Characterization of chemical species in PM2.5 and PM10 aerosols in suburban and rural sites of central Taiwan.

Aerosol samples for PM2.5 (particulate matter with aerodynamic diameters less than 2.5 microns), PM2.5-10 (particulate matter with aerodynamic diameters between 2.5 and 10 microns) and TSP were collected from June to September 1998 at THU (suburban) and HKIT (rural) sites in central Taiwan. The ratios of PM2.5/PM10 averaged 0.70 for the daytime and 0.63 for the nighttime at THU, respectively. At HKIT, the PM2.5/PM10 ratios averaged 0.56 for the daytime and 0.72 in the nighttime, respectively. These results indicated that the PM2.5 concentrations contribute the majority of the PM10 concentration and PM10 concentrations contribute the majority of the TSP at both sites. The averaged PM2.5 concentrations at THU are higher than those measured at HKIT during the daytime period. However, the average PM2.5-10 concentrations in THU are lower than those measured at HKIT during nighttime. The samples collected were also analyzed by atomic absorption spectrophotometry for the elemental analysis of Ca, Fe, Pb, Zn, Cu, Mn and Cr. Meanwhile ion chromatography was used to analyze for the water-soluble ions: sulphate, nitrate and chloride in the Universal samples. The concentrations of heavy metals in PM10 during daytime were all higher than nighttime at THU. However, the averaged concentrations of metal elements in PM10 during day and night period were distributed irregularly at HKIT. The results indicated that for metal elements collected at HKIT have different emission sources. The concentrations of metal elements during daytime in PM10 at THU were generally higher than HKIT. The phenomena owing to the averaged PM2.5 particle concentrations at THU (suburban) were higher than those measured at HKIT (rural) and PM2.5 occupied the major portions of PM10 for both sites during the day period. For anion species, there are no significant differences between day and night period in PM10 concentrations at both suburban and rural sites.     

The PM2.5 and PM10 particles in urban areas of Taiwan

This study conducted an atmospheric aerosol sampling to measure the PM10 (particles < 10 microns in aerodynamic diameter) and PM2.5 (particles < 2.5 microns in aerodynamic diameter) mass concentrations from October 1996 to June 1997 in northern (Taipei), central (Taichung) and southern (Kaohsiung), the three largest cities of Taiwan. Seventy-eight samples were obtained to measure the mass concentrations of PM10 and PM2.5 from nine sampling sites. According to those results, the PM10 mass concentrations in Taipei, Taichung and Kaohsiung were 42.19, 60.99 and 77.10 micrograms/m3, respectively. The corresponding PM2.5 mass concentrations were 23.09, 39.97 and 48.47 micrograms/m3, respectively. The PM2.5 fraction accounted for 61-67% of the PM10 mass in central and southern Taiwan, but was lower (54-59%) in northern Taiwan. Some samples in which the PM2.5 fraction was overwhelmingly dominant could reach as high as 80-95% of the PM10 mass. In addition, the PM2.5, PM10 levels and PM2.5/PM10-2.5 (particles with aerodynamic diameters ranging from 2.5 to 10 microns) ratios in metropolitan Taiwan significantly fluctuated from site-to-site and over time. Moreover, ambient daily PM2.5 and PM10-2.5 mass concentrations did not correlate well with each other at most of the sampling sites, indicated that they originated from different kinds of sources and emitted variedly over time.     

不同城市绿地类型对PM 2.5 、PM 10 以及TSP的消减效应研究

一直以来,绿地系统都是城市户外 公共活动、文化展示、景观美化等功能的重 要载体。此外,基于景观生态学的绿地系统 研究使其生态调节功能进一步强化。作为城 市的主要户外公共空间,城市绿地的空气质 量关乎整个城市居民的健康,虽然已有部分 研究开始初步探索城市绿地在消减空气颗 粒物上的作用,但仍缺乏更加系统性的对比 研究,以揭示不同绿地类型中绿地率、植物 群落结构等特征对空气颗粒物的不同消减 效应。本文采用定量测定的方法,对重庆市 典型的绿地类型进行全年的测试分析。研究 表明：绿地率最高的公园绿地对于空气颗粒 物的消减率最明显;在一年四季中,TSP在夏 季的消减率最高,PM 10 的消减率春季最高, PM 2.5 的消减率冬季最高;在不同空气颗粒物 的消减率对比分析中,各种城市绿地对TSP 的消减率最高,能达到15%以上,对PM 2.5 的 消减率最低,在夏季甚至出现负值。由此可见,城市绿地对总悬浮颗粒物（TSP）和粗颗粒物（PM 10 ）都具有很好的消减效果,但是在对细 颗粒物(PM 2.5 )的消减效果则不明显。     

Comparative PM10-PM2.5 source contribution study at rural, urban and industrial sites during PM episodes in Eastern Spain

In this study a set of 340 PM10 and PM2.5 samples collected throughout 16 months at rural, an urban kerbside and an industrial background site (affected by the emissions from the ceramic manufacture and other activities) were interpreted. On the regional scale, the main PM10 sources were mineral dust (mainly Al2O3, Fe, Ti, Sr, CaCO3, Mg, Mn and K), emissions derived from power generation (SO4=, V, Zn and Ni), vehicle exhausts (organic and elemental carbon, NO3- and trace elements) and marine aerosol (Na, Cl and Mg). The latter was not identified in PM2.5. At the industrial site, additional PM10 sources were identified (tile covering in the ceramic production, petrochemical emissions and bio-mass burning from a large orange tree cultivation area). The contribution of each PM source to PM10 and PM2.5 levels experiences significant variations depending on the type of PM episode (Local-urban mainly in autumn-winter, regional mainly in summer, African or Atlantic episode), which are discussed in this study. The results show that it would be very difficult to meet the EU limit values for PM10 established for 2010. The annual mean PM levels are 22.0 microg PM10/m3 at the rural and 49.5 microg PM10/m3 and 33.9 microg PM2.5/m3 at the urban site. The natural contribution in this region, estimated at 6 microg/m3 of natural mineral dust (resulting from the African events and natural resuspension) and 2 microg/m3 of marine aerosol, accounts for 40% of the 2010 EU annual limit value (20 microg PM10/m3). Mineral dust concentrations at the urban and industrial sites are higher than those at the rural site because of the urban road dust and the ceramic-production contributions, respectively. At the urban site, the vehicle exhaust contribution (17 microg/m3) alone is very close to the 2010 EU PM10 limit value. At the rural site, the African dust is the main contributor to PM10 levels during the highest daily mean PM10 events (100th-97th percentile range). At the urban site, the vehicle exhaust product is the main contributor to PM10 and PM2.5 levels during the highest daily mean PM events (100th-85th percentile range). Mineral dust concentrations during African dust events accounts for 20-30 microg/m3 in PM10 and 10-15 microg/m3 in PM2.5. During non-African dust events, mineral dust derived from anthropogenic activities (e.g. urban road dust) is also a significant contributor to PM10, but not to PM2.5.     

Estimating the frequency distributions of PM10 and PM2.5 by the statistics of wind speed at Sha-Lu,Taiwan

The frequency distribution of air pollutant concentration varies with the meteorological conditions and pollutant emission level. There exists a simple relation between the frequency distribution of wind speed and frequency distribution of air pollutant concentration. The concentration of air pollutant, C, at cumulative probability, p, is inversely proportional to the wind speeds, u, at probability of (100 - p) when the distributional types and shape factors of both data are the same. The relationship is shown as K=Cp u(100 - p), where K is constant. In this study, three theoretical distributions (log-normal, Weibull and type V Pearson distributions) are selected to fit the measured data of PM10, PM2.5 and wind speed. The frequency distributions of air pollutants can be estimated from the simple relationship of air pollutant concentration and wind speed. The results show that the log-normal distribution is the best one to represent the data of PM10, PM2.5 and wind speed. The K values of PM10 and PM2.5 are nearly constant from the 30-80th percentiles. It was also found that the distributions of PM10 and PM2.5 can be successfully estimated from the distribution of wind speed. The Kolmogorov-Smirnov (K-S) test shows that there is no significant discrepancy between the estimated and measured distribution of PM10 and PM2.5 at the 95% confidence level. Therefore, the distribution of air pollutants is easily estimated when the wind speed data are known.     

Hospital indoor PM10/PM2.5 and associated trace elements in Guangzhou, China

PM10 and PM2.5 samples were collected in the indoor environments of four hospitals and their adjacent outdoor environments in Guangzhou, China during the summertime. The concentrations of 18 target elements in particles were also quantified. The results showed that indoor PM2.5 levels with an average of 99 microg m(-3) were significantly higher than outdoor PM2.5 standard of 65 microg m(-3) recommended by USEPA [United States Environmental Protection Agency. Office of Air and Radiation, Office of Air Quality Planning and Standards, Fact Sheet. EPA's Revised Particulate Matter Standards, 17, July 1997] and PM2.5 constituted a large fraction of indoor respirable particles (PM10) by an average of 78% in four hospitals. High correlation between PM2.5 and PM10 (R(2) of 0.87 for indoors and 0.90 for outdoors) suggested that PM2.5 and PM10 came from similar particulate emission sources. The indoor particulate levels were correlated with the corresponding outdoors (R(2) of 0.78 for PM2.5 and 0.67 for PM10), demonstrating that outdoor infiltration could lead to direct transportation into indoors. In addition to outdoor infiltration, human activities and ventilation types could also influence indoor particulate levels in four hospitals. Total target elements accounted for 3.18-5.56% of PM2.5 and 4.38-9.20% of PM10 by mass, respectively. Na, Al, Ca, Fe, Mg, Mn and Ti were found in the coarse particles, while K, V, Cr, Ni, Cu, Zn, Cd, Sn, Pb, As and Se existed more in the fine particles. The average indoor concentrations of total elements were lower than those measured outdoors, suggesting that indoor elements originated mainly from outdoor emission sources. Enrichment factors (EF) for trace element were calculated to show that elements of anthropogenic origins (Zn, Pb, As, Se, V, Ni, Cu and Cd) were highly enriched with respect to crustal composition (Al, Fe, Ca, Ti and Mn). Factor analysis was used to identify possible pollution source-types, namely street dust, road traffic and combustion processes.     

不同建筑环境空气中颗粒物PM1.0、PM2.5和PM10的污染水平及对比研究

为了解人们常停留的建筑室内空气中不同粒径段颗粒物的污染水平,本文对写字楼、地铁站台、餐饮环境、大学教室和宿舍等不同类型建筑室内和室外空气中的颗粒物PM10、PM2.5和PM10的质量浓度水平进行了测试、统计分析和对比研究,同时对不同建筑环境内不同粒径段颗粒物的浓度大小、占比情况和主要来源进行了分析探讨.结果 表明:1)...     

Approaching PM(2.5) and PM(2.5-10) source apportionment by mass balance analysis, principal component analysis and particle size distribution

A chemical characterization was carried out for PM(2.5) and PM(2.5-10) samples collected in a suburban area and the concentrations of 12 elements were determined in 8 size segregated fractions using a Berner Impactor. Two main objectives were proposed in this work: 1) to test for closure among chemical and gravimetric measurements of PM(2.5) and PM(2.5-10) and 2) evaluate the performance of Multilinear Regression Analysis (MLRA) and Mass Balance Analysis (MBA) in the determination of source contribution to Particulate Matter (PM) concentrations. The fraction unaccounted for by chemical analysis comprised on average 17% and 34% of gravimetric PM(2.5) and PM(2.5-10), respectively. The lack of closure in PM(2.5) and PM(2.5-10) mass (i.e., constituent concentrations not adding up to gravimetrically measured) could partly result from the presence of water associated with particles and errors in the estimation of unmeasured species. MLRA and MBA showed very similar results for the temporal variation of the source contributions. However, quantitatively important discrepancies could be observed, principally due to the lack of mass closure in PM(2.5) and PM(2.5-10). Both methods indicated that the major PM(2.5) aerosol mass contributors included secondary aerosol and vehicle exhaust. In the coarse fraction, marine and mineral aerosol contributions were predominant.     

Chemical composition of PM2.5 and PM10 in Mexico City during winter 1997

PM2.5 and PM10 were measured over 24-h intervals at six core sites and at 25 satellite sites in and around Mexico City from 23 February to 22 March 1997. In addition, four 6-h samples were taken each day at three of the core sites. Sampling locations were selected to represent regional, central city, commercial, residential, and industrial portions of the city. Mass and light transmission concentrations were determined on all of the samples, while elements, ions and carbon were measured on approximately two-thirds of the samples. PM10 concentrations were highly variable, with almost three-fold differences between the highest and lowest concentrations. Fugitive dust was the major cause of PM10 differences, although carbon concentrations were also highly variable among the sampling sites. Approximately 50% of PM10 was in the PM2.5 fraction. The majority of PM mass was comprised of carbon, sulfate, nitrate, ammonium and crustal components, but in different proportions on different days and at different sites. The largest fine-particle components were carbonaceous aerosols, constituting approximately 50% of PM2.5 mass, followed by approximately 30% secondary inorganic aerosols and approximately 15% geological material. Geological material is the largest component of PM10, constituting approximately 50% of PM10 mass, followed by approximately 32% carbonaceous aerosols and approximately 17% secondary inorganic aerosols. Sulfate concentrations were twice as high as nitrate concentrations. Sulfate and nitrate were present as ammonium sulfate and ammonium nitrate. Approximately two-thirds of the ammonium sulfate measured in urban areas appears to have been transported from regions outside of the study domain, rather than formed from emissions in the urban area. Diurnal variations are apparent, with two-fold increases in concentration from night-time to daytime. Morning samples had the highest PM2.5 and PM10 mass, secondary inorganic aerosols and carbon concentrations, probably due to a shallow surface inversion and rush-hour traffic.     

地铁车站PM2.5和PM10颗粒物浓度实测及分析

为了进一步了解地铁车站内环境中的颗粒物浓度分布情况,在2015年11月对上海市A、B两个地铁车站进行了实地监测,分析了PM2.5和PM10颗粒物浓度在一天中的变化规律及其影响因素.测试结果显示站厅公共区,站台公共区与轨行区的PM2.5浓度在监测时段内逐时变化规律相似.站厅公共区,站台公共区PM10与PM2.5在监测时段...     

Temporal variations of PM2.5 in the ambient air of a suburban site in Athens, Greece

Twenty-three hour measurements of PM(2.5) particulate matter have been carried out during the period between the 1st April and the 13th November 2003 in a suburban area of Athens. The monitoring site was located in the National Research Center "DEMOKRITOS", on the foot of Hemittos Mountain and about 12 km away from the center of Athens. The site covers an area of 600 acres in a forest of pine trees close enough to the newly constructed Hemittos Mountain peripheral highway. PM(2.5) samples were collected on 47 mm filters, with the use of low volume gravimetric samplers while a meteorological station recorded meteorological data 6 m above the ground, nearby the sampling instrumentation. The daily average PM(2.5) concentration reached 21.1 microg m(-3) and all measurements were below U.S. Environmental Pollution Agency daily limit (65 microg m(-3)). A regression analysis was used to investigate the relationship among PM(2.5) concentrations and meteorological parameters. Additionally, PM(2.5) mass concentrations were correlated with other inorganic gaseous pollutants (O(3), NO, NO(2), SO(2)) while weekly and seasonal PM(2.5) variations were also investigated.     

Source apportionment of PM10 and PM2.5 in Milan (Italy) using receptor modelling

In this paper a source apportionment of particulate matter pollution in the urban area of Milan (Italy) is given. Results of PM10 and PM2.5 mass and elemental concentrations from a 1-year monitoring campaign are presented. Mean annual and daily PM10 levels are compared with the limits of the EU Air Quality Directive EC/30/1999 and the results show that the limit values established would not be met in the urban area of Milan or the large surrounding area. Moreover, high levels of PM2.5 are registered and this fraction constitutes a high portion of the PM10 mass. In Milan the winter period is characterised by a high degree of air pollution due to a greater contribution of emissions and to adverse meteorological and thermodynamic conditions of the atmosphere. The application of multivariate techniques and receptor modelling (PCFA, APCFA) to the whole data-set led to the identification of the main emitting sources and to the source apportionment of PM10 and PM2.5 in Milan. The most important sources were identified as 'soil dust', 'traffic', 'industry' and 'secondary compounds' for PM10 and as 'soil dust', 'anthropogenic' and 'secondary compounds' for PM2.5, explaining the greatest part of the total variance (91% and 75%, respectively).     

Personal exposure to PM2.5 in Chinese rural households in the Yangtze River Delta

High levels of PM_2.5 exposure and associated health risks are of great concern in rural China. For this study, we used portable PM_2.5 monitors for monitoring concentrations online, recorded personal time‐activity patterns, and analyzed the contribution from different microenvironments in rural areas of the Yangtze River Delta, China. The daily exposure levels of rural participants were 66 μg/m³ (SD 40) in winter and 65 μg/m³ (SD 16) in summer. Indoor exposure levels were usually higher than outdoor levels. The exposure levels during cooking in rural kitchens were 140 μg/m³ (SD 116) in winter and 121 μg/m³ (SD 70) in summer, the highest in all microenvironments. Winter and summer values were 252 μg/m³ (SD 103) and 204 μg/m³ (SD 105), respectively, for rural people using biomass for fuel, much higher than those for rural people using LPG and electricity. By combining PM_2.5concentrations and time spent in different microenvironments, we found that 92% (winter) and 85% (summer) of personal exposure to PM_2.5in rural areas was attributable to indoor microenvironments, of which kitchens accounted for 24% and 27%, respectively. Consequently, more effective policies and measures are needed to replace biomass fuel with LPG or electricity, which would benefit the health of the rural population in China.     

冬季高校教室温湿度及PM2.5、PM10变化规律研究

以西安某高校教室为研究目标,运用相关仪器进行实地监测及数据分析的方法,研究了教室内温湿度变化及PM2.5、P M10的变化规律.结果表明:冬季教室内温度的变化与上课时间的安排及课间人员的流动密切相关,冬季节正常天气下教室内湿度与温度两者之间的变化呈现出显著的负相关关系,降雨降雪天气教室内相对湿度变化受室外湿度影响波动较...     

Concentration and chemical characteristics of PM2.5 in Beijing, China: 2001-2002

Weekly PM2.5 samples were simultaneously collected at a semi-residential (Tsinghua University) and a downtown (Chegongzhuang) site in Beijing from August 2001 through September 2002. The ambient mass concentration and chemical composition of PM2.5 were determined. Analyses including elemental composition, water-soluble ions, and organic and elemental carbon were performed. The annual average concentrations of PM2.5 were 96.5 microg m(-3) and 106.9 microg m(-3) at CGZ and HU site, respectively. More than 80% of the PM2.5 mass concentrations were explained by carbonaceous species, secondary particles, crustal matters and trace elements at the two sites. Carbonaceous species were the most abundant components, constituting about 45% and 48% of the total PM2.5 mass concentrations at CGZ and THU site, respectively. SO4(2-), NO3- and NH4+ were three major ions, accounting for 37%, 23% and 20%, respectively, of the total mass of inorganic water-soluble ions.     

居住区室外开敞空间PM2.5、PM10时空分布特征及居民暴露风险评估

研究利用Reynolds Averaged Navier-Stokes Model与Revised Drift Flux Model模拟分析了居住区室外开敞空间中PM2.5、PM10浓度的时空分布,利用行为制图建立场地中居民活动与PM2.5、PM10浓度分布的时空映射,并依据世界卫生组织的空气质量标准(IT-1)评估了...     

Receptor modeling of PM2.5, PM10 and TSP in different seasons and long-range transport analysis at a coastal site of Tianjin, China

Atmospheric particulate matter (PM_2.5, PM₁₀ and TSP) were sampled synchronously during three monitoring campaigns from June 2007 to February 2008 at a coastal site in TEDA of Tianjin, China. Chemical compositions including 19 elements, 6 water-solubility ions, organic and elemental carbon were determined. principle components analysis (PCA) and chemical mass balance modeling (CMB) were applied to determine the PM sources and their contributions with the assistance of NSS SO₄²⁻, the mass ratios of NO₃⁻ to SO₄²⁻ and OC to EC. Air mass backward trajectory model was compared with source apportionment results to evaluate the origin of PM. Results showed that NSS SO₄²⁻ values for PM_2.5 were 2147.38, 1701.26 and 239.80 ng/m³ in summer, autumn and winter, reflecting the influence of sources from local emissions. Most of it was below zero in summer for PM₁₀ indicating the influence of sea salt. The ratios of NO₃⁻ to SO₄²⁻ was 0.19 for PM_2.5, 0.18 for PM₁₀ and 0.19 for TSP in winter indicating high amounts of coal consumed for heating purpose. Higher OC/EC values (mostly larger than 2.5) demonstrated that secondary organic aerosol was abundant at this site. The major sources were construction activities, road dust, vehicle emissions, marine aerosol, metal manufacturing, secondary sulfate aerosols, soil dust, biomass burning, some pharmaceutics industries and fuel-oil combustion according to PCA. Coal combustion, marine aerosol, vehicular emission and soil dust explained 5-31%, 1-13%, 13-44% and 3-46% for PM_2.5, PM₁₀ and TSP, respectively. Backward trajectory analysis showed air parcels originating from sea accounted for 39% in summer, while in autumn and winter the air parcels were mainly related to continental origin.     

Source apportionment of PM(10) and PM(2.5) using positive matrix factorization and chemical mass balance in Izmir, Turkey

Atmospheric particulate matter (PM) fractions (PM(10) and PM(2.5)) were sampled concurrently between June 2004 and May 2005 at two sites (urban and suburban) in Izmir, Turkey. The elemental composition of PM (Al, Ba, Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Sr, V, and Zn) was determined using inductively coupled plasma-optical emission spectrometer. Elemental compositions of several PM sources were also characterized. Positive matrix factorization (PMF) and chemical mass balance modeling (CMB) were applied to determine the PM sources and their contributions to air concentrations. The major contributors to PM were fossil fuel burning, traffic emissions, mineral industries and marine salt according to the PMF results. However, undetermined parts were more than 40%. On the other hand, the contributions to PM could be determined completely by CMB, and the dominant contributor was traffic with >70% at the two sites. Fossil fuel burning, mineral industries, marine salt and natural gas-fired power plant were the minor contributors.     

Household and behavioral determinants of indoor PM2.5 in a rural solid fuel burning Native American community

Indoor and outdoor concentrations of PM_2.5 were measured for 24 h during heating and non-heating seasons in a rural solid fuel burning Native American community. Household building characteristics were collected during the initial home sampling visit using technician walkthrough questionnaires, and behavioral factors were collected through questionnaires by interviewers. To identify seasonal behavioral factors and household characteristics associated with indoor PM_2.5, data were analyzed separately by heating and non-heating seasons using multivariable regression. Concentrations of PM_2.5 were significantly higher during the heating season (indoor: 36.2 μg/m³; outdoor: 22.1 μg/m³) compared with the non-heating season (indoor: 14.6 μg/m³; outdoor: 9.3 μg/m³). Heating season indoor PM_2.5 was strongly associated with heating fuel type, housing type, indoor pests, use of a climate control unit, number of interior doors, and indoor relative humidity. During the non-heating season, different behavioral and household characteristics were associated with indoor PM_2.5 concentrations (indoor smoking and/or burning incense, opening doors and windows, area of surrounding environment, building size and height, and outdoor PM_2.5). Homes heated with coal and/or wood, or a combination of coal and/or wood with electricity and/or natural gas had elevated indoor PM_2.5 concentrations that exceeded both the EPA ambient standard (35 μg/m³) and the WHO guideline (25 μg/m³).