International network for coordinating work on the physicochemical properties of molecules and mixtures important for atmospheric particulate matter

通过大气气溶胶的大小和化学成分的演变来预测它们的影响，依赖于使用机械模型，这些模型试图预测可能数以百万计的此类化合物在气相和冷凝相之间的分配。纯组分和凝聚相混合物的物理化学性质的不确定性影响了我们准确预测和解决这种分配的能力。我们如何应对这种不确定性？在两项正在进行的NERC赠款中，正在测量纯组分和混合物的一系列基本性质(蒸汽压、粘度和扩散常数)，目的是改善对大气功能的预测。鉴于进行此类测量的紧迫性，欧盟和北美存在着现有的NERC项目无法提供的补充工具和专业知识。同样，这些国际方案无法利用参考的NERC项目所提供的实验室设施和专门知识。为什么在方法和专门知识方面缺乏连贯性是一个问题？国际社会最近的审查突出了实验方法之间的重大差异。尽管如此，没有协调一致的努力来调和这些差异，或开始汇编适当的数据，并进行适当的筛选，以改进对改进大气气溶胶模型至关重要的预测技术。目前汇编的数据极其稀少。最重要的是，没有推荐的标准来为未来的测量建立可接受的标准，也没有一套商定的建模工具来确定数据必须有多准确才能预测不断演变的气溶胶特性。归根结底，我们不知道属性中可能达到和可以接受的精度水平是什么。这是一个独特的机会，可以在国际上解决这些问题，同时直接受益于现有和未来由NERC驱动的方案。该IOF将促进对正在进行的NERC赠款的数据的利用，将其整合到利用合作伙伴组织的测量和专业知识建立的新数据库中，通过扩大预测技术的灵活性和准确性来增加价值。我们已经确定了3笔正在进行的和2笔已完成的NERC赠款，详见案例中的支助。每个伙伴将提供对其现有测量和建模方案的访问，参与评价委员会会议，撰写出版物，接待研究人员参加相互比较(见支持函)，并在网络成熟后支持与更广泛的社区接触。虽然我们确定了在两年内开展的活动，但确保项目的可持续性是至关重要的。因此，我们不仅将创建新的数据库和一套商定的开放源码社区建模设施，而且还将建立一套商定的标准，以接受未来的测量。我们将通过公开研讨会和会议与全球社会接触。该网络由来自曼彻斯特大学[LEAD]、布里斯托尔大学[UK-COI]、瑞士ETH、丹麦奥胡斯大学、瑞典斯德哥尔摩大学、劳伦斯·伯克利实验室[美国]、太平洋西北国家实验室[美国]和不列颠哥伦比亚大学[加拿大]的研究人员组成。

Predicting the impact of atmospheric aerosols, through their evolving size and chemical composition, relies on using mechanistic models that attempt to predict the partitioning of potentially millions of such compounds between the gas phase and condensed phase. Uncertainties in the physicochemical properties of pure components and condensed phase mixtures affect our ability to accurately predict and resolve this partitioning. How do we tackle such uncertainties? In 2 ongoing NERC grants, a range of fundamental properties of pure components and mixtures (vapour pressures, viscosities and diffusion constants), are being measured with the objective of improving predictions for atmospheric functionalities. Given the urgency of making such measurements, complementary instruments and expertise exists across the EU and North America that is not available through existing NERC projects. Similarly, the laboratory facilities and expertise enabled by the referenced NERC projects are not accessible to such international programmes. Why is the lack of coherence in methodology and expertise a problem? Recent reviews by the international community highlight significant discrepancies between experimental methods. Despite this, there is no coordinated effort to reconcile these differences or to start compiling appropriate data, with appropriate screening, to improve the predictive techniques essential for improving atmospheric aerosol models. Current compiled data are extremely sparse. On top of this, there are no recommended standards to establish accepted criteria for future measurements or an agreed set of modelling tools to determine how accurate the data has to be to predict evolving aerosol properties. Ultimately, we do not know what level of accuracy in properties might be attainable and acceptable. This is a unique opportunity to address these issues internationally whilst directly benefiting existing and future NERC driven programmes. This IOF will catalyse exploitation of data from ongoing NERC grants, consolidating it into new databases built with measurements and expertise from partner organisation, adding value by expanding flexibility and accuracy of predictive techniques. We have identified 3 ongoing and 2 completed NERC grants as detailed in the case for support. Each partner will provide access to their existing measurement and modelling programmes, involvement in evaluation committee meetings, writing publications, hosting researchers to take part in intercomparisons (see letters of support) and supporting engagement with the wider community once the network matures. Whilst we identify activities to take place over a 2-year period, it is crucial to ensure project sustainability. As such, we will not only create new databanks and an agreed set of open source community modelling facilities, but an agreed set of standards for accepting future measurements will be established. We will engage with the global community through open workshops and meetings. The network comprises researchers from: The University of Manchester [lead], University of Bristol [UK-CoI], ETH [Switzerland], Aarhus University [Denmark], Stockholm University [Sweden], Lawrence Berkeley Laboratory [US], Pacific Northwest National Lab [US] and University of British Columbia [Canada].