Bioaerosols in the Amazon rain forest: temporal variations and vertical profiles of Eukarya, Bacteria, and Archaea

The Amazon rain forest plays a major role in global hydrological cycling, and biogenic aerosols are likely to influence the formation of clouds and precipitation. Information about the sources and altitude profiles of primary biological aerosol particles, however, is sparse. We used fluorescence in situ hybridization (FISH), a molecular biological staining technique largely unexplored in aerosol research, to investigate the sources and spatiotemporal distribution of Amazonian bioaerosols on the domain level. We found wet season bioaerosol number concentrations in the range of 1–5 × 105 m−3 accounting for > 70 % of the coarse mode aerosol. Eukaryotic and bacterial particles predominated, with fractions of ∼ 56 % and ∼ 26 % of the intact airborne cells. Archaea occurred at very low concentrations. Vertical profiles exhibit a steep decrease in bioaerosol numbers from the understory to 325 m height on the Amazon Tall Tower Observatory (ATTO), with a stronger decrease in Eukarya compared to Bacteria. Considering earlier investigations, our results can be regarded as representative for near-pristine Amazonian wet season conditions. The observed concentrations and profiles provide new insights into the sources and dispersion of different types of Amazonian bioaerosols as a solid basis for model studies on biosphere–atmosphere interactions such as bioprecipitation cycling.

Determination of free amino acids, saccharides, and selected microbes in biogenic atmospheric aerosols – seasonal variations, particle size distribution, chemical and microbial relations

Primary biological aerosol particles (PBAPs) play an important role in the interaction between biosphere, atmosphere, and climate, affecting cloud and precipitation formation processes. The presence of pollen, plant fragments, spores, bacteria, algae, and viruses in PBAPs is well known. In order to explore the complex interrelationships between airborne and particulate chemical tracers (amino acids, saccharides), gene copy numbers (16S and 18S for bacteria and fungi, respectively), gas phase chemistry, and the particle size distribution, 84 size-segregated aerosol samples from four particle size fractions (< 1.0, 1.0–2.5, 2.5–10, and > 10 µm) were collected at the SMEAR II station, Finland, in autumn 2017. The gene copy numbers and size distributions of bacteria, Pseudomonas, and fungi in biogenic aerosols were determined by DNA extraction and amplification. In addition, free amino acids (19) and saccharides (8) were analysed in aerosol samples by hydrophilic interaction liquid chromatography–mass spectrometry (HILIC-MS). Different machine learning (ML) approaches, such as cluster analysis, discriminant analysis, neural network analysis, and multiple linear regression (MLR), were used for the clarification of several aspects related to the composition of biogenic aerosols. Clear variations in composition as a function of the particle size were observed. In most cases, the highest concentration values and gene copy numbers (in the case of microbes) were observed for 2.5–10 µm particles, followed by > 10, 1–2.5, and < 1.0 µm particles. In addition, different variables related to the air and soil temperature, the UV radiation, and the amount of water in the soil affected the composition of biogenic aerosols. In terms of interpreting the results, MLR provided the greatest improvement over classical statistical approaches such as Pearson correlation among the ML approaches considered. In all cases, the explained variance was over 91 %. The great variability of the samples hindered the clarification of common patterns when evaluating the relation between the presence of microbes and the chemical composition of biogenic aerosols. Finally, positive correlations were observed between gas-phase VOCs (such as acetone, toluene, methanol, and 2-methyl-3-buten-2-ol) and the gene copy numbers of microbes in biogenic aerosols.

Bioaerosols in the Amazon rain forest: Temporal variations and vertical profiles of Eukarya, Bacteria and Archaea

The Amazon rain forest plays a major role in global hydrological cycling and biogenic aerosols are likely to influence the formation of clouds and precipitation. Information about the sources and altitude profiles of primary biological aerosol particles, however, is sparse. We used fluorescence in situ hybridization (FISH), a molecular biological staining technique largely unexplored in aerosol research, to investigate the sources and spatiotemporal distribution of Amazonian bioaerosols on domain level. We found wet season bioaerosol number concentrations in the range of 1–5 · 105 m−3 accounting for > 70 % of the coarse mode aerosol. Eukaryotic and bacterial particles predominated, with fractions of ~56 % and ~26 % of the intact airborne cells. Archaea occurred at very low concentrations. Vertical profiles exhibit a steep decrease of bioaerosol numbers from the understory to 325 m height on the Amazon Tall Tower Observatory, with a stronger decrease of Eukarya compared to Bacteria. Considering earlier investigations, our results can be regarded as representative for near-pristine Amazonian wet season conditions. The observed concentrations and profiles provide unprecedented insights into the sources and dispersion of different types of Amazonian bioaerosols as a solid basis for model studies on biosphere-atmosphere interactions such as bioprecipitation cycling.

Towards the connection between snow microphysics and melting layer: Insights from multi-frequency and dual-polarization radar observations during BAECC

In stratiform rainfall, the melting layer is often visible in radar observations as an enhanced reflectivity band, the so-called bright band. Despite the ongoing debate on the exact microphysical processes taking place in the melting layer and on how they translate into radar measurements, both model simulations and observations indicate that the radar-measured melting layer properties are influenced by snow microphysical processes that take place above it. There is still, however, a lack of comprehensive observations to link the two. To advance our knowledge of precipitation formation in ice clouds and provide an additional constraint on the retrieval of ice cloud microphysical properties, we have investigated this link. This study is divided into two parts. Firstly, surface-based snowfall measurements are used to devise a method for classifying rimed and unrimed snow from X- and Ka-band Doppler radar observations. In the second part, this classification is used in combination with multi-frequency and dual-polarization radar observations to investigate the impact of precipitation intensity, aggregation, riming, and dendritic growth on melting layer properties. The radar-observed melting layer characteristics show strong dependence on precipitation intensity as well as detectable differences between unrimed and rimed snow. This study is based on the data collected during the Biogenic Aerosols – Effects on Clouds and Climate (BAECC) experiment, that took place in 2014 in Hyytiala, Finland.

Development and validation of a supervised machine learning radar Doppler spectra peak-finding algorithm

In many types of clouds, multiple hydrometeor populations can be present at the same time and height. Studying the evolution of these different hydrometeors in a time–height perspective can give valuable information on cloud particle composition and microphysical growth processes. However, as a prerequisite, the number of different hydrometeor types in a certain cloud volume needs to be quantified. This can be accomplished using cloud radar Doppler velocity spectra from profiling cloud radars if the different hydrometeor types have sufficiently different terminal fall velocities to produce individual Doppler spectrum peaks. Here we present a newly developed supervised machine learning radar Doppler spectra peak-finding algorithm (named PEAKO). In this approach, three adjustable parameters (spectrum smoothing span, prominence threshold, and minimum peak width at half-height) are varied to obtain the set of parameters which yields the best agreement of user-classified and machine-marked peaks. The algorithm was developed for Ka-band ARM zenith-pointing radar (KAZR) observations obtained in thick snowfall systems during the Atmospheric Radiation Measurement Program (ARM) mobile facility AMF2 deployment at Hyytiälä, Finland, during the Biogenic Aerosols – Effects on Clouds and Climate (BAECC) field campaign. The performance of PEAKO is evaluated by comparing its results to existing Doppler peak-finding algorithms. The new algorithm consistently identifies Doppler spectra peaks and outperforms other algorithms by reducing noise and increasing temporal and height consistency in detected features. In the future, the PEAKO algorithm will be adapted to other cloud radars and other types of clouds consisting of multiple hydrometeors in the same cloud volume.

Comparison of PM10 Sources Profiles at 15 French Sites Using a Harmonized Constrained Positive Matrix Factorization Approach

Receptor-oriented models, including positive matrix factorization (PMF) analyses, are now commonly used to elaborate and/or evaluate action plans to improve air quality. In this context, the SOURCES project has been set-up to gather and investigate in a harmonized way 15 datasets of chemical compounds from PM10 collected for PMF studies during a five-year period (2012–2016) in France. The present paper aims at giving an overview of the results obtained within this project, notably illustrating the behavior of key primary sources as well as focusing on their statistical robustness and representativeness. Overall, wood burning for residential heating as well as road transport were confirmed to be the two main primary sources strongly influencing PM10 loadings across the country. While wood burning profiles, as well as those dominated by secondary inorganic aerosols, present a rather good homogeneity among the sites investigated, some significant variabilities were observed for primary traffic factors, illustrating the need to better characterize the diversity of the various vehicle exhaust and non-exhaust emissions. Finally, natural sources, such as sea salts (widely observed in internal mixing with anthropogenic compounds), primary biogenic aerosols and/or terrigenous particles, were also found as non-negligible PM10 components at every investigated site.

Development and validation of a supervised machine learning radar Doppler spectra peak finding algorithm

In many types of clouds, multiple hydrometeor populations can be present at the same time and height. Studying the evolution of these different hydrometeors in a time-height perspective can give valuable information on cloud particle composition and microphysical growth processes. However, as a prerequisite, the number of different hydrometeor types in a certain cloud volume needs to be quantified. This can be accomplished using cloud radar Doppler velocity spectra from profiling cloud radars if the different hydrometeor types have sufficiently different terminal fall velocities to produce individual Doppler spectrum peaks. Here we present a newly developed supervised machine learning radar Doppler spectra peak finding algorithm (named PEAKO). In this approach, three adjustable parameters (spectrum smoothing span, prominence threshold, and minimum peak width at half-height) are varied to obtain the set of parameters which yields the best agreement of user-classified and machine-marked peaks. The algorithm was developed for Ka-band ARM Zenith-pointing Radar (KAZR) observations obtained in thick snow fall systems during the Atmospheric Radiation Measurement Program’s (ARM) mobile facility AMF2 deployment at Hyytiälä, Finland, during the Biogenic Aerosols – Effects on Clouds and Climate (BAECC) field campaign. The performance of PEAKO is evaluated by comparing its results to existing Doppler peak finding algorithms. The new algorithm is found to perform well. Its advantage is that the detected features are less noisy and more consistent in time and height than the peak finding results of other algorithms. In the future, the PEAKO algorithm will be adapted to other cloud radars and other types of clouds consisting of multiple hydrometeors in the same cloud volume.