A new approach to make indoor air quality in the accommodation of ships understandable and actionable for seafaring staff

Resumen

Today’s society is increasingly aware of the impact of air quality on human life. Air quality in and around ships is a challenging subfield because pollution is aggravated by cargo vapours, exhaust emission and even cooking on board. The assessment of the air quality requires substantial chemical analyses at several locations over prolonged periods. In addition, the huge amounts of collected data and the complexity of the underlying relationships are important barriers for persons not trained in data science. The situation is aggravated by the plethora of guidelines, standards, recommendations, and legislations from several countries and organizations specifying permitted exposure limits. These criteria often result in contradicting information, confusing seafarers. The purpose of this study is to develop a mathematical method to translate all this complex data and opinions into actionable information, easy to understand for non-specialists. We developed a mathematical algorithm were all these opinions were brought together in a statistical model, resulting in a more nuanced interpretation. The concentration values of the pollutants is associated with an estimated risk-index. The levels of risk are presented in a simplified way using colour-maps. The method developed was applied on a dataset obtained from a measuring campaign performed on a research vessel, sailing close to the Belgian coast. Multiple parameters such as NO2, NO, CO2, CO SO2, O3 and H2S concentrations were analysed during the time of the measuring campaign. In this contribution, we will present the risk assessment we derived during the measuring campaign and the actionable interpretations we derived from them