State Key Joint Laboratory of ESPC, School of Environment, Tsinghua University, Beijing, 100084, China.
Nat Commun. 2020 Jun 3;11(1):2801. doi: 10.1038/s41467-020-16579-w.
Estimating truck emissions accurately would benefit atmospheric research and public health protection. Here, we developed a full-sample enumeration approach TrackATruck to bridge low-frequency but full-size vehicles driving big data to high-resolution emission inventories. Based on 19 billion trajectories, we show how big the emission difference could be using different approaches: 99% variation coefficients on regional total (including 31% emissions from non-local trucks), and ± as large as 15 times on individual counties. Even if total amounts are set the same, the emissions on primary cargo routes were underestimated in the former by a multiple of 2-10 using aggregated approaches. Time allocation proxies are generated, indicating the importance of day-to-day estimation because the variation reached 26-fold. Low emission zone policy reduced emissions in the zone, but raised emissions in upwind areas in Beijing's case. Comprehensive measures should be considered, e.g. the demand-side optimization.
准确估算卡车排放将有益于大气研究和公众健康保护。在这里，我们开发了一种全样本枚举方法 TrackATruck，将低频但全尺寸车辆的大数据与高分辨率排放清单联系起来。基于 190 亿条轨迹，我们展示了不同方法的排放差异有多大：区域总量的变异系数为 99%（包括 31%的非本地卡车排放），个别县的变异系数高达±15 倍。即使总量相同，使用聚合方法，主要货物运输路线的排放量也会被低估 2-10 倍。生成了时间分配代理，表明了日常估算的重要性，因为变化幅度达到了 26 倍。在北京市的案例中，低排放区政策减少了排放区的排放量，但增加了上风区的排放量。应考虑采取综合措施，例如需求方优化。
