Inverse Modeling of Formaldehyde Emissions and Assessment of Associated Cumulative Ambient Air Exposures at Fine Scale

Abstract

Among air toxics, formaldehyde (HCHO) is an important contributor to urban cancer risk. Emissions of HCHO in the United States are systematically under-reported and may enhance atmospheric ozone and particulate matter, intensifying their impacts on human health. During the 2021 Michigan-Ontario Ozone Source Experiment (MOOSE), mobile real-time (~1 s frequency) measurements of ozone, nitrogen oxides, and organic compounds were conducted in an industrialized area in metropolitan Detroit. The measured concentrations were used to infer ground-level and elevated emissions of HCHO, CO, and NO from multiple sources at a fine scale (400 m horizontal resolution) based on the 4D variational data assimilation technique and the MicroFACT air quality model. Cumulative exposure to HCHO from multiple sources of both primary (directly emitted) and secondary (atmospherically formed) HCHO was then simulated assuming emissions inferred from inverse modeling. Model-inferred HCHO emissions from larger industrial facilities were greater than 1 US ton per year while corresponding emission ratios of HCHO to CO in combustion sources were roughly 2 to 5%. Moreover, simulated ambient HCHO concentrations depended significantly on wind direction relative to the largest sources. The model helped to explain the observed HCHO concentration gradient between monitoring stations at Dearborn and River Rouge in 2021.

Keywords

• formaldehyde
• inverse modeling
• cumulative exposure
• air quality models
• emissions inventories
• air toxics