Data-driven Optimal Sensor Placement for Aerosol Monitoring in the UK

A data-driven sensor-placement framework embedding a trained convolutional Gaussian neural process in the DeepSensor optimisation framework cut mean computational time by 60% (10±3 versus 25±7 minutes) for aerosol monitoring in the United Kingdom, according to a new arXiv paper (2610.12658v1). Genetic optimisation with lazy evaluation and caching produced sequential placement performance comparable to the greedy solver, and Joint NLL improved by about 22% for Joint-MI-driven genetic placement after five aerosol-station locations were identified against a CAMS-based retrospective oracle benchmark. Seasonal and wind-regime analyses showed agreement between model-based criteria and oracle benchmarks varies with meteorological transport conditions.

arXiv:2610.12658v1 Announce Type: new Abstract: We develop a data-driven sensor-placement framework and apply it to aerosol monitoring in the United Kingdom UK . A trained convolutional Gaussian neural process is embedded within the DeepSensor optimisation framework. Genetic optimisation with lazy evaluation and caching reduces mean computational time by 60\% 10$\pm$3 versus 25$\pm$7 minutes while producing sequential placement performance comparable to the greedy solver. Using the CAMS reference field to construct a retrospective oracle benchmark, Joint NLL improves by about 22\% for Joint-MI-driven genetic placement after five locations for aerosol stations are identified. Seasonal and wind-regime analyses show that agreement between model-based criteria and oracle benchmarks varies with meteorological transport conditions. These results show a computationally efficient approach for data-informed expansion of environmental observing systems.