环境科学研究  2017, Vol. 30 Issue (12): 1859-1868  DOI: 10.13198/j.issn.1001-6929.2017.03.34

引用本文  

刘亚勇, 张文杰, 白志鹏, 等. 我国典型燃煤源和工业过程源排放PM2.5成分谱特征[J]. 环境科学研究, 2017, 30(12): 1859-1868.
LIU Yayong, ZHANG Wenjie, BAI Zhipeng, et al. Characteristics of PM2.5 Chemical Source Profiles of Coal Combustion and Industrial Process in China[J]. Research of Environmental Sciences, 2017, 30(12): 1859-1868.

基金项目

科技部科技基础性工作专项(2013FY112700);国家科技支撑计划项目(2014BAC23B02)

责任作者

张文杰(1979-), 女, 山东青州人, 研究员, 博士, 主要从事大气气溶胶与环境基准研究, zhangwj@craes.org.cn

作者简介

刘亚勇(1990-), 男, 山西晋中人, craes_sp@163.com

文章历史

收稿日期:2016-11-23
修订日期:2017-08-25
我国典型燃煤源和工业过程源排放PM2.5成分谱特征
刘亚勇 , 张文杰 , 白志鹏 , 杨文 , 赵雪艳 , 韩斌 , 王歆华     
中国环境科学研究院, 环境基准与风险评估国家重点实验室, 北京 100012
摘要:鉴于我国本地化源谱(源成分谱)数量不足的现状,采用稀释通道系统对燃煤源和工业过程源进行采样,建立了4类燃煤锅炉(链条炉、流化床、往复炉和煤粉炉)和6类工业过程源(炼铁、铝焙烧、铝煅烧、砖瓦炉、水泥窑头和窑尾)的PM2.5成分谱,并对源谱特征进行研究.结果表明:① 不同源谱组分特征差异明显.水泥窑炉排放的PM2.5中,w(Ca)、w(Si)、w(OC)、w(SO42-)较高,分别为8.51%~14.18%、5.69%~11.80%、3.47%~15.56%、8.67%~16.85%;燃煤锅炉中Al(4.50%~8.67%,质量分数,余同)、OC(6.44%~15.33%)、SO42-(9.85%~22.87%)组分贡献较大;炼铁和铝冶炼工艺源谱中主导化学组分分别为Fe(8.57%~9.88%)和Al(11.81%~16.58%);砖瓦炉颗粒物源谱中主要组分为SO42-、NH4+、Si等.② 不同污染源PM2.5成分谱的分歧系数结果显示,流化床和煤粉炉、水泥窑头和窑尾源谱较为相似,其分歧系数分别为0.26和0.28,其余源谱间均存在一定差异.进一步计算组分差异权重(R/U)发现,往复炉源谱中组分Zn、Sn与其他3类锅炉有明显不同.流化床/煤粉炉源谱中的Si、Ni,窑头/窑尾源谱中的K、Mn、OC组分差异显著,可以作为区分相似源谱的标识组分.与其他研究建立的源谱相比,燃煤源谱中w(EC)和w(SO42-)偏高.钢铁源谱中w(EC)和w(NH4+)较其他地区偏高,w(Pb)偏低;工业过程源谱中,w(Cl-)较SPECIATE相关源谱偏低,而w(Ⅴ)和w(Cr)偏高.鉴于颗粒物源谱受到不同燃料种类、燃烧方式和烟气控制设施等影响而存在差异,源谱的准确性和代表性还需进一步测试和验证.
关键词PM2.5成分谱    燃煤源    工业过程源    
Characteristics of PM2.5 Chemical Source Profiles of Coal Combustion and Industrial Process in China
LIU Yayong , ZHANG Wenjie , BAI Zhipeng , YANG Wen , ZHAO Xueyan , HAN Bin , WANG Xinhua     
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Abstract: In view of insufficient local source profiles in China, PM2.5 source profiles for coal-fired boilers and industrial processes' emissions were established.Four coal burning sources from coal-fired boilers of grate firing, fluidized bed, converters and pulverized coal, and 6industrial process emissions from metallurgy, steel production and construction materials production were discussed. Results showed that:(1) The chemical composition shows special characteristics in different source categories. Ca (8.51%-14.18%), Si (5.69%-11.80%), OC (3.47%-15.56%) and SO42- (9.85%-22.87%) were shown to be the major species of PM2.5 from cement kiln; Al, SO42- and OC marked coal-fired boiler, accounted for 4.50%-8.67%, 6.44%-15.33% and 9.85%-22.87%, respectively; Fe (8.57%-9.88%) and Al (11.81%-16.58%) were the most abundant elements in steel production and aluminum metallurgy. The highest abundances of SO42-, NH4+, Si were observed in brick kiln emissions. (2) The coefficient of divergence (CD) and the distribution of weighted differences (R/U ratio) were used to compare the similarities and differences of source profiles. Good similarities were observed between fluidized bed and pulverized coal boiler emissions, and between cement kiln head and inlet emissions. Si and Ni were expected to distinguish profiles between fluidized bed and pulverized coal boiler with the R/U>3. K, Mn and OC abundances were significant different between profiles of cement kiln head and inlet. Differences of source profiles from different studies including SPECIATE database were compared. EC and SO42- from coal burning, EC and NH4+ from steel production were higher than those of studies in other regions. Compared with source profiles in SPECIATE v4.5, Cl- abundances in metallurgy, cement and brick kiln were lower, while V and Cr were higher in this research. The discrepancies of chemical species from different source profiles are closely linked to different fuels, combustion modes and control facilities. More tests are needed for further study.
Keywords: PM2.5    source profiles    coal-fired boiler emissions    industrial process sources    

近年来,我国大范围的霾污染时有发生,PM2.5浓度居高不下,已经严重影响到大气环境质量[1-3]、气候变化[4]和人体健康[5]. ZHANG等[6]讨论了我国大气污染治理所面临的挑战,并指出我国应改变粗放型的经济发展方式,限制化石燃料的使用.2013年9月,国务院发布《大气污染防治行动计划》,力促环境空气质量改善,向PM2.5宣战[7].为了准确识别污染源,制订合理的控制措施,环境保护部于2013年8月发布《大气颗粒物来源解析技术指南(试行)》,北京[8]、天津[9]、厦门[10]、泰安[11]、济南等[12]城市先后开展了源解析工作.其中,受体模型法如化学质量平衡(CMB)和正定矩阵因子分解法(PMF)已被广泛应用于源解析工作中[13-15].

我国以煤炭为主要燃料,燃煤源是我国大气颗粒物污染的主要来源[16-17]. BI等[12]用CMB受体模型对我国北方6个城市进行了源解析,研究发现,春季燃煤飞灰对大气颗粒物的贡献达到5%~21%,而冬季达到20%~59%.工业过程源是指工业生产和加工过程中,以对工业原料进行物理和化学转化为目的的工业设备,第一级分类包括钢铁、有色冶金、建材和化工4个行业[18].工业源排放的颗粒物是大气灰霾形成的重要来源之一,不同工艺过程排放的颗粒物中重金属如w(V)、w(Ni)和w(Sb)较高[19-21],对人体健康有一定影响.目前,京津冀和长三角地区各主要城市的源解析结果[22-26]已经公布,燃煤源和工业源对PM2.5贡献率分别占13.5%~28.5%和17%~28.9%.在烟气净化技术方面,我国工业锅炉已普遍配备高效静电除尘器及脱硫装置[27].但是,传统的控制技术仍然无法满足对烟气中细颗粒物的控制,如经过除尘效率相对较高的静电除尘及湿法脱硫后,PM2.5仍占颗粒物总排放量的64.1%[28].

源谱(源成分谱)是污染源的“指纹”,可以准确定义污染源的排放特征.此外,源谱还可以作为CMB的输入数据、PMF解析因子的依据和计算排放清单的基础[29],可为大气颗粒物来源解析提供重要基础数据.自19世纪80年代起,欧美等国家就开始进行源解析和排放清单的研究工作[30]. US EPA的SPECIATE是迄今为止最全面的源谱数据库[31],目前已更新至v4.5,包含源谱数量多达5 728条,涵盖了燃煤、生物质燃烧、机动车尾气和工业锅炉等诸多污染源类,相关的分析测试方法和数据质量评价也囊括其中[32].欧洲的SPECIEUROPE颗粒物源谱数据库也于2015年对外开放;Pernigotti等[33]对SPECIEUROPE进行了介绍并用聚类分析的方法对数据库中现有源谱数据进行了分类研究.此外,中国环境科学研究院的研究者们建立了我国的源谱数据库——中国源谱数据共享平台(CSPSS, www. speciate. org. cn),目前开放的CSPSS1.0版本包括了2003—2012年以来固定燃烧源、工业过程源、机动车和开放源等我国20多个城市的500多条成分谱.但是,我国关于源谱的研究仍然相对缺乏,并且主要集中在扬尘源[34-40]、燃煤源[16-17, 41-42]、机动车排放[43]以及生物质燃烧源[42, 44].外来源谱在相关的源解析工作中占比达20%~90%[14].

该研究采用稀释通道系统对典型燃煤源和工业过程源进行采样,建立链条炉、流化床、往复炉和煤粉炉、炼铁、铝焙烧、铝煅烧、砖瓦炉、水泥窑头和窑尾排放PM2.5成分谱,并对源谱不确定性进行评估.该研究旨在开展我国典型燃煤源和工业过程源排放PM2.5成分谱特征研究,以期为国内相关城市和区域开展大气颗粒物来源解析提供基础数据,以及为国家环境空气质量管理和控制提供技术支撑.

1 研究方法 1.1 数据来源

该研究采集了4类燃煤源(链条炉、往复炉、循环流化床和煤粉炉)以及6类工业过程源(炼铁、转铝焙烧、铝煅烧、砖瓦炉、水泥窑头和窑尾)共计31个样品(见表 1),样品均采自2014—2015年.该研究中所涉及的采样、分析方法及相应的质量控制和质量保证详见文献[45].

表 1 燃煤源和工业过程源采样信息 Table 1 Sampling information about sources of coal combustion and industrial process
1.2 不确定性评估

样品经化学分析后,计算不同源样品中各组分质量分数的平均值(F)和标准偏差(SD),获得不同源类的成分谱.考虑到测试过程中的方法不确定性和平行样品的不确定性[46-47],该研究中组分不确定性的计算公式:

${U_c} = {F_c}{\left[ {{{\left( {\frac{{{\rm{MDL}}}}{{{M_c}}}} \right)}^2} + {\rm{C}}{{\rm{V}}^2}} \right]^{0.5}}$ (1)

式中:Uc为组分c的不确定性;Fc为源样品中组分c含量的平均值;MDL为仪器测量检出限;Mc为所测组分c质量的平均值;CV为变异系数,以SD/Fc计算得到.

2 结果与讨论 2.1 组分特征

燃煤锅炉、工业过程源PM2.5成分谱中主要组分含量及其不确定性如表 2图 1~2所示.10种源谱中所测得的主要组分含量为40.8%(往复炉)~79.9%(炼铁),其中燃煤锅炉和砖瓦炉源谱组分解释量偏低( < 60%),可能是由于这两种源排放较复杂导致源谱中含有未监测到的物种.郑玫等[14]建立了上海4种工业源谱,研究发现电厂锅炉源谱对PM2.5的解释量(45%~55%)偏低.

表 2 不同源类成分谱中主要组分的质量分数和不确定性 Table 2 Main chemical compositions and their uncertainties of source profiles

图 1 源谱中主要组分的质量分数 Fig.1 Main chemical compositions of source profiles

图 2 源谱中碳组分的质量分数 Fig.2 Carbonaceous compositions of source profiles

水泥窑炉排放的PM2.5中,w(Ca)、w(Si)、w(OC)、w(SO42-)等较高,分别为8.51%~14.18%、5.69%~11.80%、3.47%~15.56%、8.67%~16.85%.窑尾排放的颗粒物中OC的贡献约为窑头的3倍,窑头和窑尾排放的OC/EC值分别为6.85和0.76.可能是由于气体经窑尾至窑头冷却后燃烧较充分,因此w(OC)偏低. Ca2+/Ca( < 0.3) 偏低说明水泥窑炉颗粒物中的Ca主要以非水溶性的形态存在.燃煤锅炉中w(Al)、w(OC)、w(SO42-)分别为4.50%~8.67%、6.44%~15.33%、9.85%~22.87%. As在燃煤锅炉中的贡献较其他源谱偏高,往复炉排放颗粒物中w(As)约为0.04%. ZHANG等[16]研究了我国燃煤排放特征,发现As可以作为燃煤排放的特征组分.流化床、煤粉炉、钢铁厂的炼铁工艺采用的脱硫方法为湿法和半干半湿法,两种脱硫工艺均需以石灰作为原料,因此这3种源谱中w(Ca)偏高,分别为4.63%、5.82%和7.67%.工业过程源排放与生产原料相关,炼铁和铝冶炼工艺排放的颗粒物中,主导化学组分分别为Fe(8.57%~9.88%,质量分数,余同)和Al (11.81%~16.58%).此外,炼铁工艺排放颗粒物中OC的贡献约为其他源的1.3~9.3倍,可能与钢铁工艺过程中的有机添加剂有关.砖瓦炉颗粒物源谱中主要组分为SO42-、NH4+、Si等,相应质量分数分别为16.88%~25.64%、3.23%~7.87%、10.54~13.13%.10种源谱的Mg2+/Mg较稳定,范围为0.57~0.79,说明各类源排放的颗粒物中Mg水溶性组分的比例受工艺过程的影响较小.上述结果与已有研究结果相似,如王书肖等[41]研究发现,工业链条炉排放的PM2.5中SO42-最多,占20%~54%;郑玫等[14]建立了上海工业源谱,发现钢铁源排放的颗粒物受到生产原料和工艺添加剂的影响,SO42-、Fe、Zn、Cl-等物种贡献较大.

2.2 源谱相似性

分歧系数可以将不同成分谱中组分含量标准化,从而来比较成分谱之间的相似性[48]. CD的计算公式:

${\rm{C}}{{\rm{D}}_{jk}} = \sqrt {\frac{1}{p}\sum\limits_{i = 1}^p {} {{\left( {\frac{{{x_{ij}} - {x_{ik}}}}{{{x_{ij}} + {x_{ik}}}}} \right)}^2}} $ (2)

式中,CDjkj类源谱和k类源谱之间的分歧系数,xijj类源谱组分i含量的平均值,jk为要比较的两种源类,p为所测主要组分的数量. CD值越趋近于0,成分谱越相似.若CD>0.3,表明成分谱之间存在一定差异[49];若CD < 0.3,表明成分谱之间有一定的相似性,输入CMB模型可能会引起共线性问题. 表 3为10种污染源PM2.5成分谱间的分歧系数,结果显示,流化床和煤粉炉、水泥窑头和窑尾源谱较为相似,其CD分别为0.26和0.28,其余源谱间均存在一定差异.

表 3 源谱间的分歧系数 Table 3 Coefficient of divergence between the paired source profiles

为了进一步研究燃煤锅炉、水泥窑炉源谱间的差异,引入组分差异权重分布函数——Residual(R)/Uncertainty(U). R/U表示两种源谱中相同组分间差异性的权重,计算时将源谱中的组分含量和不确定性均考虑在内. Chow等[50]将地质尘按照采样点位的距离和源类别的相似程度进行分级,用R/U分析不同级别扬尘PM2.5源谱之间的相似性和差异性. R/U计算公式:

${\rm{R/U}} = \frac{{\left| {w{{\left( i \right)}_1} - w{{\left( i \right)}_2}} \right|}}{{\sqrt {{\sigma _{i1}}^2 + {\sigma _{i2}}^2} }}$ (3)

式中,σi1σi2为两种源谱中组分i含量平均值的标准偏差.

该研究中,若两种源谱中某种组分的|R/U|>3,则认为这种组分在两种源谱中有一定差异[50].燃煤锅炉和水泥窑炉源谱主要组分间的R/U如表 4所示.该研究中,往复炉PM2.5源谱中组分Zn、Sn与其他3类锅炉有明显不同,R/U分别为3.01~6.47、18.08~27.46.流化床、煤粉炉成分谱中w(Si)和w(Ni)有一定差异,其R/U分别为5.20、3.83,可以做为区分两种源谱的标识组分.煤粉炉源谱中w(Ca)与其他燃煤锅炉差异明显,可能是由于采用了石膏法脱硫.源谱的组分特征由于受到不同燃料种类、燃烧方式和烟气控制设施的影响而不同.水泥窑炉源谱中,窑头窑尾排放的w(K)、w(Mn)和w(OC)有一定差异(R/U=5.13、4.40、4.36),可以作为区分两种源谱的标识组分.

表 4 燃煤锅炉和水泥窑炉源谱主要组分间的R/U Table 4 R/U for main chemical compositions of source profiles of coal-fired boilers and cement kiln
2.3 国内外源谱对比

比较不同地区以及SPECIATE v4.5中相关源类的成分谱,发现不同研究得到的源谱中主要化学组分有一定差异,该差异主要与燃料种类、生产方式和研究者所用的测试方法等的不同有关[14].

SPECIATE数据库中源谱数量众多,为了便于研究,笔者将相关源类的PM2.5成分谱进行统计和整理.由于数据库中不同研究获得的源谱差异性较大,分别对同一源类的原始成分谱进行求中值处理[31].最终获得燃煤、钢铁生产、水泥生产、铝冶炼和砖瓦窑炉5种源类的平均成分谱.

不同地区的燃煤源谱如表 5所示,结果显示,该研究中w(EC)较其他地区而言相对偏高(除浙江宁波燃煤电厂外);流化床排放的颗粒物中w(SO42-)偏高,与上海电厂相似.该研究燃煤锅炉源谱主要组分含量与SPECIATE源谱相似.

表 5 不同地区燃煤源谱中主要组分的质量分数 Table 5 Main chemical compositions of coal-fired boiler source profiles from different places

不同地区的钢铁源谱如表 6所示.对比发现,该研究钢铁源谱中主要组分与其他地区差异较大.其中w(EC)和w(NH4+)较其他地区偏高,w(Pb)偏低.除上海烧结厂外,w(SO42-)较国内其他地区偏高.其余组分随工艺、研究地区的不同也有一定差异.郑玫等[14]测试的上海烧结厂源谱与其他钢铁源谱有较大的差异,其中w(SO42-)、w(Cl-)、w(Ca)和w(Pb)均较高.

表 6 不同地区钢铁源谱中主要组分的质量分数 Table 6 Main chemical compositions of iron related source profiles from different places

由于国内关于冶金、建材等行业的工业源谱报道较少,表 7比较了该研究与SPECIATE数据库中水泥生产、铝冶炼和砖瓦炉源谱的差异.对比发现,各污染源源谱中w(Cl-)较SPECIATE低,而w(V)、w(Cr)偏高,表明我国工业过程源排放的颗粒物中重金属含量相对较高,应予以重视.

表 7 该研究和SPECIATE中相关源谱主要组分的质量分数 Table 7 Main chemical compositions of source profiles from this study and SPECIATE
3 结论

a)依托中国源谱数据共享平台,建立了燃煤锅炉和工业过程源的成分谱,并对其不确定性进行了评估,发现不同源类的组分特征差异明显.10种源谱中所测主要组分的质量分数为40.8%~79.9%.水泥窑炉排放的PM2.5中,Ca、Si、OC、SO42-等组分贡献较大,其质量分数分别为8.51%~14.18%、5.69%~11.80%、3.47%~15.56%、8.67%~16.85%.燃煤锅炉中Al(4.50%~8.67%)、OC(6.44%~15.33%)、SO42-(9.85%~22.87%)等组分贡献较大.流化床、煤粉炉、钢铁厂的炼铁工艺采用的脱硫方法为湿法和半干半湿法,两种脱硫工艺均需以石灰作为原料,因此这3种源谱中w(Ca)偏高.炼铁和铝冶炼工艺源谱中主导化学组分分别为Fe(8.57%~9.88%)和Al(11.81%~16.58%).砖瓦炉颗粒物源谱中主要组分为SO42-、NH4+、Si等.

b)不同污染源PM2.5成分谱的分歧系数结果表明,流化床和煤粉炉、水泥窑头和窑尾源谱较为相似,其分歧系数分别为0.26和0.28,其余源谱间均存在一定差异.进一步计算组分差异权重R/U,发现往复炉源谱中,组分Zn、Sn与其他三类锅炉有明显不同.流化床、煤粉炉源谱中的Si、Ni,窑头窑尾源谱中K、Mn、OC组分差异显著,可以作为区分相似源谱的标识组分.

c)与其他研究建立的源谱相比,燃煤源谱中w(EC)和w(SO42-)偏高.钢铁源谱中w(EC)和w(NH4+)较其他地区偏高,w(Pb)偏低;对于其余工业源谱,w(Cl-)较SPECIATE低,而w(V)和w(Cr)偏高.鉴于颗粒物源谱受到不同燃料种类、燃烧方式和烟气控制设施等影响而存在差异,源谱的准确性和代表性还需进一步测试进行验证.

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