Formaldehyde and its relation to CO, PAN, and SO₂ in the Houston-Galveston airshed

International audienc

ACTRIS non-methane hydrocarbon intercomparison experiment in Europe to support WMO GAW and EMEP observation networks

The performance of 18 European institutions involved in long-term non-methane hydrocarbon (NMHC) measurements in ambient air within the framework of the Global Atmosphere Watch (GAW) and the European Monitoring and Evaluation Programme (EMEP) was assessed with respect to data quality objectives (DQOs) of ACTRIS (Aerosols, Clouds, and Trace gases Research InfraStructure Network) and GAW. Compared to previous intercomparison studies the DQOs define a novel approach to assess and ensure a high quality of the measurements. Having already been adopted by GAW, the ACTRIS DQOs are demanding with deviations to a reference value of less than 5% and a repeatability of better than 2% for NMHC mole fractions above 0.1 nmol mol(-1). The participants of the intercomparison analysed two dry gas mixtures in pressurised cylinders, a 30-component NMHC mixture in nitrogen (NMHC_N-2 /at approximately 1 nmol mol(-1) and a whole air sample (NMHC_air), following a standardised operation procedure including zero-and calibration gas measurements. Furthermore, participants had to report details on their instruments and assess their measurement uncertainties. The NMHCs were analysed either by gas chromatography-flame ionisation detection (GC-FID) or by gas chromatography-mass spectrometry (GC-MS). For the NMHC_N-2 measurements, 62% of the reported values were within the 5% deviation class corresponding to the ACTRIS DQOs. For NMHC_air, generally more frequent and larger deviations to the assigned values were observed, with 50% of the reported values within the 5% deviation class. Important contributors to the poorer performance in NMHC_air compared to NMHC_N-2 were a more complex matrix and a larger span of NMHC mole fractions (0.03-2.5 nmol mol(-1)). The performance of the participating laboratories were affected by the different measurement procedures such as the usage of a two-step vs. a one-step calibration, breakthroughs of C-2-C-3 hydrocarbons in the focussing trap, blank values in zero-gas measurements (especially for those systems using a Nafion (R) Dryer), adsorptive losses of aromatic compounds, and insufficient chromatographic separation.Peer reviewe

Upscaling Net Ecosystem Exchange Over Heterogeneous Landscapes With Machine Learning

This paper discusses different feature selection methods and CO2 flux data sets with a varying quality‐quantity balance for the application of a Random Forest model to predict daily CO2 fluxes at 250 m spatial resolution for the Rur catchment area in western Germany between 2010 and 2018. Measurements from eddy covariance stations of different ecosystem types, remotely sensed vegetation data from MODIS, and COSMO‐REA6 reanalysis data were used to train the model and predictions were validated by a spatial and temporal validation scheme. Results show the capabilities of a backwards feature elimination to remove irrelevant variables and an importance of high‐quality‐low‐quantity flux data set to improve predictions. However, results also show that spatial prediction is more difficult than temporal prediction by reflecting the mean value accurately though underestimating the variance of CO2 fluxes. Vegetated parts of the catchment acted as a CO2 sink during the investigation period, net capturing about 237 g C m−2 y−1. Croplands, coniferous forests, deciduous forests and grasslands were all sinks on average. The highest uptake was predicted to occur in late spring and early summer, while the catchment was a CO2 source in fall and winter. In conclusion, the Random Forest model predicted a narrower distribution of CO2 fluxes, though our methodological improvements look promising in order to achieve high‐resolution net ecosystem exchange data sets at the regional scale.Plain Language Summary: Whether ecosystems absorb or emit CO2 plays a major role in the global carbon cycle and impacts climate change. This exchange is already measured at scattered stations, but creating spatially resolved data sets remains a challenge. In this paper, we used satellite images of vegetation and meteorological data to predict the CO2 exchange of the Rur catchment area near the German‐Dutch‐Belgian border for every day from 2010 to 2018. In order to assess the prediction quality, we compared actual measurements from several stations within the catchment with the predictions at the locations of these stations. Results show that our method could increase prediction quality compared to previous process‐based models, though the error remains rather high. Vegetated parts of the catchment including coniferous forests, deciduous forests, grasslands, and croplands were all CO2 sinks on average. In late spring and early summer, they were the strongest sink, but in fall and winter a CO2 source.Key Points: CO2 flux upscaling with Random Forest can be improved with a backward feature elimination and strict quality control of flux data. Vegetated parts of the Rur catchment were predicted to be a CO2 sink on average, with the highest uptake in late spring and early summer. The Enhanced Vegetation Index and potential evapotranspiration are useful predictors for the regionalization of CO2 flux measurements.Deutsche ForschungsgemeinschaftInstitute of Environmental Meteorology of the University of TrierAgrosphere Institute of the Forschungszentrum Jülic

On the diurnal, weekly, and seasonal cycles and annual trends in atmospheric CO 2 at Mount Zugspitze, Germany, during 1981-2016

A continuous, 36-year measurement composite of atmospheric carbon dioxide (CO2) at three measurement locations on Mount Zugspitze, Germany, was studied. For a comprehensive site characterization of Mount Zugspitze, analyses of CO2 weekly periodicity and diurnal cycle were performed to provide evidence for local sources and sinks, showing clear weekday to weekend differences, with dominantly higher CO2 levels during the daytime on weekdays. A case study of atmospheric trace gases (CO and NO) and the passenger numbers to the summit indicate that CO2 sources close by did not result from tourist activities but instead obviously from anthropogenic pollution in the near vicinity. Such analysis of local effects is an indispensable requirement for selecting representative data at orographic complex measurement sites. The CO2 trend and seasonality were then analyzed by background data selection and decomposition of the long-term time series into trend and seasonal components. The mean CO2 annual growth rate over the 36-year period at Zugspitze is 1.8±0.4&thinsp;ppm&thinsp;yr−1, which is in good agreement with Mauna Loa station and global means. The peak-to-trough amplitude of the mean CO2 seasonal cycle is 12.4±0.6&thinsp;ppm at Mount Zugspitze (after data selection: 10.5±0.5&thinsp;ppm), which is much lower than at nearby measurement sites at Mount Wank (15.9±1.5&thinsp;ppm) and Schauinsland (15.9±1.0&thinsp;ppm), but following a similar seasonal pattern.</p

Comparison of different methods for the<em> in situ</em> measurement of forest litter moisture content.

Dead fine fuel (e.g., litter) moisture content is an important parameter for both forest fire and ecological applications as it is related to ignitability, fire behavior and soil respiration. Real-time availability of this value would thus be a great benefit to fire risk management and prevention. However, the comprehensive literature review in this paper shows that there is no easy-to-use method for automated measurements available. This study investigates the applicability of four different sensor types (permittivity and electrical resistance measuring principles) for this measurement. Comparisons were made to manual gravimetric reference measurements carried out almost daily for one fire season and overall agreement was good (highly significant correlations with 0.792 &lt; Combining double low line r &lt; Combining double low line 0.947, p &lt; 0.001). Standard deviations within sensor types were linearly correlated to daily sensor mean values; however, above a certain threshold they became irregular, which may be linked to exceedance of the working ranges. Thus, measurements with irregular standard deviations were considered unusable and relationships between gravimetric and automatic measurements of all individual sensors were compared only for useable periods. A large drift in these relationships became obvious from drought to drought period. This drift may be related to installation effects or settling and decomposition of the litter layer throughout the fire season. Because of the drift and the in situ calibration necessary, it cannot be recommended to use the methods presented here for monitoring purposes and thus operational hazard management. However, they may be interesting for scientific studies when some manual fuel moisture measurements are made anyway. Additionally, a number of potential methodological improvements are suggested