山东省机动车污染物排放演变研究

发布时间：2018-06-28 11:12

本文选题：机动车 + 排放清单　；参考：《济南大学》2017年硕士论文

【摘要】：改革开放以来,山东省机动车保有量逐年攀升,由此引发的区域复合型大气污染已成为当地面临的典型而严峻的环境问题。建立山东省机动车排放清单是把握当地机动车污染特征、识别影响机动车排放的关键因素、制定合理有效的机动车排放控制措施的基础。本文综合考虑山东省机动车地域特点建立了山东省2000~2014年机动车排放清单,并分析了区域内机动车的排放特征;运用LMDI法构建了机动车排放因素分解模型,考察了技术效应、里程效应、结构效应和规模效应对机动车排放的贡献;最后基于情景分析法,预测了山东省2020年机动车的排放状况,评价了各类控制措施的削减效果与协同效应。本文的主要研究结论如下:排放清单结果表明研究期内山东省机动车氮氧化物(NO_X)、可吸入颗粒物(PM_(10))、二氧化碳(CO_2)、甲烷(CH_4)和氧化亚氮(N_2O)的排放量分别从17.70、1.24、1923.97、1.13和0.61万吨上升至51.38、2.95、13841.95、1.53和3.87万吨,一氧化碳(CO)、非甲烷挥发性有机物(NMVOC)则分别由173.45和27.79万吨下降至172.33和23.42万吨。从排放总量趋势来看,山东省机动车CO、NMVOC、CH_4的排放先增后降,NO_X和PM_(10)排放前期增长迅速后期增势开始放缓,CO_2排放量一直处于高速增长状态,N_2O排放则表现出了波动增长的态势。从车型贡献来看,CO、NMVOC和CH_4的排放主要来源于轻型载客车和摩托车,NO_X和PM_(10)主要排放源为重型载货车,CO_2的主要排放源是轻型载客车和重型载货车,N_2O的排放则主要来自于轻型载客车与轻型载货车。从地区分担来看,污染物主要集中于济南、青岛、烟台、潍坊、济宁和临沂,研究期内机动车CO和NMVOC的排放在部分地区有所下降,NO_X和PM_(10)的排放在所有地区均有所上升。从空间分布来看,山东省机动车排放较高的区域集中在东部与中部,在空间上呈现出自市区向郊区的递减趋势。因素分解结果表明,N_2O中结构效应对排放增长累积贡献最大,其他污染物中规模效应则是排放最重要的驱动效应。技术效应对大部分污染物而言是非常重要的排放抑制效应。规模效应对各类污染物在所有年份都产生了驱动效应,且在初期对排放驱动的逐年效应较大,在后期对排放增量的贡献逐渐降低。多数情况下,技术效应在初期表现较弱,后期对排放抑制的逐年效应会有所上升。情景分析结果表明,基准情景下2020年山东省机动车CO、NMVOC、NO_X、PM_(10)、CO_2、CH_4、N_2O的排放量分别为142.91、20.9、62.71、3.37、23047.07、1.58、0.51万吨。单一控制措施情景中提高排放标准和老旧车辆淘汰的减排效果较为明显,常规控制措施情景能有效减少多数污染物的排放,综合控制措施情景则可以达到最佳的减排效果。协同效应评价显示提高排放标准和老旧车辆淘汰对传统污染物的减排效果要强于对温室气体的减排,公共交通普及、新能源车推广和行驶条件改善对温室气体的减排效果要比对传统污染物更好。常规控制措施情景和综合控制措施情景对传统污染物的减排效果都强于对温室气体的减排,但综合控制措施情景的协同效应要好于常规控制措施情景。
[Abstract]:Since the reform and opening up, the number of motor vehicles in Shandong has been increasing year by year. The resulting regional complex air pollution has become a typical and severe environmental problem in the local area. To establish a vehicle emission inventory in Shandong province is to grasp the characteristics of the local motor vehicle pollution, to identify the key factors affecting the emission of motor vehicles, and to formulate a reasonable and effective maneuver. On the basis of the vehicle emission control measures, in this paper, the vehicle emission list in Shandong province for 2000~2014 was established in Shandong Province, and the vehicle emission characteristics in the region were analyzed. The vehicle emission factor decomposition model was constructed by LMDI method, and the technical effect, the mileage effect, the structure effect and the scale effect were investigated. The contribution of motor vehicle emission; finally, based on the scenario analysis method, the vehicle emission status in Shandong Province in 2020 was predicted and the reduction effect and synergistic effect of various control measures were evaluated. The main conclusions of this paper are as follows: the emission inventory results show that in the study period, the nitrogen oxides (NO_X), inhalable particles (PM_ (10)), and two of the motor vehicles (10), two. Carbon oxide (CO_2), methane (CH_4) and Nitrous Oxide (N_2O) emissions increased from 17.70,1.241923.97,1.13 and 6 thousand and 100 tons to 51.38,2.9513841.95,1.53 and 38 thousand and 700 tons, carbon monoxide (CO) and non methane volatile organic compounds (NMVOC) from 173.45 and 277 thousand and 900 tons to 172.33 and 234 thousand and 200 tons, respectively. The emission of CO, NMVOC, CH_4 of motor vehicles in East Province increased first and then decreased, the increase of NO_X and PM_ (10) emissions began to slow down in the early stage of rapid growth, CO_2 emission was in a high speed growth state and N_2O emissions showed a trend of fluctuation. From the model contribution, the emissions of CO, NMVOC and CH_4 mainly came from light passenger cars and motorcycles, NO_X The main emission source of PM_ (10) is heavy truck. The main source of CO_2 emission is light carrier and heavy truck. The emission of N_2O mainly comes from light carrier and light truck. From the point of view of Regional Sharing, the pollutants are mainly concentrated in Ji'nan, Qingdao, Yantai, Weifang, Jining and Linyi, and the emission of vehicle CO and NMVOC during the study period. In some areas, the emission of NO_X and PM_ (10) increased in all regions. From the view of spatial distribution, the higher emission areas of motor vehicles in Shandong were concentrated in the East and the middle part, which showed a decreasing trend from the urban to the suburbs. The result of factor decomposition showed that the structural effect in N_2O contributed most to the accumulation of emissions. Large, scale effect in other pollutants is the most important driving effect of emission. Technical effect is a very important emission inhibition effect for most pollutants. Scale effect has a driving effect on all kinds of pollutants in all years, and the annual effect on emissions driven in the early stage is larger, and the contribution to the release increment in the later period is great. In most cases, the effect of technical effect is weaker in the early stage, and the effect of the later year on emission suppression will rise. The results of the scenario analysis show that the emission of CO, NMVOC, NO_X, PM_ (10), CO_2, CH_4, N_2O in 2020 under the baseline scenario is divided into 142.91,20.9,62.71,3.3723047.07,1.58,0.51 million tons. The effect of emission standard and old vehicle elimination is more obvious in the situation. The situation of conventional control measures can effectively reduce the emission of most of the pollutants. The situation of comprehensive control measures can achieve the best effect of emission reduction. The synergistic effect evaluation shows that the emission standards and the elimination of the old vehicles to the traditional pollutant emission reduction are improved. The effect is better than the emission reduction of greenhouse gases, public transportation is popularized, the effect of the promotion and driving conditions of new energy vehicles on greenhouse gas emission reduction is better than that of the traditional pollutants. The effect of conventional control measures and comprehensive control measures on the emission reduction of traditional pollutants is better than the emission reduction of greenhouse gases, but the comprehensive control measures are taken. The synergy effect of scenarios is better than that of conventional control measures.
【学位授予单位】：济南大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：X734.2

【参考文献】