NEXUS: A compact neural architecture for high-resolution spatiotemporal air quality forecasting in Delhi National Capital Region

TL;DR

This work presents NEXUS (Neural Extraction and Unified Spatiotemporal) architecture for forecasting carbon monoxide, nitrogen oxide, and sulfur dioxide, delivering superior predictive performance with remarkable computational efficiency, enabling real-time deployment for air quality monitoring systems.

Abstract

Urban air pollution in megacities poses critical public health challenges, particularly in Delhi National Capital Region (NCR) where severe degradation affects millions. We present NEXUS (Neural Extraction and Unified Spatiotemporal) architecture for forecasting carbon monoxide, nitrogen oxide, and sulfur dioxide. Working with four years (2018--2021) of atmospheric data across sixteen spatial grids, NEXUS achieves R$^2$ exceeding 0.94 for CO, 0.91 for NO, and 0.95 for SO$_2$ using merely 18,748 parameters -- substantially fewer than SCINet (35,552), Autoformer (68,704), and FEDformer (298,080). The architecture integrates patch embedding, low-rank projections, and adaptive fusion mechanisms to decode complex atmospheric chemistry patterns. Our investigation uncovers distinct diurnal rhythms and pronounced seasonal variations, with winter months experiencing severe pollution episodes driven by temperature inversions and agricultural biomass burning. Analysis identifies critical meteorological thresholds, quantifies wind field impacts on pollutant dispersion, and maps spatial heterogeneity across the region. Extensive ablation experiments demonstrate each architectural component's role. NEXUS delivers superior predictive performance with remarkable computational efficiency, enabling real-time deployment for air quality monitoring systems.

Figures (9)

• Figure 1: NEXUS architecture showing the complete processing pipeline from raw spatiotemporal inputs through patch embedding, low-rank projection, stacked NanoBlocks with parallel pathways (CompactKernel, MicroConv, FusionGate), weighted spatial pooling, and prediction head. The compact design achieves superior forecasting performance through strategic exploitation of atmospheric data structure---local temporal coherence enables patch-based dimensionality reduction, dominant meteorological modes justify low-rank projections, and multi-scale parallel processing captures concurrent atmospheric processes across temporal scales.
• Figure 2: Temporal evolution of predicted versus observed pollutant concentrations over year-long test period (July--December 2021). NEXUS captures sustained low-concentration baseline during summer monsoon months and dramatic concentration spikes during winter pollution episodes.
• Figure 3: Diagnostic plots for model residuals including scatter plots, quantile-quantile comparisons, and scale-location relationships. Residuals show approximately zero mean, near-normal distribution, and relatively constant variance.
• Figure 4: Relationships between weather variables and pollutant concentrations. Strong negative correlations with temperature and wind speed confirm their roles in controlling dispersion.
• Figure 5: Mean diurnal variation showing systematic 24-hour patterns. Morning peaks coincide with traffic emissions and shallow boundary layers, afternoon minima reflect enhanced mixing.