Work

Poorly Constrained Planetary Boundary Layer Height and Emissions Factors Drive High Uncertainty in Atmospheric Chemical Modeling of Emissions from Forests

Year: 2026

Type: article

Abstract: The impact of emissions on atmospheric chemistry requires a detailed chemical understanding of their transformation and fate. However, there are many sources of potential uncertainty in modeling the life cycle and transformation of biogenic volatile organic compounds (BVOC). This study investigates the sensitivity of a 0-D box model to several sources of potential uncertainty at a southeastern U.S. forest site over the summer season, including mixing ratios of critical trace oxidants (O3 and NO2), planetary boundary layer height (PBLH), and the factors that control emission rates. Within this framework, our results indicate that the model is relatively insensitive to the tested O3 input. Meanwhile NO2 input, evaluated here as a bounding comparison between rural and urban conditions, produces a substantial effect on modeled BVOC mixing ratios. Differences among currently available PBLH data sets emerged as a major source of overall uncertainty of comparable or greater magnitude than the NO2 bounding test. Daytime and nighttime model-observation correlation and bias varied considerably depending on PBLH data set and compound, with the largest discrepancies and lowest correlations occurring at night, highlighting particular challenges in modeling nocturnal BVOC mixing ratios. Compound-specific biases also varied substantially: isoprene and α-pinene, the two best-studied compounds, showed moderate bias across most scenarios, while sabinene showed substantially larger, consistent underprediction, suggesting poorly constrained emission factors for this compound specifically. These results highlight the sensitivities of this modeling framework to PBLH, oxidant mixing ratios, and emission factor uncertainty, and underscore the value of observationally constraining PBLH and isomer-resolved BVOC mixing ratios to improve current biogenic emissions modeling.

Source: ACS ES&T Air

DOI: 10.1021/acsestair.6c00165