Characterization of Elemental Species in PM2.5 Samples Collected in Four Cities of Northeast China

A monitoring program of particulate matter was conducted at eight sampling sites in four highly industrialized cities (Shenyang, Anshan, Fushun, and Jinzhou) of Liaoning Province in Northeast China to identify the major potential sources of ambient PM_2.5. A total of 814 PM_2.5 and PM_2.5–10 samples were collected between 2004 and 2005. All PM samples were collected simultaneously in four cities and analyzed gravimetrically for mass concentrations. A sum of 16 elemental species concentrations in the PM samples were determined using inductively coupled plasma atomic emission spectroscopy. Annual means of PM_2.5 concentrations ranged from 65.0 to 222.0 μg m^?3 in all the eight sampling sites, and the spatial and seasonal variations were discussed. Enrichment factors were calculated, and Cr, Cu, Zn, As, Cd, and Pb will be pollution-derived elements. Site-to-site comparisons of PM_2.5 species in each city were examined using coefficient of divergence, revealing that the two sites in each city are similar in elemental species. Principle component analysis was used for preliminary source analysis of PM_2.5. Three or four factors in each city were isolated, and similar sources (crustal source, coal combustion, vehicle exhaust, iron making, or some other metallurgical activities) were identified at four cities.     

Identification of Fine Particle Sources in Mid-Atlantic US Area

The U.S. Environmental Protection Agency (EPA) designated 20 urban areas including major cities located in mid-Atlantic US area as being in non-attainment of the new national ambient air quality standards for PM_2.5 (particulate matter ≤2.5 μm in aerodynamic diameter). To support the development of effective State Implementation Plans for PM_2.5 in the non-attainment area, 24-h integrated Speciation Trends Networks data collected in the mid-Atlantic US urban area were analyzed through the application of the positive matrix factorization (PMF). A total of 117 to 235 samples and 27 to 29 chemical species collected at the four monitoring sites between 2001 and 2003 were analyzed and six to nine sources were identified. Secondary particles provided the highest contributions to PM_2.5 mass concentrations (38–50% for secondary sulfate; 9–18% for secondary nitrate). Potential source contribution function analyses show the potential source areas and pathways of secondary particles contributing to this region, especially the regional influences of the biogenic as well as anthropogenic secondary particles. Motor vehicle emissions contributed 21–33% to the PM_2.5 mass concentration. In four sites in southern New Jersey and Delaware, gasoline vehicle and diesel emissions were tentatively separated by different abundances of organic and elemental carbons. The compositional profiles for gasoline vehicle and diesel emissions are similar across this area. In addition, other combustion sources, aged sea salt, and intercontinental dust storms were identified.     

An Assessment of the Mobile Source Contribution to PM10 and PM2.5 in the United States

Mobile sources are significant contributors to ambient particulate matter (PM) in the United States. As the emphasis shifts from PM₁₀ to PM_2.5, it becomes particularly important to account for the mobile source contribution to observed particulate levels since these sources may be the major contributor to the fine particle fraction. This is due to the fact that most mobile source mass emissions have an aerodynamic diameter less than 2.5 µm, while the particles of geological origin that tend to dominate the PM₁₀ fraction generally have an aerodynamic diameter greater than 2.5 µm. A common approach to assess the relative contributions of sources to observed particulate mass concentrations is the application of source apportionment methods. These methods include material balance, chemical mass balance (CMB), and multivariate receptor models. This paper describes a number recent source attribution studies performed in the United States in order to evaluate the range of the mobile source contribution to observed PM. In addition, a review of the methods used to apportion source contributions to ambient particulate loadings is presented.     

Atmospheric Emission Inventory for Natural and Anthropogenic Sources and Spatial Emission Mapping for the Greater Athens Area

A spatially, temporally and chemically resolved emission inventory for particulate matter and gaseous species from anthropogenic and natural sources was created for the Greater Athens Area (GAA; base year, 2007). Anthropogenic sources considered in this study include combustion (industrial, non-industrial, commercial and residential), industrial production, transportation, agriculture, waste treatment and solvent use. The annual gaseous pollutants (????x, SOx, non-methane volatile organic compounds (NMVOCs), CO and ????₃) and particulate matter (PM_2.5 and PM_2.5?C10) emissions were derived from the UNECE/EMEP database for most source sectors (SNAP 1?C9; 50?×?50 km²) and their spatial resolution was increased using surrogate spatial datasets (land cover, population density, location and emissions of large point sources, emission weighting factors for the GAA; 1?×?1 km²). The emissions were then temporally disaggregated in order to provide hourly emissions for atmospheric pollution modelling using monthly, daily and hourly disintegration coefficients, and additionally the chemical speciation of size-segregated particles and NMVOCs emissions was performed. Emissions from agriculture (SNAP 10) and natural emissions of particulate matter from the soil (by wind erosion) and the sea surface and of biogenic gaseous pollutants from vegetation were also estimated. During 2007 the anthropogenic emissions of CO, SOx, NOx, NMVOCs, NH₃, PM_2.5 and PM_2.5?C10 from the GAA were 151,150, 57,086, 68,008, 38,270, 2,219, 9,026 and 3,896 Mg, respectively. It was found that road transport was the major source for CO (73.3%), NMVOCs (31.6%) and NOx (35.3%) emissions in the area. Another important source for NOx emissions was other mobile sources and machinery (23.1%). Combustion for energy production and transformation industries was the major source for SOx (38.5%), industrial combustion for anthropogenic PM_2.5?C10 emissions (59.5%), whereas non-industrial combustion was the major source of PM_2.5 emissions (49.6%). Agriculture was the primary NH₃ source in the area (72.1%). Natural vegetation was found to be an important source of VOCs in the area which accounted for approximately the 5% of total VOCs emitted from GAA on a typical winter day. The contribution of sea-salt particles to the emissions of PM_2.5 was rather small, whereas the emissions of resuspended dust particles exceeded by far the emissions of PM_2.5 and PM_2.5?C10 from all anthropogenic sources.     

A Module to Calculate Primary Particulate Matter Emissions and Abatement Measures in Europe

Primary particulate matter is emitted directly into the atmosphere from various anthropogenic and natural sources such as power plants (combustion of fossil fuels) or forest fires. Secondary particles are formed by transformation of SO₂, NO_x, NH₃, and VOC in the atmosphere. They both contribute to ambient particulate matter concentrations, which may have adverse effects on human health. Health hazards are caused by small particulate size, high number of especially fine (< 2.5 µm) and ultra-fine (< 0.1 µm) particles and/or their chemical composition. As part of an integrated assessment model developed at IIASA, a module on primary particulate matter (PM) emissions has been added to the existing SO₂, NO_x, NH₃ and VOC sections. The module considers so far primary emissions of total suspended particles (TSP), PM₁₀ and PM_2.5 from aggregated stationary and mobile sources. A primary PM emission database has been established. Country specific emission factors for stationary sources have been calculated within the module using the ash content of solid fuels.     

Human Health Impact of Exposure to Airborne Particulate Matter in Pearl River Delta, China

To evaluate the potential public health impact of exposure to airborne particulate matter, concentrations of PM₁₀ and PM_2.5 were measured at 16 monitoring stations in Pearl River Delta. Epidemiological studies were collected, and meta-analysis method was used to get the exposure-response functions for health effects on mortality of residents in China. Chinese studies reported somewhat lower exposure-response coefficients as compared with studies abroad. Both Poisson model and life-table approach were used to estimate the health effects including acute effects and chronic effects. For short-term exposure, 2,700 (95% confidence interval (CI), 2,200?C3,400) premature deaths would be prevented annually if PM₁₀ daily concentrations reduced to below World Health Organization (WHO) guideline value. Much more benefits would be gained for long-term exposure. The annual avoidable deaths would be 42,000 (95% CI, 28,000?C55,000) and 40,000 (95% CI, 23,000?C54,000) for PM₁₀ and PM_2.5, respectively, if the particulate matter annual concentrations were reduced to below WHO guideline values. And the average lifespan of residents would prolong 2.57?years for PM₁₀ and 2.38?years for PM_2.5 if reducing the PM annual concentrations. The benefits varied greatly in different areas and different manage strategies should be carried out to protect human health effectively.     

A Morphometric and Compositional Approach to the Study of Ambient Aerosol in a Medium Industrial Town of Italy

Morphometric and compositional studies have been performed on both PM_10–2.5 and PM_2.5 aerosol collected in the city of Prato, Italy. Chemical analysis has been carried out using PIXE technique and factor analysis was applied for the source apportionment process. Industrial emissions, vehicular traffic as well as crustal dust and marine aerosol were the sources identified. SEM-EDS analysis has been employed to individually characterize particles collected during a week of the sampling campaign. The morphometric study, performed on 43,671 particles, revealed that, for both the fine and coarse fraction, about 2/3 of particles display a high roundness coefficient, 1/3 of them a medium value, while only a small number of particles (from silicates and organics) exhibits a low roundness coefficient. Similarly, particles with small surface area represent the greater portion in both fractions. Particles classified as organics, metals and oxides, chlorides, carbonates, phosphates, sulphates and silicates have been detected in the PM_10–2.5 while in the PM_2.5 chlorides and phosphates are lacking. Silicates are about the same percentage, by concentration number, in the coarse and fine fraction (20.7% and 20.5% respectively) showing that this material, at least one fifth of the total PM, might be the result of crustal erosion and anthropic activities. The purpose of this work has been that of providing a contribution to the study of particulate matter and took an effort for relating morphometric and compositional features of urban aerosol collected in a medium size industrial city.     

Diurnal Characteristics of Suspended Particulate Matter and PM<Subscript>2.5</Subscript> in the Urban and Suburban Atmosphere of the Kanto Plain,Japan

Suspended particulate matter (SPM) and PM_2.5 in the urban and suburban atmosphere of the Kanto Plain of Japan, which includes the Tokyo metropolitan area, during the period 22–26 July 2002 were characterized. Samples of SPM and PM_2.5 were collected by low-volume samplers at 6-h intervals at Suginami, Saitama, and Kisai. At all the sites, the major components of SPM and PM_2.5 were organic carbon (OC), elemental carbon (EC), and sulfate. The ion balance, the size distributions of the ionic species, and the high correlation between SO₄ ^2? and NH₄ ⁺ indicated that the main chemical form of sulfate was (NH₄)₂SO₄. The OC/EC ratios were larger in the daytime than in the nighttime. The correlation coefficients of OC, OC/EC, and SO₄ ^2? with ozone concentrations at inland sites (Saitama, Kisai) were higher than those at the coastal site (Suginami). Bound water and hydrogen and oxygen atoms associated with OC, the amounts of which were estimated with a mass closure model, contributed substantially to the total particle mass. The chemical characteristics of the particles indicated that two mechanisms led to high concentrations of SPM and PM_2.5: (i) an active photochemical process produced high concentrations of OC and sulfate, leading to a high concentration of (NH₄)₂SO₄ in the particles and to production of secondary organic aerosols; (ii) stable meteorological conditions resulted in accumulation of primary particles, mainly emitted from vehicle exhaust, resulting in high concentrations of OC and EC.     

Concentration and Sources of PM10 and its Constituents in Alsasua, Spain

Ambient concentrations of PM₁₀ were measured every fifteen minutes from November 2002 to October 2003 at Alsasua (Northern Spain) using a laser particle counter. A high volume sampler was also used to collect 24-h integrated PM₁₀ samples at a frequency of three running days per week (i.e. three consecutive PM₁₀ samples followed by five days without sampling) for gravimetric determination of PM₁₀ mass concentrations followed by chemical analysis of its chemical components. The annual mean PM₁₀ concentration obtained using the laser particle counter with gravimetric correction was 22.7 μg m^?3 (365 days), while the mean for the gravimetric samples was 29.5 μg m^?3 (134 days). A total of 94 integrated PM₁₀ samples were analyzed for 60 chemical species using a combination of inductively coupled plasma spectrometry (ICP) and ion chromatography (IC). The concentrations of the main PM₁₀ components were found to be generally in agreement with the values reported for other Spanish cities. Bilinear Positive Matrix Factorization (PMF2) was used to study the sources of PM₁₀ and its constituents. Six main sources of PM₁₀ were identified (average contribution to total PM₁₀ mass in parentheses): crustal material (35%), secondary sulfate (21%), secondary nitrate (14%), motor vehicles (12%), sea-salt aerosol (12%) and metallurgical industries (3%).     

Association of IL-6 with PM<Subscript>2.5</Subscript> Components: Importance of Characterizing Filter-Based PM<Subscript>2.5</Subscript> Following Extraction

Filter-based toxicology studies are conducted to establish the biological plausibility of the well-established health impacts associated with fine particulate matter (PM_2.5) exposure. Ambient PM_2.5 collected on filters is extracted into solution for toxicology applications, but frequently, characterization is nonexistent or only performed on filter-based PM_2.5, without consideration of compositional differences that occur during the extraction processes. To date, the impact of making associations to measured components in ambient instead of extracted PM_2.5 has not been investigated. Filter-based PM_2.5 was collected at locations (n?=?5) and detailed characterization of both ambient and extracted PM_2.5 was performed. Alveolar macrophages (AMJ2-C11) were exposed (3, 24, and 48 h) to PM_2.5 and the pro-inflammatory cytokine interleukin (IL)-6 was measured. IL-6 release differed significantly between PM_2.5 collected from different locations; surprisingly, IL-6 release was highest following treatment with PM_2.5 from the lowest ambient concentration location. IL-6 was negatively correlated with the sum of ambient metals analyzed, as well as with concentrations of specific constituents which have been previously associated with respiratory health effects. However, positive correlations of IL-6 with extracted concentrations indicated that the negative associations between IL-6 and ambient concentrations do not accurately represent the relationship between inflammation and PM_2.5 exposure. Additionally, seven organic compounds had significant associations with IL-6 release when considering ambient concentrations, but they were not detected in the extracted solution. Basing inflammatory associations on ambient concentrations that are not necessarily representative of in vitro exposures creates misleading results; this study highlights the importance of characterizing extraction solutions to conduct accurate health impact research.     

GIS-Based Emission Inventory, Dispersion Modeling, and Assessment for Source Contributions of Particulate Matter in an Urban Environment

The Industrial Source Complex Short Term (ISCST3) model was used to discern the sources responsible for high PM₁₀ levels in Kanpur City, a typical urban area in the Ganga basin, India. A systematic geographic information system-based emission inventory was developed for PM₁₀ in each of 85 grids of 2?×?2 km. The total emission of PM₁₀ was estimated at 11 t day^?1 with an overall breakup as follows: (a) industrial point sources, 2.9 t day^?1 (26%); (b) vehicles, 2.3 t day^?1 (21%); (c) domestic fuel burning, 2.1 t day^?1 (19%); (d) paved and unpaved road dust, 1.6 t day^?1 (15%); and the rest as other sources. To validate the ISCST3 model and to assess air-quality status, sampling was done in summer and winter at seven sampling sites for over 85 days; PM₁₀ levels were very high (89?C632 ??g m^?3). The results show that the model-predicted concentrations are in good agreement with observed values, and the model performance was found satisfactory. The validated model was run for each source on each day of sampling. The overall source contribution to ambient air pollution was as follows: vehicular traffic (16%), domestic fuel uses (16%), paved and unpaved road dust (14%), and industries (7%). Interestingly, the largest point source (coal-based power plant) did not contribute significantly to ambient air pollution. The reason might be due to release of pollutant at high stack height. The ISCST3 model was shown to produce source apportionment results like receptor modeling that could generate source apportionment results at any desired time and space resolution.     

Polycyclic Aromatic Hydrocarbons and their Molecular Diagnostic Ratios in Airborne Particles (PM10) Collected in Rio de Janeiro, Brazil

Atmospheric polycyclic aromatic hydrocarbons (PAHs) were determined in particulate matter (PM10) collected in a suburban area with industrial and vehicular emissions in the Metropolitan Area of Rio de Janeiro City (Brazil). A total of 22 samples were collected between March and August 2005 by means of a high volume PM10 sampler. The particulate matter contained in the filters was extracted ultrasonically with dichloromethane. The extracts were later analyzed by gas chromatography coupled to a mass spectrometer (GC/MS). The individual concentrations of PAHs ranged between the detection limit and 0.386 ng m^?3. The PAHs concentrations observed in this study were towards the lowest end of the range of values reported for other European locations and also lower than values obtained for South America. PAHs concentrations and molecular ratios showed that light cars seem to be the main contributors to PM10 emissions, but diesel vehicles are clearly minor emission sources and industrial contributions should not be disregarded until more data are obtained.     

A Study of PM2.5 and PM2.5-Associated Polycyclic Aromatic Hydrocarbons at an Urban Site in the Po Valley (Bologna, Italy)

PM_2.5 and PAHs bound to PM_2.5 were investigated in downtown Bologna, from January to June 2003, in order to determine the burden of the fine fraction in the aerosol of a typical urban environment of the Po Valley, a critical area in Northern Italy in terms of atmospheric pollution. The sampling campaign was divided into three parts: a winter sub-campaign, an intermediate campaign where PM_2.5 and PM₁₀ were simultaneously sampled and which identified PM_2.5 as the major component of PM₁₀, and a summer sub-campaign. Critical concentrations of both PM_2.5 and PAHs were observed in winter time; for example, in January 2003 the mean value for the 24-h average PM_2.5 concentration was 58 μg/m³, much higher than the annual arithmetic mean of 15 μg/m³ established by the US ambient air quality standard (NAAQS). Correspondingly, the mean value for benzo[a]pyrene (BAP) in PM_2.5 was 1.79 ng/m³, again higher than the annual mean of 1 ng/m³, required by European regulations for BAP in PM₁₀. In summer time the BAP concentration considerably decreases to 0.10 ng/m³ as the likely effect of photolysis and dilution on a higher boundary layer; PM_2.5 decreases too, but the mean concentration (22 μg/m³) is still higher than the NAAQS value. Further analysis included TEM microscopy of collected particles and correlations between PM_2.5, PAHs and gases (benzene, O₃, CO, NO₂, SO₂). All these observations identified on-road mobile sources as the main source of emissions and, in general, of the poor air quality level in the city of Bologna.     

大型自然通风奶牛舍空气颗粒物浓度监测方法中测点数和位置优化

对奶牛舍颗粒物浓度和分布进行实时连续监测,是评估其环境风险和制定科学减排措施的前提。随着大型奶牛舍的快速发展,对尽可能少且科学地布置测点数量和位置并能够对舍内颗粒物浓度进行精准监测,提出了新的挑战。为探究测点优化布置方案,该研究基于物联网技术在大型自然通风奶牛舍内3个相邻饲养区域布置17个采样点,对总悬浮颗粒物（total suspended particle, TSP）和细颗粒物（PM_2.5）浓度进行了连续6个月监测,得到舍内浓度平均值（视为“真值”）;利用系统聚类和误差分析方法得到优化的测点方案,并将其颗粒物浓度结果与6种传统测点方案进行对比,以确定最优的测点数量和位置方案。结果表明,舍内3个饲养区域内颗粒物浓度分布均匀,PM_2.5浓度在不同采样高度上无统计差异（P>0.05）,而TSP浓度在屋顶通风口下方（9.0 m）明显低于1.5和2.5 m高度（P<0.05）。与真值相比,优化测点方案的TSP和PM_2.5浓度监测误差绝对值之和分别为6.4%～22.6%和4.7%～14.2%,均低于传统测点方案。综合考虑优化方案的科学性和传统方案的易操作性,确定最优测点方案为：在奶牛舍中央屋顶通风口下方1.0～2.0 m处布置1个测点,在奶牛卧床上方2.5 m高度布置2个测点,上述3个测点按奶牛舍斜对角线进行布置;另外,分别在挤奶通道、饲喂通道、清粪通道上方2.5 m高度处各布置1个测点;共计6个测点。该优化测点方案可满足监测准确性和易操作性要求。     

Influence of Saharan Dust Transport Events on PM2.5 Concentrations and Composition over Athens

The evaluation of the contribution of natural sources to PM₁₀ and PM_2.5 concentrations is a priority especially for the countries of European south strongly influenced by Saharan dust transport events. Daily PM_2.5 concentrations and composition were monitored at an urban site at 14 m above ground level, at the National Technical University of Athens campus from February to December 2010. The typical dust constituents Si, Al, Fe, K, Ca, Mg, and Ti were determined by wavelength dispersive X-ray fluorescence spectrometry (WDXRF). Sulfur, a tracer of anthropogenic origin and major constituent of PM_2.5, was determined by both WDXRF and ionic chromatography. The contribution of dust and sulfates in PM_2.5 was calculated from the analytical determinations. An annual mean of 20 μg/m³ was calculated from the mean daily PM_2.5 concentrations data. Twenty-two per cent of daily concentrations of PM_2.5 reached or exceeded the EU annual target concentration of 25 μg/m³. The exceedances occurred during 13 short periods of 1–4 days. Back-trajectory analysis was performed for these periods in order to identify the air masses origin. From these periods, ten periods were associated to Saharan dust transport events. The most intense dust transport event occurred between February 17th and 20th and was responsible for the highest recorded PM_2.5 concentration of 100 μg/m³ where the dust contribution in PM_2.5 reached 96 %. The other dust transport events were less intense and corresponded to less pronounced enhancements of PM_2.5 concentrations, and their contribution ranged from 15 to 39 % in PM_2.5 concentrations. Air masses originated from northwest Africa while the influence of central Sahara was quite smaller.     

PM10 and PM2.5 Levels in the Eastern Mediterranean (Akrotiri Research Station, Crete, Greece)

Particulate matter measurements (PM₁₀, PM_2.5) using a beta radiation attenuation monitor were performed at the Akrotiri research station (May 2003–March 2006) on the island of Crete (Greece). The mean PM₁₀ concentration during the measuring period (05/02/03–03/09/04) was equal to 35.0?±?17.7 μg/m³ whereas the mean PM_2.5 concentration (03/10/04–04/02/06) was equal to 25.4?±?16.5 μg/m³. The aerosol concentration at the Akrotiri station shows a large variability during the year. Mean concentrations of particulate matter undergo a seasonal change characterised by higher concentrations during summer [PM₁₀, 38.7?±?10.8 μg/m³ (2003); PM_2.5, 27.9?±?8.7 μg/m³ (2004) and 27.8?±?9.7 μg/m³ (2005)] and lower concentrations during winter [PM₁₀, 28.7?±?22.5 μg/m³ (2003/2004); PM_2.5, 21.0?±?13.0 μg/m³ (2004/2005) and 21.4?±?21.9 μg/m³ (2005/2006)]. Comparative measurements of the PM₁₀ concentration between the beta radiation attenuation monitor, a standardized low volume gravimetric reference sampler and a low volume sequential particulate sampler showed that PM₁₀ concentrations measured by the beta radiation attenuation monitor were higher than values given by the gravimetric samplers (mean ratio 1.17?±?0.11 and 1.21?±?0.08, respectively). Statistical and back trajectory analysis showed that elevated PM concentrations (PM₁₀, 93.8?±?49.1 μg/m³; PM_2.5: 102.9?±?59.9 μg/m³) are associated to desert dust events. In addition regional transport contributes significantly to the aerosol concentration levels whereas low aerosol concentrations were observed during storm episodes.     

A Study of Airborne Selected Metals and Particle Size Distribution in Relation to Climatic Variables and their Source Identification

Ten selected metals (Na, K, Fe, Zn, Pb, Mn, Cr, Co, Ni & Cd) were estimated in total suspended particulate (TSP) samples collected on glass fibre filters in urban Islamabad, Pakistan, from October 2002 to May 2003, using a high volume sampling technique. The wet digestion method (HNO₃/HClO₄) was used for metal analysis by the flame atomic absorption spectrophotometry (FAAS) method. Maximum mean contribution was noted for Na (1.949 μg m^?3), followed by K (0.900 μg m^?3), Zn (0.603 μg m^?3), Fe (0.584 μg m^?3) and Pb (0.214 μg m^?3). The particle size determination on % volume basis for four fractions (PM_{< 2.5}, PM_2.5–10, PM_10–100& PM_{> 100}) was also carried out. PM_10–100 were found to be the most abundant in the local atmosphere followed by PM_2.5–10, while the respirable fraction (PM_{< 2.5}) and giant fraction (PM_{> 100}) showed comparable and lower levels. The trace metals were found to be mainly associated with PM_{< 2.5} and PM_2.5–10. The influence of climatic variables on toxic trace metals and particle size fractions was also investigated statistically and it was revealed that temperature has a significant correlation with fine particle fractions and airborne trace metal levels. The source identification was carried out by Principal Component Analysis and Cluster Analysis. Four metal sources were identified: industrial (32.6%), soil-derived dust (21.9%), traffic/road dust (19.8%), and metallurgical/garbage incineration (12.4%). The metal levels were also compared with those reported for other parts around the world.     

Measuring air particulate matter in large urban areas for health effect assessment

This study deals with Particle Matter (PM) levels in the metropolitan area of Lisbon and shows that EU directive is exceeded in a systematic way, mainly due to the inner city traffic. Results show that it is important to develop an epidemiological study in Lisbon to find a possible association between PM levels, sources and morbidity. Some important issues related with a monitor's representation of regional, sub-regional, and local air pollution exposures to the population in the metropolitan area are highlighted. PM_2.5 and PM₁₀ total mass concentration measured in several places located in both centre of Lisbon and the outskirts are quite well correlated, mainly considering that two measuring methodologies (automatic and gravimetric) were used and areas with different classifications (urban and suburban) were analysed. However, the results imply that a source-oriented evaluation of PM health effects needs to take into account the uncertainty associated with spatial representativity of the species measured at a single sampling station. Temporal correlation across sampling stations, within relatively short separation distances, varied considerably for some important elements (Zn, Sb, Cu, As and Br), indicating that the precision of population exposure estimates for specific elements can vary depending on the species.     

Using Seven Types of GM (1, 1) Model to Forecast Hourly Particulate Matter Concentration in Banciao City of Taiwan

In this study, seven types of first-order and one-variable grey differential equation model (abbreviated as GM (1, 1) model) were used to predict hourly particulate matter (PM) including PM₁₀ and PM_2.5 concentrations in Banciao City of Taiwan. Their prediction performance was also compared. The results indicated that the minimum mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and maximum correlation coefficient (R) was 14.10%, 25.62, 5.06, and 0.96, respectively, when predicting PM₁₀. When predicting PM_2.5, the minimum MAPE, MSE, RMSE, and maximum R value of 15.24%, 11.57, 3.40, and 0.93, respectively, could be achieved. All statistical values revealed that the predicting performance of GM (1, 1, x ⁽⁰⁾), GM (1, 1, a), and GM (1, 1, b) outperformed other GM (1, 1) models. According to the results, it revealed that GM (1, 1) GM (1, 1) was an efficiently early warning tool for providing PM information to the inhabitants.     

基于GIS的中国PM2.5浓度的空间分布及影响因素分析

[目的]研究中国PM2.5的空间分布特征及其影响因素,为区域可持续发展提供科学依据。[方法]利用2014年2月25日上午9时和3月23日9时来自国家环保部的PM2.5时均浓度值,以GIS为平台利用双三次B样条方法,以中国陆疆国界为内插区域,模拟两个时相PM2.5浓度的空间分布,并在此基础上对比分析了中国和美国PM2.5浓度标准的差异,进一步分析荒漠化、降水、风速和经济增长水平对PM2.5浓度空间分异的影响。[结果]模拟结果表明,京、津为中心的华北地区是中国PM2.5污染严重的区域,珠三角是另一个污染较严重的区域,西藏、新疆和贵州等西部省区是中国PM2.5浓度较低,空气质量较好的区域。[结论]我国各地区PM2.5浓度与区域经济发展水平表现出显著的相关性。