基于曲面响应建模的PM2.5可控人为源贡献解析

以东莞市PM_(2.5)重污染月份为例,使用强力法(Brute Force)和RSM/CMAQ曲面响应模型法分别解析了珠三角地区人为源排放对东莞PM_(2.5)的贡献,以及区域传输的可控人为源SO_2、NO_x和一次颗粒物(PM)在不同控制比例下(25%、50%、75%和100%)对东莞PM_(2.5)的累积浓度贡献.强力法研究结果表明,2014年1月珠三角地区人为源二次转化对东莞市PM_(2.5)的贡献(约58.10%)大于一次PM排放贡献(约41.90%),其中,人为源NH_3排放贡献最大,约占总量的21.66%.RSM/CMAQ动态源贡献结果显示,东莞市PM_(2.5)的人为可控源排放贡献(SO_2、NO_x和一次PM)占比为82.17%,受本地排放影响较大,且叠加区域排放的影响;一次PM减排对PM_(2.5)环境浓度的贡献高于仅减排SO_2和NO_x.在减排比例较低时,一次PM减排可有效削减东莞市PM_(2.5)浓度;随控制比例加大,二次前体物(SO_2和NO_x)减排对东莞市PM_(2.5)浓度削减率的影响加大.进一步使用HYSPLIT模式和轨迹聚类分析方法研究了2014年1月东莞市PM_(2.5)污染传输过程.结果显示,该时段共有6条长、短距离污染传输路径,污染物主要来自东莞市东、东北及东南方向,途经其上风向区域(惠州、深圳和广州等)传输至东莞;惠州是各主导上风向出现频率最高的城市,因而其区域传输对东莞PM_(2.5)的贡献也较大,深圳次之.

Response surface modeling based PM2.5 source contribution analysis on anthropogenic controllable emission

Taking the typical PM_2.5-polluted month in Dongguan as an example, Brute Force and RSM/CMAQ (Response surface model/community multi-scale air quality) were respectively used to analyze the contribution of anthropogenic emissions of the Pearl River Delta region to ambient PM_2.5 in Dongguan, and the cumulative contribution of regional anthropogenic controllable emissions (SO₂, NO_x and primary particulates (PM)) for ambient PM_2.5 in Dongguan at different control ratios (25%, 50%, 75% and 100%). The results of Brute Force indicated that the contribution of secondary transformation of anthropogenic emissions (about 58.10%) in the PRD Region for PM_2.5 in Dongguan was greater than that of primary particulates (PM) (about 41.90%) in January 2014, and the anthropogenic NH₃ emissions contributed the most, accounting for 21.66% of the total. The results of RSM/CMAQ showed that the contribution of total anthropogenic controllable emissions (SO₂, NO_x and PM) to PM_2.5 concentration in Dongguan was 82.17%, which was greatly affected by the local emissions and superimposed by regional emissions, and the contribution of primary particulates (PM) emission reduction was higher than that of only reducing emission of SO₂ and NO_x to the ambient concentration of PM_2.5. Moreover, when the control ratios of emission sources were low, primary particulates (PM) emission reduction can effectively reduce PM_2.5 concentration in Dongguan, with the increase of control ratios, the influence of secondary precursors (SO₂ and NO_x) emission reduction on the reduction rate of PM_2.5 concentration in Dongguan will increase. This paper further used the HYSPLIT model and trajectory clustering method to study the transmission process of PM_2.5 pollution in Dongguan in January 2014. The results showed that during this period, there were 6 long and short-distance routes of pollution transmission, the pollutants were mainly from the east, northeast, and southeast, passing through the upstream wind direction of Dongguan (Huizhou, Shenzhen, Guangzhou, etc.), in which Huizhou had the highest frequency (followed by Shenzhen) of dominant winds and is the greatest regional transmission contributor to PM_2.5 pollution in Dongguan.