TY - EJOU
AU - Alzahrani, Taher
AU - Rashid, Saima
TI - Heterogeneous Community Surveillance–Driven Physics-Informed Reformulation of Fine-Scale Convection–Diffusion Air Pollution Distribution
T2 - Computer Modeling in Engineering \& Sciences
PY - 2026
VL - 147
IS - 1
SN - 1526-1506
AB - Air pollution poses a serious public health threat in developing countries such as Pakistan, where rapid urbanization and industrialization have intensified atmospheric contamination. Although mobile sensing deployed on public transportation expands spatial coverage beyond fixed monitoring stations, accurate high-resolution pollution mapping remains constrained by sparse observations, computational burden, neglected pollutant interactions, and limited interpretability. To address these challenges, this study proposes a unified physics-informed deep learning framework for fine-grained air pollution map reconstruction and joint multi-pollutant estimation. The framework integrates mobile and stationary monitoring data with atmospheric dispersion principles to enhance physical consistency under limited observational coverage. A physics-guided air pollution map reconstruction model (Phy-APMR) is developed to jointly estimate ozone (O3), particulate matter (PM2.5), and PM10, while explicitly modeling cross-pollutant interactions through a deep interaction module. A tanh-based self-attention mechanism adaptively weights heterogeneous inputs, and a physics-constrained loss function enforces consistency with atmospheric transport behavior. An adaptive short-time update sampling strategy is further introduced to accelerate convergence and enable high-frequency updates under dynamic transportation conditions. Experiments conducted in major urban regions of Pakistan during 2023–2024 demonstrate that the proposed framework consistently outperforms conventional reconstruction and single-pollutant estimation approaches. The model achieves cross-validation R2 values of 0.92 for O3, 0.90 for PM2.5, and 0.86 for PM10, while reducing convergence time by over 80%. Interpretability analysis identifies formaldehyde, carbon monoxide, hydroxyl radicals, and temperature as dominant contributors. Spatial and seasonal analyses indicate pronounced summer O3 pollution and wintertime particulate accumulation, particularly in Punjab and Sindh. These findings demonstrate the framework’s capability for robust multi-pollutant reconstruction and its potential to support coordinated air quality management and evidence-based environmental policy in Pakistan.
KW - Air pollution map; complex networks; mobile sensing networks; machine learning paradigms; sensor networks; optimization
DO - 10.32604/cmes.2026.076957