Anisotropic Effective Medium Theory for Urban UV Monitoring with IoT Networks

Theoretical Physics (Effective Medium Theory) and Environmental Engineering (IoT Sensor Networks), the project addresses the limitations of current satellite remote sensing systems (e.g., OMI/TROPOMI), which fail to resolve micro-climatic variations at the street level.Theoretical FrameworkThe core innovation of this work is treating the urban atmosphere not as a vacuum or simple fluid, but as an anisotropic disordered dielectric medium. The framework parametrizes the effective permittivity tensor $\boldsymbol{\epsilon}_{\text{eff}}(\mathbf{r},\omega)$ using the Dyson equation and the Bethe-Salpeter equation for mean-field propagation. This approach allows for a rigorous electrodynamic description of photon scattering by particulate matter (PM10) and aerosols without the prohibitive computational costs of full-wave Maxwell simulations.Methodology & ImplementationThe project implements a linear inversion algorithm based on the Born approximation, suitable for deployment on low-power edge computing devices (e.g., ESP32/LoRaWAN). The model utilizes a distributed network of IoT sensors as a discrete sampling grid to reconstruct continuous optical fields.Key Objectives:Micro-Climatic Resolution: To resolve intra-urban UV gradients often masked by coarse satellite pixels ($13 \times 24$ km).The "Pollution Paradox": To quantitatively model the phenomenon where industrial zones with high aerosol loads act as localized radiative shields, while peripheral residential zones remain disproportionately exposed to harmful UV levels.IoT Feasibility: To validate that physics-informed algorithms can run efficiently on resource-constrained hardware ($\sim 0.5$ mW per prediction).Expected ResultsA Proof-of-Concept (PoC) using climatologically informed synthetic data for São José dos Campos (Brazil) demonstrated the model's robustness, achieving a coefficient of determination $R^2 \approx 0.89$ even under significant instrumental noise. The project aims to provide a scalable, low-cost methodology for real-time environmental monitoring, contributing to public health policies and climate justice.