| In recent years, with a rapid development of China’s economy, the industrial product is greatly increased. Due to the immature technology of the industrial waste treatment and the extensive modes of production, the environmental pollution is serious and the environment worsens rapidly. In 2013, China’s primary energy production was 3364520 kiloton of standard coal by the electric heating equivalent calculation method. Raw coal production accounts for 80.4% of the total energy production, while more than 85% of the coal is burned directly. The form of direct combustion not only has a low combustion efficiency and considerable amounts of carbon, but also will produces a large number of sulfur dioxide, carbon dioxide, soot and volatile organic compounds,ect. These factors cause acid rain and the air pollution by coal based in china. The volatile organic compounds produced by the combustion of fuel are important precursors of ozone and PM2.5. The controlling of volatile organic compounds helps to reduce the ozone and haze pollution.VOCs emission inventory is the estimates on the emission of volatile organic compounds in a certain period of time that is based on the emission coefficient and the activity level of volatile organic compounds. Based on the above, the main emission sources of VOCs in the air could be identified. It is of great significance to establish a VOCs emission inventory for controlling the secondary pollution, guiding the related industries to control the emission of VOCs, the setting of the air standard and the work on the atmospheric source apportionment. So that we can guide the adjustment of energy structure, lay a solid foundation for environmental protection and sustainable development and reduce the probability of complex air pollution.The study introduced the harm of VOCs and the importance of the establishment of VOCs emission inventory. The study used the emission coefficient method to set up a 2013-based VOCs emission inventory of the sources from the energy production industry in the Northeast China. The study also analyzed the VOCs’ share rate of the sources from different industries and different provinces. Using the Monte Carlo numerical analysis method to analyze the uncertainty of the VOCs emission inventory.The VOCs emission inventory shows that the total VOCs emission of the sources from the energy production industry in the Northeast China in 2013 is 96.300 kt. The coke production industry was the largest contributor in the energy production industry emission sources, which emission was 35.815 kt, then the petroleum refining industry, which emission was 27.179 kt, then the crude oil production industry, which emission was 20.220 kt, then the thermal power industry, which emission was 12.828 kt, and the lowest is natural gas production industry, which emission was 0.258 kt. VOCs emission in Liaoning province is the largest, which emission was 51.319 kt, then the Heilongjiang Province, which emission was 31.630 kt, and the lowest was Jilin Province, which emission was 13.351 kt.The results of uncertainty analysis shows that the uncertainty of the emission inventory comes from the emission factor, the level of activity and the removal efficiency. Use the Monte Carlo numerical analysis method to transmit the uncertainty of the data we got a lognormal distribution emission inventory uncertainty. The calculation results are as follow: The coke production industry’s mean value was 35.61 kt, the average value was 50.09 kt, and the 95% confidence interval was(48.01, 52.17), and the uncertainty was ±94.6%. The petroleum refining industry’s mean value was 26.14 kt, the average value was 37.90 kt, and the 95% confidence interval was(36.27, 39.54), and the uncertainty was ±98.5%. The crude oil production industry’s mean value was 19.75 kt, the average value was 28.35 kt, and the 95% confidence interval was(27.09, 29.60), and the uncertainty was ±101.1%. The thermal power industry’s mean value was 12.76 kt, the average value was 17.95 kt, and the 95% confidence interval was(17.20, 18.69), and the uncertainty was ±94.5%. The natural gas production industry’s mean value was 0.25 kt, the average value was 0.36 kt, and the 95% confidence interval was(0.35, 0.38), and the uncertainty was ±95.9%. The calculated results are close to the median values. The Monte Carlo model prediction results(mean values) are higher than the calculated results due to the transfer of data uncertainty. |
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