The objective of this study is to derive methane (CH₄) emissions from three landfills, which are found to be the most significant CH₄ sources in the metropolitan area of Madrid in Spain. We derive CH₄ emissions from the CH₄ enhancements observed by spaceborne and ground-based instruments. We apply satellite-based measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) together with measurements from the ground-based COllaborative Carbon Column Observing Network (COCCON) instruments. In 2018, a 2-week field campaign for measuring the atmospheric concentrations of greenhouse gases was performed in Madrid in the framework of Monitoring of the Greenhouse Gases Concentrations in Madrid (MEGEI-MAD) project. Five COCCON instruments were deployed at different locations around the Madrid city center, enabling the observation of total column-averaged CH₄ mixing ratios (XCH₄). Considering the prevalent wind regimes, we calculate the wind-assigned XCH₄ anomalies for two opposite wind directions. Pronounced bipolar plumes are found when applying the method to NO₂, which implies that our method of wind-assigned anomaly is suitable to estimate enhancements of trace gases at the urban level from satellite-based measurements. For quantifying the CH₄ emissions, the wind-assigned plume method is applied to the TROPOMI XCH₄ and to the lower tropospheric CH₄ $/$ dry-air column ratio (TXCH₄) of the combined TROPOMI+IASI product. As CH₄ emission strength we estimate 7.4 × 10²⁵ ± 6.4 × 10²⁴ molec. s⁻¹ from the TROPOMI XCH₄ data and 7.1 × 10²⁵ ± 1.0 × 10²⁵ molec. s⁻¹ from the TROPOMI+IASI merged TXCH₄ data. We use COCCON observations to estimate the local source strength as an independent method. COCCON observations indicate a weaker CH₄ emission strength of 3.7 × 10²⁵ molec. s⁻¹ from a local source (the Valdemingómez waste plant) based on observations from a single day. This strength is lower than the one derived from the satellite observations, and it is a plausible result. This is because the analysis of the satellite data refers to a larger area, covering further emission sources in the study region, whereas the signal observed by COCCON is generated by a nearby local source. All emission rates estimated from the different observations are significantly larger than the emission rates provided via the official Spanish Register of Emissions and Pollutant Sources.

This study quantifies the association between the COVID‐19 economic downturn and 2020 tropospheric ozone anomalies above Europe and western North America, and their impact on long‐term trends. Anomaly detection for an atmospheric time series is usually carried out by identifying potentially aberrant data points relative to climatological values. However, detecting ozone anomalies from sparsely sampled ozonesonde profiles (once per week at most sites) is challenging due to ozone's high temporal variability. We first demonstrate the challenges for summarizing regional trends based on independent time series from multiple nearby ozone profiling stations. We then propose a novel regional‐scale anomaly detection framework based on generalized additive mixed models, which accounts for the sampling frequency and inherent data uncertainty associated with each vertical profile data set, measured by ozonesondes, lidar or commercial aircraft. This method produces a long‐term monthly time series with high vertical resolution that reports ozone anomalies from the surface to the middle‐stratosphere under a unified framework, which can be used to quantify the regional‐scale ozone anomalies during the COVID‐19 economic downturn. By incorporating extensive commercial aircraft data and frequently sampled ozonesonde profiles above Europe, we show that the complex interannual variability of ozone can be adequately captured by our modeling approach. The results show that free tropospheric ozone negative anomalies in 2020 are the most profound since the benchmark year of 1994 for both Europe and western North America, and positive trends over 1994–2019 are diminished in both regions by the 2020 anomalies.