Fine micro- and nanoplastics concentrations in particulate matter samples from the high alpine site Sonnblick, Austria.

We report atmospheric fine micro- and nanoplastics concentrations from particulate matter (PM) samples of two size fractions (PM10, fine micro- and nanoplastics, and PM1, nanoplastics), which were collected at the remote high alpine station Sonnblick Observatory, Austria. Active sampling was performed from June 2021 until April 2022. Analysis was done using TD-PTR-MS to detect 6 different plastic types. Polyethylene terephthalate (PET), polyethylene (PE) and polypropylene/polypropylene carbonate (PP/PPC) were found to be the dominating species. PET was detected in almost all samples, while the other plastic types occurred more episodically. Furthermore, polyvinyl chloride (PVC), polystyrene (PS) and tire wear particles were detected in single samples. Considering the three main plastic types, average plastics concentrations were 35 and 21 ng m-³ with maximum concentrations of 165 and 113 ng m-³ for PM10 and PM1, respectively. Average polymer concentrations were higher in the summer/fall period than in winter/spring. In summer/fall, PM10 plastics concentrations were higher by a factor of 2 compared to PM1, while concentrations of both size classes were comparable in the winter/spring period. This suggests that in the colder season plastic particles arriving at the Eastern Alpine crests are mainly present as nanoplastics. The contribution of micro- and nanoplastics to organic matter at the remote site was found to be comparable to data determined at an urban site. We found significant correlations between the PET concentration and tracers originating from anthropogenic activities such as elemental carbon, nitrate, ammonium, and sulphate as well as organic carbon and arabitol.

Widespread Pesticide Distribution in the European Atmosphere Questions their Degradability in Air.

Risk assessment of pesticide impacts on remote ecosystems makes use of model-estimated degradation in air. Recent studies suggest these degradation rates to be overestimated, questioning current pesticide regulation. Here, we investigated the concentrations of 76 pesticides in Europe at 29 rural, coastal, mountain, and polar sites during the agricultural application season. Overall, 58 pesticides were observed in the European atmosphere. Low spatial variation of 7 pesticides suggests continental-scale atmospheric dispersal. Based on concentrations in free tropospheric air and at Arctic sites, 22 pesticides were identified to be prone to long-range atmospheric transport, which included 15 substances approved for agricultural use in Europe and 7 banned ones. Comparison between concentrations at remote sites and those found at pesticide source areas suggests long atmospheric lifetimes of atrazine, cyprodinil, spiroxamine, tebuconazole, terbuthylazine, and thiacloprid. In general, our findings suggest that atmospheric transport and persistence of pesticides have been underestimated and that their risk assessment needs to be improved.

Comparison of Bacterial and Fungal Composition and Their Chemical Interaction in Free Tropospheric Air and Snow Over an Entire Winter Season at Mount Sonnblick, Austria.

We investigated the interactions of air and snow over one entire winter accumulation period as well as the importance of chemical markers in a pristine free-tropospheric environment to explain variation in a microbiological dataset. To overcome the limitations of short term bioaerosol sampling, we sampled the atmosphere continuously onto quartzfiber air filters using a DIGITEL high volume PM10 sampler. The bacterial and fungal communities, sequenced using Illumina MiSeq, as well as the chemical components of the atmosphere were compared to those of a late season snow profile. Results reveal strong dynamics in the composition of bacterial and fungal communities in air and snow. In fall the two compartments were similar, suggesting a strong interaction between them. The overlap diminished as the season progressed due to an evolution within the snowpack throughout winter and spring. Certain bacterial and fungal genera were only detected in air samples, which implies that a distinct air microbiome might exist. These organisms are likely not incorporated in clouds and thus not precipitated or scavenged in snow. Although snow appears to be seeded by the atmosphere, both air and snow showed differing bacterial and fungal communities and chemical composition. Season and alpha diversity were major drivers for microbial variability in snow and air, and only a few chemical markers were identified as important in explaining microbial diversity. Air microbial community variation was more related to chemical markers than snow microbial composition. For air microbial communities Cl-, TC/OC, SO4 2-, Mg2+, and Fe/Al, all compounds related to dust or anthropogenic activities, were identified as related to bacterial variability while dust related Ca2+ was significant in snow. The only common driver for snow and air was SO4 2-, a tracer for anthropogenic sources. The occurrence of chemical compounds was coupled with boundary layer injections in the free troposphere (FT). Boundary layer injections also caused the observed variations in community composition and chemistry between the two compartments. Long-term monitoring is required for a more valid insight in post-depositional selection in snow.

Multisource emission retrieval within a biogas plant based on inverse dispersion calculations--a real-life example.

Open digestate storage tanks were identified as one of the main methane (CH4) emitters of a biogas plant. The main purpose of this paper is to determine these emission rates using an inverse dispersion technique in conjunction with open-path tunable diode laser spectroscopy (OP-TDLS) concentration measurements for multisource reconstruction. Since the condition number, a measure of "ill-conditioned" matrices, strongly influences the accuracy of source reconstruction, it is used as a diagnostic of error sensitivity. The investigations demonstrate that the condition number for a given source-sensor configuration in the highly disturbed flow field within the plant significantly depends on the meteorological conditions (e.g., wind speed, stratification, wind direction, etc.). The CH4 emissions are retrieved by removing unrepresentative periods with high condition numbers, which indicate uncertainty in recovering the individual sources. In a final step, the CH4 emissions are compared with the maximum biological methane potential (BMP) in the digestate analyzed under laboratory conditions. The retrieved methane emission rates represent an average of 50% of the maximum BMP of the stored digestate in the winter months, while they comprised an average of 85% during the measurement campaigns in the summer months. The results indicate that the open tanks have the potential to represent a substantial emission source even during colder periods.

Use of a Monte Carlo technique to complete a fragmented set of H2S emission rates from a wastewater treatment plant.

The impact of ambient concentrations in the vicinity of a plant can only be assessed if the emission rate is known. In this study, based on measurements of ambient H2S concentrations and meteorological parameters, the a priori unknown emission rates of a tannery wastewater treatment plant are calculated by an inverse dispersion technique. The calculations are determined using the Gaussian Austrian regulatory dispersion model. Following this method, emission data can be obtained, though only for a measurement station that is positioned such that the wind direction at the measurement station is leeward of the plant. Using the inverse transform sampling, which is a Monte Carlo technique, the dataset can also be completed for those wind directions for which no ambient concentration measurements are available. For the model validation, the measured ambient concentrations are compared with the calculated ambient concentrations obtained from the synthetic emission data of the Monte Carlo model. The cumulative frequency distribution of this new dataset agrees well with the empirical data. This inverse transform sampling method is thus a useful supplement for calculating emission rates using the inverse dispersion technique.