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.