Formaldehyde emissions in tanker truck cabins: Observation, deep learning prediction and health risk assessment.

Tanker trucks are indispensable for petroleum distribution, with driver comfort and health in the cabin microenvironment closely tied to road safety. However, long-term monitoring and prediction methods for in-cabin air quality in tanker trucks remain underexplored. This study makes the first attempt to quantify formaldehyde emission dynamics and identify key influencing factors in a new tanker truck cabin. Over one month of field campaign, the time-resolved formaldehyde concentrations in the cabin were obtained and analyzed, revealing an exceeding rate of 83.5 % according to the threshold (0.1 mg/m3) of WHO guidelines. Higher and more widely distributed formaldehyde concentrations were observed on sunny days, with lower levels on cloudy and rainy days. Material surface temperature was identified as the primary factor driving formaldehyde emissions via feature importance analysis. We then developed a deep learning model to predict long-term formaldehyde dynamics, which demonstrated excellent prediction accuracy by compared with traditional models. Exposure assessment among varied vehicle cabins highlighted significant health risks in truck cabins. This study provides the first real-world data and modelling on formaldehyde dynamics in tanker trucks, indicating the necessity of source characterization and control to keep drivers healthy.