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

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

Abstract

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].

Planned Impact

Atmospheric aerosol particles are major contributors to both climate forcing uncertainties and adverse effects on human health. Without an improved knowledge of basic data it is not possible to predict effects or simplify and / or parameterise aerosol properties with any degree of certainty. The proposed work targets improvements in those parameters that currently compromise state of the art models of aerosol behaviour. The quantitative improvement in these predictive models will then inform models used to conduct process simulations from single particle to regional scales.

Direct beneficiaries of the immediate work will be largely academic; though results obtained from this project will indirectly benefit policy driven and industrial non-academic end users. The atmospheric community will benefit from provision of a comprehensive set of fundamental property measurements and improved predictive models. Dissemination of knowledge through online software and printed reference material via collaboration with partners will vastly increase our ability to benefit researchers in other fields indirectly. The primary non-academic end-users of the proposed programme output in the UK would be the Met Office via existing links with the UKCA Climate- Chemistry-Community-Aerosol model, a joint NCAS-Met Office programme funded by NCAS, GMR and DEFRA. The impacts of aerosol on climate are still credited with the largest uncertainty in climate forcing and a large part of the radiatively active boundary layer sub-micron aerosol burden is organic. Policy decisions with respect to quantification and mitigation of the climate impacts of aerosol require policy-related model simulations with at least a rudimentary but physically-based representation of organic aerosol. Such model descriptions are currently unavailable. The study of those properties which dictate gas/particle partitioning of organic compounds will be able to inform such a climate-focused goal. Other international non-academic agencies conducting IPCC simulations would be best placed to use the same reduced complexity SOA formalisms supplied to the Met Office.

Publications

10 25 50
 
Description The importance of organic aerosol particles in the environment has been long established, influencing cloud formation and lifetime, absorbing and scattering sunlight, affecting atmospheric composition and impacting on human health. Conventionally, ambient organic particles were considered to exist as liquids. Recent observations suggest that they may instead exist as highly viscous semi-solids or amorphous glassy solids under certain conditions, with important implications for atmospheric chemistry, climate and air quality. through this project we have developed a collaborative ability to explore our understanding of aerosol particle phase, particularly as identified by measurements of the viscosity of organic particles, and the atmospheric implications of phase state.
Exploitation Route To assist in developing appropriate prognostic frameworks used to quantify impact.
Sectors Environment

 
Description This network facilitated discussions around the need for collaborative work around aerosol science. Following this, network partner Jonathan Reid of the University of Bristol submitted a joint EPSRC CDT bid on aerosol science which has recently been funded.
First Year Of Impact 2019
 
Title New online and open source facility for predicting the properties of organic molecules and mixtures. 
Description UManSysProp is an online facility for calculating the properties of individual organic molecules, ensemble mixtures and aerosol particles. Built using open source chemical informatics, and currently hosted at the University of Manchester, the facilities are provided here via: a browser-friendly web-interface. a programmer friendly JSON API that enables you to call our suite of tools from your own code. access to the source code behind all predictive techniques provided on the site. 
Type Of Technology Webtool/Application 
Year Produced 2016 
Impact There have been 1800 unique users within its first 9 months of deployment, including 4 references since the paper release. 
URL http://umansysprop.seaes.manchester.ac.uk/
 
Title PyBox is a Python based box-model generator and simulator designed for atmospheric chemistry and aerosol studies. 
Description PyBox is a Python based box-model generator and simulator designed for atmospheric chemistry and aerosol studies. The first phase of the PyBox project is to develop a gas phase model, using the reaction information within the Master Chemical Mechanism (MCM) as the basis, coupled with an idealised sectional aerosol model. PyBox also relates component properties, using molecular structural information, through the UManSysProp informatics suite. Any public release will occur according to new processes added, agreement from any partner contributions and/or associated peer-review papers. 
Type Of Technology Software 
Year Produced 2018 
Open Source License? Yes  
Impact N/A srtill in adoption phase 
URL http://joss.theoj.org/papers/10.21105/joss.00755