Scalable air-quality sensor placement via gradient-based mutual information maximization

Air pollution is a leading global health threat, affecting 99% of the world�s population who breathe air exceeding WHO safety thresholds. Yet many developing countries lack the dense monitoring infrastructure needed for accurate exposure assessment and informed policy intervention. We present a scalable, gradient-based optimization framework that treats sensor coordinates as differentiable parameters and directly maximizes mutual information for optimal sensor placement.