INTEGRATING CONTINUOUS POLLUTANT MEASUREMENTS WITH TIME-LAPSE PHOTOGRAPHY TO EVALUATE INLAND VESSEL SURROUNDINGS' INFLUENCE ON WHEELHOUSE INDOOR AIR QUALITY

Abstract

The indoor air quality in the wheelhouse of an inland tanker was assessed through monitoring campaigns. The continuous-time measurements gathered data on NO₂, O₃, NO, CO, total volatile organic compounds (TVOC), and particulate matter (PM_2.5). The time series exhibited irregular concentration profiles characterized by narrow and broader peaks atop a gradually fluctuating baseline. These peaks denote sudden environmental changes, occurring within specific time frames and locations, indicating moments of poorer indoor air quality. The synchrony between peaks of different pollutants suggests that many of the narrow pollution peaks originate from exhaust emissions. Previous research has indicated that exhaust gas in outdoor air could infiltrate the wheelhouse via the ventilation system. However, multiple factors within the ship's vicinity (e.g., nearby industries, specific manoeuvres, or passing vessels) could also contribute to the occurrence of pollution peaks in the wheelhouse. To explore the synchrony between pollution peaks in the wheelhouse and events in the surroundings of the ship, a time-lapse camera capturing time-stamped images of the ship's front view has been installed. Analysis of these images in conjunction with the simultaneous occurrence of pollution peaks as observed in time series indicates the existence of multiple pollution sources influencing the indoor air quality in the wheelhouse. The various sources of pollution form an interlinked network of hazards that collectively influence indoor air quality. Each source has the potential to induce changes within this network and can, to a certain extent, affect other hazards. Furthermore, non-polluting elements within this network also contribute significantly to the variable behaviour of the network. For example, crew decisions regarding navigation and manoeuvers play affect the dynamics of this network.