南宁市大气颗粒物TSP、PM10、PM2.5污染水平研究

2002年在南宁市的5个典型城市功能区内,共采集了125个大气样品(按季节分别采集),初步调查了大气中颗粒物TSP、PM10、PM2.5的污染状况。结果表明,南宁市TSP、PM10、PM2.5的污染很严重,超标率分别为67．5％、82．5％、92．5％,对人体健康危害更大的PM2．5占到了PM10的63．5％左右。重污染区PM2.5的浓度超过轻污染区近一倍。     

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。     

宁波市大气可吸入颗粒物PM1o和PM2.5的源解析研究

在宁波市布设4个代表性点位,于2010年春季、夏季和冬季进行大气PM10和PM2.s的采样,同时采集了多种颗粒物源样品,建立了PM10、PM2.5和源样品的化学成分谱.采用化学质量平衡模型(CMB)对宁波市PM10、PM2.5进行源解析.结果表明,城市扬尘、煤烟尘、机动车尾气尘是宁波市PM10、PM2.5的3大污染源,...     

贵阳市冬季大气PM 2.5和PM 10浓度空间插值及分布研究

为了解贵阳市冬季大气污染现状,以贵阳市污染相对严重的白云区为研究对象,连续采集PM_(2.5)、PM_(10)浓度数据,利用普通克里金法进行空间插值获取PM_(2.5)、PM_(10)分布特征。通过留一法交叉验证,比较6种半变异函数模型(三角函数、高斯函数、球面函数、指数函数、J-Bessel函数和K-Bessel函数)的空间插值精度,选出最适的函数模型;采用分区统计和格网统计的方法,对不同土地利用类型、植被覆盖度下的PM_(2.5)、PM_(10)平均浓度进行比较分析。结果表明,三角函数是PM_(2.5)空间插值的最适模型,指数函数是PM_(10)空间插值的最适模型;贵阳市白云区冬季大气PM_(2.5)、PM_(10)浓度总体表现出城区浓度高,郊区浓度低的分布特征;土地利用类型和植被覆盖度对PM_(2.5)和PM_(10)浓度有着较强的影响。     

中国近年PM2.5污染研究进展

近年来雾霾天气在中国大面积频发,PM_(2.5)已经成为中国大气颗粒物污染的首要污染物。对中国近年来PM_(2.5)的研究进展进行总结,分析了城市大气及室内环境中PM_(2.5)的来源,阐述了PM_(2.5)对大气能见度、人体健康及人们行为方式的影响,介绍了室内外关于PM_(2.5)的相关性指标以及PM_(2.5)控制的最新技术等,最后对相关研究前景进行分析并提出建议。     

济南市PM2.5化学组分及污染特征分析

为探讨济南市大气PM_(2.5)主要化学组分和污染特征,2017年在济南市开展了PM_(2.5)样品采集工作,分析了PM_(2.5)中有机碳(OC)、元素碳(EC)和水溶性离子浓度水平。结果表明:采样期间济南市PM_(2.5)中OC、EC年均质量浓度分别为9.10、2.68μg/m~3,全年OC与EC质量浓度的比值为3.4,二次有机碳污染严重;OC、EC季节分布特征明显,均为冬季浓度最高,且秋、冬季两者相关系数较高,表明秋季和冬季OC、EC来源较为一致。NO_3~-、SO_4~(2-)、NH_4~+年均质量浓度之和为34.29μg/m~3,占水溶性离子总量的88.9%,是济南市PM_(2.5)中最重要的组分;各水溶性离子浓度具有明显的季节变化特征,NO_3~-、SO_4~(2-)、NH_4~+、Cl~-和K~+均冬季浓度最高,而Ca~(2+)春季浓度最高;PM_(2.5)中NO_3~-与SO_4~(2-)质量浓度的比值为1.10,说明相比于固定污染源,移动污染源对济南市PM_(2.5)影响更大。     

不同树种配置PM2.5动态变化特征

以北京市大兴区南海子公园26种常见树种配置为研究对象,应用Dustmate手持PM_(2.5)监测仪监测各配置PM_(2.5),结合气溶胶再发生器和叶面积扫描仪,分析单位叶面积PM_(2.5)吸附量,以综合探讨不同树种配置PM_(2.5)动态变化特征。结果表明:总体上,各树种配置中PM_(2.5)浓度呈现上午高、下午低的趋势,14:00最低。各树种配置中PM_(2.5)平均值表现为针阔混交林阔叶纯林阔阔混交林针叶纯林针针混交林,且6月9月7月10月5月8月。不同树种配置对PM_(2.5)的吸附能力差异较大,表现为针叶纯林针针混交林针阔混交林阔叶纯林阔阔混交林。因植物吸附PM_(2.5)能力取决于单位叶面积PM_(2.5)吸附量及其叶面积指数,进行树种配置时需同时考虑这两个因素,将不同生活型和具不同叶习性的植物合理混交配置,从而提高植被吸附和调控PM_(2.5)的能力,为优化城市绿化植物配置、降低空气中PM_(2.5)污染提供科学依据。     

燃煤工业锅炉PM2.5排放规律

当前我国工业锅炉中最主要应用炉型为链条炉,是大气污染物排放的重要污染源之一。本研究利用基于荷电低压捕集器(ELPI)的颗粒物排放稀释系统,选取5台典型链条燃煤工业锅炉,对其除尘器的进口、出口和脱硫后3处进行细微颗粒物(PM2.5)的现场测试。粒径分布结果表明,粒数浓度较多在0.04~0.3μm范围内,质量浓度分布在0.08~0.25μm范围内呈单峰上升形态。除尘装置对PM2.5的捕集效率在50%左右,除尘效果较差;脱硫后有些级的颗粒物浓度不降反升。目前环境日趋恶劣,燃煤工业锅炉作为PM2.5的重要排放源,将是今后重点控制对象。     

国际PM2.5排放标准及其实施情况比较分析

对主要国际组织和部分国家的PM2.5排放标准及其实施情况进行了比较和分析.结果表明,世界卫生组织(WHO)和欧盟、美国、加拿大、澳大利亚、日本等均已制定了PM2.5排放标准;墨西哥和印度等发展中国家制定了PM2.5排放标准,中国也制定了PM2.5排放标准,但还未正式发布.WHO除制定了PM2.5的日均浓度限值和年均浓度限值外,还设立3个过渡时期目标值.发达国家制定的PM2.5日均浓度限值比较一致(在25～35 /μg/m3),低于发展中国家(墨西哥和印度)制定的限值标准.发达国家中澳大利亚制定的PM2.5排放标准最为严格,而日本制定的PM2.5排放标准在亚洲最为严格.WHO、欧盟、美国、加拿大和印度还规定了PM2.5的达标判断要求,各要求有所差异,而中国还未规定PM2.5达标的判断要求.美国制定了PM2.5排放标准的详细实施计划,中国拟发布的PM2.5排放标准也将分期实行.     

Elemental characterization of PM10, PM2.5 and PM1 in the town of Genoa (Italy)

The particulate matter (PM) concentration and composition, the PM10, PM2.5, PM1 fractions, were studied in the urban area of Genoa, a coastal town in the northwest of Italy. Two instruments, the continuous monitor TEOM and the sequential sampler PARTISOL, were operated almost continuously on the same site from July 2001 to September 2004. Samples collected by PARTISOL were weighted to obtain PM concentration and then analysed by PIXE (particle induced X-ray emission) and by ED-XRF (energy dispersion X-ray fluorescence), obtaining concentrations for elements from Na to Pb. Some of the filters used in the TEOM microbalance were analysed by ED-XRF to calculate Pb concentration values averaged over 7-30 d periods.     

贵阳市白云区大气PM_(10)和PM_(2.5)污染特征

使用β射线法在线监测仪连续监测了贵阳市白云区PM_(10)和PM_(2.5)浓度,分析了2014年6月1日—12月31日7个月内PM_(10)、PM_(2.5)的浓度水平、时变规律和PM_(2.5)/PM_(10)的变化情况。结果表明,监测时段内PM_(10)和PM_(2.5)的日均浓度平均值分别为76.8μg/m~3和40.0μg/m~3,均达到国家二级标准;浓度超标的天数占总观测天数的5.1%和9.3%,属污染轻微的地区。PM_(2.5)/PM_(10)在25.3%~78.8%之间周期性波动,平均值为52.1%。PM_(10)和PM_(2.5)的浓度变化具有很好的正相关性(r=0.919 8,p0.000 1);日均值在7个月中呈现明显的周期性变化,各月相对稳定,12月的PM_(10)和PM_(2.5)浓度最高且变化最为剧烈,6月最为平缓。PM_(10)和PM_(2.5)浓度小时变化总体上呈双峰型分布,最高值出现在出现在09:00—10:00和19:00—21:00前后,最低值出现在14:00—17:00之间。     

Monitoring of PM10 and PM2.5 around primary particulate anthropogenic emission sources

Investigations on the monitoring of ambient air levels of atmospheric particulates were developed around a large source of primary anthropogenic particulate emissions: the industrial ceramic area in the province of Castelló (Eastern Spain). Although these primary particulate emissions have a coarse grain-size distribution, the atmospheric transport dominated by the breeze circulation accounts for a grain-size segregation, which results in ambient air particles occurring mainly in the 2.5–10 μm range. The chemical composition of the ceramic particulate emissions is very similar to the crustal end-member but the use of high Al, Ti and Fe as tracer elements as well as a peculiar grain-size distribution in the insoluble major phases allow us to identify the ceramic input in the bulk particulate matter. PM2.5 instead of PM10 monitoring may avoid the interference of crustal particles without a major reduction in the secondary anthropogenic load, with the exception of nitrate. However, a methodology based in PM2.5 measurement alone is not adequate for monitoring the impact of primary particulate emissions (such as ceramic emissions) on air quality, since the major ambient air particles derived from these emissions are mainly in the range of 2.5–10 μm. Consequently, in areas characterised by major secondary particulate emissions, PM2.5 monitoring should detect anthropogenic particulate pollutants without crustal particulate interference, whereas PM10 measurements should be used in areas with major primary anthropogenic particulate emissions.     

Wintertime PM2.5 and PM10 Source Apportionment at Sacramento,California

ABSTRACT

The chemical mass balance (CMB) model was applied to winter (November through January) 1991–1996 PM2.5 and PM10 data from the Sacramento 13th and T Streets site in order to identify the contributions from major source categories to peak 24-hr ambient PM_2.5 and PM₁₀ levels. The average monthly PM₁₀ monitoring data for the nine-year period in Sacramento County indicate that elevated concentrations are typical in the winter months. Concentrations on days of highest PM₁₀ are dominated by the PM2.5 fraction. One factor contributing to increased PM_2.5 concentrations in the winter is meteorology (cool temperatures, low wind speeds, low inversion layers, and more humid conditions) that favors the formation of secondary nitrate and sulfate aerosols. Residential wood burning also elevates fine particulate concentrations in the Sacramento area.

The results of the CMB analysis highlight three key points. First, the source apportionment results indicate that primary motor vehicle exhaust and wood smoke are significant sources of both PM_2.5 and PM₁₀ in winter. Second, nitrates, secondarily formed as a result of motor-vehicle and other sources of nitrogen oxide (NO_x), are another principal cause of the high PM_2.5 and PM₁₀ levels during the winter months. Third, fugitive dust, whether it is resuspended soil and dust or agricultural tillage, is not the major contributor to peak winter PM_2.5 and PM10 levels in the Sacramento area.     

Pollution characteristics and toxic effects of PM1.0 and PM2.5 in Harbin,China

Environmental Science and Pollution Research - In 2019, PM2.5 and PM1.0 samples were collected in Harbin City, Heilongjiang Province, China, to research their mass concentration, number...     

天津冬季PM2.5与PM10中有机碳、元素碳的污染特征

研究了天津冬季PM2.5和PM10中碳成分的污染特征.结果表明,天津冬季PM2.5和PM10的平均质量浓度分别为(124.4±60.9)、(224.6±131.2)μg/m3;总碳(TC)、有机碳(OC)与元素碳(EC)在PM2.5中的平均质量分数比在PM10中分别高出5.0%、3.6%、1.2%;PM2.5中OC、EC的相关系数较高,为0.95,表明OC、EC的来源相对简单,可能主要反应了燃煤和机动车尾气的贡献.OC/EC的平均值在PM2.5和PM10中分别为3.9、4.9.次生有机碳(SOC)在PM2.55和PM10中的平均质量浓度分别为14.9、23.4/μg/m3,分别占OC的48.5%(质量分数,下同)、49.8%,OC/EC较高可能主要与直接排放源有关;PM2.5中的OC1与OC2的比例明显高于PM10,而聚合碳(OPC)的比例又低于PM10,同时PM2.5与PM10中的EC1含量均较高,表明天津冬季燃煤取暖和机动车尾气是重要的污染源.     

Elemental composition of PM10 and PM2.5 in urban environment in South Brazil

The purpose of the present study is to analyze the elemental composition and the concentrations of PM₁₀ and PM_2.5 in the Guaíba Hydrographic Basin with HV PM₁₀ and dichotomous samplers. Three sampling sites were selected: 8° Distrito, CEASA and Charqueadas. The sampling was conducted from October 2001 to December 2002. The mass concentrations of the samplers were evaluated, while the elemental concentrations of Si, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu and Zn were determined using the Particle-Induced X-ray Emission (PIXE) technique. Factor Analysis and Canonical Correlation Analysis were applied to the chemical and meteorological variables in order to identify the sources of particulate matter. Industrial activities such as steel plants, coal-fired power plants, hospital waste burning, vehicular emissions and soil were identified as the sources of the particulate matter. Concentration levels higher than the daily and the annual average air quality standards (150 and 50 μg m⁻³, respectively) set by the Brazilian legislation were not observed.     

A four-year size-segregated characterization study of particles PM10, PM2.5 and PM1 depending on air mass origin at Melpitz

PM₁₀ measurements were started in November 1992 at Melpitz site. The mean PM₁₀ concentration in 1993 was 38 μg m^?3 in the summer season (May until October) and about 44 μg m^?3 in the winter season (November until April). The mean PM₁₀ level decreased until 1999 and varies now in ranges from 20–34 μg m^?3 to 17–24 μg m^?3 (minimum and maximum mean values for 1999–2008) in winter and summer seasons, respectively. High volume filter samples of particles PM₁₀, PM_2.5 and PM₁ were characterized for mass, water-soluble ions, organic and elemental carbon from 2004 until 2008. The percentage of PM_2.5 in PM₁₀ varies between summer (71.6%) and winter seasons (81.9%). Mean concentrations of PM₁₀, PM_2.5 and PM₁ in Melpitz were 20, 15, and 13 μg m^?3 in 2004, 22, 18, and 13 μg m^?3 in 2005, 24, 19, and 12 μg m^?3 in 2006 and 22, 17, and 12 μg m^?3 in 2007, respectively. In the four winters the rural background concentration PM₁₀ at Melpitz exceeded the daily 50 μg m^?3 limit for Europe on 8, 8, 7 and 6 days, respectively.Findings for a simple two-sector-classification of the samples (May 2004 until April 2008) using 96-h backward trajectories for the identification of source regions are: Air masses were transported most of time (60%) from the western sector and secondly (17%) from the eastern sector. The lowest daily mean mass concentration PM₁₀ were found during western inflow in summer (17 μg m^?3) containing low amounts of sulphate (2.4 μg m^?3), nitrate (1.7 μg m^?3), ammonium (1.1 μg m^?3) and TC (3.7 μg m^?3). In opposite the highest mean mass concentration PM₁₀ was found during eastern inflow in winter (35 μg m^?3) with high amounts of sulphate (6.1 μg m^?3), nitrate (5.4 μg m^?3), ammonium (3.8 μg m^?3) and TC (9.4 μg m^?3). An estimation of secondary formed OC (SOA) shows 0.8–0.9 μg m^?3 for air masses from West and 2.1–2.2 μg m^?3 from East. The seasonal difference can be neglected.The half-hourly measurements of the particle mass concentration PM₁₀ evaluated as mean daily courses using a TEOM^® show low values (14–21 μg m^?3) in summer and winter for air masses transported from West and the highest concentrations (31–38 μg m^?3) in winter for air masses from East.The results demonstrate the influence of meteorological parameters on long-range transport, secondary particle mass formation and re-emission which modify mass concentration and composition of PM₁₀, PM_2.5 and PM₁. Melpitz site is located in the East of Germany faraway from strong local anthropogenic emissions (rural background). Therefore, this site is suitable for investigation of the influence of long-range transport of air pollution in continental air masses from the East with source regions inside and outside of the European Union.     

PM10, PM2.5 and PM1.0—Emissions from industrial plants—Results from measurement programmes in Germany

Emission measurement programmes were carried out at industrial plants in several regions of Germany to determine the fine dust in the waste gases; the PM₁₀, PM_2.5 and PM_1.0 fractions were sampled using a cascade impactor technique. The installations tested included plants used for: combustion (brown coal, heavy fuel oil, wood), cement production, glass production, asphalt mixing, and processing plants for natural stones and sand, ceramics, metallurgy, chemical production, spray painting, wood processing/chip drying, poultry farming and waste treatment. In addition waste gas samples were taken from small-scale combustion units, like domestic stoves, firing lignite briquettes or wood.In total 303 individual measurement results were obtained during 106 different measurement campaigns. In the study it was found that in more than 70% of the individual emission measurement results from industrial plants and domestic stoves the PM₁₀ portion amounted to more than 90% and the PM_2.5 portion between 50% and 90% of the total PM (particulate matter) emission. For thermal industrial processes the PM_1.0 portion constituted between 20% and 60% of the total PM emission.Typical particle size distributions for different processes were presented as cumulative frequency distributions and as frequency distributions. The particle size distributions determined for the different plant types show interesting similarities and differences depending on whether the processes are thermal, mechanical, chemical or mixed. Consequently, for the groups of plant investigated, a major finding of this study has been that the particle size distribution is a characteristic of the industrial process. Attempts to correlate particle size distributions of different plants to different gas cleaning technologies did not lead to usable results.     

Identification of dicarboxylic acids and aldehydes of PM10 and PM2.5 aerosols in Nanjing,China

In this study aerosol samples of PM10 and PM2.5 collected from 18 February 2001 to 1 May 2001 in Nanjing, China were analyzed for their water-soluble organic compounds. A series of homologous dicarboxylic acids (C_2–10) and two kinds of aldehydes (methylglyoxal and 2-oxo-malonaldehyde) were detected by GC and GC/MS. Among the identified compounds, the concentration of oxalic acid was the highest at all the five sites, which ranged from 178 to 1423 ng/m³. The second highest concentration of dicarboxylic acids were malonic and succinic acids, which ranged from 26.9 to 243 ng/m³. Higher level of azelaic acid was also observed, of which the maximum was 301 ng/m³. As the highest fraction of dicarboxylic acids, oxalic acid comprised from 28% to 86% of total dicarboxylic acids in PM10 and from 41% to 65% of total dicarboxylic acids in PM2.5. The dicarboxylic acids (C₂, C₃, C₄) together accounted for 38–95% of total dicarboxylic acids in PM10 and 59–87% of dicarboxylic acids in PM2.5. In this study, the total dicarboxylic acids accounted for 2.8–7.9% of total organic carbon (TOC) of water-soluble matters for PM10 and 3.4–11.8% of TOC for PM2.5. All dicarboxylic acids detected in this study together accounted for about 1% of particle mass. The concentration of azelaic acid was higher at one site than others, which may be resulted from higher level of volatile fat used for cooking. The amounts of dicarboxyic acids (C_2,3,4,9) and 2-oxo-malonaldehyde of PM2.5 were higher in winter and lower in spring. Compared with other major metropolitans in the world, the level of oxalic acid concentration of Nanjing is much higher, which may be contributed to higher level of particle loadings, especially for fine particles.