Identification and detection of high NO x emitting inland ships using multi-source shore-based monitoring data

Abstract

In urban areas situated along busy waterways like the Yangtze River, the diesel engines of inland navigation ships emerge as significant contributors to air pollution. Among these vessels, certain high-emission ships exhibit considerably higher levels of nitrogen oxides (NOx) emissions compared to others. To effectively identify such ships, this study employed a cost-effective ship emission monitoring sensor platform, comprising high-precision gas sensors, automatic identification system receiver, and sensitive meteorological sensors, along the Yangtze River in Wuhan City. By combining multi-source shore-based monitoring data, we identified ship emission signals and proposed a high-emission ship detection method using inverse modeling. Using this method, we successfully detected inland high-emission ships based on two months of monitoring data. Furthermore, the relationship between different ship types, sizes, speeds, and ship NO x emission rates were investigated. The results of this study are beneficial for strengthening the regulation of high-emission vessels in inland waterways, thereby reducing the adverse impact of ship emissions on the environment and climate. It also encourages the inland shipping industry to adopt more environmentally friendly technologies and fuels, as advocated by the International Maritime Organization.