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To accurately assess the current atmospheric methane budget and its future trends, it is essential to apportion and quantify the anthropogenic methane emissions to specific sources. This poses a significant challenge in the under‐sampled Middle East, where estimates predominantly depend on remote sensing observations and bottom‐up reporting of national emissions. Here, we present in situ shipborne observations of greenhouse gases (GHGs) and non‐methane hydrocarbons (NMHCs) collected along a >10,000‐km route from Vigo, Spain, to Abu Dhabi, UAE. By comparing our observations with Lagrangian dispersion model simulations, coupled with two methane emission inventories, we identify periods of considerable mismatch and apportion the responsible sources. Employing interspecies relationships with NMHCs has enabled the characterization of methane emissions from oil and gas (O&G) operations, urban centers, Red Sea deep water, enteric fermentation, and agriculture across diverse atmospheric environments. Our analysis reveals that the Suez area is a regional emission hotspot, where simulations consistently underestimate the methane emission sources. Importantly, the Middle Eastern O&G sector has been identified as an additional source of considerable uncertainty. Here, methane emissions were alternately underestimated and overestimated by the two inventories, exposing significant gaps in our understanding of fuel exploitation‐related emissions in the Middle East. This underscores the need for further targeted field campaigns and long‐term observations to improve the accuracy of emission data in the inventories. Plain Language Summary: For the mitigation of human‐induced methane emissions, a detailed characterization of its numerous sources is vital. This is particularly challenging in understudied regions where the source attribution and emission strength thus far relies on satellite observations and country reports. Although such data, representing the entire atmospheric column, are invaluable, source specific emission estimates remain highly uncertain. In this study, we used shipborne measurements of methane to evaluate the performance of two emission inventories commonly used in computer‐based models to simulate methane atmospheric concentrations. The relationships between methane and various co‐emitted reactive trace gases revealed the causes of discrepancies between observations and model simulations. Our results show that, while models are generally reliable at capturing high methane concentrations, they are deficient in the Suez and Middle East areas due to inadequate characterization of emissions from Oil and Gas operations. The research outcomes of this study underscore the crucial role of ground‐based observations in improving the accuracy of methane emission inventories and their reporting, and in supporting evidence‐based policies to mitigate climate change and improve air quality. Key Points: The Suez Canal and the Gulf of Suez are regional pollution hotspots with underestimated methane emission sourcesDiscrepancies between observed and modeled methane mixing ratios were resolved using non‐methane hydrocarbon observationsMethane sources from the oil and gas exploitation sector in the Middle East are particularly uncertain in emission inventories [ABSTRACT FROM AUTHOR]

The preventive and cautionary measures taken by the UAE and Abu Dhabi governments to reduce the spread of the coronavirus disease (COVID-19) and promote social distancing have led to a reduction of mobility and a modification of economic and social activities. This paper provides statistical analysis of the air quality data monitored by the Environment Agency - Abu Dhabi (EAD) during the first 10 months of 2020, comparing the different stages of the preventive measures. Ground monitoring data is compared with satellite images and mobility indicators. The study shows a drastic decrease during lockdown in the concentration of the gaseous pollutants analysed (NO 2 , SO 2 , CO, and C 6 H 6 ) that aligns with the results reported in other international cities and metropolitan areas. However, particulate matter (PM 10 and PM 2.5 ) averaged concentrations followed a markedly different trend from the gaseous pollutants, indicating a larger influence from natural events (sand and dust storms) and other anthropogenic sources. The ozone (O 3 ) levels increased during the lockdown, showing the complexity of O 3 formation. The end of lockdown led to an increase of the mobility and the air pollution; however, air pollutant concentrations remained in lower levels than during the same period of 2019. The results in this study show the large impact of human activities on the quality of air and present an opportunity for policymakers and decision-makers to design stimulus packages to overcome the economic slow-down, with strategies to accelerate the transition to resilient, low-emission economies and societies more connected to the nature that protect human health and the environment., Supplementary Information: The online version contains supplementary material available at 10.1007/s11869-021-01000-2., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2021.)

Background: Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual air pollution exposure in population studies. Few comparisons have however been made of the performance of these methods., Objectives: Within the European Study of Cohorts for Air Pollution Effects (ESCAPE) we explored the differences between LUR and DM estimates for NO2, PM10 and PM2.5., Methods: The ESCAPE study developed LUR models for outdoor air pollution levels based on a harmonised monitoring campaign. In thirteen ESCAPE study areas we further applied dispersion models. We compared LUR and DM estimates at the residential addresses of participants in 13 cohorts for NO2; 7 for PM10 and 4 for PM2.5. Additionally, we compared the DM estimates with measured concentrations at the 20-40 ESCAPE monitoring sites in each area., Results: The median Pearson R (range) correlation coefficients between LUR and DM estimates for the annual average concentrations of NO2, PM10 and PM2.5 were 0.75 (0.19-0.89), 0.39 (0.23-0.66) and 0.29 (0.22-0.81) for 112,971 (13 study areas), 69,591 (7) and 28,519 (4) addresses respectively. The median Pearson R correlation coefficients (range) between DM estimates and ESCAPE measurements were of 0.74 (0.09-0.86) for NO2; 0.58 (0.36-0.88) for PM10 and 0.58 (0.39-0.66) for PM2.5., Conclusions: LUR and dispersion model estimates correlated on average well for NO2 but only moderately for PM10 and PM2.5, with large variability across areas. DM predicted a moderate to large proportion of the measured variation for NO2 but less for PM10 and PM2.5., (Copyright © 2014 Elsevier Ltd. All rights reserved.)