Classifying Urban Regions by Aggregated Pollutant Weather Correlation Strength: A Spatiotemporal Study

TL;DR

This paper introduces an entropy-based statistical framework that combines multiple correlation measures into a unified index to classify urban regions based on pollutant-weather interaction strength, revealing key causal influences.

Contribution

The study develops a novel PCA-based composite index integrating heterogeneous metrics for pollutant-meteorology dependence analysis across multiple cities.

Findings

Humidity generally leads pollutant changes

Temperature tends to lag behind pollutant variations

Strong short-term interactions with limited persistence

Abstract

Understanding pollutant meteorology interactions is essential for environmental risk assessment. This study develops an entropy-based statistical framework to analyze static and temporal dependencies between urban air pollutants and meteorological variables across multiple Indian cities. Dependence is quantified using complementary linear and nonlinear measures, including Pearson correlation, mutual information, and relative conditional entropy. A key methodological contribution is a PCA based composite indexing framework that integrates these heterogeneous metrics into a unified and interpretable correlation score. For each pollutant meteorological pair within a city, PCA is used to extract a joint variability index, while spatial variability is assessed by aggregating correlations across cities. These indices are further combined to derive a comprehensive city-level correlation score that represents overall pollutant meteorology coupling strength and enables classification of cities into distinct interaction regimes. Sensitivity analysis, performed by systematically excluding individual variable pairs, demonstrates the robustness of the framework, with no single pair exerting disproportionate influence. Temporal dependencies are examined using transfer entropy and time-delayed mutual information. Results indicate that relative humidity generally leads changes in pollutant concentrations, whereas ambient temperature tends to lag, highlighting contrasting causal influences. Mutual information peaks at zero lag and decays rapidly, indicating strong short term interactions with limited persistence. Overall, the proposed framework provides a unified and interpretable approach for assessing complex pollutant meteorology interactions across diverse locations and time.