Characterisation and Modelling of Climatically Relevant Primary Biogenic Ice Nuclei in the BEACHON Southern Rocky Mountain Project

Lead Research Organisation: University of Manchester
Department Name: Earth Atmospheric and Env Sciences

Abstract

This proposal is for an invited UK contribution to an international multidisciplinary, multi-investigator study of the connections between biogeochemical cycling of carbon and water in semi-arid regions of the Western U.S comprising the Southern Rocky Mountain study region in 2011. The central hypothesis is that biogenic emission of reactive carbon gases from plants and soil, and wind transport of primary carbonaceous particles such as spores, pollen and bacterial cells, lead to the formation of cloud condensation nuclei (CCN) and ice nuclei (IN) which, in turn, influence regional precipitation patterns and thus link components of the carbon and water cycles. Conceptually, the potential linkages and feedbacks among biogenic emissions, the formation of CCN, and dynamics in the carbon and water cycles have been debate extensively and while various aspects of the coupled system have been studied in the past, e.g., emissions of biogenic volatile organic compounds (VOC) from vegetation and CCN formation from biogenic secondary organic aerosol (SOA), there are absolutely no studies that have traced the entire process in a quantitative, source to receptor fashion -- from emissions, through precipitation and back to emissions. A multidisciplinary research effort aimed at quantifying and assessing the relative importance of links among these processes has long been needed to enable regional coupled surface-atmosphere models. The strategic plans of the International Geosphere Biosphere Program - Integrated Land Ecosystem Atmosphere Processes Study (IGBP-iLEAPS) and the NCAR Bio-hydro-atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics, and Nitrogen (BEACHON) project both place a high priority on research efforts that will enhance understanding of the role of biogenic aerosols in linking and regulating the biogeochemical and water cycles and BEACHON SRM 2011provides an ideal opportunity to develop this integrative approach. The central aim of BEACHON-SRM is to use observations to assess the influence of ecosystem processes, and their responses to climate, on the number and composition of climatically-relevant biogenic aerosols and their potential to function as CCN and IN, and to modify precipitation, which feeds back to complete the loop and alter ecosystem processes. BEACHON-SRM will focus on the formation and modification of CCN and IN from biogenic sources. IN are those particles that initiate ice nucleation in the atmosphere. Although IN typically represent lessthan 1 in 10^5 of atmospheric aerosol particles, they are important in initiating ice formation at temperatures warmer than -36 C, a critical first step in most precipitation formation. In addition to dust, sources of IN include primary biological particles. The ubiquity of biological IN in precipitation from mid- to high-latitude locations has been well documented recently and biological IN have been measured directly from aerosol and cloud particle residuals. Biological IN, which again typically represent a small fraction of all primary biological particles, include ice nucleation active (INA) bacteria, fungal spores, pollen, leaf litter , and IN derived from bacterial decomposition of these particles. Biological IN are particularly efficient at warmer supercooled temperatures, and thus may play an especially important role in modestly supercooled clouds (warmer than -4.5 to - 10C. At the moment there is enormous uncertainty regarding number concentrations of INA biogenic particles in the atmosphere, and any estimates are limited due to the scarcity of data regarding the spatial and seasonal distributions of these types of particles. This proposal will contribute field measurement and modelling expertise to this ambitious project which will deliver quantitative and far reaching results for biogenic aerosol-cloud interactions and impacts on hydrological cycling for the first time.

Publications

10 25 50

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Costa A (2017) Classification of Arctic, Mid-Latitude and Tropical Clouds in the Mixed-Phase Temperature Regime in Atmospheric Chemistry and Physics Discussions

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Crawford I (2017) Real-time detection of airborne fluorescent bioparticles in Antarctica in Atmospheric Chemistry and Physics

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Crawford I (2017) Real Time Detection of Airborne Bioparticles in Antarctica in Atmospheric Chemistry and Physics Discussions

 
Description We showed that the concentration of airborne biological particles in a North American forest ecosystem increases significantly during rain and that bioparticles are correlated with atmospheric ice nuclei (IN). The greatest increase of bioparticles and IN occurred in the size range of 2-6 µm, which is characteristic for bacterial aggregates and fungal spores. By DNA analysis it was found that high diversities of airborne bacteria and fungi, including groups containing human and plant pathogens (mildew, smut and rust fungi, molds, Enterobacteriaceae, Pseudomonadaceae). In addition to detecting known bacterial and fungal IN, the project discovered two species of IN-active fungi that were not previously known as biological ice nucleators (Isaria farinosa and Acremonium implicatum). The findings suggest that atmospheric bioaerosols, IN, and rainfall are more tightly coupled than previously assumed.
There has been an upsurge of interest in use of field deployable UVLIF instrumentation to investigate and monitor airborne biological aerosols for ecosystem an, animal and human health applications based on the outcomes of this and similar projects. Unfortunately the UK now seems to be lagging behind in such research which is being maintained in a somewhat ad hoc approach to this specific type of airborne particulate pollution.
In addition we have developed parameterisations for emissions of bioaerosols as a fucntion of temperature and relative humidity specific to these pine forests of N. America. Recent studies adapting the same instruments characterised and demonstrated in this study have been evident. The main study airborne involving measurements across the US using an instrumented airship (CloudLab) was funded not by NERC but by the bBC.
Exploitation Route These findings will allow biological particle emissions to be more accurately included in climate models to assess their importance for climate change through aerosol cloud interactions.The study has led to additional collaboration with Dstl, US and Swiss instrument manufacturers (Droplet Measurement technology and PLAIR Switzerland) to characterise instruments for detection of airborne biological particles in real-time, including pollen. New proposals are in preparation with manufacturing partners to develop portable bioaerosol detectors for use in health applications (EPSRC).New mathematical techniques based on supervised machine learning algorithms have been developed for discrimination of different particle types using newer sophisticated instruments.
Sectors Aerospace, Defence and Marine,Agriculture, Food and Drink,Digital/Communication/Information Technologies (including Software),Environment,Healthcare,Manufacturing, including Industrial Biotechology

URL http://cires.colorado.edu/jimenez-group/wiki/index.php/BEACHON-RoMBAS
 
Description The BEACHON project conducts experimental and numerical research studies to enhance understanding of the roles of biogenic aerosols, nitrogen trace gases and oxidants in linking and regulating the carbon and water cycles. The findings were used (following consultancy) by other institutes to analyse large scale airborne monitoring measurement programmes in the USA, funded by the BBC (CloudLab project). Demonstration of originally UK developed UVLIG field instruments have contributed to market interest in using this technology for many health applications. The Earth system has undergone extensive change during the last 60 years, with important implications for human health, resource management, ecosystem services and the environment. The ability to predict these changes and their impacts on time scales of months to a decade is becoming increasingly important. Key to improving the predictability of Earth system behavior over these time scales is an improved understanding of the coupling between water, energy and biogeochemical cycles in a multi-scale modeling framework. Robust predictions at these time scales require coordinated modeling, observations and process studies that explicitly address the coupled water, energy and biogeochemical cycles at multiple temporal and spatial scales.
First Year Of Impact 2016
Sector Aerospace, Defence and Marine,Agriculture, Food and Drink,Environment,Healthcare
Impact Types Societal,Economic

 
Description NERC Standard Grant
Amount £11,000 (GBP)
Organisation Natural Environment Research Council 
Sector Public
Country United Kingdom
Start 10/2017 
End 09/2020
 
Title New HCA & Machine & Deep Learning Algorithms for Real-Time Airborne Particle Detection 
Description New Machine Learning tools were developed and compared with previous techniques to improve current real-time airborne bioparticle detection and discrimination using UVLIF particle spectrometers. 
Type Of Material Biological samples 
Provided To Others? No  
Impact As a result of the work we obtained a PhD partnership/secondment of a NERC DTP PhD student to Paratools-Daresbury to develop deep learning algorithms for large bioaerosol databases using supercomputing facilities there. A new NERC case studentship with Dstl was funded in collaboration with LTSC/CEA France. Manchester-NCAS have now invested in a new high resolution UVLIF spectrometer for assessment in bioparticle monitoring networks with the manufacturers (PLAIR Switzerland/MeteoSwiss). The case stduentships will have access to enw datasest provided by this new instrument. 
 
Description Dstl Partnership to assess instruments for detecting and discriminating different bioparticles in real-time for health monitoring applications 
Organisation Defence Science & Technology Laboratory (DSTL)
Country United Kingdom 
Sector Public 
PI Contribution We will be designing new chamber experiments and providing new UVLIF instruments to deliver new bioparticle training data sets to challenge machine learning and deep learning algorithms to identify airborne bioparticle types in real-time for health/advertant releases suitable for bio-PM health monitoring applications.
Collaborator Contribution Two publications have been published. The first paper presented improved methods for discriminating and quantifying airborne biological aerosol particles by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF) spectrometer data. The methods employed in this study were evaluated for accuracy against prescribed reference particle populations, biological and non-biological. The HCA method was examined and potential for false positives identified and methods to reduce the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches. This improved capacity to discriminate and quantify PBAP meta-classes.The performance of various hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set. We provided the algorithm development specific to various UVLIF aerosol spectrometers, collected the field data and conducted the laboratory experiments for this study. In a second study, much larger training bioparticle data sets from a new UVLIF particle spectrometer, provided by partner's Dstl, were evaluated using new Machine Learning Algorithms and compared with the previous hierarchical Agglomerative CLuster Approaches. In this study we provided the algorithm development, analysed the data sets and produced the publication. We also collected additional field data sets of ambient particles. This work builds on existing collaboration with Dstl originally started as part of the NERC BIOGENICE funded project. http://www.cas.manchester.ac.uk/resprojects/biogenice/
Impact A new PhD case studentship was provided with "Paratools-Daresbury" to sue supercomputing facilities to analyse and interpret large bioparticle data sets using deep learning machine algorithms. We have been provided with year long bioparticle data sets recorded in London to evaluate these algorithms. We have received a NERC-Dstl case studentship in collaboration with CEA France to conduct field experiments at European sites. We have received infrastructure support from NCAS via a new high resolution, field deployable UVLIF particle spectrometer which will be used by the case studentships to further evaluate applications for health monitoring and for delivering datasets for improving emissions parameterisations of bioparticles in regional scale and global models. We have received in-kind support from manufacturers of UVLIF instruments through shared data sets to evaluate new instrument performance and, as part of the Dstl proposed laboratory biochamber experiments, they are supplying new instruments and training for instrument intercomparison exercises.
Start Year 2015
 
Description Dstl Partnership to assess instruments for detecting and discriminating different bioparticles in real-time for health monitoring applications 
Organisation Laboratory of Climate Sciences and the Environment (LSCE)
Country France 
Sector Academic/University 
PI Contribution We will be designing new chamber experiments and providing new UVLIF instruments to deliver new bioparticle training data sets to challenge machine learning and deep learning algorithms to identify airborne bioparticle types in real-time for health/advertant releases suitable for bio-PM health monitoring applications.
Collaborator Contribution Two publications have been published. The first paper presented improved methods for discriminating and quantifying airborne biological aerosol particles by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF) spectrometer data. The methods employed in this study were evaluated for accuracy against prescribed reference particle populations, biological and non-biological. The HCA method was examined and potential for false positives identified and methods to reduce the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches. This improved capacity to discriminate and quantify PBAP meta-classes.The performance of various hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set. We provided the algorithm development specific to various UVLIF aerosol spectrometers, collected the field data and conducted the laboratory experiments for this study. In a second study, much larger training bioparticle data sets from a new UVLIF particle spectrometer, provided by partner's Dstl, were evaluated using new Machine Learning Algorithms and compared with the previous hierarchical Agglomerative CLuster Approaches. In this study we provided the algorithm development, analysed the data sets and produced the publication. We also collected additional field data sets of ambient particles. This work builds on existing collaboration with Dstl originally started as part of the NERC BIOGENICE funded project. http://www.cas.manchester.ac.uk/resprojects/biogenice/
Impact A new PhD case studentship was provided with "Paratools-Daresbury" to sue supercomputing facilities to analyse and interpret large bioparticle data sets using deep learning machine algorithms. We have been provided with year long bioparticle data sets recorded in London to evaluate these algorithms. We have received a NERC-Dstl case studentship in collaboration with CEA France to conduct field experiments at European sites. We have received infrastructure support from NCAS via a new high resolution, field deployable UVLIF particle spectrometer which will be used by the case studentships to further evaluate applications for health monitoring and for delivering datasets for improving emissions parameterisations of bioparticles in regional scale and global models. We have received in-kind support from manufacturers of UVLIF instruments through shared data sets to evaluate new instrument performance and, as part of the Dstl proposed laboratory biochamber experiments, they are supplying new instruments and training for instrument intercomparison exercises.
Start Year 2015