Self-organized Evolution Of Surface Ozone In A Typical City

Ground-level O3 is an important component of the air pollution phenomenon and popular topic in China, which as concerns have been raised about the health impacts and plant growth by high concentration of O3.The ground-level O3 pollution index,referential parameter describing ground-level O3 pollution. The accurate ground-level O3 forecast can help to develop effective warning strategies to reduce impacts on public health during episodes or poor air quality. It seems evident that the understanding of the complex dynamic characteristics of ground-level O3 can contribute to developing advanced techniques for air pollution precastingThe influence of ground-level O3 is not only the pollutant source, but also many complex factors, such as the movements and transformation of O3, pollutants emission and climate condition. These factors are not mutually independent, there are many nonlinear interactions among those factors in different time scales, which result in the mechanisms of O3 pollutant temporal evolutions are very sophisticated and not very clear up to now. According to the complexity of the O3 evolution, first of all, the nonlinear characteristics with time variation of O3 was analyzed, then the detrened cross-correlation analysis(DCCA) was introduced, which was used to study cross-correlation of O3 and NO2 time series in different season and weather. Farther more, the dynamic mechanism was discussed deeply about the O3 by self-organized criticality. Finally, to motive comparisons between O3 pollution and SOC rice pile, the SOC of the O3 was studied to reveal the micro mechanisms of O3 concentrations evolution. In this paper, the examined data set is daily O3 indexes in Chengdu from Oct. 1, 2013 to Dec. 31, 2014.The main works and conclusions in this paper are as follows:1. Statistical analysis was conducted on the characteristics of ozone concentration at different time scales in Chengdu. It finds that the highest concentration of ozone was always appeared during 3 to 5 PM, and it reachs lowest concentration in the small hours. Monthly average of ozone concentration in the summer half year is markedly higher than that of winter half year, presents the characteristic of summer > spring > autumn > winter. O3 concentration effect is not obvious at the weekend; weekday O3 concentration was slightly lower than the weekend. The basic statistics of O3 concentration were calculated, the result show that the O3 data sets deviate from the standard normal distribution, and put up the fat fail distribution, indicating that O3 time series character by nonlinear.2. The correlations between ground-level NO2 and O3 concentration are analyzed by the method of detrended cross-correlation analysis. The results show that the correlation between NO2 and O3 concentration are characterized by long-term persistence. At same time, a comparative study of the correlation of NO2 and O3 in summer and winter is made, and the reasons for the differences are analyzed. It shows that were significant differences in meteorological conditions on the different time scaling of the interaction between NO2 and O3. It was found that the differences are due to solar radiation, sunshine hours and temperature. Go a step further, the Shapiro-Wilk test was used to examine the distribution of the index value d between between NO2 and O3 in different conditions, there is spatial distribution accord with normal distribution in space.3. The correlations between ground-level O3 concentration and meteorological factors are analyzed by the method of detrended cross-correlation analysis. The results reveal that the correlation between O3 concentration and meteorological factors are characterized by long-term persistence, high temperature, wind speed long to the meteorological conditions, such as high concentration ozone synthesis is bigger.4. The temporal scaling properties of daily ozone average concentration series at six air monitoring stations of Chengdu were explored by using frequency-size analysis. The results show that the probability functions of O3 concentrations are characterized by power-law distribution, which is similar to the Gutenberg-Richter law in the earthquakes study, and suggesting there is inherent dynamical connection between small and high events of O3 pollution. Further, I want to find a answers on the emergence of long-term correlation and power-law distribution in O3 by SOC framework specially designed to model the dynamics of complex systems. It shows that the high O3 concentration act as dynamically SOC and the SOC of O3 that results in the temporal variation of O3 indexes. Therefore, this maybe indicates that a vast amount of minor air pollution sources, such as home kitchens, hotel and traffic sources, also can result in high events of air pollution by SOC behaviors.In conclusion, we hope this study will be of help to further understanding of the O3 pollution evolution process, the studies in terms of methodology and results were conducive to improving the forecast modeling of ground-level O3 concentrations and it has the theoretical and practical significance for studding high O3.