RTCA Keynote: Francis Pope - Measuring, monitoring, and modelling in a changing world

Transcript

1. Measuring, monitoring, and modelling in a changing world f.pope@bham.ac.uk www.francispope.com School of Geography, Earth and Environmental Sciences Birmingham Institute for Sustainability and Climate Action (BISCA)
2. Air pollution and human health • Exposure to air pollutants causes a range of adverse effects upon human health • Worldwide ca. 4 to 9 million premature deaths per year – with many in Asian and African countries • Mechanisms of toxic action include the following: (a) Effects on the respiratory system. (b) Effects via the cardiovascular system. (c) Several common air pollutants are known to be carcinogenic (d) Effects on cognition and mental health
3. Reframing air pollution as a cognitive and socioeconomic risk Faherty, T., Ellis-Bradford, L.J.A., Onyeaka, H., Harrison, R.M. and Pope, F.D., 2026. Reframing air pollution as a cognitive and socioeconomic risk. npj Clean Air, 2(1), p.18. https://doi.org/10.1038/s44407-026-00059-4
4. Vulnerability to air pollution Exposure Susceptibility Adaptive capacity
5. Low-cost sensor revolution $$$$$$ $$
6. Advantages and applications of air quality sensors New Applications Greater spatial resolution Greater temporal resolution Advantages Mobile devices Cheap (CAPEX and OPEX) Sacrificial devices Small size and weight Multiple pollutant measurements Low power requirements Indoor monitoring Low technical expertise required Combining with other devices Digital twins Citizen science / public outreach Art/Science Opportunities Source Apportionment
8. Low cost ≠ low quality — if used appropriately • Calibration and co-location matter • Drift, interference and environmental sensitivity remain challenges • Uncertainty depends on the application • Sensors complement rather than simply replace reference instruments
9. Birmingham Air Quality Supersite (BAQS) A state-of-the-art research grade station located at the University of Birmingham, focusing on detailed, continuous monitoring of urban pollution and atmospheric composition.
10. From sparse observations to continuous hyperlocal measurement and understanding
11. From sparse observations to continuous hyperlocal measurement and understanding
12. The Birmingham Citizen Science Pilot • The area of study is a 1x1 km block in Selly Oak (about 10,000 students). • Use of calibrated low-cost sensors for monitoring • Static and mobile measurements. • Outdoor and indoor measurements. • Inclusion of citizens to increase the amount of collected data. • Tested and novel statistical methodologies for data analysis. Bousiotis et al., (2024). Pinpointing sources of pollution using citizen science and hyperlocal low-cost mobile source apportionment. Environment international, https://doi.org/10.1016/j.envint.2024.109069 Setups designed by the University of Birmingham.
13. Source apportionment Successful air quality management and control not only requires measurement of air pollution levels, but it also requires information on the sources and their relative importance. Without this critical, targeted information on pollution sources, it is difficult to reduce air pollution. Historically source apportionment has been done using expensive regulatory grade equipment.
14. Indoor Source Apportionment Example
15. https://doi.org/10.1016/j.scitotenv.2025.180257
16. Combining multiple types sensors together • For example – Air pollution sensors and radar sensors to relate indoor room occupancy and movement with air quality Bousiotis et al. (2026). Parameterising the effect of human occupancy and kinetic energy on indoor air pollution. npj Climate and Atmospheric Science, 9(1), p.4. https://doi.org/10.1038/s41612-025-01281-9
17. Combining multiple types sensors together
18. Sensor data to study traffic noise (TrafficEar) TrafficEar co-located with the EDAR data collection to benefit from traffic counts and vehicle identification
19. Sensor data to study traffic noise (TrafficEar) Ghaffarpasand et al. 2023. Traffic Noise Assessment Using Intelligent Acoustic Sensors (Traffic Ear) and Vehicle Telematics Data. Sensors, 23(15), p.6964. https://doi.org/10.3390/s23156964
20. Increasing Number of Data Sources Example 1 – Satellite Data Donkelaar et al. Environmental Science & Technology 2021 55 (22), 15287
21. Increasing Number of Data Sources Example 2 – Telematics Data Ghaffarpasand et al, 2022. Vehicle Telematics for Safer, Cleaner and More Sustainable Urban Transport: A Review. Sustainability
22. Increasing Number of Data Sources Example 3 – Proxy Data (e.g. Visibilty Data) Singh et al, 2020. Visibility as a proxy for air quality in East Africa. Environmental Research Letters.
23. From Big Data to Environmental Intelligence • Advanced data modelling allows us to combine increasingly large, diverse and imperfect datasets. • Machine learning can integrate: • • • • • • low-cost sensors regulatory monitors Satellites Meteorology traffic/telematics/mobility/proxy data etc • This enables us to: • • • • fill spatial and temporal gaps predict pollution at previously impossible resolution identify patterns and sources estimate individual exposure • The opportunity: move from measuring where monitors happen to be to predicting pollution where and when people actually experience it.. Baruah et al., (2024). A novel spatiotemporal prediction approach to fill air pollution data gaps using mobile sensors, machine learning and citizen science techniques. npj Climate and Atmospheric Science
24. Model building and dashboard • Dashboard allows users to run health impact calculations as visualisations. • Scenario engine includes: o Future PM2.5 scenarios e.g., WHO target and percentage reductions over time with population growth o Road vs urban contribution scenarios which calculates road contributions to the health outcomes
25. Example Outputs of K-AQHA 3 Assuming average PM2.5 = 90 ug/m
26. Summary • Low-cost sensors are transforming air-quality measurement, enabling continuous, hyperlocal monitoring of pollution and exposure. • Combining air-quality sensors with other data sources provides new insight into pollution sources and human behaviour. • The next step is environmental intelligence: integrating diverse datasets and advanced modelling to move from simply measuring pollution to understanding, predicting and managing it.
27. Thank you for listening! Any questions? f.pope@bham.ac.uk www.francispope.com