An overview of non-road equipment emissions in China

As the vehicle population has dramatically increased in China in the past two decades, vehicle emissions have become one of the major sources to air pollution across the entire country, especially for the metropolitan cities such as Beijing and Shanghai. Most of the non-road equipment are diesel-fueled and have been proved to be a key source for NOx and PM emissions, contributing significantly to the formation of haze/smog. Therefore, an accurate estimation of emission inventory from non-road equipment is essential for air quality improvement policy making, which mainly depends on the data availability of equipment population, activity, and emissions factor. Compared to on-road vehicles, less studies regarding emissions characterization have been conducted and investigated for non-road mobile sources in China. Thus, in order to identify the data gaps and future research needs, the objective of this study is to review the current status of research in non-road mobile emissions. Five types of non-road equipment were addressed in this study, including agricultural equipment, industrial equipment, river/ocean-going vessels, locomotives, and commercial airplanes, with a focus on the former two. The equipment are further classified mainly based on national standards and data availability to account for fuel type, job duties and others. This investigation has found that the research regarding emissions from non-road equipment is still at its early stage and there is a huge data gap for both activity and emissions factors. For most of the study, data used for emission inventory estimation were based on either literature with similar equipment or as-developed emissions models such as NONROAD or CORPERT. The representativeness of these data to the localities was not much discussed in those studies, which might have weakened the accuracy of the estimated emission inventory. For future study, real-world in-use measurements of activities and emissions for the non-road equipment are desperately needed. (C) 2016 Elsevier Ltd. All rights reserved.