同化反演方法在污染源清单更新中的应用

Application of data assimilation method in updating emission inventory

污染源同化反演方法是利用观测数据对污染源进行"自上而下"约束更新,可为大气污染源清单提供一种快速校验和更新的方法,还可为"自下而上"的污染源清单补充更多时空分布信息。综述了质量平衡法、集合卡尔曼滤波法和四维变分法等同化反演方法的基本原理及在污染源清单反演优化中的应用方法;介绍了观测数据、初始污染源清单的获取和其不确定性的计算方法以及污染源与观测数据之间关系的获取方法;总结了污染源同化反演方法优化在不同尺度污染源清单反演中的应用,以及在国内外的应用情况。采用集合卡尔曼滤波法或四维变分法并利用伴随模式或去耦合直接法确定污染源与观测数据的关系,可在一定程度上避免模式中的非线性问题,但提高污染源同化反演效果仍需在观测数据质量和模式模拟性能方面进一步改进。

Data assimilation is to use the observed data to update the pollution sources from top to bottom,which could provide a fast way to update and verify the emission inventories,and also add further information about the temporal variation and some sources that could not easily be quantified by traditional bottom-up methods.The basic principles of several assimilation inversion methods,including the mass balance,ensemble Calman filter and four-dimensional variation methods were reviewed,and their application methods in emission inventory inversion and optimization introduced.The acquisition method of observation data and the primary emission inventory,the calculation method of uncertainty,and the acquisition method of the relationship between pollution sources and observation data were also introduced.The application of pollution source assimilation inversion methods at different scales of pollution source inventory inversion,both at home and abroad,were summarized.The ensemble Kalman filter or the 4D variation method combined with adjoint model or decoupling direct method was used to determine the relationship between pollution sources and observation data,able to avoid the nonlinearities in the model to a certain extent.The more accurate observations and more precise air quality model were needed in the future to improve the effect of pollution sources inversion.