Omi Based On Satellite Data And Numerical Simulation Of The Air So < Sub > 2 < / Sub > Emissions Concentration Monitoring And Estimation

Sulfur dioxide (SO2) is an important trace gas in the atmosphere and one of the main air pollutants, which has a serious impact on atmospheric environment and human health. The SO2in the atmosphere mainly derives from human discharge, and the process of the economic booming since reform and opening up in China is accompanied by the increase of SO2emission. The increasing emission of SO2leads to serious environmental pollution, and it has been regarded as a restricting environment factor in the development of Chinese economy. During the10th Five Year Plan (FYP) period, the totale missions of SO2increased by27.8%from19.95Mt to25.49Mt. In an attempt to address these challenges, the Chinese government published the11th FYP for national environment protection. The11th FYP included only one goal for air pollution control policy to reduce SO2emissions in2010by10%from the2005level.The spatiotemporal variation in SO2concentration during2005-2010over China was monitored from the planetary boundary layer (PBL) SO2column concentration retrieved from Aura ozone monitoring instrument (OMI) data, in this study. The Community Multi-Scale Air Quality (CMAQ) modeling system and the Weather Research and Forecasting (WRF) Model were applied to assess the source-receptor relationships of SO2, and a new methodology was developed to constrain the emissions of SO2based on OMI data, so as to provide theoretic reference for national emission control.The main studies and results are as follows.(1) The acquisition and procession of OMI data:The data used in this study are acquired from the OMI/Aura Level-2SO2data product between2005and2010. The study abandons data affected by large solar zenith angle and big cloud, rectifies the valid data by AMF, and calculates the average value of each month in different area in China. After comparing the SO2concentration data acquired from EANET china station and API data in key cities, this study concludes that the satellite data corresponds to the observed data in season trend well and has a good correlation.(2) The temporal and spatial distribution of SO2concentration in china based on OMI data:By the analysis of satellite data, the distribution of SO2in china has obvious temporal and spatial feature. In the spatial distribution, the volume of SO2 varies greatly in China, and there is a big difference between the east and the west. The concentration in the east is much higher than that of the west, with the maximum appeared in the border of ShanXi, HeBei and ShanDong provinces. In the seasonal variation, there is an obvious seasonal change in the east; the concentration is in the lowest in summer and in the highest in winter. Conversely, there is not an obvious seasonal change in the west; the concentration is in the highest in summer and in the lowest in winter. In the annual change trend, there is obvious concentration fluctuations in the five years, which is in the highest in2007and in the lowest in2009, yet restart to increase in2010. With the analysis of correlation between SO2concentration and energy consumption, it is found that the SO2concentration correlates best with coal consumptions, which indicates that the SO2in the atmosphere is mainly from coal burning.(3) The simulation of SO2concentration in china:Using data of INTEX-B as the emission scheme to the atmosphere, this study simulates the SO2concentration in different seasons in China by WRF and CMAQ model. It shows that the spatial distribution and the seasonal variation of simulation are similar to satellite monitoring results. The concentration of SO2in the east is much higher than that in the northwest, and winter is the season with highest concentration. The simulation also shows the daily variations of SO2concentration varies in different regions. The curve of Beijing is just like "N" with two peaks. In the vertical change, the SO2distributes mainly under800hpa. The SO2deposition area is the region with high SO2emission, mainly in mid-eastern China, and the heaviest deposition season is fall and winter.(4) Source-receptor relationships of SO2concentration and deposition in China. Four simulated schemes are designed to find the source-receptor relationships of SO2concentration and deposition in china. After the comparison of these schemes, it can be found that the100RM_S scheme is far good than others because of the better correlation and less errors. The error is mainly appeared on the surrounding area of simulated region. In most regions in china, especially the mid-eastern China where the SO2concentration is high, the error is under3%. So the source-receptor relationships were calculated by100RM_S model. From the results, it can be found that CTR area has most impact on its surrounding area, while SW area is mainly affected by the emission from its own. HeBei and Shanxi provinces have more impact on other regions, while Beijing and Tianjin cities are heavily influenced by their neighboring areas. Temporally, there is a specific source-receptor relationship in January, and its own emission will be the primary cause in July. SO2concentration has a specific source-receptor relationship, and SO2deposition is mainly determined by its own emission.(5) Estimation of SO2emission based on satellite data:SO2concentration in the atmosphere is controlled by some physical and chemical processes, such as surface emission, transport, chemical transform and dry deposition, called SO2budget. A new methodology was developed to constrain the emissions of SO2based on OMI data, Using the linear relationship between concentration and emission, and the source-receptor relationships of SO2. Based on the concentration and the source-receptor relationships simulated by CMAQ and OMI data in January, April, July and October,2006, the SO2emission in the East of China was estimated. It can be found that SO2emission from the statistic data is less than data of INTEX-B and estimation based on satellite. Assessments of the implications of SO2emissions usually are based on "bottom-up" inventories as estimated by using geographical and statistical data to extrapolate measurements of emission factors, typically available only on a sparse spatial and temporal network and subject to uncertainty."Top-down" constraints on SO2emissions through satellite observations could provide valuable data to inform emission inventory development and evaluation.