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Across many cities, estimates of methane emissions from natural gas (NG) distribution and end use based on atmospheric measurements have generally been more than double bottom-up estimates. We present a top-down study of NG methane emissions from the Boston urban region spanning 8 y (2012 to 2020) to assess total emissions, their seasonality, and trends. We used methane and ethane observations from five sites in and around Boston, combined with a high-resolution transport model, to calculate methane emissions of 76 ± 18 Gg/yr, with 49 ± 9 Gg/yr attributed to NG losses. We found no significant trend in the NG loss rate over 8 y, despite efforts from the city and state to increase the rate of repairing NG pipeline leaks. We estimate that 2.5 ± 0.5% of the gas entering the urban region is lost, approximately three times higher than bottom-up estimates. We saw a strong correlation between top-down NG emissions and NG consumed on a seasonal basis. This suggests that consumption-driven losses, such as in transmission or end-use, may be a large component of emissions that is missing from inventories, and require future policy action. We also compared top-down NG emission estimates from six US cities, all of which indicate significant missing sources in bottom-up inventories. Across these cities, we estimate NG losses from distribution and end use amount to 20 to 36% of all losses from the US NG supply chain, with a total loss rate of 3.3 to 4.7% of NG from well pad to urban consumer, notably larger than the current Environmental Protection Agency estimate of 1.4% [R. A. Alvarez et al., Science 361, 186–188 (2018)].

Significance Most recent analyses of the environmental impact of natural gas have focused on production, with very sparse information on emissions from distribution and end use. This study quantifies the full seasonal cycle of methane emissions and the fractional contribution of natural gas for the urbanized region centered on Boston. Emissions from natural gas are found to be two to three times larger than predicted by existing inventory methodologies and industry reports. Our findings suggest that natural-gas–consuming regions may be larger sources of methane to the atmosphere than is currently estimated and represent areas of significant resource loss. Methane emissions from natural gas delivery and end use must be quantified to evaluate the environmental impacts of natural gas and to develop and assess the efficacy of emission reduction strategies. We report natural gas emission rates for 1 y in the urban region of Boston, using a comprehensive atmospheric measurement and modeling framework. Continuous methane observations from four stations are combined with a high-resolution transport model to quantify the regional average emission flux, 18.5 ± 3.7 (95% confidence interval) g CH ₄⋅m ⁻²⋅y ⁻¹. Simultaneous observations of atmospheric ethane, compared with the ethane-to-methane ratio in the pipeline gas delivered to the region, demonstrate that natural gas accounted for ∼60–100% of methane emissions, depending on season. Using government statistics and geospatial data on natural gas use, we find the average fractional loss rate to the atmosphere from all downstream components of the natural gas system, including transmission, distribution, and end use, was 2.7 ± 0.6% in the Boston urban region, with little seasonal variability. This fraction is notably higher than the 1.1% implied by the most closely comparable emission inventory.

Cities are greenhouse gas emission hot spots, making them targets for emission reduction policies. Effective emission reduction policies must be supported by accurate and transparent emissions accounting. Top-down approaches to emissions estimation, based on atmospheric greenhouse gas measurements, are an important and complementary tool to assess, improve, and update the emission inventories on which policy decisions are based and assessed. In this study, we present results from 9 research flights measuring CO2 and CH4 around New York City during the nongrowing seasons of 2018–2020. We used an ensemble of dispersion model runs in a Bayesian inverse modeling framework to derive campaign-average posterior emission estimates for the New York–Newark, NJ, urban area of (125 ± 39) kmol CO2 s–1 and (0.62 ± 0.19) kmol CH4 s–1 (reported as mean ± 1σ variability across the nine flights). We also derived emission estimates of (45 ± 18) kmol CO2 s–1 and (0.20 ± 0.07) kmol CH4 s–1 for the 5 boroughs of New York City. These emission rates, among the first top-down estimates for New York City, are consistent with inventory estimates for CO2 but are 2.4 times larger than the gridded EPA CH4 inventory, consistent with previous work suggesting CH4 emissions from cities throughout the northeast United States are currently underestimated.

Using the Purdue University Airborne Laboratory for Atmospheric Research, we measured concentrations of methane and ethane emanating from seven U.S. cities (New York, NY, Philadelphia, PA, Washington, D.C./Baltimore, MD, Boston, MA, Chicago, IL, Richmond, VA, and Indianapolis, IN), in order to determine (with a median 95% CI of roughly 7%) the fraction of methane emissions attributable to natural gas (Thermogenic Methane Emission Ratio [TMER]), for both summer and winter months. New methodology is introduced to compute inflow concentrations and to accurately define the spatial domain of the sampling region, using upwind measurements coupled with Lagrangian trajectory modeling. We show discrepancies in inventory-estimated TMER from cities when the sample domain is defined using political boundaries versus urban centers encircled by the flight track and highlight this as a potential source of error common to top-down studies. We found that methane emissions of natural gas were greater than winter biogenic emissions for all cities except Richmond, where multiple landfills dominate. Biogenic emissions increased in summer, but natural gas remained important or dominant (20%–80%). National inventories should be updated to reflect the dominance of natural gas emissions for urban environments and to account for seasonal increases in biogenic methane in summer.

Urban areas are the major sources of greenhouse gas emissions but also leaders in emission reduction efforts. Appropriate techniques to quantify emissions and any potential reductions over time are necessary to effectively inform these mitigation efforts. The aircraft mass balance experiment (MBE) is an established technique used for such a purpose. In this work, we use a series of 55 MBEs downwind of power plants to assess the technique’s bias and precision. In addition, we investigate what factors drive the absolute error, determined as the absolute difference between observed and reported emission rates, in individual experiments using multilinear regressions. Power plants are required to monitor their carbon dioxide emissions with an hourly resolution, and these publicly available reported emissions can be directly compared to the mass balance estimates as a pseudo-known release. To quantify the bias we calculated the mean error, which was 10 ± 240 Mg·h−1 (1σ), regressed mass balance emission rates against reported emission rates to yield a slope of 0.967 ± 0.062, and compared the sum across all mass balance emission rates, 31,000 ± 1,000 Mg·h−1, to the sum across all reported emissions, 30,660 ± 740 Mg·h−1. All three of these approaches suggest no systematic bias. Then to quantify the precision for individual determinations we calculated the slope of a regression between the standard deviation across repeated MBEs and the corresponding average emission rate, which is 30.7% ± 6.7%. The main drivers of the absolute error were sparse sampling of the plume, poor horizontal and vertical mixing of the plume, and smaller signal-to-noise ratios. Quantifying the capabilities of this technique provides context for previous analyses and allows stakeholders and researchers to make informed decisions when choosing quantification methods. Identifying the factors that drive the absolute error also allows us to adjust flight design to minimize it and potentially improve uncertainty estimates.

To effectively address the unprecedented acceleration of climate change, cities across the United States are leading efforts to reduce greenhouse gas emissions. Coherent, aggressive, and lasting mitigation policies in controlling carbon emissions are beginning to be adopted to help strengthen climate resilience across different sectors. However, evaluating the effectiveness of current climate legislation requires careful monitoring of emissions through measurable and verifiable means to inform policy decisions. As a part of this effort, we developed a new method to spatially allocate aircraft-based mass balance carbon dioxide (CO2) emissions. In this work, we conducted 7 aircraft flights, performed downwind of New York City (NYC) to quantify CO2 emissions during the nongrowing seasons between 2018 and 2020. We used an ensemble of emission inventories and transport models to calculate the fraction of enhancements (Φ) produced by sources within the policy-relevant boundaries of the 5 NYC boroughs and then applied that to the bulk emissions calculated using the mass balance approach. We derived a campaign-averaged source-apportioned mass balance CO2 emission rate of (57 ± 24) (1σ) kmol/s for NYC. We evaluated the performance of this approach against other top-down methods for NYC including inventory scaling and inverse modeling, with our mean emissions estimate resulting in a 6.5% difference from the average emission rate reported by the 2 complementary approaches. By combining mass balance and transport model approaches, we improve upon traditional mass balance experiment methods to enable quantification of emissions in complex emission environments. We conducted an assessment using an ensemble of emission inventories and transport models to determine the sources of variability in the final calculated emission rates. Our findings indicate that the choice of inventory accounted for 2.0% of the variability in the emission estimates and that the atmospheric transport model contributed 3.9% at the campaign level. Additionally, on average, at the daily scale, the transport model contributed 7.6% and the inventory accounted for 14.1%. The daily flight-to-flight variability contributed a significant portion, at 42.1%. This approach provides a solution to the difficulty of interpreting aircraft-based mass balance results in complex emission environments.

Appropriate techniques to quantify greenhouse gas emission reductions in cities over time are necessary to monitor the progress of these efforts and effectively inform continuing mitigation. We introduce a scaling factor (SF) method that combines aircraft measurements and dispersion modeling to estimate urban emissions and apply it to 9 nongrowing season research aircraft flights around New York City (NYC) in 2018–2020. This SF approach uses a weighting function to focus on an area of interest while still accounting for upwind emissions. We estimate carbon dioxide (CO2) emissions from NYC and the Greater New York Area (GNA) and compare to nested inversion analyses of the same data. The average calculated CO2 emission rates for NYC and the GNA, representative of daytime emissions for the flights, were (49 ± 16) kmol/s and (144 ± 44) kmol/s, respectively (uncertainties reported as ±1σ variability across the 9 flights). These emissions are within ∼15% of an inversion analysis and agree well with inventory estimates. By using an ensemble, we also investigate the variability introduced by several sources and find that day-to-day variability dominates the overall variability. This work investigates and demonstrates the capability of an SF method to quantify emissions specific to particular areas of interest.

The Marcellus Shale is a rapidly developing unconventional natural gas resource found in part of the Appalachian region. Air quality and climate concerns have been raised regarding development of unconventional natural gas resources. Two ground-based mobile measurement campaigns were conducted to assess the impact of Marcellus Shale natural gas development on local scale atmospheric background concentrations of air pollution and climate relevant pollutants in Pennsylvania. The first campaign took place in Northeastern and Southwestern PA in the summer of 2012. Compounds monitored included methane (CH4), ethane, carbon monoxide (CO), nitrogen dioxide, and Proton Transfer Reaction Mass Spectrometer (PTR-MS) measured volatile organic compounds (VOC) including oxygenated and aromatic VOC. The second campaign took place in Northeastern PA in the summer of 2015. The mobile monitoring data were analyzed using interval percentile smoothing to remove bias from local unmixed emissions to isolate local-scale background concentrations. Comparisons were made to other ambient monitoring in the Marcellus region including a NOAA SENEX flight in 2013. Local background CH4 mole fractions were 140 ppbv greater in Southwestern PA compared to Northeastern PA in 2012 and background CH4 increased 100 ppbv from 2012 to 2015. CH4 local background mole fractions were not found to have a detectable relationship between well density or production rates in either region. In Northeastern PA, CO was observed to decrease 75 ppbv over the three year period. Toluene to benzene ratios in both study regions were found to be most similar to aged rural air masses indicating that the emission of aromatic VOC from Marcellus Shale activity may not be significantly impacting local background concentrations. In addition to understanding local background concentrations the ground-based mobile measurements were useful for investigating the composition of natural gas emissions in the region.

The Aerodyne Mobile Laboratory was deployed to the Houston Ship Channel and surrounding areas during the Benzene and Other Toxics Exposure field study in February 2015. We evaluated atmospheric concentrations of volatile organic hydrocarbons and other hazardous air pollutants of importance to human health, including benzene, 1,3-butadiene, toluene, xylenes, ethylbenzenes, styrene, and NO2. Ambient concentration measurements were focused on the neighborhoods of Manchester, Harrisburg, and Galena Park. The most likely measured concentration of 1,3-butadiene in the Manchester neighborhood (0.17 ppb) exceeds the Environmental Protection Agency's E-5 lifetime cancer risk level of 0.14 ppb. In all the three neighborhoods, the measured benzene concentration falls below or within the E-5 lifetime cancer risk levels of 0.4–1.4 ppb for benzene. Pollution maps as a function of wind direction show the impact of nearby sources.

An endophytic fungus of Persea indica was identified, on the basis of its anamorphic stage, as Nodulosporium sp. by SEM. Partial sequence analysis of ITS rDNA revealed the identity of the teleomorphic stage of the fungus as Hypoxylon sp. It produces an impressive spectrum of volatile organic compounds (VOCs), most notably 1,8-cineole, 1-methyl-1,4-cyclohexadiene, and tentatively identified (+)-.alpha.-methylene-.alpha.-fenchocamphorone, among many others, most of which are unidentified. Six-day-old cultures of Hypoxylon sp. displayed maximal VOC-antimicrobial activity against Botrytis cinerea, Phytophthora cinnamomi, Cercospora beticola, and Sclerotinia sclerotiorum suggesting that the VOCs may play some role in the biology of the fungus and its survival in its host plant. Media containing starch- or sugar-related substrates best supported VOC production by the fungus. Direct on-line quantification of VOCs was measured by proton transfer mass spectrometry covering a continuous range with optimum VOC production occurred at 6 days at 145 ppmv with a rate of production of 7.65 ppmv/h. This report unequivocally demonstrates that 1,8-cineole (a monoterpene) is produced by a microorganism, which represents a novel and important source of this compound. This monoterpene is an octane derivative and has potential use as a fuel additive as do the other VOCs of this organism. Thus, fungal sourcing of this compound and other VOCs as produced by Hypoxylon sp. greatly expands their potential applications in medicine, industry, and energy production.