Utilising BC observations to estimate CO contributions from fossil fuel and biomass burning in the Central Himalayan region

Priyanka Srivastava; M Naja; P Bhardwaj; R Kumar; M C Rajwar; T R Seshadri

doi:10.1016/j.envpol.2023.122975

Utilising BC observations to estimate CO contributions from fossil fuel and biomass burning in the Central Himalayan region

Environ Pollut. 2024 Jan 15:341:122975. doi: 10.1016/j.envpol.2023.122975. Epub 2023 Nov 20.

Authors

Priyanka Srivastava¹, M Naja², P Bhardwaj³, R Kumar⁴, M C Rajwar⁵, T R Seshadri⁶

Affiliations

¹ National Institute for Environmental Studies (NIES), Tsukuba, 305-8506, Japan.
² Aryabhatta Research Institute of Observational Sciences, Manora Peak, Nainital, 263001, India. Electronic address: manish@aries.res.in.
³ Center for Study of Science, Technology and Policy (CSTEP), Bengaluru, 560094, India.
⁴ National Center for Atmospheric Research (NCAR), Boulder, CO, 80307-3000, USA.
⁵ Aryabhatta Research Institute of Observational Sciences, Manora Peak, Nainital, 263001, India.
⁶ Department of Physics and Astrophysics, University of Delhi, Delhi, 110007, India.

PMID: 37992951
DOI: 10.1016/j.envpol.2023.122975

Abstract

The Himalayan region is adversely affected by the increasing anthropogenic emissions from the adjacent Indo-Gangetic plain. However, source apportionment studies for the Himalayan region that are crucial for estimating CO concentration, are grossly insufficient, to say the least. It is in this context that our study reported here assumes significance. This study utilizes five years (2014-2018) of ground-based observations of eBC and multiple linear regression framework (MLR) to estimate CO and segregate its fossil fuel and biomass emission fractions at a high-altitude (1958 m) site in the Central Himalayas. The results show that MERRA2 always underestimates the observed CO; MOPITT has a high monthly difference ranging from -32% to +57% while WRF-Chem simulations underestimate CO from February to June and overestimate in other months. In contrast, CO estimated from MLR replicates diurnal and monthly variations and estimates CO with an r² > 0.8 for 2014-2017. The CO predicted during 2018 closely follows the observed variations, and its mixing ratios lie within ±17% of the observed CO. The results reveal a unimodal diurnal variation of CO, CO_ff (ff: fossil fuel) and CO_bb (bb: biomass burning) governed by the boundary layer evolution and upslope winds. CO_ff has a higher diurnal amplitude (39.1-67.8 ppb) than CO_bb (5.7-33.5 ppb). Overall, CO_ff is the major contributor (27%) in CO after its background fraction (58%). CO_bb fraction reaches a maximum (28%) during spring, a period of increased agricultural and forest fires in Northern India. In comparison, WRF-Chem tracer runs underestimate CO_bb (-38% to -98%) while they overestimate the anthropogenic CO during monsoon. This study thus attempts to address the lack of continuous CO monitoring and the need to segregate its fossil fuel and biomass sources, specifically over the Central Himalayas, by employing a methodology that utilizes the existing network of eBC observations.

Keywords: Biomass burning; Black carbon; Carbon monoxide; Fossil fuel combustion; Himalaya.

MeSH terms

Aerosols / analysis
Air Pollutants* / analysis
Biomass
Carbon / analysis
Environmental Monitoring / methods
Fossil Fuels / analysis
Seasons
Wildfires*

Substances

Air Pollutants
Fossil Fuels
Aerosols
Carbon