Direct Validated Improvement of Atmospheric Aerosol Property Prediction Using Laboratory Measurements

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

Abstract

Aerosol particles influence climate directly by the scattering and absorption of solar radiation (direct effect) and indirectly through their role as cloud condensation nuclei (indirect effect), the latter effect comprising the largest uncertainty in climate change. Similarly, aerosol particles have a large impact on air quality. Unfortunately, there are many uncertainties which hinder our ability to model the behaviour of aerosol particles and thus asses the impacts they can have. These uncertainties are largely caused by the complexity of organic compounds, which represent a significant fraction of the chemical composition, and subsequent coupling with inorganic compounds. Whilst speciation is difficult we know certain compounds reside in this fraction yet detailed laboratory/theoretical studies focusing on specific parameters are lacking. 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. However, development of large scale models which aim to assess the effect of aerosols on climate, for example, rely heavily on such parameterisations. Thus, current unavailability of data propagates through to uncertainty in the aerosol impact. The most important uncertainties are in those parameters which dictate the aerosol water content and gas / aerosol partitioning. The former is necessary for predicting the direct and indirect climatic effect; the latter determines the evolving chemical composition of the aerosol and hence is necessary for predicting aerosol loading and composition which is also important for air quality considerations. To determine effects on water uptake below 100% relative humidity, investigations of aqueous thermodynamics are required through measurements / predictions of a quantity known as the water activity, which represents an 'effective' concentration. For predictions of water uptake above 100%RH, the solution surface tension is a crucial parameter for predictions of cloud activation. In describing the changing composition of aerosol particles, it is important to know how readily a compound will partition between the gas and particulate phase. Two parameters are important here. Solute activity coefficients, a measure of chemical interactions taking place in solution, describes how 'comfortable' a compound is in the aqueous aerosol, and is thus important for modelling condensation. Similarly, compounds with low vapour pressure have higher tendency to partition to aerosol particle and is thus an important parameter yet remains highly uncertain. This proposal seeks to conduct a range of detailed laboratory measurements, using well-established techniques, on key parameters which at the present time critically compromise the predictive capability of state of the art models of multicomponent aerosol behaviour. Improvement in the base models and predictive techniques from the laboratory programme will thus find its way directly to improved climate predictions and assesment of air quality.

Publications

10 25 50

 
Description By constructing a new instrument to measure the vapour pressure of organics, we have found that huge uncertainties exist which hinder our ability to assess the impact aerosols have on the environment. However, by having this new facility we have also shown that we can actively reduce these uncertainties with targeted studies in the laboratory and through new modelling frameworks.
Exploitation Route 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. Dissemination of knowledge through distributed software, online and printed reference material via collaboration with industrial partners (the Dortmund Databank) will vastly increase our ability to benefit researchers in other fields indirectly, as discussed shortly. 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, and the improvements this grant will ultimately lead to, will be able to inform such a climate-focused goal. We have opened collaboration with the Dortmund Databank who would greatly benefit from inclusion of data which is not covered in their extensive datasets and models. Formal collaborations will somewhat depend on successful reciept of future grants but our partners are more than willing to be involved with ourselves and the vapour rpessure community to disseminate knowledge via distribution of peer reviewed data derived from our instrument, and direct improvement of models. They have also suggested future routes which would enable even more improvements in predictive techniques, the ultimate aim of this grant.
Sectors Chemicals,Environment

 
Description We have used results from this project to directly engage with developers of 'impact driven' models of air quality and climate to identify the dangers in prescribing overly simplified representations of aerosol volatility in models. Challenging this will be a long steady process, one which without this grant we could not have identified.
 
Description Improvement of composition and property prediction techniques for for Secondary Organic Aerosol (SOA)
Amount £366,032 (GBP)
Funding ID NE/J009202/1 
Organisation Natural Environment Research Council 
Sector Public
Country United Kingdom
Start 09/2012 
 
Description Novel approaches for quantifying the highly uncertain thermodynamics and kinetics of atmospheric gas-to-particle conversion
Amount £426,177 (GBP)
Funding ID NE/J02175X/1 
Organisation Natural Environment Research Council 
Sector Public
Country United Kingdom
Start 09/2013 
End 01/2017
 
Description Collaboration with the Dortmund Databank 
Organisation Dortmund Data Bank
Country Germany 
Sector Private 
PI Contribution We have opened collaboration with the Dortmund Databank who would greatly benefit from inclusion of data which is not covered in their extensive datasets and models. Formal collaborations will somewhat depend on successful reciept of future grants but our partners are more than willing to be involved with ourselves and the vapour rpessure community to disseminate knowledge via distribution of peer reviewed data derived from our instrument, and direct improvement of models. They have also suggested future routes which would enable even more improvements in predictive techniques, the ultimate aim of this grant.
Start Year 2010
 
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/