本文關(guān)鍵詞： 響應(yīng)曲面模型 CMAQ 細顆粒物 控制減排　出處：《浙江大學》2015年碩士論文　論文類型：學位論文

【摘要】：區(qū)域多尺度空氣質(zhì)量模型(CMAQ)是當前大氣環(huán)境管理和科研領(lǐng)域的重要研究工具,已經(jīng)在國內(nèi)外取得了較好的應(yīng)用。但是,由于CMAQ在實際使用過程中過于復雜、專業(yè)性太強,CMAQ的優(yōu)勢大打折扣。基于CMAQ實驗排放控制矩陣模擬得到的污染物濃度響應(yīng),統(tǒng)計歸納出各污染物排放控制因子比例與污染物濃度之間的函數(shù)關(guān)系,也即響應(yīng)曲面模型(RSM),可建立高效快速的“污染物排放-空氣質(zhì)量”響應(yīng)模型,提高空氣質(zhì)量預測的系統(tǒng)性能,方便快捷地評估減排措施對污染物的控制效果、為制定優(yōu)化控制方案提供科學依據(jù)。基于以上背景,本文開發(fā)了適用于杭州市細顆粒物(PM2.5)污染濃度預測的RSM。模型只需要輸入有機碳(OC),黑碳(BC),可吸入顆粒物(PM1o),交通源排放氮氧化物(NOx),電廠源排放NOx,工業(yè)源排放NOx,交通源排放二氧化硫(SO2),電廠源排放S02,氨(NH3)以及揮發(fā)性有機物(VOC)等10個控制因子的參數(shù)組合,便可快速響應(yīng)出該排放控制情景下對應(yīng)的PM2.5濃度變化。在通過留一法交叉驗證和外部驗證對本文所建立的RSM的可靠性進行評價之后,應(yīng)用該RSM對杭州市PM2.5的污染機制和來源進行了解析,評估了杭州市NH3排放與VOC排放對PM2.5的影響以及杭州市2020年的空氣質(zhì)量和減排政策的控制效果。研究的主要結(jié)論有：OC、BC口PM1o排放對PM2.5的貢獻具有線性響應(yīng)特征,NOx、SO2、NH3和VOC具有明顯的非線性響應(yīng)特征；同一污染物在不同季節(jié)對PM2.5的貢獻不一樣。杭州市冬季的PM2.5中一次污染物所占的比例比較多,夏季時,二次氣溶膠所占的比例遠遠大于一次組分；在冬季,控制OC、BC和PM1o的排放可以有效減少PM2.5污染,當控制政策收緊時,NOx與NH3的影響才能明顯顯現(xiàn)。夏季,PM1o對PM2.5污染影響依舊較大,電廠NOx和工業(yè)NOx的影響明顯高于其他。
[Abstract]:Multi scale model of regional air quality (CMAQ) is an important tool in the study of the atmospheric environmental management and scientific research, has achieved good application at home and abroad. However, due to the CMAQ in the actual use of the process is too complicated, too professional, the advantage of CMAQ greatly. CMAQ experimental emission control matrix simulation pollutant concentration obtained based on the response, summed up the statistical relationship between pollutant emission control factor and the proportion of pollutant concentration, i.e. the response surface model (RSM), can be established fast and efficient "emissions - air quality" response model, the system performance is improved air quality prediction, convenient to evaluate the control effect of pollutants emission reduction measures for the formulation, optimization control scheme and provide a scientific basis. Based on the above background, this paper developed for fine particles in Hangzhou city (PM2.5) pollution concentration forecast RSM. The model only need to enter the organic carbon (OC), black carbon (BC), respirable particulate matter (PM1o), traffic source emissions of nitrogen oxides (NOx), NOx power source emissions, NOx emissions from industrial sources, traffic source emissions of sulfur dioxide (SO2) emissions, power plant S02 (NH3), ammonia and volatile organic compound (VOC) parameters of 10 control factors, can quickly respond to changes in concentration of PM2.5 corresponding to the emission control scenarios. After the evaluation of reliability by leave one out cross validation and external validation to the establishment of RSM, application of pollution mechanism and source of the RSM PM2.5 in Hangzhou City the analysis, evaluation of control effect on PM2.5 NH3 emissions and VOC emissions in Hangzhou city and Hangzhou city in 2020 the air quality and emission reduction policy. The main conclusions of the study are: OC, BC and PM1o emissions contribute to PM2.5 with linear response characteristics, NOx, SO2, NH3 and VOC have obvious The nonlinear response characteristics; the same pollutants in different seasons. The contribution to PM2.5 of primary pollutants in Hangzhou city in winter PM2.5 in the proportion of more, in summer, two aerosol proportion is far greater than a component; in winter, the control of OC, BC and PM1o emissions can be effectively to reduce the pollution of PM2.5, when the control policy tightening, influence to NOx and NH3 was revealed. In summer, the effect of PM1o on PM2.5 pollution is still large, affect the plant NOx and industrial NOx is significantly higher than the other.

【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：X51

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