Thomas C. Moore

Nitrous oxide (N2O) is a potent greenhouse gas and the most abundant stratospheric ozone-depleting substance. Atmospheric mixing ratios of N2O have rapidly increased in recent years, and quantifying sources of N2O emissions is critical for climate change mitigation. We quantified and characterized N2O emission sources at street level using mobile laboratory measurements in New York City taken over 10 days in January and June 2025. N2O plume enhancements were strongly correlated with carbon monoxide (CO) and carbon dioxide (CO2). We identified city buses using clean air technology as the source of these enhancements. Using estimates of bus-related CO2 emissions, we estimated annual N2O emissions from buses in New York City of 31 (±14) tonne-N2O yr–1. With increased deployment of clean air technologies to reduce NOx pollution from diesel vehicles globally, N2O emissions from transportation are likely under-reported and should be accounted for in emissions inventories.

Reducing methane (CH ₄ ) emissions has become increasingly important in recent years due to its importance for radiative forcing. Fugitive emissions of CH ₄ from natural gas distribution infrastructure are of particular interest as a mitigation target within the oil and gas sector. Previous studies have shown the ability to detect these emissions by use of mobile surveys measuring CH ₄ , with some studies using ratios to secondary co-emitted compounds as a means of predicting the source of emission. This study aims to adapt existing algorithm parameters by investigating the limitations of equipment within the platform used for mobile surveys. These changes suggest that previous methods may underpredict the number of Leak Indications (LIs) by 53.5 % with 27 LIs detected with the old methodology compared to 58 LIs detected with the new methodology. The majority of these LIs were found to be emitting in a leak rate category of 0–2 L min ⁻¹ . Source determination was included as a core step within the algorithm, which was shown to reduce the misassignment of LIs, suggesting when not using this step, emissions from pyrogenics and biogenics are included within LI assignments.

Assessing the impact of indoor volatile organic compound (VOC) sources on outdoor concentrations remains challenging due to their variability, rapid dispersion, and chemical reactions in the atmosphere. Mobile monitoring can address these challenges by providing spatial and temporal resolution of localized emission sources. In this study, we developed a new approach to characterize the impact of indoor emissions on outdoor air quality using mobile measurements. We used geographic information to identify the locations of hundreds of individual source types in Bradford, England, including restaurants, beauty salons, and automobile repair shops. For each source type, we modeled the potential hourly contribution using an advanced Gaussian plume modelincorporating wind speed and directionacross 22 mobile measurement circuits (approximately 56 modeled hours). The outcome is a single source factor for each latitude-longitude coordinate at each hour of the measurement campaign, representing the influence level of each source type. We then applied K-means clustering to group source factors based on their spatial distributions and influence levels, and analyzed their relationship with the incremental concentrations of VOCs and NO _x using bootstrap-validated generalized additive models (GAMs). Several previously identified key tracer compounds showed robust associations with specific source factors, including m/z 102 (tentatively assigned as butanone) with auto repair source factor, m/z 59 (acetone) with beauty salon source factor, and m/z 68 (isobaric compounds: isoprene and furan) with restaurant source factor. This method offers a new perspective on air quality monitoring by using source location information to inform the analysis of mobile measurements, providing a robust framework for identifying the outdoor signatures of commercial indoor activities.

Results are shown for methane emissions associated with oil loading operations to a shuttle tanker, using measurements obtained from a research aircraft and an unmanned aerial vehicle (UAV), together with multiple modeling approaches.

Assessing the impact of indoor volatile organic compound (VOC) sources on outdoor concentrations remains challenging due to their variability, rapid dispersion, and chemical reactions in the atmosphere. Mobile monitoring can address these challenges by providing spatial and temporal resolution of localized emission sources. In this study, we developed a new approach to characterize the impact of indoor emissions on outdoor air quality using mobile measurements. We used geographic information to identify the locations of hundreds of individual source types in Bradford, England, including restaurants, beauty salons, and automobile repair shops. For each source type, we modeled the potential hourly contribution using an advanced Gaussian plume modeling system across approximately 57 hours of mobile measurements. The outcome is a single source factor for each latitude-longitude coordinate at each hour of the measurement campaign, representing the influential level of each source type. We then applied K-means clustering to group source factors based on their spatial distributions and influence levels, and analyzed their relationship with the incremental concentrations of VOCs and NOx using a generalized additive model. Several previously identified key tracer compounds showed strong correlations with specific source factors. These include m/z 102 (tentatively assigned as butanone) with auto repair source factor, m/z 88 (acetone) with beauty salon source factor, and m/z 68 (isobaric compounds: isoprene and furan) with restaurant source factor. By clustering the source factor metric, we linked these emission sources to VOC concentrations at different locations along the mobile measurement route. Our method offers a new perspective on air quality monitoring by using source location information to inform the analysis of mobile VOC measurements, complementing existing source characterization approaches.

The methane pledge brought about during COP26 requires its signatories to reduce their 2020 methane emissions by 30% by 2030 (European Commission and United States of America, 2021). 65 % of all methane emissions are thought to be anthropogenic in nature (Saunois et al., 2025)One of the major anthropogenic sectors contributing to methane emissions is from the oil and gas industry. Fugitive emissions of methane are one of the major contributors to emissions from this industry, this may refer to unwanted emission during transportation of product (colloquially referred to as gas leaks), or flaring, where methane undergoes combustion to CO2. As of 2023 the oil and gas industry was responsible for 1.2 % of the UK’s methane emissions, with flaring emissions representing 69 % of this sector's emissions. (North Sea Transition Authority, 2025).Flaring may occur for one of three reasons; Routine flaring, where an oil producing facility is unable to use the produced gas; Safety flaring, where flaring ensures the safe operation of the facility; Non-routine flaring encompasses all other flaring. The North Sea is one of the most active areas in the world for oil and gas activities. Recent attempts between 2018 and 2022 have reduced flaring activities in the North Sea by 50%, with an aim for zero flaring to take place by 2030. This is an important step to reducing emissions in this region as one fifth of all emissions in the North Sea related to oil and gas production activities are attributed to flaring (North Sea Transition Authority, 2023). While flaring activity continues to be reduced, some facilities require the continued use of flares, in these cases attempts have been made to adapt the flare itself and improve its overall efficiency and ensure that more methane is converted to CO2. This work features data collected from one sampling flight of a platform with a highly efficient flare that was intentionally flaring while on task. We explore the feasibility of using previous detection methodologies, such that present in (Shaw et al., 2023), while adding additional stages to confirm the detection of a flare, including source identification using NOx : CO2 ratios as well as modelling the dispersion of multiple sources on the platform using ADMS to understand the likelihood of detecting the flare.

Reducing methane emissions has become increasingly important in recent years due to its importance for radiative forcing. Of the many sources of methane, fugitive emissions from a country's domestic natural gas network are one that can have a direct impact on the citizens of a country. Previous studies have shown the ability to detect these emissions by use of mobile surveys measuring methane, some of these use secondary co-emitted compounds as a means of confirming the nature of the emission. This study aims to adapt existing algorithms parameters by investigating the limitations of equipment used within the platform used for mobile surveys. This has led to reduced enhancement parameters as well as reduced time clustering parameters. These changes suggest that previous methods may underpredict the number of Leak Indications (LIs) by 53.5 % with number of LIs detected in the old method being 27 and the new method detecting 58. When source appointment was included as a core step within the algorithm itself, the total fugitive natural gas emissions within a city was reduced from 185.10 L min^-1 to 60.23 L min^-1, nearly three times lower.

Reducing methane emissions has become increasingly important in recent years due to its importance for radiative forcing. Of the many sources of methane, fugitive emissions from a country's domestic natural gas network are one that can have a direct impact on the citizens of a country. Previous studies have shown the ability to detect these emissions by use of mobile surveys measuring methane, some of these use secondary co-emitted compounds as a means of confirming the nature of the emission. This study aims to adapt existing algorithms parameters by investigating the limitations of equipment used within the platform used for mobile surveys. This has led to reduced enhancement parameters as well as reduced time clustering parameters. These changes suggest that previous methods may underpredict the number of Leak Indications (LIs) by 53.5 % with number of LIs detected in the old method being 27 and the new method detecting 58. When source appointment was included as a core step within the algorithm itself, the total fugitive natural gas emissions within a city was reduced from 185.10 L min^-1 to 60.23 L min^-1, nearly three times lower.

Reducing methane emissions has become increasingly important in recent years due to its importance for radiative forcing. Of the many sources of methane, fugitive emissions from a country's domestic natural gas network are one that can have a direct impact on the citizens of a country. Previous studies have shown the ability to detect these emissions by use of mobile surveys measuring methane, some of these use secondary co-emitted compounds as a means of confirming the nature of the emission. This study aims to adapt existing algorithms parameters by investigating the limitations of equipment used within the platform used for mobile surveys. This has led to reduced enhancement parameters as well as reduced time clustering parameters. These changes suggest that previous methods may underpredict the number of Leak Indications (LIs) by 53.5 % with number of LIs detected in the old method being 27 and the new method detecting 58. When source appointment was included as a core step within the algorithm itself, the total fugitive natural gas emissions within a city was reduced from 185.10 L min^-1 to 60.23 L min^-1, nearly three times lower.

Reducing methane emissions has become increasingly important in recent years due to its importance for radiative forcing. Of the many sources of methane, fugitive emissions from a country's domestic natural gas network are one that can have a direct impact on the citizens of a country. Previous studies have shown the ability to detect these emissions by use of mobile surveys measuring methane, some of these use secondary co-emitted compounds as a means of confirming the nature of the emission. This study aims to adapt existing algorithms parameters by investigating the limitations of equipment used within the platform used for mobile surveys. This has led to reduced enhancement parameters as well as reduced time clustering parameters. These changes suggest that previous methods may underpredict the number of Leak Indications (LIs) by 53.5 % with number of LIs detected in the old method being 27 and the new method detecting 58. When source appointment was included as a core step within the algorithm itself, the total fugitive natural gas emissions within a city was reduced from 185.10 L min^-1 to 60.23 L min^-1, nearly three times lower.