北京市典型排放源PM_(2.5)成分谱研究

PM_(2.5) profiles of typical sources in Beijing

为了建立和完善北京市PM_(2.5)本地化源谱,对北京市11类排放源PM_(2.5)进行采集,并测定其26种组分,分析了不同排放源源谱的组分特征.结果表明,在有组织排放源中,燃煤电厂PM_(2.5)中OC和Si含量很高,占PM_(2.5)的质量分数分别为8.56%和6.19%(平均值),而供热/工业锅炉排放PM_(2.5)中则是SO_4^(2-)(占48.38%)和OC(11.0%)比例最高,水泥窑炉PM_(2.5)中OC(7.12%)、Ca(4.81)和Si(4.41%)占有较大比例;垃圾焚烧排放的PM_(2.5)中Si、Ca、K和SO_4^(2-)均较高,分别占8.15%、9.36%、7.17%和6.79%,且Cl^-含量(2.5%)高于其他所有源,生物质燃烧源PM_(2.5)中OC(21.7%)、Si(6.75%)、Ca(6.15%)较为丰富,餐饮源PM_(2.5)中OC(19.44%)、SO_4^(2-)(5.76%)和K(3.11%)含量均较高;无组织开放源中,道路扬尘和土壤风沙PM_(2.5)化学组分含量变化较为一致,均是Si(分别为16.8%和9.3%)和OC(分别为8.89%和6.61%)最高,建筑水泥尘PM_(2.5)中Ca(17.46%)含量高于其他源;流动排放源PM_(2.5)中OC、EC比例最高,其中,重型柴油车的OC(29.79%)与EC(26.5%)排放比例相当,而轻型汽油车OC排放占有绝对优势(占75%).本文通过对比国内外部分排放源PM_(2.5)成分谱的差异,指出不同区域相同源类排放的PM_(2.5)化学组分差异较大,在应用受体模型中的化学质量平衡模型(CMB)判断受体颗粒物来源时,应基于本地的排放源成分谱,以避免较大的误差.

For the purpose of establishing and improving the local PM2.5 source profiles of Beijing, PM2.5 samples were collected from 11 typical emission sources. 26 different chemical constituents were characterized to identify source profiles. The results revealed that in stationary pollution sources, OC and Si were the highest constituents in coal-fired power plants, with the mean mass percentage of 8.56% and 6.19%, respectively. Dominant species were SO2-4 （48.38%） and OC（11.0%） in heating/industrial boiler emissions, OC（7.12%）, Ca（4.81%） and Si（4.41%） in cement kilns, SO2-4（8.15%）, Ca （9.36%）, K（7.17%）, Si（6.79%） and C1-（2.5%） in waste incineration, OC（21.7%）, Si（6.75%） and Ca（6.15%） in biomass burning, and OC （ 19.44%）, SO2-（5.76%） and K（3.11%） in catering services sources. In open sources, PM2.5 chemical composition in uncontrolled road dust and sand soil were relatively consistent, with Si（ 16.8% and 9.3% ） and OC （8.89% and 6.61% ） the most abundant constituents. Ca（ 17.46% ）concentration in the construction of cement dust was higher than other sources. OC and EC had the highest percentages in mobile emission sources. Their contributions were similar for heavy diesel vehicles, but OC（ 75% ）emissions were much higher for light-duty gasoline vehicles. PM2.5 source profiles reported in China and overseas showed significant differences. Therefore, local source profiles should be preferentially utilized when applying chemical mass balance（CMB） for source apportionment of PM2.5.