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

Talha Ansar; Waqar Muhammad Ashraf; Alejandro Coca-Castro; Manvendra Janmaijaya; Ramit Debnath; J. Scott Hosking

cs.LGOct 8, 2026

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

Authors: Talha Ansar, Waqar Muhammad Ashraf, Alejandro Coca-Castro, Manvendra Janmaijaya, Ramit Debnath, J. Scott Hosking

Organizations: University of Engineering and Technology, Lahore, Pakistan · The Alan Turing Institute, London, UK · University of Cambridge, Cambridge, UK

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.

Figures & tables

Figure 1: Sequential sensor placement performance with optimisation solvers. (a) Joint NLL, Marginal NLL, and RMSE as five stations are added using the model-based acquisition criteria. (b) Joint-MI-selected locations for greedy and genetic search. (c) Wall-clock time required to identify five sensor locations for each acquisition criterion.

Figure 2: Wind-regime dependence of acquisition-function agreement. (a) Mean Spearman correlation between paired model-based and oracle acquisition functions, stratified by domain-mean wind direction. The pairs are Joint MI with Joint NLL, Marginal MI with Marginal NLL, and Mean Var with RMSE. (b) Monthly percentage of evaluation days assigned to north (N), east (E), south (S), and west (W) wind sectors.

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Figure A.1: Final five-sensor placement maps for the acquisition criteria. (a) three model-based acquisition criteria and, (b) their corresponding oracle counterparts. Columns identify the acquisition criterion and rows compare greedy and genetic search.

Figure A.2: Correlation between paired model-based and oracle acquisition functions. The three pairs are Joint MI with Joint NLL, Marginal MI with Marginal NLL, and Mean Var with RMSE.

Figure A.3: Monthly mean Spearman correlation between paired model-based and oracle acquisition functions.

Bayesian Experimental Design

Bayesian experimental design uses probabilistic models to choose measurements that improve learning or support better decisions.

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Model Calibration

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Low-cost air quality sensors (LCS) provide a practical alternative to expensive regulatory-grade instruments, making dense urban monitoring networks possible. Yet their adoption is limited by calibration challenges, including sensor drift, environmental cross-sensitivity, and variability in performance from device to device. This work presents a deep learning framework for calibrating LCS measurements of PM

_{2.5}

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_{10}

, and NO

_2

using a Long Short-Term Memory (LSTM) network, trained on co-located reference data from the OxAria network in Oxford, UK. Unlike the Random Forest (RF) baseline, which treats each observation independently, the proposed approach captures temporal dependencies and delayed environmental effects through sequence-based learning, achieving higher

R^2

values across training, validation, and test sets for all three pollutants. A feature set is constructed combining time-lagged parameters, harmonic encodings, and interaction terms to improve generalization on unseen temporal windows. Validation of unseen calibrated values against the Equivalence Spreadsheet Tool 3.1 demonstrates regulatory compliance with expanded uncertainties of 22.11% for NO

_2

, 12.42% for PM

_{10}

, and 9.1% for PM

_{2.5}

Abstract

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