Source Quantification of South Asian Black Carbon Aerosols with Isotopes and Modeling.

Sanjeev Dasari,Andreas Stohl,Nikolaos Evangeliou,Srinivas Bikkina,August Andersson,Krishnakant Budhavant,Henry Holmstrand,Örjan Gustafsson,Abdus Salam

doi:10.1021/acs.est.0c02193

Abstract

Black carbon (BC) aerosols perturb climate and impoverish air quality/human health—affecting ∼1.5 billion people in South Asia. However, the lack of source-diagnostic observations of BC is hindering the evaluation of uncertain bottom-up emission inventories (EIs) and thereby also models/policies. Here, we present dual-isotope-based (Δ14C/δ13C) fingerprinting of wintertime BC at two receptor sites of the continental outflow. Our results show a remarkable similarity in contributions of biomass and fossil combustion, both from the site capturing the highly populated highly polluted Indo-Gangetic Plain footprint (IGP; Δ14C-fbiomass = 50 ± 3%) and the second site in the N. Indian Ocean representing a wider South Asian footprint (52 ± 6%). Yet, both sites reflect distinct δ13C-fingerprints, indicating a distinguishable contribution of C4-biomass burning from peninsular India (PI). Tailored-model-predicted season-averaged BC concentrations (700 ± 440 ng m–3) match observations (740 ± 250 ng m–3), however, unveiling a systematically increasing model-observation bias (+19% to −53%) through winter. Inclusion of BC from open burning alone does not reconcile predictions (fbiomass = 44 ± 8%) with observations. Direct source-segregated comparison reveals regional offsets in anthropogenic emission fluxes in EIs, overestimated fossil-BC in the IGP, and underestimated biomass-BC in PI, which contributes to the model-observation bias. This ground-truthing pinpoints uncertainties in BC emission sources, which benefit both climate/air-quality modeling and mitigation policies in South Asia.

Highlights

The high-intensity South Asian Pollution Experiment 2016 (SAPOEX-16) campaign was conducted from 4 January to 24 March 2016.27 Sampling was carried out at the Bangladesh Climate Observatory at Bhola (BCOB; 22.17°N, 90.71°E; 10 m agl), located on the remote southern end of the Bhola Island, which is on the outflow edge of the Indo-Gangetic Plain footprint (IGP), and at the Maldives Climate Observatory at Hanimaadhoo (MCOH; 6.78°N, 73.18°E; 1.5 m agl), located on the northern tip of a northern island in the northernmost atoll of the Republic of the Maldives, south of peninsular India (PI) (i.e., ∼ south of 23.4°N) in the N
Samples collected during January at both BCOB and MCOH were influenced by air masses from the IGP because of synoptic meteorology.[27]
During February and March, samples collected at MCOH were influenced by air masses received from the Arabian Sea (ARS) and South East Asia (SE Asia), respectively

Summary

■ INTRODUCTION

The impacts of combustion-derived black carbon (BC) on regional warming and effects on air quality and human health are large, yet remain highly uncertain, despite considerable recent scientific attention.[1−7] This is troublesome for highly populated, high-pollution regions such as South Asia.[1−3] BC is implicated to cause a multitude of secondary effects in this region such as monsoon shifts, increased frequencies of storms, surface dimming, melting of glaciers, and disturbance of precipitation patterns affecting both freshwater supply and agriculture.[1,4−6]. Multiple data points, suppression of the influence of endmember variability (e.g., partial overlapping δ13C-signatures of fossil coal and liquid fossil fuel; see Figure 2 and Supporting Information Table S5) on the computed relative source contributions is obtained, following the original method paper.[17] To examine different possible source combination scenarios, three different MCMC computations were carried out for each site (methodological details in Supporting Information Note S5). From these numerical simulations, statistical estimates of the relative source contributions (e.g., mean, standard deviation, and median) are obtained, while the estimated probability density functions are visualized (Supporting Information Figure S5). The potential source regions were identified with (i) cluster analysis, (ii) fractional cluster contributions, and (iii) concentrated weighted trajectory (CWT) analysis of BC concentrations (methodological details in Supporting Information Note S7)

■ RESULTS AND DISCUSSION

■ ACKNOWLEDGMENTS

■ REFERENCES