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

TL;DR

The paper addresses how urban pollutant concentrations interact with meteorological variables across multiple Indian cities by developing an entropy-based framework that combines linear and nonlinear dependence measures. A PCA-based compound scoring scheme integrates Pearson correlation, Mutual Information, and Relative Conditional Entropy to yield intra-city Local Correlation Scores (LCS) and a cross-city Comprehensive Correlation Score (CCS), enabling robust city classifications. Temporal dependencies are characterized using Transfer Entropy and Time-Delayed Mutual Information, revealing that relative humidity often leads pollutant changes while temperature tends to lag, with TDMI showing zero-lag peaks and exponential decay, indicating short-term coupling. The resulting spatiotemporal, multi-metric approach highlights substantial spatial heterogeneity in pollutant–meteorology coupling and provides a transferable framework for region-specific forecasting and environmental management.

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.

Figures (6)

• Figure 1: Map of India depicting monitoring stations across the studied cities. Individual stations are shown as dots, and the total number of stations is indicated for each city.
• Figure 2: Time-series plots for concentration of the pollutants and the meteorological parameters for two representative stations in Kolkata (left panel) and Mumbai (right panel) for the period Jan, 2020 to Dec, 2024.
• Figure 3: Kernel regression plots illustrating relationships between pollutants and meteorological variables in selected cities.
• Figure 4: Spatial visualization of city clusters according to comprehensive correlation score $\mathcal{C}$. The map shows the cities corresponding to very high (red), high (orange), moderate (violet), and low (green) values of $\mathcal{C}$.
• Figure 5: Exponential decay for all four pollutant-RH pairs across selected representative cities, plotted on a semi-logarithmic scale with $\Delta_{X,Y}/a$ as a function of the scaled variable $\tau/\tau_0$. In each panel, the black solid line denotes the function $e^{-\tau/\tau_0}$.