Towards Causal Understanding of Urban Air Pollution: Mechanistic Models under Sparse Sensing

Ankit Bhardwaj, Lakshmi Subramanian

Keywords: Source Apportionment, Air Pollution, Physics-Based Causality

TL;DR: We present a position paper framing urban air pollution source attribution as a causal inference problem, using a parameterized Gaussian plume model to show how mechanistic approaches yield interpretable insights under sparse, uncertain data.

Abstract: Understanding the causal drivers of urban air pollution remains a central challenge for environmental science and policy. While high-resolution source apportionment typically relies on dense monitoring networks or receptor-based chemical analysis, many cities must operate with sparse sensors and incomplete emission inventories. We frame air pollution source attribution as a causal inference problem, linking emissions to observed concentrations through mechanistic dispersion models. Using Gaussian plume formulations, we combine multiple emission categories; vehicular traffic, domestic emissions, brick kilns, industries, and power plants; with real-world sensor data from New Delhi. Our methodology estimates source-specific contributions under sparse observations via parameterized dispersion modeling, while also capturing the influence of missing or unobserved sources. By situating source apportionment within a causal modeling perspective, we emphasize both the opportunities and limitations of mechanistic approaches under real-world constraints, and propose a causal learning framework at the intersection of environmental science and machine learning.

Submission Number: 43