Ammonia (NH3) is a central component of atmospheric reactive nitrogen, and a precursor gas that forms airborne particulate matter including nitrate and sulfate aerosol. Reducing ammonia emissions, primarily from agriculture, is a major challenge for air pollution mitigation and environmental protection. This work assesses the effects of ammonia emission reductions in Sweden, which is one of the EU Member States that are currently not meeting their emission reduction commitments. We apply the regional chemical transport model MATCH with improved NH3 treatment, and benchmark the updated model setup against surface observations of atmospheric NH3 and other nitrogen species for a large-scale model domain over Europe. We perform high-resolution regional simulations over Sweden, and compare the effects of reducing the emissions of (1) agricultural NH3, and (2) nitrogen oxides (NOx) from road transport, which is the second major nitrogen source in Sweden. The results show the potential of national NH3 emission reductions to mitigate NH3 levels, fine particulate matter (PM2.5) and nitrogen deposition within Sweden. While cutting NOx emissions is important for air pollution abatement through NOx mitigation, in Sweden the reductions have only minor or negligible effects on PM2.5 and nitrogen deposition. This demonstrates the benefits of NH3 reduction for both air quality and environment, and the impact of national abatement actions.
Mobile, near-source measurements are broadly used for determining δ13CH4 of individual methane (CH4) emissions sources. To answer the need for robust and comparable measurement methods, we aim to define the best practices to determine isotopic signatures of CH4 sources from atmospheric measurements, considering instrument accuracy and precision. Using the Keeling and Miller-Tans methods, we verify the impact of linear fitting methods, averaging approaches, and for the Miller-Tans method, different background composition. Measurement techniques include Isotope Ratio Mass Spectrometry (IRMS) and Cavity Ring Down Spectroscopy (CRDS). The use of the active AirCore system for sampling, coupled to CRDS for measurement, is examined. Due to their higher precision and accuracy, the chosen data processing strategy does not significantly influence IRMS results. Comparatively lower-precision CRDS measurements are more sensitive to methodological choices. Fitting methods with forced symmetry like Major Axis or Bivariate Correlated Errors and Intrinsic Scatter (BCES) with orthogonal sub-method introduce significant bias in the determined δ13CH4 signatures using measurements from the lower-precision CRDS. The most reliable results are obtained for non-averaged data using fitting methods, which include uncertainties of x- and y-axis values, like York fitting or BCES (Y|X) sub-method, where x is treated as an independent variable. The Ordinary Least Squares method provides sufficiently robust results and can be used to determine δ13CH4 in near-source conditions. The present recommendations are aimed at laboratories measuring δ13CH4 source signatures to encourage consistency in the required methods for data analysis.
We studied the influence of the Planetary Boundary Layer (PBL) on the air masses sampled at the mountaintop Hellenic Atmospheric Aerosol and Climate Change station ((HAC) 2 ) at Mount Helmos (Greece) during the Cloud-AerosoL InteractionS in the Helmos background TropOsphere (CALISTHO) Campaign from September 2021 to March 2022. The PBL Height (PBLH) was determined from the standard deviation of the vertical wind velocity ( sigma w ) measured by a wind Doppler lidar (over a 30 -min time window with 30 m spatial resolution); the height for which sigma w drops below a characteristic threshold of 0.1 m s -1 corresponds to the PBLH. The air mass characterization is independently carried out using in situ measurements sampled at (HAC) 2 (equivalent black carbon, eBC ; fluorescent particle number, aerosol size distributions, absolute humidity). We found that a distinct diurnal cycle of aerosol properties is seen when the station is inside the PBL (i.e., PBLH exceeds the (HAC) 2 altitude); and a complete lack thereof when it is in the Free Tropospheric Layer (FTL). Additionally, we identified transition periods where the (HAC) 2 site location alternates between the FTL (usually during the early morning hours) and the PBL (usually during the midday and late afternoon hours), during which the concentration and characteristics of the aerosols vary the most. Transition periods are also when orographic clouds are formed. The highest PBLH values occur in September [400 m above (HAC) 2 ] followed by a transition period in November, while the lowest ones occur in January [200 m below (HAC) 2 ]. We found also that the PBLH increases by 16 m per 1 degrees C increase of the ground temperature.
Aerosol effects on cloud properties are notoriously difficult to disentangle from variations driven by meteorological factors. Here, a machine learning model is trained on reanalysis data and satellite retrievals to predict cloud microphysical properties, as a way to illustrate the relative importance of meteorology and aerosol, respectively, on cloud properties. It is found that cloud droplet effective radius can be predicted with some skill from only meteorological information, including estimated air mass origin and cloud top height. For ten geographical regions the mean coefficient of determination is 0.41 and normalised root-mean square error 24%. The machine learning model thereby performs better than a reference linear regression model, and a model predicting the climatological mean. A gradient boosting regression performs on par with a neural network regression model. Adding aerosol information as input to the model improves its skill somewhat, but the difference is small and the direction of the influence of changing aerosol burden on cloud droplet effective radius is not consistent across regions, and thereby also not always consistent with what is expected from cloud brightening.
Accurate quantification of air-sea gas transfer velocity is critical for our understanding of air-sea CO2 gas fluxes, global carbon budget and climate responses. CO2 transfer velocity is predominantly subject to constraints of wave-related dynamic processes at the ocean surface layer but is typically parameterized with wind speed. This study proposes and compares two parameterizations which accommodate dimensionless wave terms. The validations are conducted using both laboratory and field measurements of CO2 transfer and wave statistics. A scaling of bubble-mediated gas transfer is implemented into the formula that is linked to wave breaking probability. The improved parameterizations are capable of collapsing combined laboratory and field data sets which comprise diversified conditions of wind, wave and wave breaking.
