Heavy-duty diesel vehicles are important sources of urban nitrogen oxides (NO_x) in actual applications for environmental compliance, emitting more than 80% of NO_x and more than 90% of particulate matter (PM) in total vehicle emissions. The detection and control of heavy-duty diesel emissions are critical for protecting public health. Currently, vehicles on the road must be regularly tested, every six months or once a year, to filter out high-emission mobile sources at vehicle inspection stations. However, it is difficult to effectively screen high-emission vehicles in time with a long interval between annual inspections, and the fixed threshold cannot adapt to the dynamic changes of vehicle driving conditions. An on-board diagnostic device (OBD) is installed inside the vehicle and can record the vehicle’s emission data in real time. In this paper, we propose a temporal optimization long short-term memory (LSTM) and adaptive dynamic threshold approach to identify heavy-duty high-emitters by using OBD data, which can continuously track and record the emission status in real time. First, a temporal optimization LSTM emission prediction model is established to solve the attention bias discrepancy problem on time steps that is caused by the large number of OBD data streams in practice. Then, the concentration prediction error sequence is detected and distinguished from the anomalous emission contexts using flexible criteria, calculated by an adaptive dynamic threshold with changing driving conditions. Finally, a similarity metric strategy for the time series is introduced to correct some pseudo anomalous results. Experiments on three real OBD time-series emission datasets demonstrate that our method can achieve high accuracy anomalous emission identification.

重型柴油车是环境达标实际应用中城市氮氧化物(NO _{x )的}重要来源，其排放量占车辆总排放量的80%以上，占颗粒物(PM)的90%以上。重型柴油机排放的检测和控制对于保护公众健康至关重要。目前，道路上的车辆必须定期进行检测，每六个月或每年一次，以过滤车辆检查站的高排放移动源。但年检间隔较长，难以及时有效筛查高排放车辆，且固定门槛无法适应车辆行驶工况的动态变化。车载诊断设备（OBD）安装在车辆内部，可以实时记录车辆的排放数据。在本文中，我们提出了一种时间优化长短期记忆（LSTM）和自适应动态阈值方法，利用OBD数据来识别重型高排放者，可以连续实时跟踪和记录排放状态。首先，建立时间优化LSTM排放预测模型，以解决实际中大量OBD数据流引起的时间步上的注意力偏差差异问题。然后，使用灵活的标准检测浓度预测误差序列并将其与异常排放环境区分开来，这些标准是通过随驾驶条件变化的自适应动态阈值计算的。最后，引入时间序列的相似性度量策略来纠正一些伪异常结果。在三个真实OBD时间序列排放数据集上的实验表明，我们的方法可以实现高精度的异常排放识别。