Michel Grutter

Accurate measurements of the composition of fine particulate matter (PM_2.5) are important for understanding its sources and health impacts. Water, a portion of PM_2.5 mass, is difficult to measure. We describe developments to the Surface PARTiculate mAtter Network (SPARTAN) to better characterize the chemical composition of PM_2.5 on polytetrafluoroethylene filters. A robotic weighing facility is used to estimate water content of PM_2.5 sampled at three sites (Fajardo, Puerto Rico; Bujumbura, Burundi; Abu Dhabi, United Arab Emirates), and the chemical composition is used to attribute water content to three hygroscopic categories: high growth (sodium chloride), medium growth (ammonium-nitrate-sulfate-potassium), and low growth (organics). The growth rates are tested using two additional sites (Beijing, China; Halifax, Canada). The water content at 35 % relative humidity (RH) is estimated to be 16.5 % (11.6 %–26.5 %, 95 % confidence) for the high growth category, 3.9 % (3.0 %–4.2 %, 95 % confidence) for medium growth, and 1.0 % (0.6–1.3 %, 95 % confidence) for low growth. We calculate the average water content at 35 % RH for 2442 filters from 24 globally distributed sites from December 2019 to September 2024 to be 2.06 % (0.85 %–5.47 %, range), where 58 % of the aerosol water is associated with the medium growth category (22 % low growth, 19 % high growth).

Accurate measurements of the composition of fine particulate matter (PM_2.5) are important for understanding its sources and health impacts. Water, a portion of PM_2.5 mass, is difficult to measure. We describe developments to the Surface PARTiculate mAtter Network (SPARTAN) to better characterize the chemical composition of PM_2.5 on polytetrafluoroethylene filters. A robotic weighing facility is used to estimate water content of PM_2.5 sampled at three sites (Fajardo, Puerto Rico; Bujumbura, Burundi; Abu Dhabi, United Arab Emirates), and the chemical composition is used to attribute water content to three hygroscopic categories: high growth (sodium chloride), medium growth (ammonium-nitrate-sulfate-potassium), and low growth (organics). The growth rates are tested using two additional sites (Beijing, China; Halifax, Canada). The water content at 35 % relative humidity (RH) is estimated to be 16.5 % (11.6 %–26.5 %, 95 % confidence) for the high growth category, 3.9 % (3.0 %–4.2 %, 95 % confidence) for medium growth, and 1.0 % (0.6–1.3 %, 95 % confidence) for low growth. We calculate the average water content at 35 % RH for 2442 filters from 24 globally distributed sites from December 2019 to September 2024 to be 2.06 % (0.85 %–5.47 %, range), where 58 % of the aerosol water is associated with the medium growth category (22 % low growth, 19 % high growth).

Accurate measurements of the composition of fine particulate matter (PM2.5) are important for understanding its sources and health impacts. Water, a portion of PM2.5 mass, is difficult to measure. We describe developments to the Surface PARTiculate mAtter Network (SPARTAN) to better characterize the chemical composition of PM2.5 on polytetrafluoroethylene filters. A robotic weighing facility is used to estimate water content of PM2.5 sampled at three sites (Fajardo, Puerto Rico; Bujumbura, Burundi; Abu Dhabi, United Arab Emirates), and the chemical composition is used to attribute water content to three hygroscopic categories: high growth (sodium chloride), medium growth (ammonium-nitrate-sulfate-potassium), and low growth (organics). The growth rates are tested using two additional sites (Beijing, China; Halifax, Canada). The water content at 35 % relative humidity (RH) is estimated to be 16.5 % (11.6 %–26.5 %, 95 % confidence) for the high growth category, 3.9 % (3.0 %–4.2 %, 95 % confidence) for medium growth, and 1.0 % (0.6–1.3 %, 95 % confidence) for low growth. We calculate the average water content at 35 % RH for 2442 filters from 24 globally distributed sites from December 2019 to September 2024 to be 2.06 % (0.85 %–5.47 %, range), where 58 % of the aerosol water is associated with the medium growth category (22 % low growth, 19 % high growth).

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 .

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.

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.

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 datasets, 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.

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.