State Key Laboratory of Earth Surface Processes and Resource Ecology, and College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China.
State Key Laboratory of Earth Surface Processes and Resource Ecology, and College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China.
Environ Pollut. 2020 Dec;267:115164. doi: 10.1016/j.envpol.2020.115164. Epub 2020 Sep 2.
Carbon monoxide (CO) is an important gas that affects human health and causes air pollution. However, the estimates of CO emissions in China are still subject to large uncertainties. Based on the CO mass concentration and the coupled Weather Research and Forecast (WRF) and Stochastic Time-Inverted Lagrangian Transport (STILT) model (WRF-STILT), this study estimates the CO emissions over Zhengzhou, China. The results show that the mean CO mass concentration was 1.17 mg m from November 2017 to February 2018, with a clear diurnal variation. There were two periods of rapidly increasing CO concentration in the diurnal variation, which are 06:00-09:00 and 16:00-20:00 local time. The footprint analysis shows that the observation site is highly influenced by local emissions. The most influential regions to the site observations are northeast and northwest Zhengzhou, which are associated with the geographical barrier of the Taihang Mountains in the north and narrow Fenwei Plain in the west. The inversion result shows that the actual emissions are lower than the inventory estimates. Using the optimal scaling factors, the WRF-STILT simulations of CO concentration agree closely with the CO measurements with the linear fitting regression equation y = 0.87x + 0.15. The slopes of the linear fitting regressions between the WRF-STILT-simulated CO concentrations determined using the optimal emissions and the observations range from 0.72 to 0.89 for four months, and all the fitting results passed the significance test (P < 0.001). These results indicate that the new optimal emissions derived with the scaling factors could better represent the real emission conditions than the a priori emissions if the WRF-STILT model is assumed to be reliable.
一氧化碳（CO）是一种重要的气体，它会影响人类健康并造成空气污染。然而，中国 CO 排放量的估算仍存在较大不确定性。本研究基于 CO 质量浓度和耦合天气研究与预报（WRF）与随机时间反转拉格朗日输送（STILT）模型（WRF-STILT），估算了中国郑州的 CO 排放量。结果表明，2017 年 11 月至 2018 年 2 月期间，CO 质量浓度的平均值为 1.17mg/m3，呈明显的日变化特征。在日变化中，CO 浓度有两个快速上升的阶段，分别是当地时间 06:00-09:00 和 16:00-20:00。足迹分析表明，观测点受局地排放的影响较大。对站点观测影响最大的区域是郑州的东北部和西北部，这与北部的太行山地理屏障和西部狭窄的汾渭平原有关。反演结果表明，实际排放量低于清单估计值。利用最优缩放因子，WRF-STILT 模拟的 CO 浓度与 CO 测量值吻合较好，线性拟合回归方程为 y=0.87x+0.15。四个月中，使用最优排放源进行 WRF-STILT 模拟的 CO 浓度与观测值之间的线性拟合回归斜率范围为 0.72-0.89，所有拟合结果均通过了显著性检验（P<0.001）。这些结果表明，如果假设 WRF-STILT 模型是可靠的，那么使用缩放因子得出的新的最优排放源比先验排放源更能代表真实的排放情况。
