Abstract. Ammonia (NH3) is an important constituent in the global nitrogen cycle, present in both urban and remote environments. It is a source of reactive nitrogen and a precursor for particulate matter, thereby affecting atmospheric chemistry and radiative forcing. This work presents the seasonal and diurnal variability, along with long-term trends, of atmospheric NH3 total columns retrieved from Fourier transform infrared (FTIR) spectroscopic solar absorption measurements at 22 ground-based sites, globally distributed from 45° S to 80° N. Comparisons are made with simulations from the GEOS-Chem High Performance (GCHP) chemical transport model and the Tropospheric Chemistry Reanalysis (TCR-2) NH3 product. The mean NH3 total columns from the FTIR time series ranged from 0.12×1015 to 19.20×1015 molecules cm−2, with the smallest columns found at the Arctic and high-altitude sites, and the largest in urban areas. Significant enhancements were attributed to biomass burning, and NH3 emissions from volcanic eruptions were detected at the Izana site. The seasonal patterns are similar across most sites, with maxima mainly related to the volatilization of NH3 due to higher temperatures. The diurnal variability differs significantly and depends on the characteristics of each site and local sources. Most sites have positive trends in the total column, with a mean value (and 95 % confidence interval) for all sites of 3.82 (3.29–4.35) % for the FTIR measurements, 3.66 (3.35–3.97) % for GCHP, and 6.49 (2.00–10.98) % for TCR-2. GCHP exhibited a better general agreement with the FTIR observations than TCR-2; potential reasons for this are explored, including a sensitivity test on the emissions used.

Formaldehyde (HCHO) is an important proxy for volatile organic compounds and photochemical activity, with significant implications for air quality and atmospheric chemistry. The recently launched geostationary satellite instrument Tropospheric Emissions: Monitoring of Pollution (TEMPO) provides the first-ever hourly observations of HCHO and other key pollutants over North America. To evaluate the accuracy of the TEMPO Level 2 Version 3 retrieval, we compared HCHO total columns (TC) with co-located ground-based measurements from Fourier Transform Infrared (FTIR) and Pandora spectrometers at three sites: Boulder, Colorado, USA; Mexico City, Mexico; and Toronto, Ontario, Canada. The consistency between FTIR and Pandora HCHO TC measurements was first assessed to characterize the performance of the Pandora instruments relative to established FTIR retrievals, which is important given the rapid global expansion of the Pandora network and limited overlap with reference instruments. Our results show good correlation and consistent hourly and seasonal patterns between FTIR and Pandora, while highlighting the importance of routine field calibrations and performance assessments to ensure long-term data reliability. By establishing agreement and understanding differences between the two independent ground-based data sets, the TEMPO evaluation was strengthened. TEMPO HCHO TC retrievals exhibit a consistent low bias of approximately 30% relative to ground-based observations at all three sites, with the largest discrepancies under elevated HCHO conditions. Nonetheless, TEMPO accurately captures both diurnal and seasonal variability, demonstrating its utility for characterizing short-lived atmospheric species. These findings highlight the value of coordinated satellite and ground-based measurements for validating and improving satellite products for air quality research and monitoring.

Abstract The Orbiting Carbon Observatory‐2 and ‐3 (collectively termed “OCO‐2/3,” hereafter) missions, together, provide precise and accurate global data records that contribute to a better understanding of the variability in atmospheric carbon dioxide (CO 2 ). The retrieval algorithm used to process the satellite data, Atmospheric Carbon Observations from Space (ACOS), continues to be updated. The latest v11.2 OCO‐2 and v11 OCO‐3 data releases include significant improvements compared to past data versions. The Total Carbon Column Observing Network (TCCON), a network of ground‐based Fourier Transform Spectrometers (FTS), has historically, been the validation data source for OCO‐2/3. The COllaborative Carbon Column Observing Network (COCCON), an emerging network of ground‐based portable Fourier Transform Infrared Spectrometers (EM27/SUN), aids satellite validation efforts by providing observations in geographic locations where there are no TCCON measurements being made. This study provides the first global comparison between the OCO‐2/3 data sets and the COCCON observations. Currently, the COCCON data set, most useful for global analysis, is version 1 (v1), which our analysis shows is typically lower than the satellites by ∼0.50–1.00 ppm, with scatter against OCO‐2/3 roughly similar to TCCON versus OCO‐2/3. This analysis includes comparisons of results from the latest v2.x COCCON data version for the sites with the data available. Comparisons with the v2.x COCCON data version generally show improved comparisons against OCO‐2/3. Our analysis illustrates the ways in which the COCCON data set, used globally, regionally, or in specific locations, can provide insight into the quality of satellite observations of atmospheric CO 2 .

The Orbiting Carbon Observatory-2 and -3 (collectively termed "OCO-2/3," hereafter) missions, together, provide precise and accurate global data records that contribute to a better understanding of the variability in atmospheric carbon dioxide (CO2). The retrieval algorithm used to process the satellite data, Atmospheric Carbon Observations from Space (ACOS), continues to be updated. The latest v11.2 OCO-2 and v11 OCO-3 data releases include significant improvements compared to past data versions. The Total Carbon Column Observing Network (TCCON), a network of ground-based Fourier Transform Spectrometers (FTS), has historically, been the validation data source for OCO-2/3. The COllaborative Carbon Column Observing Network (COCCON), an emerging network of ground-based portable Fourier Transform Infrared Spectrometers (EM27/SUN), aids satellite validation efforts by providing observations in geographic locations where there are no TCCON measurements being made. This study provides the first global comparison between the OCO-2/3 data sets and the COCCON observations. Currently, the COCCON data set, most useful for global analysis, is version 1 (v1), which our analysis shows is typically lower than the satellites by similar to 0.50-1.00 ppm, with scatter against OCO-2/3 roughly similar to TCCON versus OCO-2/3. This analysis includes comparisons of results from the latest v2.x COCCON data version for the sites with the data available. Comparisons with the v2.x COCCON data version generally show improved comparisons against OCO-2/3. Our analysis illustrates the ways in which the COCCON data set, used globally, regionally, or in specific locations, can provide insight into the quality of satellite observations of atmospheric CO2.

Ethane is the most abundant non-methane hydrocarbon in Earth’s atmosphere and acts as an indirect greenhouse gas, influencing the atmospheric lifetime of methane. Therefore, understanding the development of trends and identifying trend reversals in atmospheric ethane is crucial. Ethane abundance is measured at different ground-based stations worldwide using Fourier transform infrared remote sensing techniques. We compile a new dataset comprising 26 ethane time series from the Northern and Southern Hemispheres. We analyze their long-term trends using different econometric techniques capable of handling missing data and strong seasonal components present in the data. The resulting trend patterns are consistent across the different methods, with similar estimated trends at the various stations. In the Northern Hemisphere, the common trend across stations declined from the 1990s to 2005, gradually increased over the next decade, and then resumed a similar downward trajectory from 2015 onward. The estimated trends reveal a pronounced peak around 2014/2015, marking a reversal from an upward to a downward trend.

Characterizing black carbon (BC) on a fine scale globally is essential for understanding its climate and health impacts. However, sparse BC mass measurements in different parts of the world and coarse model resolution have inhibited evaluation of global BC emission inventories. Here, we apply globally distributed BC mass measurements from the Surface Particulate Matter Network (SPARTAN) and complementary measurement networks to evaluate contemporary BC emission inventories. We use a global chemical transport model (GEOS-Chem) in its high-performance configuration (GCHP) for high-resolution simulations to relate BC emissions to ambient concentrations for comparison with measurements. Here we find that simulations using the Community Emissions Data System (CEDS) emission inventory exhibit skill (r2 = 0.73) in representing variability in SPARTAN measurements across primarily developed regions with low BC concentrations but exhibit pronounced discrepancy (r2 = 0.00019) across high-BC regions in the Global South, underestimating BC by 38%. Alternative inventories (EDGAR, HTAP) yield similar results. These findings motivate renewed attention to the challenging task of characterizing BC emissions from low- and middle-income countries.

The COllaborative Carbon Column Observing Network has become a reliable source of high-quality ground-based remote sensing network data that provide column-averaged dry-air mole fractions of carbon dioxide (XCO2), methane (XCH4), and carbon monoxide (XCO). The fiducial reference measurements of these gases from the COCCON complement the TCCON and NDACC-IRWG data. This study shows the application of COCCON data for the validation of existing greenhouse gas satellite products. This study includes the validation of XCH4 and XCO products from the European Copernicus Sentinel-5 Precursor (S5P) mission, XCO2 products from the American Orbiting Carbon Observatory-2 (OCO-2) mission, and XCO2 and XCH4 products from the Japanese Greenhouse gases Observing SATellite (GOSAT). A total of 27 datasets contributed to this study; some of these were collected in the framework of campaign activities and covered only a short time period. In addition, several permanent stations provided long-term observations. The random uncertainties in the validation results, specifically for S5P with a lot of coincidences pairs, are found to be similar to the comparison with the TCCON. The comparison results of OCO-2 land nadir and land glint observation modes to the COCCON on a global scale, despite limited coincidences, are very promising. The stations can, therefore, expand on the coverage of the already existing ground-based reference remote sensing sites from the TCCON and the NDACC network. The COCCON data can be used for future satellite and model validation studies and carbon cycle studies.

The Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument, launched in April 2023, is North America's first geostationary air pollution monitoring satellite mission. Together with Asia's Geostationary Environment Monitoring Spectrometer (GEMS) launched in 2020 and Europe's upcoming Sentinel-4, TEMPO contributes to nearly global coverage provided by geostationary satellite constellation. TEMPO and GEMS offer hourly, high-resolution data of ozone surpassing the once-daily observations of instruments like the TROPOspheric Monitoring Instrument (TROPOMI) in temporal resolution. This study presents TEMPO's total ozone data, demonstrating TEMPO's ability to observe sudden changes in ozone and UV index. Furthermore, TEMPO and GEMS measurements are validated using ground-based monitoring networks (Brewer, Dobson, and Pandora). Results show good agreement but also highlight latitude-dependent discrepancies between the satellite and ground-based data sets (-2% to 2% for TEMPO, -1% to -3% for GEMS). Findings are further validated using TROPOMI data and reanalysis models.

Accurate estimates of greenhouse gas emissions and sinks are critical for understanding the carbon cycle and identifying key drivers of anthropogenic climate change. In this study, we investigate the variability in CO and CO2 concentrations and their ratio over the Mexico City metropolitan area (MCMA) using long-term, time-resolved columnar measurements at three stations, employing solar-absorption Fourier transform infrared spectroscopy (FTIR). Using a simple model and the mixed-layer height derived from a ceilometer, we determined the CO and CO2 concentrations in the mixed layer from the total column measurements and found good agreement with surface cavity ring-down spectroscopy measurements. In addition, we used the diurnal pattern of CO columnar measurements at specific time intervals to estimate an average growth rate that, when combined with the space-based Tropospheric Monitoring Instrument (TROPOMI) CO measurements, allowed for the derivation of annual CO and CO2 MCMA emissions from 2016 to 2021. A CO emission decrease of more than 50 % was found during the COVID-19 lockdown period with respect to the year 2018. These results demonstrate the feasibility of using long-term EM27/SUN column measurements to monitor the annual variability in the anthropogenic CO2 and CO emissions in Mexico City without recourse to complex transport models. This simple methodology could be adapted to other urban areas if the orography of the regions favours low ventilation for several hours per day and the column growth rate is dominated by the emission flux.

Satellite measurements of urban CO2 plumes offer a global approach to track CO2 emissions for large cities. To examine and to quantify the feasibility of space-based monitoring, an intensive measurement campaign (MERCI-CO2) using seven solar-tracking Fourier transform infrared (FTIR) spectrometers has been conducted over the Mexico City Metropolitan Area (MCMA) to monitor urban emissions and to evaluate Snapshot Area Map (SAM) observations from the NASA's Orbiting Carbon Observatory-3 (OCO-3) mission. Once adjusted for their respective averaging kernels, we diagnosed a positive difference between OCO-3 and FTIR column measurements (1.06 ppm). Thanks to this unprecedented amount of column observations over a large city, we demonstrate that XCO2 gradients within OCO-3 SAMs align with the inter-calibrated FTIR measurements (mean bias of 0.3 ppm), confirming the potential to track CO2 emissions from space over large metropolitan areas. XCO2 urban-rural differences across the FTIR network, show a strong correlation with observed gradients, with Pearson's correlation coefficients (R) around 0.92. The correlation is significantly lower when considering intra-urban gradients, where R drop to around 0.24. Simulated XCO2 enhancements (Delta XCO2) based on X-STILT for both FTIR and OCO-3 show relatively high correlations (R is around 0.6) using high-resolution footprints and two gridded inventories. Spatial correlations with OCO-3 improve when aggregating satellite retrievals at coarser resolutions (10 km). Our study demonstrates the capabilities of detecting urban gradients by FTIR network and OCO-3 SAM observations over MCMA, a promising result to evaluate the evolution of MCMA's emissions over the coming decade. Plain Language Summary Despite the fact that large metropolitan areas across the world account for a significant fraction of the global CO2 emissions from fossil fuels, city-scale CO2 emissions estimates from inventories remain highly uncertain and usually lag real time by several years. Observations obtained from satellites and ground-based sensors are expected to improve the quantification of CO2 emissions in urban regions. Observed CO2 gradients (site-to-site differences) over urban areas is essential for quantifying CO2 emissions. In this study, we assess intra-urban CO2 gradients over the Mexico City Metropolitan Area utilizing dense observations from both space and ground. Our analysis reveals that space and ground observations exhibit greater consistency when comparing urban-to-rural gradients, as opposed to gradients observed exclusively within urban areas. Furthermore, simulated urban gradients based on inventories show good coherence with our observed gradients, underscoring the potential for utilizing both space and ground spatial gradients in city-scale CO2 emissions estimation.

Spaceborne formaldehyde (HCHO) measurements constitute an excellent proxy for the sources of non-methane volatile organic compounds (NMVOCs). Past studies suggested substantial overestimations of NMVOC emissions in state-of-the-art inventories over major source regions. Here, the QA4ECV (Quality Assurance for Essential Climate Variables) retrieval of HCHO columns from OMI (Ozone Monitoring Instrument) is evaluated against (1) FTIR (Fourier-transform infrared) column observations at 26 stations worldwide and (2) aircraft in situ HCHO concentration measurements from campaigns conducted over the USA during 2012–2013. Both validation exercises show that OMI underestimates high columns and overestimates low columns. The linear regression of OMI and aircraft-based columns gives ΩOMI=0.651Ωairc+2.95×1015 molec.cm-2, with ΩOMI and Ωairc the OMI and aircraft-derived vertical columns, whereas the regression of OMI and FTIR data gives ΩOMI=0.659ΩFTIR+2.02×1015 molec.cm-2. Inverse modelling of NMVOC emissions with a global model based on OMI columns corrected for biases based on those relationships leads to much-improved agreement against FTIR data and HCHO concentrations from 11 aircraft campaigns. The optimized global isoprene emissions (∼445Tgyr-1) are 25 % higher than those obtained without bias correction. The optimized isoprene emissions bear both striking similarities and differences with recently published emissions based on spaceborne isoprene columns from the CrIS (Cross-track Infrared Sounder) sensor. Although the interannual variability of OMI HCHO columns is well understood over regions where biogenic emissions are dominant, and the HCHO trends over China and India clearly reflect anthropogenic emission changes, the observed HCHO decline over the southeastern USA remains imperfectly elucidated.

The Mexico City Metropolitan Area (MCMA) stands as one of the most densely populated urban regions globally. To quantify the urban CO2 ${\text{CO}}_{2}$ emissions in the MCMA, we independently assimilated observations from a dense column-integrated Fourier transform infrared (FTIR) network and OCO-3 Snapshot Area Map observations between October 2020 and May 2021. Applying a computationally efficient analytical Bayesian inversion technique, we inverted for surface fluxes at high spatio-temporal resolutions (1-km and 1-hr). The fossil fuel (FF) emission estimates of 5.08 and 6.77 GgCO2 ${\text{CO}}_{2}$/hr reported by the global and local emission inventories were optimized to 4.85 and 5.51 GgCO2 ${\text{CO}}_{2}$/hr based on FTIR observations over this 7 month period, highlighting a convergence of posterior estimates. The modeled biogenic flux estimate of -0.14 GgCO2 ${\text{CO}}_{2}$/hr was improved to -0.33 to -0.27 GgCO2 ${\text{CO}}_{2}$/hr, respectively. It is worth noting that utilizing observations from three primary sites significantly enhanced the accuracy of estimates (13.6 similar to ${\sim} $ 29.2%) around the other four. Using FTIR posterior estimates can improve simulation with the OCO-3 data set. OCO-3 shows a similar decreasing trend in FF emissions (from 6.37 GgCO2 ${\text{CO}}_{2}$/hr to 6.36 and 5.04 GgCO2 ${\text{CO}}_{2}$/hr) as FTIR, but its correction trends for biogenic sources differ, changing from 0.37 to 0.48 GgCO2 ${\text{CO}}_{2}$/hr. The primary reason is OCO-3's lower temporal sampling density. Aligning the FTIR inversion timing with that of OCO-3 yielded comparable corrections for FF emissions, yet discrepancies in biogenic emissions persisted, which can be attributed to their different sampling locations in the rural region and discrepancy in XCO2 ${\text{CO}}_{2}$ observations. Our findings mark a significant step toward validating OCO-3 and FTIR inversion results in metropolitan region.

Global ground-level measurements of elements in ambient particulate matter (PM) can provide valuable information to understand the distribution of dust and trace elements, assess health impacts, and investigate emission sources. We use X-ray fluorescence spectroscopy to characterize the elemental composition of PM samples collected from 27 globally distributed sites in the Surface PARTiculate mAtter Network (SPARTAN) over 2019-2023. Consistent protocols are applied to collect all samples and analyze them at one central laboratory, which facilitates comparison across different sites. Multiple quality assurance measures are performed, including applying reference materials that resemble typical PM samples, acceptance testing, and routine quality control. Method detection limits and uncertainties are estimated. Concentrations of dust and trace element oxides (TEO) are determined from the elemental dataset. In addition to sites in arid regions, a moderately high mean dust concentration (6 μg/m3) in PM2.5 is also found in Dhaka (Bangladesh) along with a high average TEO level (6 μg/m3). High carcinogenic risk (>1 cancer case per 100000 adults) from airborne arsenic is observed in Dhaka (Bangladesh), Kanpur (India), and Hanoi (Vietnam). Industries of informal lead-acid battery and e-waste recycling as well as coal-fired brick kilns likely contribute to the elevated trace element concentrations found in Dhaka.

Volcanic CO 2 emissions inventories have great importance in the understanding of the geological carbon cycle. Volcanoes provide the primary pathway for solid-earth volatiles to reach the Earth’s atmosphere and have the potential to significantly contribute to the carbon-climate feedback. Volcanic carbon emissions (both passive and eruptive degassing) included in inventories, largely stem from patchy surface measurements that suffer from difficulties in removing the atmospheric background. With a 27-year-long ongoing open-vent eruption, Popocatépetl ranks as one of the highest permanent volcanic CO 2 emitters worldwide and provides an excellent natural laboratory to design and experiment with new remote sensing methods for volcanic gas emission measurements. Since October 2012, infrared spectra at different spectral regions have been recorded with a solar occultation FTIR spectrometer. The near-infrared spectra allow for high precision measurements of CO 2 and HCl columns. Under favorable conditions, the continuous observations during sunrise allow the reconstruction of a plume cross-section of HCl and the estimation of the emission flux using wind data. Despite that the detection of CO 2 is more challenging, on April 26 th , 2015 we captured a volcanic plume under favourable wind conditions which allowed us to reconstruct from this particular event a CO 2 emission rate of 116.10 ± 17.2 kg/s. The volcanic HCl emission on this event was the highest detected during the 2012-2016 period. An annual average CO 2 emission estimate of (41.2 ± 16.7) kg/s ((1.30 ± 0.53) Tg/yr) could be determined from a statistical treatment of the detected CO 2 and HCl columns in the IR spectra, and their corresponding molecular ratios, during this period. A total of 25 events were used to derive a mean CO 2 /HCl molecule ratio of 11.4 ± 4.4 and an average HCl emission rate of (3.0 ± 0.3) kg/s could be determined. The CO 2 emissions of Popocatépetl were found to be around 0.32% of the total anthropogenic CO 2 emissions reported in the country and 3.6% of those corresponding to the Mexico City Metropolitan Area (MCMA). CO 2 emissions from the Popocatépetl volcano can be considered to play a negligible role in the global CO 2 budget, but should be taken into account.

Volcanic plume composition is strongly influenced by both changes in magmatic systems and plume-atmosphere interactions. Understanding the degassing mechanisms controlling the type of volcanic activity implies deciphering the contributions of magmatic gases reaching the surface and their posterior chemical transformations in contact with the atmosphere. Remote sensing techniques based on direct solar absorption spectroscopy provide valuable information about most of the emitted magmatic gases but also on gas species formed and converted within the plumes. In this study, we explore the procedures, performances and benefits of combining two direct solar absorption techniques, high resolution Fourier Transform Infrared Spectroscopy (FTIR) and Ultraviolet Differential Optical Absorption Spectroscopy (UV-DOAS), to observe the composition changes in the Popocatépetl’s plume with high temporal resolution. The SO2 vertical columns obtained from three instruments (DOAS, high resolution FTIR and Pandora) were found similar (median difference <12%) after their intercalibration. We combined them to determine with high temporal resolution the different hydrogen halide and halogen species to sulfur ratios (HF/SO2, BrO/SO2, HCl/SO2, SiF4/SO2, detection limit of HBr/SO2) and HCl/BrO in the Popocatépetl’s plume over a 2.5-years period (2017 to mid-2019). BrO/SO2, BrO/HCl, and HCl/SO2 ratios were found in the range of (0.63 ± 0.06 to 1.14 ± 0.20) × 10−4, (2.6 ± 0.5 to 6.9 ± 2.6) × 10−4, and 0.08 ± 0.01 to 0.21 ± 0.01 respectively, while the SiF4/SO2 and HF/SO2 ratios were found fairly constant at (1.56 ± 0.25) × 10−3 and 0.049 ± 0.001. We especially focused on the full growth/destruction cycle of the most voluminous lava dome of the period that took place between February and April 2019. A decrease of the HCl/SO2 ratio was observed with the decrease of the extrusive activity. Furthermore, the short-term variability of BrO/SO2 is measured for the first time at Popocatépetl volcano together with HCl/SO2, revealing different behaviors with respect to the volcanic activity. More generally, providing such temporally resolved and near-real-time time series of both primary and secondary volcanic gaseous species is critical for the management of volcanic emergencies, as well as for the understanding of the volcanic degassing processes and their impact on the atmospheric chemistry.

We validate formaldehyde (HCHO) vertical column densities (VCDs) from Ozone Mapping and Profiler Suite Nadir Mapper (OMPS‐NM) instruments onboard the Suomi National Polar‐orbiting Partnership (Suomi NPP) satellite for 2012–2020 and National Oceanic and Atmospheric Administration‐20 (NOAA‐20) satellite for 2018–2020, hereafter referred to as OMPS‐NPP and OMPS‐N20, with ground‐based Fourier‐Transform Infrared (FTIR) observations of the Network for the Detection of Atmospheric Composition Change (NDACC). OMPS‐NPP/N20 HCHO products reproduce seasonal variability at 24 FTIR sites. Monthly variability of OMPS‐NPP/N20 has a very good agreement with FTIR, showing correlation coefficients of 0.83 and 0.88, respectively. OMPS‐NPP (N20) biases averaged over all sites are −0.9 (4) ± 3 (6)%. However, at clean sites (with VCDs < 4.0 × 10 15 molecules cm −2 ), positive biases of 20 (32) ± 6 (18)% occur for OMPS‐NPP (N20). At sites with HCHO VCDs > 4.0 × 10 15 molecules cm −2 , negative biases of −15% ± 4% appear for OMPS‐NPP, but OMPS‐N20 shows smaller bias of 0.5% ± 6% due to its smaller ground pixel footprints. Therefore, smaller satellite footprint sizes are important in distinguishing small‐scale plumes. In addition, we discuss a bias correction and provide lower limit for the monthly uncertainty of OMPS‐NPP/N20 HCHO products. The total uncertainty for OMPS‐NPP (N20) at clean sites is 0.7 (0.8) × 10 15 molecules cm −2 , corresponding to a relative uncertainty of 32 (30)%. In the case of HCHO VCDs > 4.0 × 10 15 molecules cm −2 , however, the relative uncertainty in HCHO VCDs for OMPS‐NPP (N20) decreases to 31 (18)%.

Fossil fuel carbon dioxide (CO2ff), the main driver of global warming and climate change, is often co-emitted with nitrogen oxides (NOx) and precursors to ground-level ozone from anthropogenic sources like power plants or vehicles. In urban and suburban areas, satellite-based NO2 can be used as a proxy to track the emissions of CO2ff. Because of NO2's shorter lifetime, urban NO2 plumes are more distinguishable from backgrounds and more sensitive to variations in emissions. However, the combination of these two gases is limited by the asynchrony among NO2 and CO2 monitoring satellites. We used CO2ff simulated by the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model to reconcile the tropospheric NO2 vertical column density (VCD) from Tropospheric Monitoring Instrument (TROPOMI) and column-averaged dry-air mole fractions of carbon dioxide enhancements (ΔXCO2) from Orbiting Carbon Observatory 3 (OCO-3) Snapshot Area Maps (SAMs) over a multicity area, Washington D.C.-Baltimore (DC-Balt), and a basin city, Mexico City. NO2/CO2ff ratios over DC-Balt are smaller than Mexico City, indicative of stricter emission restrictions, a more combustion-efficient vehicle fleet, and higher combustion efficiency due to lower altitude in DC-Balt. For single-track cases, the spatial correlations between NO2 and ΔXCO2 over Mexico City are stronger than DC-Balt because the NO2 and CO2 are mostly trapped in the valley of Mexico City, while DC-Balt is severely affected by distant sources (i.e., US East Coast cities). Using multi-track averaging, spatial correlation coefficients increase with the number of days used for averaging. The correlations reached a maximum when averaging >12 continuous images for DC-Balt and >10 continuous images for Mexico City. This finding indicates that multi-track averaging using modeled CO2ff as a proxy is helpful to filter the noise in single-track images, to cancel the interference from distant sources, and to magnify correlations between NO2 and CO2ff. Mexico City showed stronger spatial correlations but weaker temporal correlations than DC-Balt due to biomass burning hot spots and large transport errors caused by the trapping effects of the surrounding mountains. Tracking the 20-day moving average of CO2ff emissions using TROPOMI NO2 seems technically feasible, considering the relationship between correlation coefficients and the number of available satellite images.

Background Machine-learning algorithms are becoming popular techniques to predict ambient air PM 2.5 concentrations at high spatial resolutions (1 × 1 km) using satellite-based aerosol optical depth (AOD). Most machine-learning models have aimed to predict 24 h-averaged PM 2.5 concentrations (mean PM 2.5 ) in high-income regions. Over Mexico, none have been developed to predict subdaily peak levels, such as the maximum daily 1-h concentration (max PM 2.5 ). Objective Our goal was to develop a machine-learning model to predict mean PM 2.5 and max PM 2.5 concentrations in the Mexico City Metropolitan Area from 2004 through 2019. Methods We present a new modeling approach based on extreme gradient boosting (XGBoost) and inverse-distance weighting that uses AOD, meteorology, and land-use variables. We also investigated applications of our mean PM 2.5 predictions that can aid local authorities in air-quality management and public-health surveillance, such as the co-occurrence of high PM 2.5 and heat, compliance with local air-quality standards, and the relationship of PM 2.5 exposure with social marginalization. Results Our models for mean and max PM 2.5 exhibited good performance, with overall cross-validated mean absolute errors (MAE) of 3.68 and 9.20 μg/m 3 , respectively, compared to mean absolute deviations from the median (MAD) of 8.55 and 15.64 μg/m 3 . In 2010, everybody in the study region was exposed to unhealthy levels of PM 2.5 . Hotter days had greater PM 2.5 concentrations. Finally, we found similar exposure to PM 2.5 across levels of social marginalization. Significance Machine learning algorithms can be used to predict highly spatiotemporally resolved PM 2.5 concentrations even in regions with sparse monitoring. Impact Our PM 2.5 predictions can aid local authorities in air-quality management and public-health surveillance, and they can advance epidemiological research in Central Mexico with state-of-the-art exposure assessment methods.

Total column H2O is measured by two remote sensing techniques at the Altzomoni Atmospheric Observatory (19 degrees 12'N, 98 degrees 65'W, 4000 m above sea level), a high-altitude, tropical background site in central Mexico. A ground-based solar absorption FTIR spectrometer that is part of the Network for Detection of Atmospheric Composition Change (NDACC) is used to retrieve water vapor in three spectral regions (6074-6471, 2925-2941, and 1110-1253 cm(-1)) and is compared to data obtained from a global positioning system (GPS) receiver that is part of the TLALOCNet GPS-meteorological network. Strong correlations are obtained between the coincident hourly means from the three FTIR products and small relative bias and correction factors could be determined for each when compared to the more consistent GPS data. Retrievals from the 2925-2941 cm(-1 )spectral region have the highest correlation with GPS [coefficient of determination (R2) = 0.998, standard deviation (STD) = 0.18 cm (78.39%), mean difference = 0.04 cm (8.33%)], although the other products are also highly correlated [R-2 >= 0.99, STD <= 0.20 cm (< 90%), mean difference <= 0.1 cm (< 24%)]. Clear-sky dry bias (CSDB) values are reduced to < 10% (< 0.20 cm) when coincident hourly means are used in the comparison. The use of GPS and FTIR water vapor products simultaneously leads to a more complete and better description of the diurnal and seasonal cycles of water vapor. We describe the water vapor climatology with both complementary datasets, nevertheless, pointing out the importance of considering the clear-sky dry bias arising from the large diurnal and seasonal variability of water vapor at this high-altitude tropical site.

Carbonyl sulfide (OCS) is a non‐hygroscopic trace species in the free troposphere and a large sulfur reservoir maintained by both direct oceanic, geologic, biogenic, and anthropogenic emissions and the oxidation of other sulfur‐containing source species. It is the largest source of sulfur transported to the stratosphere during volcanically quiescent periods. Data from 22 ground‐based globally dispersed stations are used to derive trends in total and partial column OCS. Middle infrared spectral data are recorded by solar‐viewing Fourier transform interferometers that are operated as part of the Network for the Detection of Atmospheric Composition Change between 1986 and 2020. Vertical information in the retrieved profiles provides analysis of discreet altitudinal regions. Trends are found to have well‐defined inflection points. In two linear trend time periods ∼2002 to 2008 and ∼2008 to 2016 tropospheric trends range from ∼0.0 to (1.55 ± 0.30%/yr) in contrast to the prior period where all tropospheric trends are negative. Regression analyses show strongest correlation in the free troposphere with anthropogenic emissions. Stratospheric trends in the period ∼2008 to 2016 are positive up to (1.93 ± 0.26%/yr) except notably low latitude stations that have negative stratospheric trends. Since ∼2016, all stations show a free tropospheric decrease to 2020. Stratospheric OCS is regressed with simultaneously measured N2O to derive a trend accounting for dynamical variability. Stratospheric lifetimes are derived and range from (54.1 ± 9.7)yr in the sub‐tropics to (103.4 ± 18.3)yr in Antarctica. These unique long‐term measurements provide new and critical constraints on the global OCS budget.