Abstract
Skin exposome encapsulates all internal and environmental exposures that affect skin health. Of these, photo-pollution refers to the combined effect on human skin of the simultaneous exposure to solar radiation (especially UV) and air pollution. Providing personalised photo-pollution exposure warnings and dose monitoring to an individual through a smartphone app could help in reducing skin ageing and degradation as well as in managing skin conditions (for example Atopic Dermatitis). However, accurate monitoring is challenging without a potentially expensive or cumbersome sensor device. In this work we present an innovative satellite-based air pollutant monitoring software service, ExpoPol, developed by siHealth Ltd. ExpoPol synthesises several inputs including live satellite imagery in real-time into an artificial intelligence (AI) model to provide assessment of the exposure of a smartphone user to relevant air pollutants, such as nitrogen oxides (NOx), poly-aromatic hydrocarbons (PAH) and ozone (O3). When combined with siHealth’s patented technology HappySun® for solar radiation monitoring, ExpoPol can effectively provide a sensor-less personal skin photo-pollution dosimetry. By downscaling satellite data using local geographic data, ExpoPol is capable of monitoring pollutants with street-level resolution and global coverage in near real-time. We evaluate the accuracy of ExpoPol against ground-station monitoring data for three pollutants across three continental regions (Europe, Asia, North America) and find R2 values of 0.62, 0.65, 0.74 for PM10, PM2.5, NO2 respectively. ExpoPol is shown to be significantly more accurate than a state-of-the-art global atmospheric forecasting system (CAMS) over the same ground-station dataset and provide data on much finer spatial resolutions. The presented system can support the real-time automatic assessment of the user’s skin exposome, anywhere and anytime. This paves the way for the development of mobile applications empowering users and clinicians to make informed decisions about skin health, or assisting dermocosmetic manufacturers in the creation of personalised products for personal care (e.g., skin ageing prevention or hair care).
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The datasets analysed during the current study are available from; Open Street Map (https://www.openstreetmap.org/), Sentinel 5P (https://s5phub.copernicus.eu/), NASA (https://www.earthdata.nasa.gov/), European Environmental Agency (https://discomap.eea.europa.eu/), China National Environmental Monitoring Centre (http://www.cnemc.cn/), and the Environmental Protection Agency (https://www.epa.gov/aqs).
References
Araki S, Shima M, Yamamoto K (2018) Spatiotemporal land use random forest model for estimating metropolitan NO2 exposure in Japan. Sci Total Environ 634:1269–1277. https://doi.org/10.1016/j.scitotenv.2018.03.324
Armstrong BK, Kricker A (2001) The epidemiology of UV induced skin cancer. J Photochem Photobiol B 63:8–18. https://doi.org/10.1016/S1011-1344(01)00198-1
Barrington-Leigh C, Millard-Ball A (2017) The world’s user-generated road map is more than 80% complete. PLoS ONE 12:e0180698. https://doi.org/10.1371/journal.pone.0180698
Bocheva G, Slominski RM, Slominski AT (2023) Environmental air pollutants affecting skin functions with systemic implications. Int J Mol Sci 24:10502. https://doi.org/10.3390/ijms241310502
Briggs DJ, Collins S, Elliott P et al (1997) Mapping urban air pollution using GIS: a regression-based approach. Int J Geogr Inf Sci 11:699–718. https://doi.org/10.1080/136588197242158
Cork MJ, Danby SG, Ogg GS (2020) Atopic dermatitis epidemiology and unmet need in the United Kingdom. J Dermatol Treat 31:801–809. https://doi.org/10.1080/09546634.2019.1655137
de Paula CM, Germano Marciano A, Silveira Barreto Carvalho V et al (2021) Exposome extrinsic factors in the tropics: The need for skin protection beyond solar UV radiation. Sci Total Environ 782:146921. https://doi.org/10.1016/j.scitotenv.2021.146921
Denisow-Pietrzyk M (2021) Human Skin Reflects Air Pollution – a Review of the Mechanisms and Clinical Manifestations of Environment-Derived Skin Pathologies. Pol J Environ Stud 30:3433–3444. https://doi.org/10.15244/pjoes/130525
Flohr C (2023) How we treat atopic dermatitis now and how that will change over the next 5 years. Br J Dermatol 188:718–725. https://doi.org/10.1093/bjd/ljac116
Gilbert NL, Woodhouse S, Stieb DM, Brook JR (2003) Ambient nitrogen dioxide and distance from a major highway. Sci Total Environ 312:43–46. https://doi.org/10.1016/S0048-9697(03)00228-6
Hertel O, Berkowicz R, Larssen S (1991) The operational street pollution model (OSPM). In: van Dop H, Steyn DG (eds) Air Pollution Modeling and Its Application VIII, vol 15. Springer, Boston, MA, pp 741-750. https://doi.org/10.1007/978-1-4615-3720-5_86
Hüls A, Vierkötter A, Gao W et al (2016) Traffic-Related Air Pollution Contributes to Development of Facial Lentigines: Further Epidemiological Evidence from Caucasians and Asians. J Invest Dermatol 136:1053–1056. https://doi.org/10.1016/j.jid.2015.12.045
Ialongo I, Virta H, Eskes H et al (2020) Comparison of TROPOMI/Sentinel-5 Precursor NO2; observations with ground-based measurements in Helsinki. Atmospheric Meas Tech 13:205–218. https://doi.org/10.5194/amt-13-205-2020
Kokhanovsky AA, Leeuw G, Kokhanovsky AA (2009) Satellite Aerosol Remote Sensing over Land. Satellite Aerosol Remote Sensing over Land. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 193–225
Krutmann J, Bouloc A, Sore G et al (2017) The skin aging exposome. J Dermatol Sci 85:152–161. https://doi.org/10.1016/j.jdermsci.2016.09.015
Lee CJ, Brook JR, Evans GJ et al (2011) Novel application of satellite and in-situ measurements to map surface-level NO2 in the Great Lakes region. Atmospheric Chem Phys 11:11761–11775. https://doi.org/10.5194/acp-11-11761-2011
Lin C, Li Y, Yuan Z et al (2015) Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM2.5. Remote Sens Environ 156:117–128. https://doi.org/10.1016/j.rse.2014.09.015
Marionnet C, Tricaud C, Bernerd F (2014) Exposure to Non-Extreme Solar UV Daylight: Spectral Characterization, Effects on Skin and Photoprotection. Int J Mol Sci 16:68–90. https://doi.org/10.3390/ijms16010068
McLellan LJ, Morelli M, Simeone E et al (2020) SmartPDT®: Smartphone enabled real-time dosimetry via satellite observation for daylight photodynamic therapy. Photodiagnosis Photodyn Ther 31:101914. https://doi.org/10.1016/j.pdpdt.2020.101914
Misra N, Marrot L (2020) In Vitro Methods to Simulate Pollution and Photo-Pollution Exposure in Human Skin Epidermis. In: Turksen K (ed) Imaging and Tracking Stem Cells. Springer, US, New York, NY, pp 227–241
Morelli M, Masini A, Simeone E, Khazova M (2016) Validation and in vivo assessment of an innovative satellite-based solar UV dosimeter for a mobile app dedicated to skin health. Photochem Photobiol Sci 15:1170–1175. https://doi.org/10.1039/c6pp00129g
Morelli M, Michelozzi B, Simeone E, Khazova M (2021) Validation of a satellite-based solar UV-A radiation dosimeter for mobile healthcare applications. J Atmospheric Sol-Terr Phys 215:105529. https://doi.org/10.1016/j.jastp.2020.105529
Morgado-Carrasco D, Granger C, Trullas C, Piquero-Casals J (2021) Impact of ultraviolet radiation and exposome on rosacea: Key role of photoprotection in optimizing treatment. J Cosmet Dermatol 20:3415–3421. https://doi.org/10.1111/jocd.14020
Mukherjee A, Agrawal M (2017) World air particulate matter: sources, distribution and health effects. Environ Chem Lett 15:283–309. https://doi.org/10.1007/s10311-017-0611-9
Park S-Y, Byun E, Lee J et al (2018) Air Pollution, Autophagy, and Skin Aging: Impact of Particulate Matter (PM10) on Human Dermal Fibroblasts. Int J Mol Sci 19:2727. https://doi.org/10.3390/ijms19092727
Passeron T, Krutmann J, Andersen ML et al (2020) Clinical and biological impact of the exposome on the skin. J Eur Acad Dermatol Venereol 34:4–25. https://doi.org/10.1111/jdv.16614
Pleijel H, Pihl Karlsson G, Binsell Gerdin E (2004) On the logarithmic relationship between NO2 concentration and the distance from a highroad. Sci Total Environ 332:261–264. https://doi.org/10.1016/j.scitotenv.2004.03.020
Rahman MM, Yeganeh B, Clifford S et al (2017) Development of a land use regression model for daily NO2 and NOx concentrations in the Brisbane metropolitan area, Australia. Environ Model Softw 95:168–179. https://doi.org/10.1016/j.envsoft.2017.06.029
Ramírez L, Vindel JM (2017) Forecasting and nowcasting of DNI for concentrating solar thermal systems. In: Advances in Concentrating Solar Thermal Research and Technology. Elsevier, pp 293–310 https://doi.org/10.1016/B978-0-08-100516-3.00013-7
Schikowski T, Hüls A (2020) Air Pollution and Skin Aging. Curr Environ Health Rep 7:58–64. https://doi.org/10.1007/s40572-020-00262-9
Soeur J, Belaïdi J-P, Chollet C et al (2017) Photo-pollution stress in skin: Traces of pollutants (PAH and particulate matter) impair redox homeostasis in keratinocytes exposed to UVA1. J Dermatol Sci 86:162–169. https://doi.org/10.1016/j.jdermsci.2017.01.007
Thomas G, Poulsen C, Povey A, McGarragh G, Jerg M, Siddans R, Grainger D (2014) Towards a true aerosol-and-cloud retrieval scheme. In: EGU General Assembly Conference Abstracts, p 14434. https://ui.adsabs.harvard.edu/abs/2014EGUGA..1614434T
Veefkind JP, Aben I, McMullan K et al (2012) TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications. Remote Sens Environ 120:70–83. https://doi.org/10.1016/j.rse.2011.09.027
Venturini M, Temple RC, Morelli M, Calzavara-Pinton P (2023) Field validation of a satellite-based personal solar exposure dosimeter. EADV Congress, Berlin, Germany. https://eadv.org/scientific/abstract-books/
Vierkötter A, Schikowski T, Ranft U et al (2010) Airborne Particle Exposure and Extrinsic Skin Aging. J Invest Dermatol 130:2719–2726. https://doi.org/10.1038/jid.2010.204
WHO (2010) WHO Guidelines for Indoor Air Quality: Selected Pollutants. World Health Organization, Geneva
Xiong X, King MD, Salomonson VV, Barnes WL, Wenny BN, Angal A, Wu A, Madhavan S, Link DO (2015) Moderate resolution imaging spectroradiometer on Terra and Aqua missions. Optical Payloads for Space Missions, John Wiley & Sons, pp 53–89. https://doi.org/10.1002/9781118945179.ch3
Xu H, Bechle MJ, Wang M et al (2019) National PM2.5 and NO2 exposure models for China based on land use regression, satellite measurements, and universal kriging. Sci Total Environ 655:423–433. https://doi.org/10.1016/j.scitotenv.2018.11.125
Young AR, Schalka S, Temple RC et al (2022) Innovative digital solution supporting sun protection and vitamin D synthesis by using satellite-based monitoring of solar radiation. Photochem Photobiol Sci 21:1853–1868. https://doi.org/10.1007/s43630-022-00263-7
Zhang H, Wei Z, Henderson BH et al (2022) Nowcasting Applications of Geostationary Satellite Hourly Surface PM2.5 Data. Weather Forecast 37:2313–2329. https://doi.org/10.1175/WAF-D-22-0114.1
Zhao X, Griffin D, Fioletov V et al (2020) Assessment of the quality of TROPOMI high-spatial-resolution NO2; data products in the Greater Toronto Area. Atmospheric Meas Tech 13:2131–2159. https://doi.org/10.5194/amt-13-2131-2020
Zheng Y, Zhang Q, Liu Y et al (2016) Estimating ground-level PM2.5 concentrations over three megalopolises in China using satellite-derived aerosol optical depth measurements. Atmos Environ 124:232–242. https://doi.org/10.1016/j.atmosenv.2015.06.046
Acknowledgements
The authors are grateful to:
- The Environmental Protection Agency (EPA), the European Environmental Agency (EEA) and the China National Environmental Monitoring Centre (CNMEC) for the access to ground-based air pollution measurement data
- The European Space Agency (ESA) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) for the access to the Sentinel-5 Precursor satellite data (TROPOMI sensor)
- The National Aeronautics and Space Administration (NASA) and the National Oceanic and Atmospheric Administration (NOAA) for the access to the EOS satellite data (MODIS sensor)
- RAL Space (UKRI-STFC) for the access to the ORAC data, which was supported by the Copernicus Climate Change Service (C3S)
- ECMWF for use of the CAMS data. Neither the European Commission nor ECMWF is responsible for any use that may be made of the information it contains.
- National Institute for Health and Care Research (NIHR) for funding support
Funding
This work has been partly supported by the funding provided by the National Institute for Health and Care Research (NIHR) in the frame of the “Sun4Health-AD” project (i4i FAST project NIHR205894). All authors received support from siHealth.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
RCT and MM are respectively Head of Technology Innovation and Chief Technology Officer at siHealth. PFL is a Professor at the University of Cambridge and received a consultancy fee paid by siHealth. BL is an Atmospheric Research Scientist at RAL Space (UKRI-STFC), that received a consultancy fee paid by siHealth. JM and SFW contributed to this work as Innovation Scientist and Research & Development Intern at siHealth respectively.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Temple, R.C., May, J., Linden, P.F. et al. A satellite-based, near real-time, street-level resolution air pollutants monitoring system using machine learning for personalised skin health applications. Air Qual Atmos Health (2024). https://doi.org/10.1007/s11869-024-01577-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11869-024-01577-4