of magnitude higher than concentrations reported in ambient air measurements above landfills in Canada and Germany.

As of February 2022, we have sampled 28 landfills including sites in arid (<51 cm), moderate (51-102 cm) and wet (>102 cm) regions of the U.S. as defined by the U.S. EPA in the mandatory greenhouse gas reporting rule. PFAS concentration data will be combined with estimates of U.S. LFG generation and emissions to estimate total PFAS fugitive emissions from U.S landfills.

Our group is investigating the effects of waste burning on the release of harmful organic additives from polystyrene plastic wastes. Open pit burning is commonly used as a waste management practice in rural areas, particularly in Native American communities. The burning of plastic waste is known to release toxic airborne contaminants and leave behind plastic particles in the bottom ash. During the production of plastics, additives are mixed with polymers to improve the final products’ chemical and mechanical properties and can often be harmful to human health. Previous research observed that the release of phthalates, a common plasticizing additive and reproductive toxin, increased after photodegradation, suggesting that thermal degradation may induce the same effect. In this study, leachability of phthalates from incinerated polystyrene is evaluated using gas chromatography–mass spectrometry (GC-MS). Changes in surface chemistry and phthalate content of burned polystyrene are determined using X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA), and Fourier-transform infrared spectroscopy (FTIR). The polystyrene surface chemistry and phthalate leachability are used to deduce the mechanisms of phthalate release during and after incineration. Investigating these mechanisms will aid in the understanding of the fate of phthalates and other plastic additives in the environment and the potential health exposures.

Air pollution and subsequent exposures are a byproduct of policy- and technology-driven decisions and should be mitigated by considering solutions derived from both perspectives. Federally-backed redlining led to race-based mortgage lending and zoning, which is now correlated with higher heat and air pollution exposure and adverse health effects. Presently, the e-commerce industry is exacerbating these historical inequities due to the tendency of good movements corridors and fulfillment centers to be nearby communities that are already impacted by over-industrialization and/or socioeconomic stressors. This, in turn, leads to higher air pollution emissions near these communities from rail networks, local and highway automobile corridors, and shipyards. Here, findings are presented from a community-grounded investigation of the impacts of goods movement infrastructure on personal exposure to fine particulate matter pollution. This study considers the impacts of personal mobility, housing characteristics, and ambient air pollution on daily exposures of a southern California community that is adjacent to a high-capacity railyard and a major freight highway. This effort generated household level, data-driven evidence to support community advocacy efforts to mitigate railyard impacts, following onto a previously conducted qualitative-methods investigation. Preliminary conclusions suggest that land use, building characteristics, and indoor activity all compound to worsen air pollution exposures beyond what is expected for exposures in non-industrialized areas. Findings prompt a call for stronger local, state, or federal regulation, not only for emissions, but also for indoor air quality and zoning standards specifically for the protection of communities that are impacted by historical and present-day inequities.