Improving Light-Dependent Monoterpene Emission Parameterizations to Enhance Chemical Transport Model Predictions of Ozone and SOA in South China

Abstract

Biogenic volatile organic compounds (BVOCs) play a crucial role in atmospheric photochemical, with monoterpenes (MTs) accounting for approximately 15% of total BVOC emissions. However, the mechanisms underlying MT production in plants and their subsequent release into the atmosphere remain poorly understood. Existing parameterization schemes for MT emissions in BVOC models require refinement, particularly in tropical and subtropical regions where recent field observations provide new insights. In this study, the light-temperature dependence algorithm in MEGAN2.1 was updated based on in situ measurements. The revised algorithm significantly reduced estimated MT emissions in South China, with reductions ranging from ∼9% (∼8 Gg) in summer to ∼60% (∼6.5 Gg) in winter over Guangdong province. As a result, chemical transport model simulations showed that the maximum daily 8-hr average (MDA8) ozone (O₃) concentrations decreased by 3%–5% (1.0–1.5 ppb), with a maximum reduction of up to 7% (2 ppb). Secondary organic aerosols (SOA) concentrations declined by 10%–20% (0.5–1 μg/m³), with the maximum reduction of 24% (1.2 μg/m³) in summer 2022. These decreases were primarily driven by a reduction in atmospheric oxidation capacity (AOC). Moreover, the revised algorithm amplified reduction of MTs-derived SOA through various formation pathways. These findings suggest that previous models may overestimated BVOC emissions, leading to inflated predictions of both O₃ and SOA concentrations. This overestimation could be further exacerbated by AOC changes and shifts in SOA formation pathways associated with MT emissions.