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

通过大气气溶胶不断变化的大小和化学成分来预测其影响，依赖于使用机械模型，这些模型试图预测可能有数以百万计的这种化合物在气相和凝聚相之间的分配。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。如何应对这些不确定性？在2项正在进行的NERC拨款中，正在测量纯组分和混合物的一系列基本特性（蒸汽压力、粘度和扩散常数），目的是改进对大气功能的预测。鉴于进行此类测量的紧迫性，欧盟和北美存在现有的NERC项目无法提供的互补工具和专业知识。同样，上述NERC项目所提供的实验室设施和专门知识也无法为这些国际方案所用。为什么方法论和专业知识缺乏一致性是一个问题？国际社会最近的评论强调了实验方法之间的重大差异。尽管如此，目前还没有协调一致的努力来调和这些差异，或开始编制适当的数据，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。当前编译的数据非常稀疏。最重要的是，目前还没有推荐的标准来为未来的测量建立公认的标准，也没有一套公认的建模工具来确定数据的准确性，以预测不断变化的气溶胶特性。最终，我们不知道属性的准确度可以达到和接受的程度。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的NERC驱动的项目。该IOF将促进正在进行的NERC赠款数据的利用，将其整合到新的数据库中，这些数据库由合作伙伴组织的测量和专业知识建立，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个正在进行的和2个已完成的NERC赠款，如案例中详细说明的支持。每个合作伙伴将提供对其现有的测量和建模项目的访问，参与评估委员会会议，撰写出版物，接待科学家参与相互比较（见支持信），并在网络成熟后支持与更广泛的社区进行接触。当我们确定在2年内进行的活动时，确保项目的可持续性至关重要。因此，我们不仅将创建新的数据库和一套公认的开源社区建模工具，而且还将建立一套公认的用于接受未来度量的标准。我们将通过公开研讨会和会议与国际社会接触。该网络的研究人员来自：曼彻斯特大学[领导]、布里斯托尔大学[英国- coi]、ETH[瑞士]、奥胡斯大学[丹麦]、斯德哥尔摩大学[瑞典]、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]。

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].