Pollutants control the process networks of urban environmental-meteorology

Abstract

The dynamics of interactions between the environmental and the meteorological variables in an urban region is extremely complex due to continuously evolving coupled human–natural processes in an urban setting. We attempt to understand the same with the networks of variables using information theory, known as process network. We monitored local meteorological variables at half-hourly scale using an eddy covariance observation system combined with available concentration of pollutants from other sources. Both the datasets are for Powai, Mumbai, India, from January to April 2020 that includes pre-lockdown and lockdown periods associated with interventions in response to COVID-19. Analysis of the weekly process networks developed with the same data shows that they are more dominated by memory during the lockdown period. We find that a high concentration of pollutants under no-lockdown scenarios, during specific work commute hours, interrupts the memory of the network. A seasonal transition in temperature during the pre-lockdown period failed to make any major changes. Our analysis shows that the dynamics of pollutant concentration drives the interaction between the variables of urban environmental meteorological system.