Combining real-time airborne bioaerosol spectrometry with neural network algorithms to quantify different bioaerosol emissions from agriculture.

该奖学金将结合联合收割机最先进的实时单粒子集成光电生物气溶胶和灰尘气溶胶光谱分析，结合神经网络数据分析和微气象通量测量技术，通过实验室风洞研究和高分辨率扩散模型增强，改变我们对不同大气条件下农业景观生物气溶胶排放通量的理解和量化，农业活动的影响。实验将在Rothamsted研究所的农业现场进行，这些农业现场将提供多站点湍流和气象测量系统。解决的问题将包括了解不同农业生态系统中不同农业活动对空气中生物气溶胶扩散和沉积的贡献。在冠层传输下，生物气溶胶颗粒的沉积和空气扩散将使用新的，可现场部署的光电仪器，包括多波段紫外激发荧光光谱仪，用于识别生物气溶胶类别，单粒子荧光寿命，以区分生物和非生物以及来自土壤和植物的生物粉尘混合物。这项工作将支持正在进行的与生物气溶胶仪器制造商的合作，包括DMT-USA，PLAIR CH和Swisens-CH以及赫特福德大学开发的仪器，我们与他们有着长期的合作。不同生物气溶胶类别之间的区分将使用从Eurochamp气溶胶建模和生物气溶胶研究室（ChAMBRe）的仪器表征研究中生成的AI训练数据集进行改进，热那亚在自然花粉排放事件期间，将使用标准便携式单粒子全息成像光谱仪实时量化不同物种的空气花粉浓度，以监测和比较自然排放事件与农业活动产生的生物气溶胶。新的研究表明，降雨对增加特定类别生物气溶胶排放的影响，这些生物气溶胶也将被监测土壤，播种，播种和种植活动。将使用在不同农业活动下收集的新的生物气溶胶光谱仪数据库，对一系列不同的排放通量方法进行审查，以评估未来的参考标准，改进对与人类、动物和生态系统健康有关的此类排放的监测和缓解。如果有时间和仪器，这些测量将通过使用高速液滴光谱仪分析农业冠层内的喷雾农药分散和叶鳞沉积效率来补充。最后，还将研究使用无人机气溶胶测量的局部尺度3D羽流测绘技术，收集用于逆排放通量模型研究的数据，以提高对农药沉积效率和羽流在田间尺度上扩散的理解。研究结果将用于改善不同农业活动产生的生物气溶胶排放量的量化，与自然排放机制及其与不同环境因素的相互关系相比，通过应用新的降维算法结合强大的，抗离群值的AI聚类技术。其目的是首次将直接测量的生物气溶胶通量纳入新的多维生物气溶胶和微气象数据库，这些数据库可用于测试一系列监测农业生态系统排放的新分析方法。

This studentship will combine state of the art real-time single particle integrated optoelectronic bio-aerosol and dust-aerosol spectrometry combined with neural net data analysis and micrometeorological flux measurement techniques, augmented by laboratory wind tunnel studies, and high resolution dispersion models, to transform our understanding and quantification of bioaerosol emission fluxes from agricultural landscapes under different atmospheric conditions and due to the influence from agricultural activities. Experiments will be carried out at the Rothamsted Institutes agricultural sites who will provide access to multi-site turbulence and meteorological measurement systems.Questions addressed will include understanding the contributions of different farming activities to airborne bioaerosol dispersion and deposition for different agricultural ecosystems.Below canopy transmission, deposition and airborne dispersion of bioaerosol particulates will be measured using novel, field deployable optoelectronic instruments comprising multi band UV excitation-fluorescence spectrometers to identify bioaerosol classes, single particle fluorescence lifetime to discriminate between biological and non-biological as well as bio-dust mixtures from soils and plants. This work will support ongoing collaboration with bioaerosol instrument manufacturers including DMT-USA, PLAIR CH and Swisens-CH as well as instruments developed by the University of Hertfordshire with which we have long-standing collaboration.Discrimination between different bioaerosol classes will be improved using AI training data sets generated from instrument characterisation studies in the Eurochamp Chamber for Aerosol Modelling and Bio-aerosol Research (ChAMBRe), Genoa. Additional training experiments at the ChAMBRe are planned for 2024 which will contribute to this project.During natural pollen emission events a standard portable single particle, holographic- imaging spectrometer will be used to quantify airborne pollen concentrations of different species in real-time to monitor and compare natural emission events with farming activity generated bioaerosols. New studies have shown the influence of rainfall on enhancing emissions of specific classes of bioaerosols and these will also be monitored for soil, seeding, threshing and planting activities.. A range of different emission flux methodologies will be examined using the new boaerosol spectrometer databases collected under different farming activities to assess future reference standards to improve monitoring and mitigation of such emissions relevant to human, animal and ecosystem health. If time and instruments are available, these measurements will be supplemented by analysis of spray pesticide dispersion and leaf scale deposition efficiencies within agricultural canopies using high speed liquid droplet spectrometers. Finally local scale 3D plume mapping techniques using drone-based aerosol measurements will also be investigated collecting data for inverse emission flux model studies to improve understanding of pesticide deposition efficiencies and plume dispersal across field scales. The results will be used to improve quantification of bioaerosol emissions generated by different farming activities compared to natural emission mechanisms and their inter-relationships with different environmental factors by applying novel dimensional reduction algorithms combined with robust, outlier resistant AI clustering techniques. The aim will be, for the first time, to include directly measured bioaerosol fluxes into new multi-dimensional bioaerosol and micrometeorological databases that may be used for testing a range of new analytical approaches to monitoring emissions from agricultural ecosystems.