Spatiotemporal Analysis of Complex Emission Dynamics in Port Areas Using High-Density Air Sensor Network.

Abstract

Cargo terminals, as pivotal hubs of mechanical activities, maritime shipping, and land transportation, are significant sources of air pollutants, exhibiting considerable spatiotemporal heterogeneity due to the complex and irregular nature of emissions. This study employed a high-density air sensor network with 17 sites across four functional zones in two Shanghai cargo terminals to monitor NO and NO₂ concentrations with high spatiotemporal resolution post sensor data validation against regulatory monitoring stations. Notably, NO and NO₂ concentrations within the terminal surged during the night, peaking at 06:00 h, likely due to local regulations on heavy-duty diesel trucks. Spatial analysis revealed the highest NO concentrations in the core operational areas and adjacent roads, with significantly lower levels in the outer ring, indicating strong emission sources and limited dispersion. Employing the lowest percentile method for baseline extraction from high-resolution data, this study identified local emissions as the primary source of NO, constituting over 80% of total emissions. Elevated background concentrations of NO₂ suggested a gradual oxidation of NO into NO₂, with local emissions contributing to 32-70% of the total NO₂ concentration. These findings provide valuable insights into the NO and NO₂ emission characteristics across different terminal areas, aiding decision-makers in developing targeted emission control policies.