The transport capacity, sediment supply, and sediment availability in an aeolian landscape exert control on its bedforms. In this work, we use a 200 m long megaripple field forming on the 400 m high El Chingue escarpment in Chilean Patagonia as a case study in evaluating how spatial differences in these factors affect bedforms. Strong westerly winds encounter the steep escarpment of El Chingue, which creates a speed-up effect and increased transport capacity at the ridgeline followed by a trend of decreasing wind speed and transport capacity across the megaripple field. We describe and analyze the morphological, granulometric, and structural responses of the megaripples to this spatial trend. Results indicate that upwind megaripples are smaller, have poor sorting, thin armor layers, and no cross-bedding. In the middle of the field, the megaripples are larger, have thicker armor layers, a distinct bimodal distribution, and well-developed cross-bedding. At the downwind margin, megaripples are increasingly vegetated, generally flatter, and lose the distinct cross-bedding and bimodality. This transition of morphology, granulometry, and structure through the megaripple field reflects the spatial trend in transport capacity and aligns with current understanding of megaripple morphodynamic regimes. However, it highlights that different regimes may occur simultaneously within the same field. Broadly, these results emphasize the dynamic nature of megaripples and encourages the further application of a spatial analysis and sediment state understanding for ripple-scale bedforms to improve understanding of similar features across environments.

Accurately quantifying air emissions from heterogeneous area sources like landfills remains challenging, in part due to spatially variable plumes and incomplete sampling. To address this, we developed and evaluated a Gaussian inversion framework that integrates near‑field, high‑frequency mobile measurements to estimate site‑level emissions and intra‑site variability. We evaluated this framework through 18 controlled methane release experiments spanning 0.05–3.40 g CH4/s using six ground‑level emitters distributed in a 4,000 m2 test area. Measurements were collected with a truck‑mounted system collecting 10 Hz methane, wind, and positional data to densely characterize plume structure. The inversion employed a conservative stochastic hill‑climbing (STHC) method that accepts only error‑reducing perturbations to approach the optimal source-specific emission rate. Without emission source location knowledge, the framework recovered proportional emission signals with moderate negative bias. Incorporating emission source locations markedly improved solution stability and produced strong proportional accuracy. While incorporating emission source locations also substantially improving the model’s ability to estimate emission rates at individual release points, yielding point‑specific estimates that, although notably more accurate than in the prior‑free case, still retained a consistent negative bias relative to the true release rates. For practical applications such as landfills, these findings highlight that the inversion framework is most effective when operators supply information about emission source priors or hotspots and apply appropriate correction factors to address the low bias in flux estimation.

Cities emit methane (CH4) and have a role to play in mitigating the climate impacts of their emissions. Research suggests that CH4 emissions from most North American cities have large contributions from natural gas distribution and end use. In this work, we examine trace gas measurements from a central air monitoring station in Calgary, Alberta, Canada to attribute the city's CH4 emissions to major source categories. Using positive matrix factorization (PMF), we identified four primary CH4 emissions source categories: natural gas-fugitives, natural gas-incomplete combustion, waste/biogenic, and petroleum product processing. Results from PMF modeling indicate that the bulk of CH4 emissions in Calgary are from natural gas fugitives and incomplete combustion (81% +/- 35%). This is much higher than the proportion derived from available bottom-up emissions inventories. The CH4 emissions from natural gas sources increase in winter and may be related to increased natural gas use for space heating. Emissions from waste/biogenic sources were the next largest contributor, which doubled in warmer months, consistent with temperature-driven microbial activity. Though relatively small, CH4 emissions from petroleum product processing are non-negligible and consistent. Overall, these findings underscore the need for targeted mitigation strategies focused on the natural gas supply chain, while also highlighting the influence of seasonal dynamics on urban CH4 emissions.

Cities are major aggregated sources of methane (CH _4 ) emissions and can therefore play a role in mitigating climate warming. However, diverse, spatially distributed sources make characterizing urban CH _4 emissions challenging. A limited synthesis of existing research has hindered understanding of source characteristics and contributions, implicating research priorities, policies, and mitigation. This review consolidates findings from 106 peer-reviewed articles on CH _4 emissions in U.S. and Canadian cities, identifying key insights, gaps, and opportunities. We found that top-down (TD) estimates of city-scale CH _4 emissions from 34 studies exceeded, on average, bottom-up (BU) estimates by a factor of 3.9 (±6.7). Urban CH _4 footprints were dominated by sources from natural gas distribution and end-use and landfills. Across 11 U.S. studies, the estimated mean CH _4 loss rate from delivered natural gas corrected for CH _4 content in cities was 2.3% (±0.9%). TD estimates of CH _4 emissions from six U.S. landfills were, on average, 2.4 (±1.7) times greater than self-reported BU estimates. Preferred methods for reporting may miss large fugitive point sources, systematically underestimating landfill CH _4 emissions. The studies indicated that wastewater systems emit less CH _4 than landfills and natural gas sources, but the research remains limited, and many wastewater sources are poorly characterized. Mitigation effectiveness varied by source, with scalability a challenge for small, distributed sources such as sewers, and the confirmation of reductions sensitive to measurement scale. Overall, results highlight challenges in quantifying, attributing, and mitigating CH _4 emissions in urban settings. Key research priorities are: (i) expanding CH _4 measurements from urban natural gas (distribution and end-use) and wastewater sources, and granular investigations to pinpoint and understand the causes of emissions; (ii) new emissions data to improve BU models and integrate into BU estimates; (iii) improving measurement-model coupling for landfill CH _4 quantification; and (iv) evaluating mitigation strategies for urban CH _4 sources.

In Canada, cold heavy oil production with sand (CHOPS) has a high methane emissions intensity. This study uses TROPOMI satellite observations and mass balance modeling to estimate multiyear (2019-2023) methane emissions rates for a key CHOPS region spanning Alberta and Saskatchewan. The iterative 3-year mean emissions estimates were found to be similar to 4.5 times higher than industry-reported data but show a notable downward trend, with a 71 +/- 34% reduction over the study period. The methane emissions intensity decreased by 63 +/- 31%, reaching 0.69 +/- 0.25 gCH4/MJ, but remains substantially higher than that of other oil production basins globally. Although the TROPOMI-based emission reductions were found higher than the industry-reported reductions, our emission estimates remain notably higher than the industry-reported emissions. Deficient industry reporting makes identifying root causes difficult, underscoring the need for robust measurement systems to benchmark and drive performance improvements. Potential drivers for the observed reductions include regulatory efforts targeting vent gas and fugitive emissions, an increased use of solution gas combustors, and a 19% decline in production during the period. While the exact causes remain uncertain, the measurable reductions demonstrate progress toward lowering methane emissions in the region.

Cities are important sources of anthropogenic methane emissions. Municipal governments can play a role in reducing those emissions to support climate change mitigation, but they need information on the emission rate to contextualize mitigation actions and track progress. Herein, we examine the application of satellite data from the TROPOspheric Monitoring Instrument (TROPOMI) to estimate city-level methane emission rates in a case study of the City of Calgary, Alberta, Canada. Due to low and variable annual observational coverage, we integrated valid TROPOMI observations over three years (2020–2022) and used mass balance modeling to derive a long-term mean estimate of the emission rate. The resulting column-mean dry-air mole fraction (XCH4) enhancement over Calgary was small (4.7 ppb), but within the city boundaries, we identified local hot spots in the vicinity of known emission sources (wastewater treatment facilities and landfills). The city-level emission estimate from mass balance was 215.4 ± 132.8 t CH4/d. This estimate is approximately four times larger than estimates from Canada’s gridded National Inventory Report of anthropogenic CH4 emissions and six times larger than the Emissions Database for Global Atmospheric Research (EDGAR v8.0). We note that valid TROPOMI observations are more common in warmer months and occur during a narrow daily overpass time slot over Calgary. The limited valid observations in combination with the constrained temporal observational coverage may bias the emission estimate. Overall, the findings from this case study highlight an approach to derive a screening-level estimate of city-level methane emission rates using TROPOMI data in settings with low observational coverage.

We provide a critical review of research paradigms for classifying intermediate-scale aeolian bedforms on Mars and the new terminology that has emerged. The systematic classification of bedforms has always been challenging and debated, and no paradigmatic knowledge organization system exists beyond general agreement on the importance of a distinction between ripples and dunes. The diverse aeolian landscapes of Mars have introduced further topics and challenges to these debates. We argue that Martian aeolian geomorphology's knowledge organization system for intermediate-scale bedforms is preparadigmatic and that consensus over terminology and their definitions has not been established in the literature. A preparadigmatic science can be functional only if scientists operating in the discipline provide precise, falsifiable definitions or use abductive logic. Drawing on evidence and examples from the literature, we argue that the replacement of the conventional abductive paradigm used in bedform classification with inductive logic has created an emerging disciplinary paradigm based on scientific hesitancy and a dependence on complex inductive research structures, epitomized by the concepts of 'transverse aeolian ridges' (TARs) and 'large Martian ripples' (LMRs). We show that TAR and, increasingly, LMR are inductive constructs that have been popularized despite them causing significant confusion. Notably, we highlight how the terms are irreconcilably used as both a class of bedform and as a non-genetic placeholder term for bedforms. Suggestions for moving beyond the need for TAR and LMR are provided, focusing on a return to more direct and local hypothesis-driven research inspired by W.M. Davis's notion of outrageous geological hypotheses. Recent debate surrounding bedforms in Gale crater is presented as an example of the productivity of such an approach, and it is recommended that TAR and LMR no longer be used. This work interrogates the literature on intermediate-scale Martian aeolian bedforms to enquire why terminology in this field has become more complex than aeolian research on Earth. We find that this complexity has emerged from an inductive research paradigm that contrasts with conventional abductive research structures on Earth. Differences between the two are addressed, and it is recommended that research simply cease using placeholder terms like 'transverse aeolian ridge' and 'large Martian ripple' to realign terrestrial and planetary research paradigms.image

As major sources of methane (CH4) emissions, cities have an important role in mitigating near-term global temperature rise. However, cities are challenging environments for characterizing CH4 emissions due to the diversity and spatial extent of sources. Furthermore, the characteristics and contributions of different sources are poorly understood due to a lack of synthesis and integration of the literature, with knock-on implications for policies and mitigation. Here, we review peer-reviewed journal articles on CH4 emissions from cities in the U.S. and Canada to consolidate the current state of knowledge and highlight key research priorities. From 32 of 94 studies reviewed, we find that estimates of total city-level CH4 emissions derived from top-down measurements are on average 5.6 (± 7.8) times larger than bottom-up inventory estimates. Emissions from natural gas distribution and end use, and landfills, dominate city-level CH4 footprints. The average urban natural gas loss rate of 1.8% ± 0.9% from 12 studies increases the overall natural gas supply chain loss rate estimate to 4.0% ± 0.9%. Top-down estimates of CH4 emissions from landfills were on average 10.6 times greater than Greenhouse Gas Reporting Program estimates. Landfill studies indicate that better accounting of spatial and temporal phenomena such as fugitives, hotspots, and variations in weather and soil conditions is central to improving emissions rate estimates. A handful of studies examined mitigation and highlighted the role of measurement to identify specific mitigation opportunities and verify CH4 emissions reductions. The review findings raise questions and highlight challenges around existing bottom-up inventory approaches, urban natural gas loss rates and slip, landfill emissions estimation techniques, and mitigation effectiveness. The review concludes with recommendations on research priorities to address key knowledge gaps: (i) new source-level measurement datasets and modeling approaches for bottom-up emissions estimation, (ii) more granular investigations to understand the specific sources and causes of CH4 emissions from urban natural gas infrastructure and end use, (iii) a better coupling between measurement and modeling of landfill CH4 emissions, and (iv) mitigation-focused studies.

Research on methane (CH4) emissions from the oil and gas (O&G) industry informs policies, regulations, and international initiatives that target reductions. However, there has been little integration and synthesis of the literature to document the state of knowledge, identify gaps, and determine key insights that can guide research priorities and mitigation. To address this, we performed a scoping review of 237 English-language peer-reviewed articles on CH4 emissions from onshore O&G sources, charting data on five research themes: publication trends, geography, measurement levels and methods, emissions sources, and emissions rates. Almost all articles (98%) were published between 2012 and 2022 with an increasing publication rate, indicating a nascent and evolving understanding of the science. Most articles (72%) focused on CH4 emissions from the U.S. O&G industry and were written by U.S.-based authors (69%), while other major O&G-producing countries like Saudi Arabia, Russia, and China were under-represented. Upstream was the most frequently studied supply chain segment, where U.S.-focused articles accounted for 75% of the research. Nearly half the articles (43%) included in the review reported site-level measurements, limiting the identification of equipment- and component-level emissions sources and root cause. Articles that measured or identified equipment-level sources (18%) noted high emissions from tanks, unlit flares, and compressors. The most common stand-off measurement platforms were vehicles and aircraft, while the use of satellites increased in articles published since 2019. Reported emissions profiles were consistently heavy-tailed and indicate method-based and geographic differences in magnitude and skew. All articles (n = 26) that compared inventory- to measurement-based estimates of emissions found large discrepancies in that inventories under-estimated the latter by a factor of 1.2-10 times. We recommend future research focus on: (i) field-based emissions studies for under-represented regions and source categories, (ii) identifying root causes and linking measurements to mitigation, and (iii) multi-level measurement integration.

Residential natural gas meter set assemblies (MSAs) emit methane (CH4), but reported emissions factors vary. To test existing emissions factors, we quantified CH4 emissions from 37 residential MSAs in Calgary, Alberta, Canada. A notable difference with previous studies is the targeted measurement of regulator vents in this study, which were measured with a static chamber, while fugitives were measured with a modified hi-flow sampler. Emissions were dominated by pressure regulator vents (emissions factor = 1.18 g CH4/h/MSA), but 7 fugitives were found (emissions factor = 0.018 g CH4/h/MSA). Six regulator vents were emitting at notably higher rates (≥ 1.79 g CH4/h/MSA). The total empirical emissions factor was 1.20 g CH4/h/MSA (95 % CI, 1.03 to 1.37 g/h/MSA). This is ∼7 times higher than the emissions factor for residential MSAs used in the U.S. EPA's Greenhouse Gas Inventory, which may not include emissions from regulator vents. Upscaling to annual CH4 emissions in Calgary indicates 3234.6 t CH4/yr (95 % CI, 2776.4 t to 3692.9 t CH4/yr) could be emitted from MSAs. This is equivalent to 4.1 % (95 % CI, 3.5 % to 4.7 %) of total city-level CH4 emissions as estimated with satellite data. Results suggest residential MSA emissions may be under-estimated and further study isolating root causes of regulator vent emissions is required to guide mitigation and improve emissions modeling.

Satellite observations have been used to measure methane (CH 4 ) emissions from the oil and gas (O&G) industry, particularly by revealing previously undocumented, very large emission events and basin-level emission estimates. However, most satellite systems use passive remote sensing to retrieve CH 4 mixing ratios, which is sensitive to sunlight, earth surface properties, and atmospheric conditions. Accordingly, the reliability of satellites for routine CH 4 emissions monitoring varies across the globe. To better understand the potentials and limitations of routine monitoring of CH 4 emissions with satellites, we investigated the global observational coverage of the TROPOMI instrument onboard the Sentinel-5P satellite—the only satellite system currently with daily global coverage. A 0.1° × 0.1° gridded global map that indicates the average number of days with valid observations from TROPOMI for 2019–2021 was generated by following the measurement retrieval quality-assurance threshold (≥ 0.5). We found TROPOMI had promising observational coverage over dryland regions (maximum: 58.6%) but limited coverage over tropical regions and high latitudes (minimum: 0%). Cloud cover and solar zenith angle were the primary factors affecting observational coverage at high latitudes, while aerosol optical thickness was the primary factor over dryland regions. To further assess the country-level reliability of satellites for detecting and quantifying CH 4 emissions from the onshore O&G sector, we extracted the average annual TROPOMI observational coverage (TOC) over onshore O&G infrastructure for 160 countries. Seven of the top-10 O&G-producing countries had an average annual TOC < 10% (< 36 days per year), which indicates the limited ability to routinely identify large emissions events, track their duration, and quantify emissions rates using inverse modelling. We further assessed the potential performance of the latter by combining TOC and the uncertainties from the global O&G inventory. Results indicate that the accuracy of emissions quantifications of onshore O&G sources using TROPOMI data and inverse modeling will be higher in countries located in dryland and mid-latitude regions and lower in tropical and high-latitude regions. Therefore, current passive-sensing satellites have low potential for frequent monitoring of large methane emissions from O&G sectors in countries located in tropical and high latitudes (e.g., Canada, Russia, Brazil, Norway, and Venezuela). Alternative methods should be considered for routine emissions monitoring in these regions.

There are two primary algorithms for autonomous multiple odor source localization (MOSL) in an environment with turbulent fluid flow: Independent Posteriors (IP) and Dempster–Shafer (DS) theory algorithms. Both of these algorithms use a form of occupancy grid mapping to map the probability that a given location is a source. They have potential applications to assist in locating emitting sources using mobile point sensors. However, the performance and limitations of these two algorithms is currently unknown, and a better understanding of their effectiveness under various conditions is required prior to application. To address this knowledge gap, we tested the response of both algorithms to different environmental and odor search parameters. The localization performance of the algorithms was measured using the earth mover’s distance. Results indicate that the IP algorithm outperformed the DS theory algorithm by minimizing source attribution in locations where there were no sources, while correctly identifying source locations. The DS theory algorithm also identified actual sources correctly but incorrectly attributed emissions to many locations where there were no sources. These results suggest that the IP algorithm offers a more appropriate approach for solving the MOSL problem in environments with turbulent fluid flow.

Low-cost fixed sensors are an emerging option to aid in the management and reduction of methane emissions at upstream oil and gas sites. They have been touted as a cost-effective continuous monitoring technology to detect, localize, and quantify fugitive emissions. However, to support emissions management, the efficacy of low-cost fixed sensors must be assessed in the context of the sites, technologies, methods, work practices, action thresholds, and outcomes that constitute a broader program to manage and reduce emissions. Here, we build on technology-focused research and testing by defining a prototypical low-cost fixed sensor program framework and considering the deployment from an operational perspective. We outline potentially large operational cost penalties and risks to industry relative to incumbent programs. Most costs are caused by (i) follow-up callouts, (ii) nontarget emissions, and (iii) maintenance requirements. These represent core areas for improvement. Results highlight a need for careful consideration in regulations, ensuring that alerts protocols are carefully codified and system performance is maintained.

Methane is a potent greenhouse gas that tends to leak from equipment at oil and gas (O&G) sites. Conventional leak detection and repair methods for fugitive methane emissions are labor-intensive and costly because they involve time-consuming close-range, component-level inspections at each site. This has prompted duty holders to examine new methods and strategies that could be more cost-effective. We examined a cooperative model in which multiple duty holders of upstream O&G sites in a region use shared services to inspect on-site equipment using optical gas imaging camera or Method 21. This approach was hypothesized to be more efficient and cost-effective than independent inspection programs by each duty holder in the region. To test this hypothesis, we developed a geospatial simulation model using empirical data from 11 O&G-producing regions in Canada and the United States. We used the model to compare labor cost, transit time, mileage, vehicle emissions, and driving risk between independent and co-op leak inspection programs. The results indicate that co-op leak inspection programs can generate relative savings in labor costs (1.8%–34.2%), transit time (0.6%–38.6%), mileage (0.2%–43.1%), vehicle emissions (0.01–4.0 tCO2), and driving risk (1.9%–31.9%). The largest relative savings and efficiency gains resulting from co-op leak inspection programs were in regions with a high diversity of duty holders, which was confirmed with simulations of fictitious O&G sites and road networks spanning diverse conditions. We also found reducing leak inspection time by 75% with streamlined methods can additionally reduce labor cost by 8.8%–41.1%, transit time by 5.6%–20.2%, and mileage by 2.60%–34.3% in co-op leak inspection programs. Overall, this study demonstrates that co-op leak inspection programs can be more efficient and cost-effective, particularly in regions with a large diversity of O&G duty holders, and that methods to reduce leak inspection time can create additional savings.

Multi-sensor vehicle systems have been implemented in large-scale field programs to detect, attribute, and estimate emissions rates of methane (CH4) and other compounds from oil and gas wells and facilities. Most vehicle systems use passive sensing; they must be positioned downwind of sources to detect emissions. A major deployment challenge is predicting the best measurement locations and driving routes to sample infrastructure. Here, we present and validate a methodology that incorporates high-resolution weather forecast and geospatial data to predict measurement locations and optimize driving routes. The methodology estimates the downwind road intersection point (DRIP) of theoretical CH4 plumes emitted from each well or facility. DRIPs serve as waypoints for Dijkstra’s shortest path algorithm to determine the optimal driving route. We present a case study to demonstrate the methodology for planning and executing a vehicle-based concentration mapping survey of 50 oil and gas wells near Pecos, Texas. Validation was performed by comparing DRIPs with 174 CH4 plumes measured by vehicle surveys of oil and gas wells and facilities in Alberta, Canada. Results indicate median Manhattan distances of 145.8 m between DRIPs and plume midpoints and 160.3 m between DRIPs and peak plume enhancements. A total of 46 (26%) of the plume segments overlapped DRIPs. Locational errors of DRIPs are related to misattributions of emissions sources and discrepancies between modeled and instantaneous wind direction measured when the vehicle intersects plumes. Although the development of the methodology was motivated by CH4 emissions from oil and gas facilities, it should be applicable to other types of point source air emissions from known facilities.Implications: This paper presents and validates a method that addresses the challenge of measuring industrial emissions from public roads. The method can increase the effectiveness and efficiency of targeted vehicle-based emissions surveys where the locations of potential sources are known. We believe the method has broad application in addition to the upstream oil and gas context it was designed for.

Methane emissions from oil and gas sites are often characterized by mixed plumes from multiple sources in close proximity. This presents a challenge for screening methods that rely on emissions quantification to direct and prioritize follow-up inspections. Here, we present results from experiments evaluating mixed-source quantifications using the University of Calgary Portable Methane Leak Observatory (PoMELO) conducted at the Colorado State University Methane Emissions Technology Evaluation Center (CSU METEC). PoMELO is a vehicle-based screening system that is designed for operator-led surveys of methane emissions at upstream oil and gas sites, producing detections, localizations, and emissions quantifications while on site. Mock upstream pads were configured with 1-6 emissions points and the PoMELO system was used to quantify emissions rates at the equipment scale for each piece of equipment. Over 5 days of testing in a wide diversity of conditions, 88 individual experiment pads were surveyed at the equipment scale, with 1-6 emitting equipment per survey (total surveyed equipment = 209). The uncalibrated model was effective at measuring differences in rates: compared against real releases there was a linear calibration factor of 6.77 (r2 = 0.71). Results were more accurate in conditions with stable flow. Experiments with measurements further downwind were more accurate, and results improved when considering pooled data on each pad (linear model fit r2 = 0.84), reflecting errors in the model attributable to disambiguating methane in mixed plumes. Results suggest PoMELO has practical utility for understanding upstream methane emissions at the equipment and total pad scale.

Complex multi-source emissions quantification results for the PoMELO 1 vehicle measurement system, test results from the CSU METEC facility 2 3 Thomas E. Barchyn, Chris H. Hugenholtz 4 5 Department of Geography, University of Calgary, 2500 University Drive NW, T2N 1N4, Calgary, 6 Alberta, Canada 7 8 email: tbarchyn@ucalgary.ca, twitter: @tbarchyn 9 10 This manuscript is a non-peer reviewed preprint submitted to EarthArXiv 11

Yardangs are streamlined ridges that form in arid environments on Earth and Mars through wind‐driven abrasion of consolidated substrates. Currently, there is limited consensus on the mechanisms that initiate and establish patterns of yardangs on the landscape. In this work, we examine the spatial organization of yardangs in the Campo de Piedra Pómez ignimbrite deposit of north‐western Argentina and identify evidence of antecedent controls on yardang patterns and formation. We mapped 14,826 yardangs in the region using a high‐resolution digital elevation model (DEM) and satellite imagery. We classified yardangs as points using a two‐stage decision rule based on morphology and spectral characteristics. Point pattern analysis shows that yardangs in the study area are not randomly distributed and commonly exhibit directional anisotropy in point pattern. The anisotropic pattern manifests as bands of closely‐spaced yardangs oriented transverse to the dominant northwesterly wind direction. We hypothesize that banding is controlled by pre‐existing antecedent topography in the bedrock, such as fumaroles or ridges associated with pyroclastic flow deposits. We present evidence from other locations on Earth and Mars to illustrate that the transverse banding is a common pattern in yardang landscapes.

New mobile platforms such as vehicles, drones, aircraft, and satellites have emerged to help identify and reduce fugitive methane emissions from the oil and gas sector. When deployed as part of leak detection and repair (LDAR) programs, most of these technologies use multi-visit LDAR (MVL), which consists of four steps: (a) rapidly screen all facilities, (b) triage by emission rate, (c) follow-up with close-range methods at the highest-emitting sites, and (d) conduct repairs. The proposed value of MVL is to identify large leaks soon after they arise. Whether MVL offers an improvement over traditional single-visit LDAR (SVL), which relies on undirected close-range surveys, remains poorly understood. We use the Leak Detection and Repair Simulator (LDAR-Sim) to examine the performance and cost-effectiveness of MVL relative to SVL. Results suggest that facility-scale MVL programs can achieve fugitive emission reductions equivalent to SVL, but that improved cost-effectiveness is not guaranteed. Under a best-case scenario, we find that screening must cost < USD 100 per site for MVL to achieve 30% cost reductions relative to SVL. In scenarios with non-target vented emissions and screening quantification uncertainty, triaging errors force excessive close-range follow-up to achieve emissions reduction equivalence. The viability of MVL as a cost-effective alternative to SVL for reducing fugitive methane emissions hinges on accurate triaging after the screening phase.