Optimal estimation of initial concentrations and emission sources with 4D-Var for air pollution prediction in a 2D transport model.

Attributing sources of air pollution events by deploying an efficient observational network is an important and interesting problem in air quality control and forecast studies, but it is very challenging. In order to estimate the sensitivities of pollution events to emission sources, a comprehensive framework is built based on a horizontal 2-dimensional transport model and its adjoint in solving this problem. In an analysis of an idealized air pollution event of PM2.5 over the region of North China, an objective function is defined to optimally estimate the initial concentrations and emission sources through a series of minimization procedures. Results by means of the 4-dimensional variational approach show that, with the optimal initial conditions and emission sources, the model can successfully forecast the pollution event in a few days. The optimal observing network based on sensitivity analysis takes only one third of the cost but greatly retains predictability skill compared to the full-grid observing system, while nearly no predictability skill is detectable if the same number of observational sites is randomly deployed. We evaluate air pollution predictability in the point of focusing on to what degree the root mean square errors between the modeled concentration and the targeted air pollution are limited by the optimal observational network. Results show that air pollution predictability in association with the optimal observational network is limited in the time scales about 6 days. With the high efficiency and in an economic fashion, such a sensitivity-based optimal observing system holds promise for accurately predicting an air pollution event in the targeted area once the adjoint and variational procedure of a realistic atmosphere model including transport and chemical processes is performed.

Inertial particle segregation by turbulence.

We study collections of heavy and light small spherical particles initially well mixed with each other, subjected to linear (Stokes) drag force and gravity, and falling through a fluid turbulence. We introduce the segregation power spectrum, which we use to define the segregation length scale. Kinematic simulation predicts that the turbulence can segregate heavy and light falling particles and leads to a well-defined segregation length scale. The properties of this length scale and of the segregation power spectrum used to define it are discussed and, where possible, explained.