Bioprecipitation: a feedback cycle linking Earth history, ecosystem dynamics and land use through biological ice nucleators in the atmosphere

Morris, Cindy E.; Conen, Franz; Huffman, J. A.; Phillips, Vaughan T. J.; Pöschl, Ulrich; Sands, David C.

doi:10.1111/gcb.12447

Cited by 250 publications

(233 citation statements)

References 105 publications

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“…This in turn facilitates precipitation, which is beneficial for the growth of plants and microorganisms (Morris et al, 2014). Within such a cycle it may be the case that biological particles initiate secondary ice nucleation processes, also at warmer temperatures (Crawford et al, 2012), leading to more rapid cloud glaciation which may also impact the development of precipitation.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of machine learning algorithms for classification of primary biological aerosol using a new UV-LIF spectrometer

et al. 2017

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Abstract. Characterisation of bioaerosols has important implications within environment and public health sectors. Recent developments in ultraviolet light-induced fluorescence (UV-LIF) detectors such as the Wideband Integrated Bioaerosol Spectrometer (WIBS) and the newly introduced Multiparameter Bioaerosol Spectrometer (MBS) have allowed for the real-time collection of fluorescence, size and morphology measurements for the purpose of discriminating between bacteria, fungal spores and pollen.This new generation of instruments has enabled ever larger data sets to be compiled with the aim of studying more complex environments. In real world data sets, particularly those from an urban environment, the population may be dominated by non-biological fluorescent interferents, bringing into question the accuracy of measurements of quantities such as concentrations. It is therefore imperative that we validate the performance of different algorithms which can be used for the task of classification.For unsupervised learning we tested hierarchical agglomerative clustering with various different linkages. For supervised learning, 11 methods were tested, including decision trees, ensemble methods (random forests, gradient boosting and AdaBoost), two implementations for support vector machines (libsvm and liblinear) and Gaussian methods (Gaussian naïve Bayesian, quadratic and linear discriminant analysis, the k-nearest neighbours algorithm and artificial neural networks).The methods were applied to two different data sets produced using the new MBS, which provides multichannel UV-LIF fluorescence signatures for single airborne biological particles. The first data set contained mixed PSLs and the second contained a variety of laboratory-generated aerosol.Clustering in general performs slightly worse than the supervised learning methods, correctly classifying, at best, only 67.6 and 91.1 % for the two data sets respectively. For supervised learning the gradient boosting algorithm was found to be the most effective, on average correctly classifying 82.8 and 98.27 % of the testing data, respectively, across the two data sets.A possible alternative to gradient boosting is neural networks. We do however note that this method requires much more user input than the other methods, and we suggest that further research should be conducted using this method, especially using parallelised hardware such as the GPU, which would allow for larger networks to be trained, which could possibly yield better results.We also saw that some methods, such as clustering, failed to utilise the additional shape information provided by the instrument, whilst for others, such as the decision trees, ensemble methods and neural networks, improved performance could be attained with the inclusion of such information.

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Section: Introductionmentioning

confidence: 99%

Evaluation of machine learning algorithms for classification of primary biological aerosol using a new UV-LIF spectrometer

et al. 2017

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“…These feedbacks may change the local structure of the atmospheric circulation, cloud properties, and the intensity of precipitation (Rosenfeld et al, 2013;Lappalainen et al, 2016). Precipitation, in turn, causes emissions bursts into the atmosphere of primary bioaerosols and submicron degradation products containing hygroscopic water-soluble inorganic ions (potassium, sodium, chlorides, and phosphates) and polysaccharides (hexoses, mannitol) (Morris et al, 2014;Bigg et al, 2015;Huffman et al, 2013). As temperatures increase, population outbreaks of tree-damaging insects can occur more frequently.…”

Section: Introductionmentioning

confidence: 99%

Long-term measurements (2010–2014) of carbonaceous aerosol and carbon monoxide at the Zotino Tall Tower Observatory (ZOTTO) in central Siberia

et al. 2017

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Abstract. We present long-term (5-year) measurements of particulate matter with an upper diameter limit of ∼ 10 µm (PM 10 ), elemental carbon (EC), organic carbon (OC), and water-soluble organic carbon (WSOC) in aerosol filter samples collected at the Zotino Tall Tower Observatory in the middle-taiga subzone (Siberia). The data are complemented with carbon monoxide (CO) measurements. Air mass back trajectory analysis and satellite image analysis were used to characterise potential source regions and the transport pathway of haze plumes. Polluted and background periods were selected using a non-parametric statistical approach and analysed separately. In addition, near-pristine air masses were selected based on their EC concentrations being below the detection limit of our thermal-optical instrument. Over the entire sampling campaign, 75 and 48 % of air masses in winter and in summer, respectively, and 42 % in spring and fall are classified as polluted. The observed background concentrations of CO and EC showed a sine-like behaviour with a period of 365 ± 4 days, mostly due to different degrees of dilution and the removal of polluted air masses arriving at the Zotino Tall Tower Observatory (ZOTTO) from remote sources. Our analysis of the near-pristine conditions shows that the longest periods with clean air masses were observed in summer, with a frequency of 17 %, while in wintertime only 1 % can be classified as a clean. Against a background of low concentrations of CO, EC, and OC in the near-pristine summertime, it was possible to identify pollution plumes that most likely came from crude-oil production sites located in the oil-rich regions of Western Siberia. Overall, our analysis indicates that most of the time the Siberian region is impacted by atmospheric pollution arising from biomass burning and anthropogenic emissions. A relatively clean atmosphere can be observed mainly in summer, when polluted species are removed by precipitation and the aerosol burden returns to near-pristine conditions.

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“…Pseudomonas syringae was the first such organism discovered [1]. Occurring throughout the water cycle, it has sparked the bioprecipitation hypothesis [2,3]. However, at mixed-phase cloud height, P. syringae probably constitutes only a minor fraction of the total INP −8 population [4].…”

Section: Introductionmentioning

confidence: 99%

Ice Nucleating Particle Concentrations Increase When Leaves Fall in Autumn

et al. 2017

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Bioprecipitation: a feedback cycle linking Earth history, ecosystem dynamics and land use through biological ice nucleators in the atmosphere

Cited by 250 publications

References 105 publications

Evaluation of machine learning algorithms for classification of primary biological aerosol using a new UV-LIF spectrometer

Evaluation of machine learning algorithms for classification of primary biological aerosol using a new UV-LIF spectrometer

Long-term measurements (2010–2014) of carbonaceous aerosol and carbon monoxide at the Zotino Tall Tower Observatory (ZOTTO) in central Siberia

Ice Nucleating Particle Concentrations Increase When Leaves Fall in Autumn

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