INHALE activity
Publications
- Abhijith, K. V., Kumar, P., 2020. Quantifying particulate matter reduction and their deposition on the leaves of green infrastructure. Environmental Pollution. Vol: 265, article: 114884.
- Abhijith, K.V., Kumar, P., 2021. Evaluation of respiratory deposition doses in the presence of green infrastructure. Air Qual Atmos Health.
- Abubakar-Waziri H., Fang F., Dilliway C., Wu P., Li J., Yao R., Bhavsar P., Kumar P., Pain C.C., Chung K.F., 2023. Modelling for understanding of coronavirus disease-2019 (COVID-19) spread and design of an isolation room in a hospital. Physics of Fluids, Vol: 35, 025111.
- Ahmed, T., Kumar, P., Mottet, L., 2021. Natural ventilation in warm climates: The challenges of thermal comfort, heatwave resilience and indoor air quality. Renewable and Sustainable Energy Reviews. Vol: 138, article: 110669.
- Amendola, M., Arcucci, R., Mottet, L., Casas, C.Q., Fan, S., Pain, C., Linden, P., Guo, Y.K., 2020. Data Assimilation in the Latent Space of a Neural Network. arXiv preprint. arXiv:2012.12056
- Arcucci, R. Effective Data Assimilation with Machine Learning - Data Science Book 2020, accepted.
- Arcucci, R., Mottet, L., Quilodran Casas, C. A., Guitton, F., Pain, C. and Guo, Y., 2020. Adaptive Domain Decomposition for Effective Data Assimilation - Lecture Notes in Computer Science book series (EuroPAR2019).
- Arcucci, R., Moutiq, L. and Guo, Y. Neural Assimilation. Lecture Notes in Computer Science book series (ICCS 2020). Vol 12142, pp. 155-168.
- Arcucci, R., Quilodran Casas, C., Xiao, D., Mottet, L., Fang, F., Wu, P., Pain, C. and Guo, Y., 2020. A Domain Decomposition Reduced Order Model with Data Assimilation (DD-RODA). Advances in Parallel Computing. Vol: 36, pages: 189-198, DOI: 10.3233/APC200040.
- Arcucci, R., Zhu, J., Hu, S., Guo, Y., 2021. Deep Data Assimilation: Integrating Deep Learning with Data Assimilation. Applied Sciences. Vol: 11 (3), article: 1114.
- Barwise, Y., Kumar, P., 2020. Designing vegetation barriers for urban air pollution abatement: a practical review for appropriate plant species selection. npj Climate and Atmospheric Sciences. Vol: 3, article: 12.
- Barwise, Y., Kumar, P., Tiwari, A., Rafi-Butt, F., McNabola, A., Cole, S., Field, B. C.T., Fuller, J., Mendis, J. and Wyles, K. J., 2021. The co-development of HedgeDATE, a public engagement and decision support tool for air pollution exposure mitigation by green infrastructure. Sustainable Cities and Society. Vol. 75, article: 103299.
- Buizza, C., Quilodrán Casas, C., Nadler, P., Mack, J., Marrone, S., Titus, Z., Le Cornec, C., Heylen, E., Dur, T., Baca Ruiz, L., Heaney, C., Díaz López, J. A., Arcucci, R., 2020. Data Learning: Integrating Data Assimilation and Machine Learning. Journal of Computational Science. Vol: 58, 101525.
- César Quilodrán-Casas, C., Arcucci, R., Mottet, L., Guo, Y. and Pain, C.C. (2021) Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations (submitted to SimDL workshop at ICLR May 2021) arxiv.org/abs/2104.06297.
- Cheng, M., Fang, F., Pain, C.C. and Navon, I. M., 2020. An advanced hybrid deep adversarial autoencoder for parameterized nonlinear fluid flow modelling. Computer Methods in Applied Mechanics and Engineering, Vol: 372, article: 113375.
- Cheng, M., Fang, F., Kinouchi, T., Navon, I. M. and Pain, C. C., 2020. Long lead-time daily and monthly streamflow forecasting using machine learning methods. Journal of Hydrology, Vol: 590, article: 125376.
- Cheng, M., Fang, F., Navon, I. M., Zheng, J., Tang, X., Zhu, J. and Pain, C. C. 2021. Spatio-Temporal Hourly and Daily Ozone Forecasting in China Using a Hybrid Machine Learning Model: Autoencoder and Generative Adversarial Networks. Journal of Advances in Modelling Earth Systems.
- Cheng, M., Fang, F., Navon, I.M., Pain, C. C. 2021. A real-time flow forecasting with deep convolutional generative adversarial network: Application to flooding event in Denmark. Physics of Fluids. Vol: 33.
- Cheng, M., Fang, F., Pain, C. C. and Navon, I. M., 2020. Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network. Computer Methods in Applied Mechanics and Engineering, Vol: 365, article: 113000.
- Cheng, M., Fang, F., Navon, I. M., Pain, C.C., 2021. A real-time flow forecasting with deep convolutional generative adversarial network: Application to flooding event in Denmark. Physics of Fluids. Vol: 33, ISSN: 1070-6631.
- Dilliway, C., Dyer, O., Mandrou, E., Mitchell, D., Menon, G., Sparks, H., Kapitany, V., Payne-Dwyer, A., 2022. Working at the interface of physics and biology: An early career researcher perspective. iScience. Vol: 25, Issue 12, 105615.
- Dur, T. , Arcucci, R., Mottet, L., Molina Solana, M., Pain, C., Guo, Y., 2020. Weak Constraint Guassian Process for optimal sensor placement. Journal of Computational Science. Vol: 42, DOI: 10.1016/j.jocs.2020.101110.
- Drummond, G. B., Fischer, D., Arvind D. K. 2020. Current clinical methods of measurement of respiratory rate give imprecise values. ERJ Open Research, Vol 6, Issue 3 00023-2020. DOI: 10.1183/23120541.00023-2020.
- Heaney, C. E., Buchan, A. G., Pain, C.C., Jewer, S., 2021. Reduced-Order Modelling Applied to the Multigroup Neutron Diffusion Equation Using a Nonlinear Interpolation Method for Control-Rod Movement. Energies. Vol: 14 (5), article: 1350.
- Heaney, C. E., Wolffs, Z., Tómasson, J. A., Kahouadji, L., Salinas, P., Nicolle, A., Navon, I. M., Matar, O. K., Srinil, N., Pain, C. C. 2022. An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in Pipes. Physics of Fluids. Vol: 34.
- Hu, R., Fang, F., Pain, C. C., Navon, I. M., 2019. Rapid spatio-temporal flood prediction and uncertainty quantification using a deep learning method. Journal of Hydrology. Vol: 575, article: 911920.
- Kumar, P., Druckman, A., Gallagher, J., Gatersleben, B., Allison, S., Eisenman, T.S., Hoang, U., Hama, S., Tiwari, A., Sharma, A., Abhijith, KV, Adlakha, D., McNabola, A., Astell-Burt, T., Feng, X., Skeldon, A.C., de Lusignan, S., Morawska, L., 2019. The Nexus between Air Pollution, Green Infrastructure and Human Health. Environment International. Vol: 133, article: 105181.
- Kumar, P., Hama, S., Omidvarborna, H., Sharma, A., Sahani, J., Abhijith K.V., Debele, S. E., Zavala-Reyes, J. C., Barwise, Y., Tiwari, A., 2020. Temporary reduction in fine particulate matter due to ‘anthropogenic emissions switch-off’ during COVID-19 lockdown in Indian cities. Science Direct. Vol: 62, article: 102382.
- Kumar, P., Kalaiarasan, G., Bhagat, R.K., Mumby, S., Adcock, I.M., Porter, A.E., Ransome, E., Abubakar-Waziri, H., Bhavsar, P., Shishodia, S., Dilliway, C., Fang, F., Pain, C.C., Chung, K.F., 2022. Active Air Monitoring for Understanding the Ventilation and Infection Risks of SARS-CoV-2 Transmission in Public Indoor Spaces. Atmosphere. Vol: 13, 2067.
- Kumar, P., Kalaiarasan, G., Porter, A. E., Pinna, A., Kłosowski, M. M., Demokritou, P., Chung K. F., Pain, C. C., Arvind, D. K., Arcucci, R., Adcock, I. M., Dilliway, C., 2020. An overview of methods of fine and ultrafine particle collection for physicochemical characterisation and toxicity assessments. Science of The Total Environment, Vol: TBC, article 143553.
- Kumar, P., Omidvarborna, H., Francesco, P., Lewin, N., 2020. A primary school driven initiative to influence commuting style for dropping-off and picking-up of pupils. Science of the Total Environment. Vol: 727, article: 138360.
- Kumar, P., Omidvarborna, H., Kooloth Valappil, A., and Bristow, A. 2022. Noise and air pollution during Covid-19 lockdown easing around a school site. The Journal of the Acoustical Society of America Vol: 151, pg. 881.
- Kumar, P., Morawska, L., 2020. Could fighting airborne transmission be the next line of defence against COVID-19 spread? City and Environment Interactions. Vol: 4, article: 100033.
- Kumar, P., Zavala-Reyes, J.C., Kalaiarasan, G., Abubakar-Waziri, H., Young, G., Mudway, I., Dilliway, C., Lakhdar, R., Mumby, S., Kłosowski, M.M., Pain, C.C., Adcock, I.M., Watson, J.S., Sephton, M.A., Chung, K.F., Porter, A.E., 2023. Characteristics of fine and ultrafine aerosols in the London underground. Science of The Total Environment. Vol: 858, Part 1, 159315.
- Kumar, P., Zavala-Reyes, J.C., Tomson, M., Kalaiarasan, G., 2022. Understanding the effects of roadside hedges on the horizontal and vertical distributions of air pollutants in street canyons. Environment International. Vol:158, 106883.
- Mack, J., Arcucci, R., Molina-Solana, M., Guo Y., 2020. Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation. Computer Methods in Applied Mechanics and Engineering. Vol: 372, article: 113291.
- Mortaz, E., Zadin, S. S., Shahir, M., Folkerts, G., Garssen, J., Mumby, S., Adcock, I. M., 2019. Does Neutrophil Phenotype Predict the Survival of Trauma Patients? Frontiers in Immunology. 10:2122. doi: 10.3389/fimmu.2019.02122
- Nikiteas, I., Dargaville, S., Pain, C. C., Smith, P. N. and Smedley-Stevenson, R. P. Impact of Load Balancing on Parallel Performance with Haar Wavelets Angular Adaptivity. EPJ Web of Conferences, International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future. Vol: 247, 03025.
- Phillips, T. R. F., Heaney, C. E., Smith, P. N., and Pain, C. 2021. An autoencoder‐based reduced‐order model for eigenvalue problems with application to neutron diffusion. International Journal for Numerical Methods in Engineering. Vol: 122, Issue 15.
- Phillips, T.R.F.; Heaney, C.E.; Tollit, B.S.; Smith, P.N.; Pain, C.C., 2021. Reduced-Order Modelling with Domain Decomposition Applied to Multi-Group Neutron Transport. Energies. Vol: 14 (15), article: 1369.
- Quilodrán Casas, C., Arcucci, R., Guo, Y., 2020. Reduced order deep Bayesian correction for urban air pollution physics and its healthcare applications. KDD 2020. Manuscript submitted to the Applied Data Science track of the KDD 2020 conference. (Under review)
- Quilodrán Casas, C., Arcucci, R., Guo, Y., 2020. Urban air pollution forecasts generated from latent space representation. International Conference on Learning Representation 2020; "Integration of Deep Neural Models and Differential Equations" workshop.
- Quilodrán-Casas, C., Arcucci, R., Mottet, L., Guo, Y. and Pain, C. C. 2021 Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations (submitted to SimDL workshop at ICLR May 2021).
- Quilodrán-Casas, C., Arcucci, R., Pain, C., Guo, Y. Adversarially trained LSTMs on reduced order models of urban air pollution simulations, see also poster. Machine Learning and the Physical Sciences workshop at NeurIPS 2020.
- Quilodrán Casas, C., Arcucci, R., Wu, P., Pain, C., Guo, Y., 2020. A reduced order deep data assimilation model. Physica D. Vol: 412, article: 132615.
- Quilodrán-Casas, C., Silva, V. L. S., Arcucci, R., Heaney, C. E., Guo, Y. and Pain, C. C. 2022 Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic. Neurocomputing. Vol: 470, pp 11-28.
- Rawat, N. and Kumar, P., 2023, Interventions for improving indoor and outdoor air quality in and around schools. Science of The Total Environment. Vol: 858, Part 2, 159813.
- Salinas, P., Bahlali, M. L., Jacquemyn, C., Pain, C. C., Butler, A. P. and Jackson, M. Dynamic Mesh Optimisation for High Precision Saline Intrusion Modelling Conference paper presented to AGU Fall Meeting 17th December, 2021.
- Silva, V. L. S., Heaney C. E., Li, Y. and Pain, C. C. (2021) Data Assimilation Predictive GAN (DA-PredGAN): applied to determine the spread of COVID-19. arXiv.org/abs/2105.07729.
- Silva, V. L. S., Heaney, C. E. and Pain, C. C. 2021. GAN for time series prediction, data assimilation and uncertainty quantification. arXiv. arXiv:2105.13859v2.
- Tajnafoi, G., Arcucci R., Mottet, L. Vouriot, C., Molina Solana, M., Pain, C., Guo, Y., 2021. Variational Gaussian Processes for optimal sensor placement. Journal of Applied Mathematics. Vol: 66 (2), pp. 287-317.
- Titus, Z., Heaney, C. E., Jacquemyn, C., Salinas, P., Jackson, M. D., Pain, C. C. 2020. Conditioning surface-based geological models to well data using artificial neural networks. European Association of Geoscientists & Engineers. Conference Proceedings, ECMOR XVII, Sep 2020, Vol: 2020, p.1 – 12.
- Tiwari, A, and Kumar, P., 2022. Quantification of green infrastructure effects on airborne nanoparticles dispersion at an urban scale. Science of The Total Environment. Vol: 838, Part 1, 155778.
- Tiwari, A., Kumar, P., 2020. Integrated dispersion-deposition modelling for air pollutant reduction via green infrastructure at an urban scale. Science of the Total Environment. Vol: 723, article: 138078.
- Tiwari, A., Kumar, P., Kalaiarasan, G., Ottosen, T-B., 2020. The impacts of existing and hypothetical green infrastructure scenarios on urban heat island formation. Environmental Pollution. Article: 115898.
- Tomson, M., Kumar, P., Barwise, Y., Perez, P., Forehead, H., French, K., Morawska, L., Watts, J. F., 2021. Green infrastructure for air quality improvement in street canyons. Environment International. Vol: 146, article: 106288.
- Tomson, M., Kumar P., Kalaiarasan G., Zavala-Reyes J., Chiapasco M., Sephton M., Young G., Porter A., 2023. Pollutant concentrations and exposure variability in four urban microenvironments of London. Atmospheric Environment, Vol: 298, 119624.
- Wu, P., Gong, S., Pan, K., Qiu, F., Feng, W. and Pain, C. C. 2021. Reduced order model using convolutional auto-encoder with self-attention. Physics of Fluids. Vol: 33, Issue 7.
- Wu, H-W., Kumar, P., Cao, S-J., 2022. Implementation of green infrastructure for improving the building environment of elderly care centres. Journal of Building Engineering. Vol: 54, 104682.
- Wu H-W, Kumar P, Yu CW, Cao S-J.. 2022. A global challenge for smart and healthy care homes for the elderly. Indoor and Built Environment. Vol: 31(7):1733-1737.
- Wu, P., Sun, J., Chang, X., Zhang, W., Arcucci, R., Guo, Y., Pain, C.C., 2020. Data-driven reduced order model with temporal convolutional neural network. Computer Methods in Applied Mechanics and Engineering. Vol: 360, article: 112766.
- Xiao, D., Fang, F., Zheng, J., Pain, C. C., Navon, I. M., 2019. Machine learning-based rapid response tools for regional air pollution modelling. Atmospheric Environment. Vol: 199, article: 463473.
- Zheng, J., Fang, F., Wang, Z., Zhu, J., Li, J., Li, J., Xiao, H., Pain, C.C., 2020. A new anisotropic adaptive mesh photochemical model for ozone formation in power plant plumes. Atmospheric Environment, Vol: 229, article: 117431, ISSN: 1352-2310.
- Zheng, J., Wu, X., Fang, F., Li, J., Wang, Z., Xiao, H., Zhu, J., Pain, C. C., Linden, P., Xiang, B. 2021. Numerical study of COVID-19 spatial-temporal spreading in London. Physics of Fluids. Vol: 33.
Presentations, conferences and workshops
- Dr. Soma Sekhara Rao Kolluru attended the 10th Urban Fluid Mechanics meeting on ‘High-resolution prediction for urban modelling and design applications’ at the University of Reading on 1st and 2nd September 2022. During this meeting, he met with many young researchers and Professors to discuss his research based on INHALE project.
- Professor Christopher Pain et al. gave a semi-plenary lecture entitled ‘AI modelling of Fluid Flows’, at the Eccomas 2022 Congress in Oslo on 7th June, 2022.
- Professor Christopher Pain presented a talk to the session on COVID-19 modelling using methods from Nuclear Engineering Epidemiology and Air Flows to the Modelling in Nuclear Science and Engineering conference, held April 7-8th 2022.
- Dr Boyang Chen presented a talk to the session on COVID-19 modelling using methods from Nuclear Engineering, infection risk modelling in schools using CFD to the Modelling in Nuclear Science and Engineering conference, held April 7-8th 2022.
- Professor Christopher Pain presented ‘Applications of Machine Learning to Urban Environmental Problems’ to the Urban Fluid Mechanics Special Interest Group on March 21st, 2022 which focussed on Artificial Intelligence and Data-driven Approaches.
- Dr Fangxin Fang, et al. gave a talk entitled ‘Multi-Physics and Multi-Scale Adaptive Mesh Predictive Modelling using Machine Learning and Data Assimilation’ to the Global Summit on Applied Science, Engineering and Technology Organized by The Scientist, 17, March 2022
- Professor Christopher Pain presented a talk entitled ‘Multi-physics and multi-scale adaptive mesh AI modelling for urban environment’ to the 2021 International Conference on Sustainable Development in the Building and Environment (SuDBE) held in Chongqing from 10th to 12th December 2021
- Dr Fangxin Fang presented a talk entitled ‘Machine learning-based predictive modelling for environmental issues’ to the 2021 International Conference on Sustainable Development in the Building and Environment (SuDBE) held in Chongqing from 10th to 12th December 2021.
- Dr Rossella Arcucci, César Quilodrán Casas et al, presented ‘Forecasting emissions through Kaya identity using Neural ODEs’ to the Climate Change AI workshop ICML held on 14th December 2021.
- Dr Fangxin Fang gave a talk entitled ‘Multi-scale Adaptive Mesh Predictive Modelling in Fluid Dynamics’ to the 6th International Electronic Conference on Water Sciences from 15th to 30th November 2021.
- Professor Christopher Pain presented ‘Trends in nuclear modelling: fluids, solids, coupling and Artificial Intelligence’ to the ANSWERS seminar on 10th November 2021.
- Professor Christopher Pain gave a talk entitled ‘Multi-physics and multi-scale adaptive mesh AI-modelling for the urban environment’ to the 10th International Conference on Sustainable Development in the Building and Environment (SuDBE2021) held in Chongqing from 4th to 7th November 2021.
- Professor Christopher Pain presented a talk entitled ‘Can Nuclear Modelling Techniques Help National Efforts to Combat COVID-19?’ on the 28th September 2021 to the Nuclear Institute.
- Dr Fangxin Fang et al. presented ‘Deep learning applied to nonlinear fluid flow problems’ to the Computational Fluid Dynamic and Artificial Intelligence Workshop 2021 at Shanghai University, on 30th August 2021.
- Professor Christopher Pain presented ‘Fluids and Artificial Intelligence Modelling Methods and Applications’ to the Computational Fluid Dynamic and Artificial Intelligence Workshop 2021 at Shanghai University, on 30th August 2021.
- Dr Juan Zavala Reyes, Mamatha Tomson, Dr Gopinath Kalaiarasan and Professor Prashant Kumar presented ‘Preliminary results of variation of particle size distribution and coagulation impact on size resolved particles indoor-outdoor microenvironment’ at European Aerosol Conference (EAC 2021), 30 August – 3 September 2021, to the AH P1 Aerosol sources and exposure poster session on 30th August 2021
- Teodora Georgescu, Dr Michał Kłosowski and Dr Xiaofei Wu presented their INHALE research to an event sponsored and hosted by the Physics of Life Network; ‘Physics of Life from the perspective of early career researchers: How do collaborative, interdisciplinary projects work to address key challenges at the interface of physics and the life sciences?’ on 13th July 2021, chaired by Claire Dilliway. See the recording of the presentations and science sketch note by Mathis Riehle, University of Glasgow.
- Professor Christopher Pain gave a talk entitled ‘AI for Modelling Fluid Flow and Multi-Physics Problems’ to the SIAM Conference on Mathematical & Computational Issues in the Geosciences on 22nd June 2021, Politecnico di Milano, Italy.
- Professor Christopher Pain, Dr Rossella Arcucci and César Quilodrán Casas et al, presented Data assimilation in the latent space of a convolutional autoencoder to the International Conference on Computational Science 2021 on 18th June 2021 in Kraków.
- Professor D. K. Arvind gave a talk entitled ‘Air Pollution Data in RBKC (Royal Borough of Kensington and Chelsea) during 2020 lockdown release’ to the Local Government Technical Advisors Group President’s Conference 2021, June 15th – 17th 2021.
- Professor Prashant Kumar, Dr Gopinath Kalaiarasan, Mamatha Tomson, Dr Juan Zavala Reyes, Arvind Tiwari and Sarkawt Hama presented on ‘Fine and ultra fine particles in micro-environments of London: Findings of the INHALE project’ at the HARMO20 conference (20th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes) between 14-18 June 2021.
- Professor Christopher Pain and Dr Fangxin Fang presented a talk entitled ‘CFD, reduced-order models and neural networks for Urban and Indoor flows: Results from INHALE, MAGIC, PREMIERE consortia’ to Chongqing University on 2nd June 2021.
- Professor Christopher Pain, Dr Rossella Arcucci and César Quilodrán Casas et al presented Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations to the SimDL ICLR 2021 workshop on 7th May 2021.
- Dr Fangxin Fang gave a talk entitled ‘Deep Learning Predictive Modelling in Combination with Data Assimilation and Applications to Geophysical Dynamics’ to the DataLearning Working Group - on 13 April 2021.
- Dr Rossella Arcucci was an Invited Speaker to give a talk entitled ‘Data-driven models based on Data Learning (Data Assimilation and Machine Learning) approaches’ at PI DX Spotlight: Data Governance Traceability - https://dx.pi.tv/spotlights/101/data_governance_and_traceability - on 17 February 2021.
- Dr Rossella Arcucci was an Invited Speaker to give a talk entitled ‘Data Learning: Integrating Data Assimilation and Machine Learning in real world applications’ at Physics informed Artificial Intelligence in Plasma Science, Seminar Series - http://www.ppl.eng.osaka-u.ac.jp/JSPS_Core/seminars.html - on 8 February 2021.
- Dr Xiaofei Wu presented a talk entitled ‘Processes in the development of the Fluidity-Urban model’ to a MAGIC consortium meeting on 13 October 2020.
- Dr Rossella Arcucci was an Invited Speaker to give a talk entitled ‘Artificial Neural Network at the service of Data Assimilation (and vice versa)’ at ECMWF - ESA Workshop on Machine Learning for Earth System Observation and Prediction, a virtual event - https://vimeo.com/465348878 - on 6 October 2020.
- Fang. F., Multiscale physical green and thermal dynamical modelling in urban environment, the international conference on Sustainable Development in Building and Environment and International Forum of Green Buildings and Healthy Buildings, SuDBE2020, China, 26, August 2020.
- Professor Kian Fan Chung gave a plenary talk on “Exposomes and Gene Interaction in Asthma” at the European Academy of Allergy and Immunology Digital Congress on 6 June 2020, describing the INHALE project amongst several Exposome projects.
- Dr Rossella Arcucci organised the second edition of the Workshop on Machine Learning and Data Assimilation for Dynamical Systems (MLDADS) 2020 - ICCS 3-5 June 2020.
- Dr Rossella Arcucci was a Keynote speaker at SIAM-IMA (Institute of Mathematics and its applications) with a talk entitled "Artificial Neural Network at the service of Data Assimilation" on 1 June 2020, online.
- Dr Rossella Arcucci presented a talk entitled "Data Assimilation and Machine Learning" to the Leverhulme Wildfires workshop ‘Approaches to Data Analysis’ on 19 May 2020, online.
- Dr Rossella Arcucci presented a talk entitled "Data Learning: Data Assimilation with Machine Learning" at the University of Reading on 11 March 2020.
- Professor Chris Pain and Professor Alex Porter presented the INHALE project at the 2019 Physics of Life Town Meeting at the Royal Society on 3 December 2019.
- Professor Arvind presented a ticketed invited talk entitled “Every breath you take” at the 2019 Edinburgh International Science Festival, which was reviewed in Lancet Respiratory Medicine (pdf).
- Professor Arvind was invited to participate on an expert panel in BodyNets 2019, Florence, Italy on 2-3 Oct 2019 on the topic "Smart IoT and big data for intelligent health management”, and present at The Royal Society Science+ meeting on Air Quality, past, present and future on 11-12 Nov 2019.
Media
- Further to the aforementioned publication of ‘Interventions for improving indoor and outdoor air quality in and around schools’, a press article was published by the University of Surrey on 29th November 2022.
- Further to the aforementioned publication of ‘Characteristics of fine and ultrafine aerosols in the London underground’, a press article was published by the University of Surrey on 23rd October 2022. This was subsequently picked up and reported on by a number of news outlets including Felix, Evening Standard and Science Daily.
- A public event was planned on Guildford High Street for Car Free Day on 25 September 2022. GCARE researchers provided information about Guildford Living Lab and projects such as INHALE to city residents. Prof. Prashant gave short talks in Guildhall and to the public about the air pollution and its effects on human health. The crowd along with the local MP showed great interest.
- Further to the aforementioned publication of ‘Quantification of green infrastructure effects on airborne nanoparticles dispersion at an urban scale’, a press article entitled ‘Surrey’s Global Centre for Clean Air Research identifies tall, dense trees as effective weapon against traffic’s toxic nanoparticles’ was published by the University of Surrey on 16th May 2022.
- The INHALE management team worked with UKRI on a commissioned animation explaining the INHALE project which was released with 3 others in February 2022 to showcase the impacts of the projects funded by the first round of successful UKRI Physics of Life projects as part of their activities supporting the announcement of the second round.
- Imperial College London published an article entitled Wearable sensors help researchers understand effects of air pollution on health detailing the INHALE clinical study and how people in West London can get involved, on 28th October 2021.
- The Evening Standard ran an article entitled Londoners to wear pollution monitors in toxic air study, detailing the INHALE clinical study and how it might link with the expansion of the London Ultra Low Emission Zone expansion on 27th October 2021.
- Further to the aforementioned publication of ‘Understanding the effects of roadside hedges on the horizontal and vertical distributions of air pollutants in street canyons’, a press article was published by the University of Surrey on 14th October 2021.
- The University of Surrey’s Car Free Day brought Guildford Living Lab and the local community together to discuss their individual contribution and exposure to air pollution and what we can do to reduce that on 26th September 2021.
- Further to the aforementioned publication of ‘The co-development of HedgeDATE, a public engagement and decision support tool for air pollution exposure mitigation by green infrastructure’ and the associated online tool, a press article was published by the University of Surrey on 22nd September 2021.
- INHALE research is being disseminated via @ImperialRSM, @AirPollSurrey & @GuildfordLivingLab Twitter accounts as well as investigators account @pk_shishodia – usually linked with programme Twitter @PhysicsofLifeUK.
- Imperial College London published an article New remote projects enhance student learning during the pandemic which details a student project connected with INHALE and COVAIR on 31 March 2021.
- Further to the aforementioned publication of ‘Green infrastructure for air quality improvement in street canyons’, a press article was published by the University of Surrey on 14 December 2020.
- Further to the aforementioned publication of ‘The impacts of existing and hypothetical green infrastructure scenarios on urban heat island formation’, a press article was published by the University of Surrey on 18 November 2020.
- Further to the aforementioned publication of ‘Temporary reduction in fine particulate matter due to ‘anthropogenic emissions switch-off’ during COVID-19 lockdown in Indian cities’, a press article was published by the University of Surrey.
- Imperial College London published an article ‘Air pollution during lockdown and beyond’ reporting that air pollution monitors had been installed near South Kensington campus to measure pollution during and after the COVID-19 Lockdown.
- Further to the aforementioned publication of ‘Could fighting airborne transmission be the next line of defence against COVID-19 spread?’, a press article was published by the University of Surrey.
- Further to the aforementioned publication of ‘The Nexus between Air Pollution, Green Infrastructure and Human Health’, the associated press release was widely published by many media outlets including Science Daily, EnvironTech and DovMed.
- Imperial College London published an article ‘Study to provide new insights into health impact of urban pollution’.
- Further to the aforementioned publication of ‘Designing vegetation barriers for urban air pollution abatement: a practical review for appropriate plant species selection’ the associated press release generated media interest, for example in the DailyMail, BBC and the Times.
- Further to the aforementioned publication of ‘A primary school driven initiative to influence commuting style for dropping-off and picking-up of pupils’, the associated press release generated media interest, for example in the Times.
Guidance and tools
- Prashant Kumar, Rana Alaa Abbass et. al., at the Global Centre for Clean Air Research, University of Surrey, produced Kitchen Pollution Guidance, designed to mitigate exposure to cooking emissions in kitchens.
- Professor Prashant Kumar, Yendle Barwise, Arvind Tiwari and Fahad Rafi-Butt contributed to the development of HedgeDATE, a tool to aid hedge design for the abatement of traffic emissions.
- Kumar, P., Omidvarborna, H., Barwise, Y., Tiwari, A. developed Mitigating Exposure to Traffic Pollution in and around Schools: Guidance for Children Schools and Local Communities. This guidance document offers tangible measures that can be taken to improve the air children breathe in and around schools and has been published in a number of different languages which can be found here.
- Prashant, K., Abhijith, K. V., Barwise, Y. (2019). Implementing Green Infrastructure for Air Pollution Abatement: General Recommendations for Management and Plant Species Selection. 10.6084/m9.figshare.8198261.v1.