ClearfLo: Clean Air for London
Lead Research Organisation:
University of Manchester
Department Name: Earth Atmospheric and Env Sciences
Abstract
Poor air quality, particularly in urban areas, has a demonstrable effect on human health, but the processes responsible for producing the main pollutants, namely particulate matter, ozone, nitrogen dioxide and heat are not well understood and are poorly predicted. The ambition of ClearfLo is to provide integrated measurements of the meteorology, composition and particulate loading of London's urban atmosphere, made at street level and at elevated sites, complemented by modelling, to improve predictive capability for air quality. This ambition will be addressed by establishing new measurement capabilities in London, which will be used for long-term measurements and intensive observation periods, and by analysis and modelling of the measurements to establish key processes.
Publications
Bannan T
(2015)
The first UK measurements of nitryl chloride using a chemical ionization mass spectrometer in central London in the summer of 2012, and an investigation of the role of Cl atom oxidation
in Journal of Geophysical Research: Atmospheres
Belcher S
(2015)
Meteorology, Air Quality, and Health in London: The ClearfLo Project
in Bulletin of the American Meteorological Society
Crilley L
(2015)
Sources and contributions of wood smoke during winter in London: assessing local and regional influences
in Atmospheric Chemistry and Physics
Harrison R
(2012)
Atmospheric chemistry and physics in the atmosphere of a developed megacity (London): an overview of the REPARTEE experiment and its conclusions
in Atmospheric Chemistry and Physics
Le Breton M
(2012)
Airborne observations of formic acid using a chemical ionization mass spectrometer
in Atmospheric Measurement Techniques
Liu D
(2015)
The effect of complex black carbon microphysics on the determination of the optical properties of brown carbon
in Geophysical Research Letters
Liu D
(2014)
Size distribution, mixing state and source apportionment of black carbon aerosol in London during wintertime
in Atmospheric Chemistry and Physics
Lowe D
(2015)
WRF-Chem model predictions of the regional impacts of N<sub>2</sub>O<sub>5</sub> heterogeneous processes on night-time chemistry over north-western Europe
in Atmospheric Chemistry and Physics
| Description | The first formic and nitric acid measurements over London from the Winter ClearfLo campaign are reported. The results indicate there is a significant direct source of anthropogenic formic acid HCOOH emitted from vehicles. The study suggests that vehicle emissions can be responsible for up to 30% of HCOOH in the urban environment. This study has given a better understanding of why there is such a variance in the modelled and observed concentrations of formic acid. It can be inferred that the importance of direct emissions of formic acid from vehicles emissions is significantly under-represented in global models. Its inclusion in global models reduces the gap between the observed and predicted concentrations of formic acid, especially in the northern hemisphere where vehicle use and anthropogenic activity is at its highest. Measurements of formic acid in winter in London were shown to arise from vehicular combustion, through comparison with NOx and CO combustion markers. In addition, the very low surface ozone levels preclude the ozonolysis of alkenes as a source of formic acid. Formic acid emission factors with respect to NOx have been derived and used in a global model integration. This integration shows that although ozonolysis of 1-alkenes is the dominant source, direct vehicle emissions can be up to 30% of the emission in mid-high latitudes in the northern hemisphere. Data sets were provided to the modelling partners to assess secondary organic aerosol emissions from missing diesel-related sources. Modelled concentrations were evaluated against hourly and daily measurements of organic aerosol (OA) components derived from the Manchester aerosol mass spectrometer (AMS) measurements made during the ClearfLo campaign. According to the model simulations, diesel-related IVOCs could explain on average only ~ 30 % of the annual Secondary OA (SOA) in and around London. Furthermore, the 90th percentile of modelled daily SOA concentrations for the whole year was 3.8 µg m-3, constituting a notable addition to total particulate matter. More measurements of these precursors (currently not included in official emissions inventories) was recommended. During the period of concurrent measurements, SOA concentrations at rural background locations east of London were greater than at the central London location. The model study showed that this was caused by an intense pollution plume with a strong gradient of imported SOA passing over the rural location. This demonstrated the value of combined modelling and monitoring of OA for important short duration events. The first intensive study of the size distribution, mixing state and source apportionment of black carbon aerosol in London during wintertime was made. An important output of this work was demonstration of a novel methodology to attribute the black carbon aerosol number concentrations and mass abundances from traffic ( and from solid fuel burning thus updating missing elements of current emission inventories. |
| Exploitation Route | New methods for identification of different black carbon and SOA aerosol sources were developed and are providing important new insigths and updates to current pollution emission inventories for regulatory, policy adherence and mitigation strategy. Formic acid emission factors with respect to NOx were derived and used in global model integrations. This shows that although ozonolysis of 1-alkenes is the dominant source, direct vehicle emissions can contrinute up to 30% of the emission in mid-high latitudes in the northern hemisphere. |
| Sectors | Chemicals,Construction,Energy,Environment,Healthcare,Manufacturing, including Industrial Biotechology,Security and Diplomacy,Transport |
| URL | http://www.clearflo.ac.uk/outreach/papers/ |
| Description | Many outreach activities were conducted to enhance public awareness of pollution levels in London. These included: Planet Earth podcast - 'London's air pollution from the BT Tower'. High ozone levels in London - BBC news. RAL Space news item on ClearfLo. NERC Press Release on 9 August 2012 focusing on project impacts. The ClearfLo IOP experiment campaign was conducted during the London Olympics and reported on BBC News on 8 August 2012. Vidoe available via BBC website. Research student produced podcast articles published on the Barometer Podcast: Part 1 and Part 2 for student/publica consumption. Interviews were conducted by BBC including on BBC Radio Berkshire on 27 August 2012. Planet Earth article on ClearfLo: Bohnenstengel S.I. (2012): Something in the air, Olympic Special article, PlanetEarth magazine NERC, June 2012. The project has assisted in planning further airborne moniroting campaigns for London Air Pollution. Biotechnology developments encouraged a long-term monitoring project to be implemented between ICL, University of Manchester and Dstl Porton Down supported by a PhD student 9since graduated). Several publications were produced by this work and one is in progress. |
| First Year Of Impact | 2014 |
| Sector | Energy,Environment,Healthcare,Transport |
| Impact Types | Societal,Economic,Policy & public services |
| Description | Atmospheric Composition and Radiative forcing changes due to UN International Ship Emissions regulations (ACRUISE) |
| Amount | £363,917 (GBP) |
| Funding ID | NE/S004467/1 |
| Organisation | Natural Environment Research Council |
| Sector | Public |
| Country | United Kingdom |
| Start | 02/2019 |
| End | 02/2024 |
| Description | COntrails Spreading Into Cirrus (COSIC) |
| Amount | £135,369 (GBP) |
| Funding ID | NE/G00479X/1 |
| Organisation | Natural Environment Research Council |
| Sector | Public |
| Country | United Kingdom |
| Start | 07/2009 |
| End | 06/2012 |
| Description | Characterisation and Modelling of Climatically Relevant Primary Biogenic Ice Nuclei in the BEACHON Southern Rocky Mountain Project |
| Amount | £244,499 (GBP) |
| Funding ID | NE/H019049/1 |
| Organisation | Natural Environment Research Council |
| Sector | Public |
| Country | United Kingdom |
| Start | 03/2011 |
| End | 09/2013 |
| Description | EUREC4A-UK: Elucidating the role of cloud-circulation coupling in climate |
| Amount | £797,842 (GBP) |
| Funding ID | NE/S015752/1 |
| Organisation | Natural Environment Research Council |
| Sector | Public |
| Country | United Kingdom |
| Start | 01/2020 |
| End | 10/2024 |
| Description | Greenhouse gAs Uk and Global Emissions (GAUGE) |
| Amount | £333,169 (GBP) |
| Funding ID | NE/K00221X/1 |
| Organisation | Natural Environment Research Council |
| Sector | Public |
| Country | United Kingdom |
| Start | 01/2013 |
| End | 01/2017 |
| Description | Methane and other greenhouse gases in the Arctic - measurements, process studies and modelling (MAMM) |
| Amount | £442,780 (GBP) |
| Funding ID | NE/I029293/1 |
| Organisation | Natural Environment Research Council |
| Sector | Public |
| Country | United Kingdom |
| Start | 01/2012 |
| End | 07/2015 |
| Description | The Global Methane Budget |
| Amount | £200,894 (GBP) |
| Funding ID | NE/N015835/1 |
| Organisation | Natural Environment Research Council |
| Sector | Public |
| Country | United Kingdom |
| Start | 05/2016 |
| End | 10/2020 |
| Title | ClearfLo (Clean Air for London) Data |
| Description | Data from the ClearfLo (Clean Air for London) Project. ClearfLo is a collaborative scientific project involving several academic institutions in the UK, to set up air pollution monitoring sites alongside meteorological measurements to investigate boundary layer pollution across London. The ambition of ClearfLo is to provide long-term integrated measurements of the meteorology, composition and particulate loading of London's urban atmosphere, made at street level and at elevated sites, complemented by modelling to improve predictive capability for air quality. ClearfLo is funded by the Natural Environment Research Council (NERC) for three years from Jan 2010, and is coordinated by the National Centre for Atmospheric Science (NCAS). |
| Type Of Material | Database/Collection of data |
| Year Produced | 2015 |
| Provided To Others? | Yes |
| Impact | Two PhD students were awarded PhD's based on use of the databases. Databases are archived at: Keywords: NE/S002049/1 http://catalogue.ceda.ac.uk/search/?search_term=ClearFlo&return_obj=ob&search_obj=ob Full BIOARC database now available at CEDA wards a UK Airborne Bioaerosol Climatology (BIOARC) project. Data was collected at the following ground sites: Cardington Meteorological Research Unit: MBS-M, 11/04/2019 - 09/06/2019 Chilbolton Observatory: WIBS-4D, 14/05/2019 - 14/06/2019 Weybourne Atmospheric Observatory: WIBS-4M, 03/06/2019 - 01/08/2019 Chilbolton Observatory: WIBS-4M, 10/09/2020 - 21/06/2021 Weybourne Atmospheric Observatory: MBS-M, 15/09/2020 - 03/11/2019 Weybourne Atmospheric Observatory: MBS-M, 15/04/2021 - 16/07/2021 NERC reference NE/S002049/1 Citable as: Crawford, I. (2022): BIOARC: ground site real-time bioaerosol spectrometer datasets (2019-2021). NERC EDS Centre for Environmental Data Analysis, date of citation. https://catalogue.ceda.ac.uk/uuid/14dfd0ba5212422c9c72b5184cbf5330 |
| URL | http://catalogue.ceda.ac.uk/search/?search_term=ClearFlo&return_obj=ob&search_obj=ob |
| Description | Dstl Partnership to assess instruments for detecting and discriminating different bioparticles in real-time for health monitoring applications |
| Organisation | Defence Science & Technology Laboratory (DSTL) |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | We will be designing new chamber experiments and providing new UVLIF instruments to deliver new bioparticle training data sets to challenge machine learning and deep learning algorithms to identify airborne bioparticle types in real-time for health/advertant releases suitable for bio-PM health monitoring applications. |
| Collaborator Contribution | Two publications have been published. The first paper presented improved methods for discriminating and quantifying airborne biological aerosol particles by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF) spectrometer data. The methods employed in this study were evaluated for accuracy against prescribed reference particle populations, biological and non-biological. The HCA method was examined and potential for false positives identified and methods to reduce the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches. This improved capacity to discriminate and quantify PBAP meta-classes.The performance of various hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set. We provided the algorithm development specific to various UVLIF aerosol spectrometers, collected the field data and conducted the laboratory experiments for this study. In a second study, much larger training bioparticle data sets from a new UVLIF particle spectrometer, provided by partner's Dstl, were evaluated using new Machine Learning Algorithms and compared with the previous hierarchical Agglomerative CLuster Approaches. In this study we provided the algorithm development, analysed the data sets and produced the publication. We also collected additional field data sets of ambient particles. This work builds on existing collaboration with Dstl originally started as part of the NERC BIOGENICE funded project. http://www.cas.manchester.ac.uk/resprojects/biogenice/ |
| Impact | A new PhD case studentship was provided with "Paratools-Daresbury" to sue supercomputing facilities to analyse and interpret large bioparticle data sets using deep learning machine algorithms. We have been provided with year long bioparticle data sets recorded in London to evaluate these algorithms. We have received a NERC-Dstl case studentship in collaboration with CEA France to conduct field experiments at European sites. We have received infrastructure support from NCAS via a new high resolution, field deployable UVLIF particle spectrometer which will be used by the case studentships to further evaluate applications for health monitoring and for delivering datasets for improving emissions parameterisations of bioparticles in regional scale and global models. We have received in-kind support from manufacturers of UVLIF instruments through shared data sets to evaluate new instrument performance and, as part of the Dstl proposed laboratory biochamber experiments, they are supplying new instruments and training for instrument intercomparison exercises. |
| Start Year | 2015 |
| Description | Dstl Partnership to assess instruments for detecting and discriminating different bioparticles in real-time for health monitoring applications |
| Organisation | Laboratory of Climate Sciences and the Environment (LSCE) |
| Country | France |
| Sector | Academic/University |
| PI Contribution | We will be designing new chamber experiments and providing new UVLIF instruments to deliver new bioparticle training data sets to challenge machine learning and deep learning algorithms to identify airborne bioparticle types in real-time for health/advertant releases suitable for bio-PM health monitoring applications. |
| Collaborator Contribution | Two publications have been published. The first paper presented improved methods for discriminating and quantifying airborne biological aerosol particles by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF) spectrometer data. The methods employed in this study were evaluated for accuracy against prescribed reference particle populations, biological and non-biological. The HCA method was examined and potential for false positives identified and methods to reduce the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches. This improved capacity to discriminate and quantify PBAP meta-classes.The performance of various hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set. We provided the algorithm development specific to various UVLIF aerosol spectrometers, collected the field data and conducted the laboratory experiments for this study. In a second study, much larger training bioparticle data sets from a new UVLIF particle spectrometer, provided by partner's Dstl, were evaluated using new Machine Learning Algorithms and compared with the previous hierarchical Agglomerative CLuster Approaches. In this study we provided the algorithm development, analysed the data sets and produced the publication. We also collected additional field data sets of ambient particles. This work builds on existing collaboration with Dstl originally started as part of the NERC BIOGENICE funded project. http://www.cas.manchester.ac.uk/resprojects/biogenice/ |
| Impact | A new PhD case studentship was provided with "Paratools-Daresbury" to sue supercomputing facilities to analyse and interpret large bioparticle data sets using deep learning machine algorithms. We have been provided with year long bioparticle data sets recorded in London to evaluate these algorithms. We have received a NERC-Dstl case studentship in collaboration with CEA France to conduct field experiments at European sites. We have received infrastructure support from NCAS via a new high resolution, field deployable UVLIF particle spectrometer which will be used by the case studentships to further evaluate applications for health monitoring and for delivering datasets for improving emissions parameterisations of bioparticles in regional scale and global models. We have received in-kind support from manufacturers of UVLIF instruments through shared data sets to evaluate new instrument performance and, as part of the Dstl proposed laboratory biochamber experiments, they are supplying new instruments and training for instrument intercomparison exercises. |
| Start Year | 2015 |