PM_(2.5)手工标准方法与自动监测法比对分析

选取金华、衢州、温州、丽水、宁波、杭州6个城市开展PM2.5手工标准方法和自动监测法比对实验,并用相关性和相对偏差两个指标对比对结果进行分析和评价。结果表明:(1)2013年6个采样城市采集的PM2.5手工和自动监测值均具有较好的相关性(相关系数均在0.95以上),截距均在-0.010~0.010mg/m3,但斜率相差较大(衢州和丽水在0.90以上;金华、温州和杭州在0.85~0.90;宁波在0.80以下)。(2)2013年6个城市采集的PM2.5手工和自动监测值的相对偏差为-34.2%~36.5%;PM2.5手工和自动监测值相对偏差在±15%范围内的数据占总数据量的82.6%;负偏差数据占总数据量的80.0%。(3)PM2.5手工标准方法和自动监测法的比对差异与地域、季节和PM2.5浓度等条件有关。总体上,不同地区PM2.5手工与自动监测值相对偏差绝对值(︱RD︱)年平均值为衢州丽水金华宁波温州杭州;春季PM2.5手工与自动监测值︱RD︱平均值高于夏季,秋季高于冬季;各采样城市PM2.5手工和自动监测值︱RD︱平均值在高质量浓度(PM2.5手工监测值(ρ1)0.150mg/m3)下最小,中质量浓度(0.050≤ρ1≤0.150mg/m3)下最大,低质量浓度(ρ10.050mg/m3)下介于两者之间。     

京津冀地区环境空气PM2.5自动监测现场比对研究

采用手工采样器对京津冀地区环境空气PM2.5自动监测数据进行现场比对,依据自动仪器与手工仪器数据的相对偏差来评价自动监测数据质量.研究发现,京津冀地区PM2.5自动监测数据质量总体较好,2013年较2012年有所提高.各城市PM2.5自动监测与手工监测数据的相对偏差之间差异较大,相对偏差绝对值的平均值的波动范围为4.3％ ～29.5％.当PM2.5浓度较低时,相对偏差在±25％范围内的数据比例较小,低浓度时的数据质量应引起重视.     

PM10-PM2.5冲击采样器的研制与开发

在颗粒物研究中,分级采样是一种常用的监测方法,而冲击采样器是颗粒物分级采样的重要仪器.根据斯托克斯数,对PM10-PM2.5冲击采样器设计参数进行了详细分析,并对PM10-PM2.5的捕集效率特征进行了分析.结果表明,PM10-PM2.5冲击采样器具备理想的PM10和PM2.5捕集效率,PM10冲击采样器、PM2.5冲击采样器切割粒径分别为9.94、2.43μm,均在其允许误差范围内.     

冷凝湿度控制对微振荡天平监测细颗粒物性能的改进

为提高细颗粒物(PM2.5)测量的准确性,尝试采用一种新型的气溶胶冷凝湿度控制器(简称冷凝湿度控制器)作为微振荡天平法颗粒物监测仪(TEOM)的除湿方式,在广东大气超级监测站开展了TEOM自动监测(一台采用传统的加热除湿方式,记为TEOM1405;另一台采用冷凝湿度控制器除湿,记为TEOM1405+除湿)和手工监测结果的对比。结果表明,根据PM2.5日均值相关性的拟合结果,TEOM1405监测较手工监测结果总体偏低约13%,加装冷凝湿度控制器后,TEOM1405+除湿监测较手工监测结果总体偏低在5%以内。加装冷凝湿度控制器后,显著提高了PM2.5的监测准确性;在相对湿度较高、二次颗粒物生成量较少的大气环境中,TEOM1405+除湿系统对PM2.5的监测结果是可靠的,而且在降雨过程中监测结果更为稳定;但在相对湿度较高、且二次颗粒物生成量较多的大气环境中,其对PM2.5的监测性能仍待进一步考察;在PM2.5污染比较严重的高污染时段,TEOM1405、TEOM1405+除湿监测到的PM2.5日均质量浓度分别比手工监测结果偏低26%和11%,偏低较多。但这种高污染情况在珠三角地区出现的概率很低,故采用TEOM1405+除湿系统进行PM2.5长期自动监测是可取的。     

北京市2005年夏季大气颗粒物污染特征及影响因素

对2005年7～8月北京市不同功能区8个采样点PM10和PM2.5的浓度水平、空间分布、PM10/PM2.5比值进行了分析,并讨论了PM10和PM2.5的日变化特征及影响因素.结果表明,北京市夏季PM10和PM2.5日均浓度为155.37 μg/m3和87.70 μg/m3,分别为国家二级标准和美国PM2.5标准的1.04倍和1.35倍;PM2.5、PM10浓度在不同功能区存在一定差异;PM2.5和PM10的日变化以白天高,夜间低为主,且不同功能区的最高值对应于城市居民活动的不同高峰期;在湿度较高的情况下,PM2.5、PM10与湿度呈一定正相关性,且湿度对PM2.5的影响更大;降水前后PM2.5、PM10浓度变化情况表明降水的主要作用是清除粗粒子,对PM2.5的影响则较小.     

冲击采样器设计参数分析

在颗粒物研究中，分级采样是一种常用的监测方法，而冲击采样器是颗粒物分级采样的重要仪器。从颗粒物的受力分析着手，得到了冲击采样器重要参数——斯托克斯数的表达式，并对TSP、PM10和PM2.5冲击采样器设计参数进行了详细分析，得到了采样器设计参数的关系式及相关曲线。     

区域大气环境中PM2．5／PM10空间分布研究

提出了一种利用移动监测技术研究区域大气环境中PM2.5／PM10空间分布的方法，并在2004年12月进行了宁波市全市域PM2.5／PM10空间分布的研究。数据显示：相同路径所代表的地区PM2.5和PM10具有很好的相关性，多数路径上PM2.5与PM10数据的相关系数平方在0．95以上，而不同路径上PM2.5与PM10的比值不同。文中给出了宁波市PM2.5／PM10污染的空间分布图，直观地显示出PM2.5／PM10污染的空间分布情况，突出了污染的重点点位和地区。     

重庆主城区春季典型天气的大气颗粒物浓度变化分析

选取重庆大气超级站2010年春季典型天气时段的颗粒物实时监测数据,将β射线法和震荡天平法(TEOM法)的PM10监测值进行了比对,分析了PM10、PM2.5和PM1质量浓度百分比例关系及10μm以下颗粒物数浓度随粒径大小的分布规律。结果表明,β射线法与TEOM法的PM10监测结果基本一致,β射线法比TEOM法监测值平均偏低5.4%;PM2.5、PM1和PM0.5的数浓度均占PM10数浓度的98%以上;PM0.25数浓度占PM10数浓度的平均比例为34.9%,占PM1数浓度的平均比例为35.1%;TEOM法监测的PM2.5占PM10日均质量浓度平均比例为51.2%;β射线法监测的PM2.5占PM10日均质量浓度平均比例为56.9%,PM1占PM10平均比例为30.9%。     

乌鲁木齐市大气可吸入颗粒物中多环芳烃的污染特征及来源解析

在乌鲁木齐市南、北设置2个采样点,从2011年3-12月采集可吸入颗粒物(PM2.5、PM2.5-10)样品,分析了美国环境保护署优控的13种多环芳烃(PAHs)的浓度,采用比值法、主成分分析法和多元线性回归法对乌鲁木齐市大气PM2.5、PM2.5-10中PAHs的来源进行了分析。结果表明,科学院站PM2.5中13种PAHs的总质量浓度平均值为247.2ng/m3,变动范围为1.14~2 113.33ng/m3;新大站PAHs的总质量浓度平均值为240.84ng/m3,变动范围为4.96~1 359.41ng/m3。而科学院站PM2.5-10中13种PAHs的总质量浓度平均值为57.78ng/m3,变动范围为1.18~519.87ng/m3;新大站的总质量浓度平均值为49.18ng/m3,变动范围为1.38~412.52ng/m3。比值法分析结果表明,所采集样品的2/3来自煤和生物质的燃烧排放;主成分分析法和多元线性回归分析法结果表明,采暖期汽油和煤源对PM2.5中总PAHs的贡献率为46%,而非采暖期混合源的贡献率高达85%。采暖期汽油和柴油源对PM2.5-10中总PAHs的贡献率为66%,而非采暖期混合源的贡献率为78%。     

西安市灰霾天气下PM_(2.5)浓度与气象条件分析

分析了2013年1—3月西安市12个空气监测子站监测的PM10、PM2.5以及相关气象参数;绘制了不同月的主城区浓度分布等值线图。运用单样本K-S非参数检验法检验表明,PM2.5浓度符合对数正态分布;各站点间的PM2.5浓度相关性非常高,变化趋势一致;PM10和PM2.5的变化规律呈现"W"型三峰分布;PM2.5日均值与能见度、净辐射量、平均气温、最高气温、最低气温均呈现显著负相关,且相关性较强;与平均湿度、最大湿度、最小湿度呈现显著正相关;与总辐射量、日照时数、气压、露点温度的相关性较弱;节日烟花燃放、沙尘天气容易造成严重大气污染,其中节日烟花燃放、沙尘天气对PM10的贡献量大于对PM2.5的贡献。     

Sources and processes affecting concentrations of PM10 and PM2.5 particulate matter in Birmingham (U.K.)

Hourly average concentrations of PM₁₀ and PM_2.5 have been measured simultaneously at a site within Birmingham U.K. between October 1994 and October 1995. Comparison of PM₁₀ and NO_x data with two other sites in the same city shows comparable summer and winter mean concentrations and highly significant inter-site correlations for both hourly and daily mean data. Over a four-month period samples were also collected for chemical analysis of sulphate, nitrate, chloride, ammonium and elemental and organic carbon. Analysis of the data indicates a marked difference between summer and winter periods. In the winter months PM_2.5 comprises about 80% of PM₁₀ and is strongly correlated with NO_x indicating the importance of road traffic as a source. In the summer months, coarse particles (PM₁₀−PM_2.5) account for almost 50% of PM₁₀ and the influence of resuspended surface dusts and soils and of secondary particulate matter is evident. The chemical analysis data are also consistent with three sources dominating the PM₁₀ composition: vehicle exhaust emissions, secondary ammonium salts and resuspended surface dusts. Coarse particles from resuspension showed a positive dependence on windspeed, whilst elemental carbon derived from road traffic exhibited a negative dependence.     

Biomagnetic monitoring of industry-derived particulate pollution

Clear association exists between ambient PM₁₀ concentrations and adverse health outcomes. However, determination of the strength of associations between exposure and illness is limited by low spatial-resolution of particulate concentration measurements. Conventional fixed monitoring stations provide high temporal-resolution data, but cannot capture fine-scale spatial variations. Here we examine the utility of biomagnetic monitoring for spatial mapping of PM₁₀ concentrations around a major industrial site. We combine leaf magnetic measurements with co-located PM₁₀ measurements to achieve inter-calibration. Comparison of the leaf-calculated and measured PM₁₀ concentrations with PM₁₀ predictions from a widely-used atmospheric dispersion model indicates that modelling of stack emissions alone substantially under-predicts ambient PM₁₀ concentrations in parts of the study area. Some of this discrepancy might be attributable to fugitive emissions from the industrial site. The composition of the magnetic particulates from vehicle and industry-derived sources differ, indicating the potential of magnetic techniques for source attribution.     

Rates of particulate pollution deposition onto leaf surfaces: Temporal and inter-species magnetic analyses

Evaluation of health impacts arising from inhalation of pollutant particles <10 μm (PM₁₀) is an active research area. However, lack of exposure data at high spatial resolution impedes identification of causal associations between exposure and illness. Biomagnetic monitoring of PM₁₀ deposited on tree leaves may provide a means of obtaining exposure data at high spatial resolution. To calculate ambient PM₁₀ concentrations from leaf magnetic values, the relationship between the magnetic signal and total PM₁₀ mass must be quantified, and the exposure time (via magnetic deposition velocity (MV_d) calculations) known. Birches display higher MV_d (∼5 cm⁻¹) than lime trees (∼2 cm⁻¹). Leaf saturation remanence values reached ‘equilibrium’ with ambient PM₁₀ concentrations after ∼6 ‘dry’ days (<3 mm/day rainfall). Other co-located species displayed within-species consistency in MV_d; robust inter-calibration can thus be achieved, enabling magnetic PM₁₀ biomonitoring at unprecedented spatial resolution.     

The impacts of different kinds of dust events on PM10 pollution in northern China

In this study the frequencies of PM₁₀ (as key urban pollutant) in 14 key environmental protection cities in northern China were analyzed. It follows that the PM₁₀ concentration in the high-frequency period is higher with an extent 0.009–0.066 mg m⁻³ than in the low-frequency period of 2001–2002. Further the impacts of three kinds of dust events on the PM₁₀ concentration in four cities (Beijing, Hohhot, Xi’an and Lanzhou) were explored. The results showed that different kinds of dust events have different influences on variation of PM₁₀ concentration in these four cities. In Lanzhou and Hohhot, which are near the source areas of dust events, the contribution degree of these three dust events to the PM₁₀ is: floating dust>dust storm>blowing dust. Whereas, in Beijing and Xi’an situated in dust event passing areas, the mean value of PM₁₀ concentration is higher in blowing dust than in floating dust (no dust storm). In addition, the influences of dust events on PM₁₀ concentration are different in the cities on different dust event paths. In Beijing and Hohhot (on the northern path), the high PM₁₀ concentration is usually caused by blowing dust. But in both Lanzhou and Xi’an (on the western/northwestern path) the high PM₁₀ pollution concentration is usually caused by floating dust.     

Traffic generated non-exhaust particulate emissions from concrete pavement: A mass and particle size study for two-wheelers and small cars

This study aimed to understand the non-exhaust (NE) emission of particles from wear of summer tire and concrete pavement, especially for two wheelers and small cars. A fully enclosed laboratory-scale model was fabricated to simulate road tire interaction with a facility to collect particles in different sizes. A road was cast using the M-45 concrete mixture and the centrifugal casting method. It was observed that emission of large particle non exhaust emission (LPNE) as well as PM₁₀ and PM_2.5 increased with increasing load. The LPNE was 3.5 mg tire⁻¹ km⁻¹ for a two wheeler and 6.4 mg tire⁻¹ km⁻¹ for a small car. The LPNE can lead to water pollution through water run-off from the roads. The contribution of the PM₁₀ and PM_2.5 was smaller compared to the LPNE particles (less than 0.1%). About 32 percent of particle mass of PM₁₀ was present below 1 μm. The number as well as mass size distribution for PM₁₀ was observed to be bi-modal with peaks at 0.3 μm and 4–5 μm. The NE emissions did not show any significant trend with change in tire pressure.     

Analysis of particle-associated semi-volatile aromatic and aliphatic hydrocarbons in urban particulate matter on a daily basis

PM_2.5 Particle-associated semi-volatile organic compounds (SVOC) were determined in the city of Augsburg, Germany. Daily samples were collected at a central monitoring station from late summer to late autumn 2002. The concentrations of polycyclic aromatic hydrocarbons (PAH), oxidized PAH (O-PAH), n-alkanes, hopanes and long chain linear alkylbenzenes were determined by direct thermal desorption-gas chromatography-time of flight mass spectrometry (DTD-GC-TOFMS). Additionally, PM_2.5 particle mass and number concentrations were measured. The sampling campaign can be divided into two parts, distinguished by a lower temperature level in the second part of the campaign. The particulate mass concentration showed no significant changes, whereas most of the SVOC had significant higher mean and peak concentrations in the colder period. The analysis of the data showed an increased influence of non-traffic sources in the colder period, reflected by a weak shift in the PAH profile and a significant shift in the hopane pattern. Statistical analysis of the inter-group correlations was carried out. Eight clusters partly representing different sources of the aerosol have been identified.     

Source characterization of ambient fine particles at multiple sites in the Seattle area

To identify major PM_2.5 (particulate matter ≤2.5 μm in aerodynamic diameter) sources with a particular emphasis on the ship engine emissions from a major port, integrated 24 h PM_2.5 speciation data collected between 2000 and 2005 at five United State Environmental Protection Agency's Speciation Trends Network monitoring sites in Seattle, WA were analyzed. Seven to ten PM_2.5 sources were identified through the application of positive matrix factorization (PMF). Secondary particles (12–26% for secondary nitrate; 17–20% for secondary sulfate) and gasoline vehicle emissions (13–31%) made the largest contributions to the PM_2.5 mass concentrations at all of the monitoring sites except for the residential Lake Forest site, where wood smoke contributed the most PM_2.5 mass (31%). Other identified sources include diesel vehicle emissions, airborne soil, residual oil combustion, sea salt, aged sea salt, metal processing, and cement kiln. Residual oil combustion sources identified at multiple monitoring sites point clearly to the Port of Seattle suggesting ship emissions as the source of oil combustion particles. In addition, the relationship between sulfate concentrations and the oil combustion emissions indicated contributions of ship emissions to the local sulfate concentrations. The analysis of spatial variability of PM_2.5 sources shows that the spatial distributions of several PM_2.5 sources were heterogeneous within a given air shed.     

Long-term variation of PM2.5 levels and composition at rural, urban, and roadside sites in Hong Kong: Increasing impact of regional air pollution

Long-term study of air pollution plays a decisive role in formulating and refining pollution control strategies. In this study, two 12-month measurements of PM_2.5 mass and speciation were conducted in 00/01 and 04/05 to determine long-term trend and spatial variations of PM_2.5 mass and chemical composition in Hong Kong. This study covered three sites with different land-use characteristics, namely roadside, urban, and rural environments. The highest annual average PM_2.5 concentration was observed at the roadside site (58.0±2.0 μg m⁻³ (average±2σ) in 00/01 and 53.0±2.7 μg m⁻³ in 04/05), followed by the urban site (33.9±2.5 μg m⁻³ in 00/01 and 39.0±2.0 μg m⁻³ in 04/05), and the rural site (23.7±1.9 μg m⁻³ in 00/01 and 28.4±2.4 μg m⁻³ in 04/05). The lowest PM_2.5 level measured at the rural site was still higher than the United States’ annual average National Ambient Air Quality Standard of 15 μg m⁻³. As expected, seasonal variations of PM_2.5 mass concentration at the three sites were similar: higher in autumn/winter and lower in summer. Comparing PM_2.5 data in 04/05 with those collected in 00/01, a reduction in PM_2.5 mass concentration at the roadside (8.7%) but an increase at the urban (15%) and rural (20%) sites were observed. The reduction of PM_2.5 at the roadside was attributed to the decrease of carbonaceous aerosols (organic carbon and elemental carbon) (>30%), indicating the effective control of motor vehicle emissions over the period. On the other hand, the sulfate concentration at the three sites was consistent regardless of different land-use characteristics in both studies. The lack of spatial variation of sulfate concentrations in PM_2.5 implied its origin of regional contribution. Moreover, over 36% growth in sulfate concentration was found from 00/01 to 04/05, suggesting a significant increase in regional sulfate pollution over the years. More quantitative techniques such as receptor models and chemical transport models are required to assess the temporal variations of source contributions to ambient PM_2.5 mass and chemical speciation in Hong Kong.     

Results from a pilot-scale air quality study in Addis Ababa, Ethiopia

Twenty-one samples were collected during the dry season (26 January–28 February 2004) at 12 sites in and around Addis Ababa, Ethiopia and analyzed for particulate matter with aerodynamic diameter <10 μm (PM₁₀) mass and composition. Teflon-membrane filters were analyzed for PM₁₀ mass and concentrations of 40 elements. Quartz-fiber filters were analyzed for chloride, sulfate, nitrate, and ammonium ions as well as elemental carbon (EC) and organic carbon (OC) content. Measured 24-h PM₁₀ mass concentrations were <100 and 40 μg m⁻³ at urban and suburban sites, respectively. PM₁₀ lead concentrations were <0.1 μg m⁻³ for all samples collected, an important finding because the government of Ethiopia had stopped the distribution of leaded gasoline a few months prior to this study. Mass concentrations reconstructed from chemical composition indicated that 34–66% of the PM₁₀ mass was due to geologically derived material, probably owing to the widespread presence of unpaved roads and road shoulders. At urban sites, EC and OC compounds contributed between 31% and 60% of the measured PM₁₀ while at suburban sites carbon compounds contributed between 24% and 26%. Secondary sulfate aerosols were responsible for <10% of the reconstructed mass in urban areas but as much as 15% in suburban sites, where PM₁₀ mass concentrations were lower. Non-volatile particulate nitrate, a lower limit for atmospheric nitrate, constituted <5% and 7% of PM₁₀ at the urban and suburban sites, respectively. At seven of the 12 sites, real-time PM₁₀ mass, real-time carbon monoxide (CO), and instantaneous ozone (O₃) concentrations were measured with portable nephelometers, electrochemical analyzers, and indicator test sticks, respectively. Both PM₁₀ and CO concentrations exhibited daily maxima around 7:00 and secondary peaks in the late afternoon and evening, suggesting that those pollutants were emitted during periods associated with motor-vehicle traffic, food preparation, and heating of homes. The morning concentration maxima were likely accentuated by stable atmospheric conditions associated with overnight surface temperature inversions. Ozone concentrations were measured near mid-day on filter sample collection days and were in all cases <45 parts per billion.     

Appraisal of measurement methods, chemical composition and sources of fine atmospheric particles over six different areas of Northern Belgium

Daily and seasonal variation in the total elemental, organic carbon (OC) and elemental carbon (EC) content and mass of PM_2.5 were studied at industrial, urban, suburban and agricultural/rural areas. Continuous (optical Dustscan, standard tapered element oscillating micro-balance (TEOM), TEOM with filter dynamics measurement system), semi-continuous (Partisol filter-sampling) and non-continuous (Dekati-impactor sampling and gravimetry) methods of PM_2.5 mass monitoring were critically evaluated. The average elemental fraction accounted for 2-6% of the PM_2.5 mass measured by gravimetry. Metals, like K, Mn, Fe, Cu, Zn and Pb were strongly inter-correlated, also frequently with non-metallic elements (P, S, Cl and/or Br) and EC/OC. A high OC/EC ratio (2-9) was generally observed. The total carbon content of PM_2.5 ranged between 3 and 77% (averages: 12-32%), peaking near industrial/heavy trafficked sites. Principal component analysis identified heavy oil burning, ferrous/non-ferrous industry and vehicular emissions as the main sources of metal pollution.