夏收和秋收期间泰州市空气质量特征研究

利用2012—2015年泰州市空气质量监测数据,分析夏、秋收期间城市环境空气质量特征,探讨引发重污染天气的原因。结果表明,夏收期间空气质量整体优于秋收,2012年、2013年秋收期间空气质量最差,达到重污染以上的天数分别为10 d、6 d,颗粒物尤其是PM_(2.5)超标较严重,2015年秋收期间空气质量显著好转。秸秆焚烧日PM_(2.5)和PM_(10)质量浓度呈较高相关性,PM_(2.5)/PM_(10)值比非秸秆焚烧日高。基于气团后向轨迹及秸秆焚烧卫星遥感监测火点图将污染事件分类,研究得出秸秆焚烧和区域输送是导致城市污染加重的主要因素。     

南京市城区PM2.5中化学组分演变特征

于2011—2017年在江苏省南京环境监测中心办公楼顶开展PM_(2.5)监测采样,分析其样品中OC、EC、水溶性离子和20余种无机元素等组分演变特征。结果表明,NO3-、SO24-、NH4+、OC、EC等是PM_(2.5)的主要组分,且大部分组分值随ρ(PM_(2.5))降低呈下降趋势; OC在2016—2017年成为占比最大的组分;ρ(NO_3~-)/ρ(SO_4~(2-))由0. 9上升至1. 3,ρ(OC)/ρ(EC)由3. 2上升至3. 6,均呈持续上升趋势;机动车污染和有机碳污染明显加重,南京大气污染类型从传统煤烟型污染向煤烟型与氧化型污染共同主导的复合型污染转变; K~-、Cl~-、SO_4~(2-)等水溶性离子和痕量元素K、Al、Ca、Na、Mg等值持续下降,说明工业污染减排、燃煤总量控制和污染治理、扬尘管控和秸秆禁烧效果显著。     

采暖期北京大气PM2.5中碳组分的分布特征及来源解析

在冬季采暖期采集北京大气中的PM_(2.5)样品,利用自动称重系统AWS-1和热/光碳分析仪测定样品中PM_(2.5)和OC/EC,研究碳组分的变化特征,并通过OC/EC的值和单颗粒气溶胶质谱仪(SPAMS 0515)分析大气颗粒物中碳气溶胶的可能来源。结果表明:PM_(2.5)污染天气的OC、EC在PM_(2.5)中的占比要比清洁天气时低,其中SOC在PM_(2.5)中的占比由清洁天气时的22.9%减少到了重污染天气的15.4%,这是因为大气中的PM_(2.5)有较强的消光作用,导致气溶胶的氧化能力降低,造成了SOC的生成量减少;通过分析OC/EC值表明,冬季采暖期北京大气碳气溶胶的主要来源为机动车尾气和燃煤,这与SPAMS 0515在线解析的结果一致。采用SPAMS 0515进行在线OC、EC分析,在PM_(2.5)质量浓度≤250μg/m3时同手工方法有较好的相关性。解析结果表明,燃煤和机动车尾气是北京冬季采暖期的首要污染物来源,占比分别为34.0%和26.4%。     

西宁市城区冬季PM2.5和PM10中有机碳、元素碳污染特征

2014年11月—2015年1月对西宁市冬季开展PM_(2.5)和PM_(10)的连续监测。利用DRI 2001A型热光碳分析仪(美国)对有机碳和元素碳进行分析,结果表明:西宁市冬季PM_(2.5)和PM_(10)中碳气溶胶所占比例分别为33.13%±6.83%、24.21%±6.27%,说明碳气溶胶主要集中在PM_(2.5)中;OC/EC值均大于2,说明西宁市大气中存在二次污染;SOC占PM_(2.5)和PM_(10)的质量浓度比例分别为46.50%和57.40%,PM_(2.5)中SOC浓度占PM_(10)中SOC浓度的61.88%,说明SOC主要存在于PM_(2.5)中,且SOC形成的二次污染和直接排放的一次污染都是西宁市碳气溶胶的主要来源;与其他城市比较发现,西宁市冬季PM_(2.5)中的碳气溶胶含量普遍高于其他城市,PM_(10)中OC质量浓度相对其他城市较高,EC质量浓度偏低;OC和EC的相关性不显著,说明来源不统一;进一步对OC和EC各组分质量浓度进行分析知,西宁市冬季碳气溶胶主要来源于机动车汽油排放、燃煤和生物质燃烧。     

石家庄市大气颗粒物中有机碳和元素碳的季节变化特征

为了解石家庄市大气颗粒物中有机碳和元素碳的季节变化特征,对春、夏、秋、冬四季采集的PM_(10)、PM_(2.5)样品中的有机碳(OC)和元素碳(EC)进行了分析。结果表明,石家庄市PM_(10)、PM_(2.5)污染严重;PM_(10)、PM_(2.5)中ρ(OC)和ρ(EC)季节变化特征均为夏季春季秋季冬季。冬季PM_(10)中ρ(OC)和ρ(EC)分别为42.85和8.88μg/m~3;PM_(2.5)中ρ(OC)和ρ(EC)分别为41.2和8.59μg/m~3。PM_(2.5)中EC占比最高为3.9%,EC更容易在PM_(2.5)中富集;在四个季节中,冬季PM_(10)、PM_(2.5)中ρ(OC)/ρ(EC)为最高,分别为4.83和4.80,冬季取暖用燃煤加重了OC、EC的污染。冬季PM_(10)中二次有机碳ρ(SOC)为20.92μg/m~3,PM_(2.5)中ρ(SOC)为23.50μg/m~3。     

APEC期间京津冀及周边地区PM2.5中碳组分变化特征及来源

在APEC会议期间和会期之后,分别采集北京、天津、石家庄、保定、济南5个采样点的PM2.5样品,通过分析碳组分的变化特征,研究京津冀地区污染物减排的影响以及减排后各指标的变化特征,分析大气颗粒物中碳气溶胶的可能来源。采用重量法测定组分中PM2.5的含量,利用热/光碳分析仪测定组分中OC、EC的含量,结果表明,由于采取了污染源减排措施,会议期间PM2.5、OC、EC的质量浓度均低于会期之后;会议期间和会期之后OC与EC均表现出了较好的相关性,r2为0.789~0.983,说明OC与EC的排放源基本相同;会议期间OC/EC为3.11~3.62,表明含碳气溶胶的来源主要是机动车排放,同时也存在一定的燃煤排放,会期之后为3.08~6.10,表明燃煤的排放在碳气溶胶中的比重明显增加,另外OC/EC也表明APEC会议期间和会期之后二次有机碳在各采样点均普遍存在。     

烟花爆竹燃放对北京市空气质量的影响研究

结合常规污染物浓度和PM_(2.5)化学组分浓度,分析了2015年春节期间烟花爆竹燃放对北京市空气质量的影响。结果表明:烟花爆竹燃放会在短时间内造成严重的大气污染,其中对SO2、PM_(2.5)和PM10的影响最为显著。除夕夜间良乡、官园和怀柔3个监测站点的PM_(2.5)质量浓度峰值分别达730.5、343.4、762.2μg/m~3,为2月17—25日和3月4—8日(观测期间)平均值的5.2、3.1、7.1倍。烟花爆竹燃放对PM_(2.5)组分中的SO_4~(2-)、K+和Cl-的影响最为显著,除夕夜间监测中心点位的SO_4~(2-)、K~+和Cl~-质量浓度峰值分别达92.2、95.6、57.4μg/m~3,为观测期间平均值的4.5、10.5、6.8倍。烟花爆竹燃放产生的气态前体物和NO_3~-、SO_4~(2-)、NH+4、OC等PM_(2.5)二次化学组分在不利的气象条件下会发生化学反应和物理积累,造成PM_(2.5)浓度升高,产生持续性的大气污染。根据各污染物与NH+4的质量浓度比推算得出,除夕、"破五"和元宵节3个时段烟花爆竹燃放对K~+、Cl~-、SO_4~(2-)、SO_2和PM_(2.5)浓度的平均贡献率分别为78.4%、61.1%、37.4%、38.7%和30.1%。     

重庆市主城区二次有机碳气溶胶污染特征研究

为研究重庆市大气PM_(2.5)中二次有机气溶胶污染特征,于2013年1—12月运用URG-3000ABC型中流量颗粒物采样仪连续同步采集重庆市主城区大气PM_(2.5)样品,选取OC/EC比值对PM_(2.5)中的SOC污染进行估算,结果表明,该市主城区PM_(2.5)中SOC年平均质量浓度为12.5μg/m3,占OC质量浓度的50.0%,占PM_(2.5)质量浓度的10.1%,SOC质量浓度为冬季秋季夏季春季。机动车排放是SOC前体物的主要来源。     

南通市不同空气质量级别对应的EC和OC变化特征

采用EC/OC在线分析仪和空气自动站连续监测数据(2016年3月—2017年2月),对南通市不同空气质量级别下的EC、OC变化特征进行了分析。结果表明,EC、OC小时均值分别为1.25~6.55,4.16~24.90μg/m~3,与空气质量级别呈正相关(r=0.999,0.963,p0.01);ρ(EC)/ρ(PM_(2.5))、ρ(OC)/ρ(PM_(2.5))分别为3.54%~6.64%,11.53%~22.18%,总体随空气质量级别的升高而下降,存在明显二次有机碳(SOC)污染,ρ(SOC)为2.29~14.18μg/m~3,与空气质量级别呈正相关(r=0.921,p0.05);ρ(SOC)/ρ(PM_(2.5))为5.44%~12.22%,总体随空气质量级别的升高而下降。"优"—"轻"空气质量级别下,EC小时值日变化曲线呈双峰型,OC小时值日变化曲线呈单峰型,"中""重"空气质量级别下,EC、OC小时值日变化规律不明显。"优、良、中、重"空气质量级别下的EC和各空气质量级别下的OC的季节平均值均为夏季最高,其余季节分布规律不明显,EC、OC总均值季节分布为:夏冬春秋。     

百色市PM10和PM2.5中有机碳和元素碳污染特征及来源解析

2015—2016年在百色市布设3个采样点采集PM_(10)和PM_(2.5)样品,分析其中有机碳(OC)和元素碳(EC)的含量。结果表明,PM_(10)和PM_(2.5)中OC、EC四季均值分别为15.0μg/m~3、5.55μg/m~3和11.7μg/m~3、4.72μg/m~3;OC与EC相关性不显著,存在不同的污染来源;OC/EC值多数2,存在二次污染,主要来源于柴油、汽油车尾气和燃煤的排放。由总碳质气溶胶(TCA)和8个碳组分丰度分析可知,百色市碳气溶胶(CA)来源于汽车尾气、道路扬尘、燃煤的排放。二次有机碳(SOC)在OC中的占比均75%,表明百色市大气颗粒物中OC以SOC为主,夜间污染重于昼间。     

利用SPAMS初探盘锦市冬季PM2.5污染特征及来源

利用SPAMS 0515于2015年1月在盘锦市兴隆台空气质量自动监测点位采集PM_(2.5)样品,并分析其污染特征和来源。研究结果表明,盘锦市冬季PM_(2.5)的颗粒类型主要以OC颗粒、富钾颗粒、EC颗粒组成。其中,OC颗粒占比最高,为52.5%;PM_(2.5)污染的主要贡献源为燃煤、生物质燃烧、机动车尾气排放,占比分别为33.2%、25.7%、17.5%,特别是在PM_(2.5)质量浓度较高时段,燃煤和机动车尾气排放对污染的贡献较大。     

南京大气细颗粒中有机碳与元素碳污染特征

为了解南京城区大气细颗粒物中有机碳与元素碳的污染特征,在国控点草场门进行了连续一年的PM2.5采样,分析了有机碳(OC)、元素碳(EC)、ρ(OC)/ρ(EC)污染特征和变化规律。结果表明,采样期间有些PM2.5的日均值超过了《环境空气质量标准》(GB 3095-2012)二级标准,ρ(OC)/ρ(EC)为0.77～4.98,平均值为1.92。PM2.5样品中OC约占18%、EC约占9%。     

合肥市冬季细颗粒物中碳组分特征分析

利用2020年12月1日至2021年2月28日合肥市细颗粒物(PM_2.5)、有机碳(OC)和元素碳(EC)等环境空气质量监测数据和气象观测数据，分析了合肥市大气PM_2.5中OC和EC的污染特征，并探讨了其来源以及气象因素影响。结果表明：合肥市冬季碳质气溶胶是PM_2.5中主要组分，随着污染程度的加重，碳质气溶胶的质量浓度逐步增加，但其在PM_2.5中的占比先减小后增加。在以PM_2.5为首要污染物的不同污染级别天气条件下，OC和EC的相关性说明不同程度下碳质气溶胶来源复杂。OC/EC表明机动车尾气和燃煤源排放是碳质气溶胶的主要来源。二次有机碳(SOC)会随着污染程度的加重而呈现升高趋势。OC和EC在冬季受温度影响较小；较大的相对湿度对OC和EC具有一定的清除作用，明显降水或连续降水的清除作用更加显著；而风速对含碳气溶胶的影响主要出现在污染天气背景下。     

Chemical composition of ambient particulate matter and redox activity

Exposure to ambient particulate matter (PM) has been associated with a number of adverse health effects. Increasing studies have suggested that such adverse health effects may derive from oxidative stress, initiated by the formation of reactive oxygen species (ROS) within affected cells. The study aimed to assess physical characteristics and chemical compositions of PM and to correlate the results to their redox activity. PM_2.5 (mass aerodynamic diameter ≤2.5 μm) and ultrafine particles (UFPs, mass media aerodynamic diameter <0.1 μm) were collected in an urban area, which had heavy traffic and represented ambient air pollution associated with vehicle exhaust. Background samples were collected in a rural area, with low traffic flow. Organic carbon (OC), elemental carbon (EC), polycyclic aromatic hydrocarbons (PAHs), and metals were analyzed. The dithiothreitol activity assay was used to measure the redox activity of PM. Results showed that UFPs have higher concentrations of OC, EC, and PAHs than those of PM_2.5. Several metals, including Fe, Cu, Zn, Ti, Pb, and Mn, were detected. Among them, Cu had the highest concentrations, followed by Fe and Zn. Organic carbon constituted 22.8% to 59.7% of the content on the surface of PM_2.5 and UFPs. Our results showed higher redox activity on a per PM mass basis for UFPs as compared to PM_2.5. Linear multivariable regression analyses showed that redox activity highly correlated with PAH concentrations and organic compounds, and insignificantly correlated with EC and metals, except soluble Fe, which increased redox activity in particle suspension due to the presence of ROS.     

Overview of particulate exposures in the US trucking industry

As part of a large epidemiologic study of lung cancer, 55,000 subjects, we have conducted a nation-wide survey of particulate exposures in the US trucking industry. The goal is to differentiate the risks from various types of particulate exposures, such as traffic emissions and general air pollution. We hypothesize that exposures defined by job and work site characteristics can be linked with subjects using their personal job histories. This report covers exposures at 36 randomly chosen large truck freight terminals in the US. Measurements were made of PM2.5, elemental carbon (EC), and organic carbon (OC) upwind of the terminal (background) and in work areas, and by personal samples. Significant differences in exposure intensity, microg m(-3), were found for work locations and jobs relative to background levels (GM[GSD]) at terminal sites: PM2.5 9.8[2.34], EC 0.5[3.24], and OC 5.0[1.76]. Using EC as a marker for diesel particles, work locations varied significantly: office 0.3[3.7], dock area 0.7[2.89] and shop area 1.5[3.52]), as did job titles (non-smokers): clerk 0.1[9.98], dock worker 0.8[2.13], and mechanic 2.0[3.82]. Cigarette smoking contributed substantially to personal exposures, approximately doubling PM2.5 and OC, but having less of an effect on EC. Large differences were seen across the terminal sites due to differences in local regional air pollution levels from traffic and other sources. We conclude that it will be possible to estimate current exposures of the cohort using an exposure assignment matrix based on job title, work location, and terminal site. This distribution overlaps substantially with the general public's exposure to these sources.     

2014-2018年南京PM2.5中碳组分污染特征分析

利用在线高分辨率仪器对2014-2018年南京市PM2.5中有机碳(OC)、元素碳(EC)进行了连续监测,结果表明:离线分析法与在线分析法对OC、EC的测定结果具有很好的线性相关性,离线分析的EC、OC浓度高于在线自动监测值;2014-2018年南京OC与EC的平均质量浓度分别为(6. 38±3. 91)μg/m^3和(3. 12±1. 76)μg/m^3,整体呈下降趋势,冬季OC与EC均较高,夏季两者质量浓度较低。OC和EC均呈现夜间高、白天低的日变化规律,OC与EC第一个峰值均出现在08:00左右,OC第二个峰值出现在20:00前后;夏季OC与EC相关性最低,冬季最高,NO2、CO与OC、EC的相关性总体高于SO2,表明燃料燃烧对碳气溶胶有一定贡献,但没有交通源的贡献显著,夏季O3与OC呈现一定程度的正相关性。利用最小相关系数法(MRS)计算大气OC中一次有机碳(POC)和二次有机碳(SOC),结果显示OC中以POC为主,但SOC呈逐年上升趋势,2018年SOC质量浓度达1. 96μg/m3,在OC中占比达31. 9%,后续颗粒物污染治理的重点可能应关注VOCs。     

新冠疫情管控期间西安细颗粒物中碳组分浓度变化特征

于2019年1月27日—3月18日及2020年1月27日—3月18日对西安市细颗粒物(PM_2.5)的碳组分浓度进行了在线观测，对比分析了非疫情与疫情期间各常规污染因子、气象要素、PM_2.5中有机碳(OC)和元素碳(EC)的污染特征。结果表明：非疫情与疫情期间西安市的气象条件总体水平较为相近。疫情期间的二氧化硫(SO₂)、臭氧(O₃)浓度相对升高。重污染天气下，除PM_2.5外，其他污染物浓度均降低，说明疫情管制对重污染天气污染物浓度的削弱作用明显。疫情期间，PM_2.5中的OC组分浓度及占比有显著升高，与疫情期间的各类交通管制导致的机动车尾气排放量显著降低有关。另外，OC与EC的相关性较强，说明污染来源与人类日常生活有关。疫情期间西安市颗粒物中碳组分主要来自各类生物质燃烧，并且存在SOC污染，SOC在OC中的占比达到37.8%。疫情期间重污染天气下，SOC在OC中的占比达到87.5%,说明SOC对重污染天气OC的贡献较大。     

Chemical Composition and Sources of PM10 and PM2.5 Aerosols in Guangzhou, China

Aerosol samples of PM10 and PM2.5 are collected in summertime at four monitoring sites in Guangzhou, China. The concentrations of organic and elemental carbons (OC/EC), inorganic ions, and elements in PM10 and PM2.5 are also quantified. Our study aims to: (1) characterize the particulate concentrations and associated chemical species in urban atmosphere (2) identify the potential sources and estimate their apportionment. The results show that average concentration of PM2.5 (97.54 μg m⁻³) in Guangzhou significantly exceeds the National Ambient Air Quality Standard (NAAQS) 24-h average of 65 μg m⁻³. OC, EC, Sulfate, ammonium, K, V, Ni, Cu, Zn, Pb, As, Cd and Se are mainly in PM2.5 fraction of particles, while chloride, nitrate, Na, Mg, Al, Fe, Ca, Ti and Mn are mainly in PM2.5-10 fraction. The major components such as sulfate, OC and EC account for about 70–90% of the particulate mass. Enrichment factors (EF) for elements are calculated to indicate that elements of anthropogenic origins (Zn, Pb, As, Se, V, Ni, Cu and Cd) are highly enriched with respect to crustal composition (Al, Fe, Ca, Ti and Mn). Ambient and source data are used in the multi-variable linearly regression analysis for source identification and apportionment, indicating that major sources and their apportionments of ambient particulate aerosols in Guangzhou are vehicle exhaust by 38.4% and coal combustion by 26.0%, respetively.     

Local and regional air pollution in Ireland during an intensive aerosol measurement campaign

An intensive two month measurement campaign has been performed during a two year study of major component composition of urban PM10 and PM2.5 in Ireland (J. Yin, A. G. Allen, R. M. Harrison, S. G. Jennings, E. Wright, M. Fitzpatrick, T. Healy, E. Barry, D. Ceburnis and D. McCusker, Atmos. Res., 2005, 78(3-4), 149-165). Measurements included size-segregated mass, soluble ions, elemental carbon (EC) distributions, fine and coarse fraction organic carbon (OC) and major gases along with standard meteorological measurements. The study revealed that urban emissions in Ireland had mainly a local character and therefore were confined within a limited area of 20-30 km radius, without significantly affecting regional air quality. Gaseous measurements have shown that urban emissions in Ireland had clear, but fairly limited influence on the regional air quality due to favorable mixing conditions at higher wind speeds, in particular from the western sector. Size-segregated mass and chemical measurements revealed a clear demarcation size between accumulation and coarse modes at about 0.8 microm which was constant at all sites. Carbonaceous compounds at the urban site accounted for up to 90% of the particle mass in a size range of 0.066-0.61 microm. Nss SO4(2-) concentrations in PM2.5 were only slightly higher at the urban site compared to the rural or coastal sites, while NO3- and NH4+ concentrations were similar at the urban and coastal sites, but were a factor of 2 to 3 higher than at the rural site. OC was highly variable between the sites and revealed clear seasonal differences. Natural or biogenic OC component accounted for <10% in winter and up to 30% in summer of the PM2.5 OC at urban sites. A contribution of biogenic OC component to PM2.5 OC mass at rural site was dominant.