On deriving influences of upwind agricultural and anthropogenic emissions on greenhouse gas concentrations and air quality over Delhi in India: A stochastic Lagrangian footprint approach

Delhi, the capital city of India witnesses severe degradation of air quality and rapid enhancement of trace gases during winter. Still it is unclear about the relative role of the meteorological conditions and the post-monsoon agricultural stubble burning on the occurrence of these events. To overcome this, we examine the use of applying high-resolution transport model to establish the link between atmospheric concentrations and upstream surface fluxes. This study reports the implementation of a Lagrangian approach and demonstrates its capability in deriving the upwind influences over Delhi. We simulate stochastic back trajectories over Delhi by implementing stochastic time-inverted Lagrangian transport (STILT) model, driven by the meteorological fields from the European Centre for Medium Range Weather Forecasts (ECMWF) model. During the post-monsoon, when mixing layer height is shallow, we find high near-field influence. The variations in footprint simulations with receptor heights show the effect of mixing layer dynamics on the air-parcels. By using atemporal emission fields, we find a considerable impact of meteorological conditions during November that contributes to the enhancements of trace gases. Together with strong emissions (anthropogenic and biomass burning), these enhancements can be several orders higher compared to other seasons. Through the receptor-oriented STILT implementation over India, we envision a wide range of applications spanning from air quality to climate change. An advantage of this implementation is that it allows the use of pre-calculated footprints in simulating any trace gas species and particulate matter, making it computationally less demanding than running an ensemble of full atmospheric transport model. Research highlights Our study elaborates a method to estimate the near-field influences on a region of interest or receptor location (Delhi), which is pivotal in devising mitigation strategies to curb the increasing pollution events (one of the country's greatest concerns).For this, we implement STILT that uses ECMWF meteorological data to generate simulations of realistic atmospheric trajectories and footprints to the Indian subcontinent domain. From this data, the influence matrix is derived. We demonstrate the usefulness of the STILT modeling framework by deriving air-parcel trajectories and footprint over Delhi, which shows a higher influence of Haryana and Punjab region as upwind location to Delhi during the pre-monsoon and post-monsoon season. We highlight the importance of proper accounting of vertical mixing in the atmospheric model by simulating footprint and trajectory measurements at different heights. This shows that during postmonsoon when PBL height is shallow there is a higher influence function thus choking Delhi in the winter. Our analysis shows the usefulness of pre-calculated footprints in simulating any trace gas species and particulate matter concentration thus saving a lot of computational costs incurred. This is illustrated by generating concentration signals using EDGAR global inventory for CO and CO2 emissions from biomass burning and CO 2 , CO, N 2 O and CH 4 from anthropogenic emissions. We observed concentration enhancement in agreement with the diurnal PBL dynamics (higher in lower layers (20m) compared to higher layers (500m)). We envision extending the STILT network to have wider insights on regional and local fluxes of CO 2 , CO, and CH 4 . The study can be improvised by utilizing hourly-varying emission fluxes and also by accounting for other sources of emissions.