Concentration and source rate of precursor vapors participating in particle formation and subsequent growth were investigated during the Pearl River Delta intensive campaign （PRD2004, October 2004） in southeastern China. Four new particle formation event days and a typical non-event day were selected for our analysis. Atmospheric sulphuric acid, the important precursor vapor in nucleation and growth, were simulated with a pseudo steady-state model based on the measurements of SO2, NOx, 03, CO, non-methane hydrocarbon （NMHC） and ambient particle number concentrations as well as modeled photolysis frequencies obtained from measurements. The maximum midday sulphuric acid concentrations vary from 4.53 × 10^7 to 2.17 × 10^8 molecules cm^-3, the corresponding source rate via reaction of OH and SO2 range between 2.37 × 10^6 and 1.16 × 10^7 molecules cm^-3 s^-1. Nucleation mode growth rate was derived from size spectral evolution during the events to be 6.8-13.8 nm h^-1. Based on the growth rate, concentration of the vapors participating in subsequent growth were estimated to vary from 1.32 × 10^8 to 2.80 × 10^8 molecules cm^-3 with corresponding source rate between 7.26 × 10^6 and 1.64 × 10^7 molecules cm^-3 s^-1. Our results show the degree of pollution is larger in PRD. Sulphuric acid concentrations are fairly high and have a close correlation with new particle formation events. Budget analysis shows that sulphuric acid alone is not enough for required growth; other nonvolatile vapors are needed. However, sulphuric acid plays an important role in growth; the contribution of sulphuric acid to growth in PRD is 12.4%-65.2%.
The optimum planning of industrial pollutant sources, which optimizes the economic object without violating environmental constraints, is an important and hard task to be conquered. In this paper, an adjoint method is developed to solve the problem. The penalty function is in-troduced to deal with the environmental inequality constraints, and Lagrange function is con-structed to derive the adjoint equation and the gradient of the object function. In this means, the gradient of the object function can be calculated by solving the adjoint equation, and the infor-mation from the gradient is used to make the object function descend and approach to an optimal solution after some iterations. A two-dimensional, simplified model is used for numerical experi-ments. The theoretical derivations are verified by the results of the experiments. Furthermore, the adjoint method is shown to be of excellent convergence and efficiency, which is adaptive to the fast development of air quality numerical models and super computers.
LIU Feng1,2, HU Fei2 & ZHU Jiang2 1. College of Environmental Sciences, Peking University, Beijing 100871, China
