Multivariate statistical study of simultaneously monitored cloud water, aerosol and rainwater data from different elevation levels in an Alpine valley (Achenkirch, Tyrol, Austria)

The present paper deals with data interpretation of monitoring of various atmospheric events (cloud water, aerosol and rainwater) at three different elevation levels at Achenkirch profile in an Alpine valley, Tyrol, Austria (Christlumkopf-1758 m, Christlumalm-1280 m and Talboden-930 m a.s.l.) by the use of principal components analysis. From October 1995 to September 1996 sampling sessions for all sites from the profile and for all events were performed for the major ions NH₄⁺, Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl⁻, NO₃⁻, SO₄²⁻-44 cases with eight variables for rainwater; 117 cases with eight variables for cloud water samples and 50 cases with seven variables for aerosol (the major ions as in rain- and cloud water but without magnesium) at any of the elevations. The aim of the multivariate statistical treatment was to extract information about latent factors determining the data structure in all of the cases in order to compare and interpret similarities and dissimilarities with respect to the elevation or the type of the atmospheric event. Four latent factors seem to explain over 85% of the total variance for almost all sites and events but the factors have different identification for the different events or sites (e.g. ‘anthropogenic’, ‘crustal’, ‘neutralization’, ‘salt’). Thus, a comparison between sites and between events becomes possible. It was found that cloud water and aerosol events are much more similar with respect to data structure (relevant to emission sources or processes of formation) than the same events and rainwater. Further, the upper sites of the profile (Christlumkopf and Christlumalm) also reveal data structure similarity differing from that of the lowest site Talboden.     

Chemometric analysis of groundwater quality data of alluvial aquifer of Gangetic plain, North India

Water quality data set from the alluvial region in the Gangetic plain in northern India, which is known for high fluoride levels in soil and groundwater, has been analysed by chemometric techniques, such as principal component analysis (PCA), discriminant analysis (DA) and partial least squares (PLS) in order to investigate the compositional differences between surface and groundwater samples, spatial variations in groundwater composition and influence of natural and anthropogenic factors. Trilinear plots of major ions showed that the groundwater in this region is mainly of Na/K-bicarbonate type. PCA performed on complete data matrix yielded six significant PCs explaining 65% of the data variance. Although, PCA rendered considerable data reduction, it could not clearly group and distinguish the sample types (dug well, hand-pump and surface water). However, a visible differentiation between the water samples pertaining to two watersheds (Khar and Loni) was obtained. DA identified six discriminating variables between surface and groundwater and also between different types of samples (dug well, hand pump and surface water). Distinct grouping of the surface and groundwater samples was achieved using the PLS technique. It further showed that the groundwater samples are dominated by variables having origin both in natural and anthropogenic sources in the region, whereas, variables of industrial origin dominate the surface water samples. It also suggested that the groundwater sources are contaminated with various industrial contaminants in the region.     

Assessment of Water Quality for Human Consumption

 This study deals with the application of chemometric approaches (cluster analysis and principal components analysis) to a potable water monitoring demonstrated on a data set from the region of Kavala, Greece, being analysed according to the standard instructions and directives of the European Union. It is shown that the data classification by cluster analysis and data structure modeling by principal components analysis reveals similar results, namely four different patterns of water source sites are identified depending on the geographical site location (near to Nestos river, near to Strimon river, elevated sites and near-to-coast sites). Three latent factors, explaining over 85% of the total variance, are responsible for the data structure as follows: “water acidity (anthropogenic)”, “water hardness (natural)” and the “marine factor”. Their importance for the different sites is related to the site location. Finally, it is recommended to involve the environmetric data treatment as a substantial standard procedure in assessment of the quality of water intended for human consumption. Received October 18, 2001; accepted June 24, 2002     

Atmospheric trace metal characterization in industrial area of Lisbon, Portugal

Summary A set of 15 atmospheric aerosol samples was collected in an industrial area of Lisbon, Portugal and then analyzed by instrumental neutron activation analysis (INAA). Both fine and coarse aerosol samples were collected during November and December 2001 on polycarbonate filters with Gent samplers. The INAA methodology utilized both thermal and epithermal neutron irradiations. Compton suppressed and normal gamma-ray spectra were acquired simultaneously for each measurement and the elemental concentrations of 30 elements were determined. Enrichment factors, wind speed comparison and receptor modeling techniques were applied to obtain the different source contributions of the aerosols. Crustal, marine and anthropogenic sources were identified. The anthropogenic elements have origin mainly in the area close to the sampling site (<5 km), with the exception of Ca and V. A direct relationship was observed between the anthropogenic atmospheric aerosol concentrations and wind speed.     

Multivariate statistical analysis of aerosol immission data — An exemplary investigation on the North Frisian island of Pellworm

Element concentrations of 23 elements in different particle size fractions of aerosol samples from the island of Pellworm in the German Bight were analyzed by the help of total reflection X-ray fluorescence analysis. These immission data were investigated with multivariate statistical methods. Multivariate correlation analysis, as a newer chemometric method, demonstrates the autocorrelation of the immission state for different particle size fractions of dustlike aerosols. The immission of smaller particles is more strongly autocorrelated than that of larger particles. The factor analysis of each particle size fraction allows the extraction of two pollution factors for each fraction. The weights of these factors, anthropogenic and sea spray, change with size. The anthropogenic factor is more highly weighed for smaller particles, the sea spray factor is stronger in larger particles. The dependence of factor scores for smaller particles on wind direction indicates the sources of the extracted factors.     

Carbonaceous aerosol in the breathable particulate matter (PM10) in urban area

Carbonaceous material is a large fraction of urban aerosol and it is classified into Elemental Carbon (EC) and Organic Carbon (OC). EC particles are emitted from combustion sources. Because most combustion sources are anthropogenic and generally EC does not undergo chemical transformations, EC is a good indicator of primary anthropogenic primary pollution. OC particles species are emitted from primary emission sources either anthropogenic or biogenic sources. In this paper we have measured the ground concentration of Particulate Matter (PM), Total Carbon (TC), EC and OC in two Monitoring Stations in Rome. The first station is situated downtown Rome (near S.M. Maggiore Cathedral) where the traffic emission flux is strong. The second station is located in the inner a green park (Villa Ada Park): this site is not directly influenced by anthropogenic emissions. The results show that in Rome the TC contribution is about 30% of PM and the OC/EC vary between 0.5 and 1.5 according to the site we are considering. About the chemical particle composition the long-chain carboxylic have been identified as major constituent of organic aerosol and a range values are reported for two important compound class, the Polyciclic Aromatic Hydrocarbons and the nitro-PAHs wich are at very low levels.     

Arsenic speciation and other parameters of surface and ground water samples of Jamshoro,Pakistan

This study evaluated and interpreted complex data sets of water samples collected from different sampling origins of ground water (hand pump and tube well) and surface water (municipal, river and canal). The aim was to provide information concerning the apportionment of pollution sources to obtain better information about water quality and possible distribution of As with respect to its speciation. The As (III) formed complex with ammonium pyrrolidinedithiocarbamate (APDC) and extracted by surfactant-rich phases in the non-ionic surfactant Triton X-114, while total iAs in water samples was adsorbed on titanium dioxide (TiO₂) and determined by electrothermal atomic absorption spectrometry. The accuracy of the proposed methodologies was confirmed by standard addition method. The recoveries of As (III) and total inorganic arsenic (iAs) were found to be >98%. The results revealed that the ground water of the area under study was more contaminated as compared to surface water samples. The mean concentration of As (III) and As (V) in the surface water samples was found to be 15.8 and 6.00?µg?L^?1, respectively, whereas, in the case of ground water samples, the contents of As (III) and As (V) ranged from 6.20 to 51.0 and 6.40 to 53.0?µg?L^?1, respectively. Principal component analysis performed on a combined (tube well and hand pump) samples data set extracted two significant factors explaining more than 60% of total variance, which suggested that the contamination sources might be natural or anthropogenic.     

Multivariate statistical interpretation of coastal sediment monitoring data

Multivariate statistical analysis of sediment data (input matrix 122 x 15) collected from 122 sampling sites from the western coastline of the USA and analyzed for 15 analytes indicates that the data structure could be explained by four latent factors. These factors are conditionally named "anthropogenic", "organic", "natural", and "hot spots". They explain over 85% of the total variance of the data system, which is an acceptable value for the PCA model. The receptor models obtained after regression of the mass on the absolute principal components scores ensures reliable estimation of the contribution of each possible natural or anthropogenic source to the mass of each chemical component. It can be concluded that the region of interest reveals a different pattern of pollution compared with the eastern coastline treated statistically in a previous study.     

Multivariate statistical interpretation of coastal sediment monitoring data

Multivariate statistical analysis of sediment data (input matrix 122 × 15) collected from 122 sampling sites from the western coastline of the USA and analyzed for 15 analytes indicates that the data structure could be explained by four latent factors. These factors are conditionally named “anthropogenic”, “organic”, “natural”, and “hot spots”. They explain over 85% of the total variance of the data system, which is an acceptable value for the PCA model. The receptor models obtained after regression of the mass on the absolute principal components scores ensures reliable estimation of the contribution of each possible natural or anthropogenic source to the mass of each chemical component. It can be concluded that the region of interest reveals a different pattern of pollution compared with the eastern coastline treated statistically in a previous study.     

Danube River Water Data Modelling by Multivariate Data Analysis

 A data set (48×19) consisting of Danube river water analytical data collected at Galati site, Romania, during a four-year period has been treated by principal components analysis (PCA). The PCA indicated that seven latent factors (“hardness”, “biochemical”, “waste inlets”, “turbidity”, “acidity”, “soil extracts” and “organic wastes”) are responsible for the data structure and explain over 80 % of the total variance of the system. Its complexity is further proved by the application of multiple linear regression analysis on the absolute principal components scores (APCS) where the contribution of each natural or anthropogenic sources in the factor formation is shown. The apportioning makes clear that each variable participates to a different extent to each source and, in this way, no pure natural or pure anthropogenic influence could be determined. No specific seasonality for the variables in consideration is found. Received January 24, 2001. Revision July 6, 2001.     

Collection and Identification of Trace Organic Compounds in Atmospheric Deposition from a Semi-Rural Site in the U.K.

Abstract

The collection of rainwater, aerosol and vapour samples at a semi-rural site in the UK was achieved using a PTFE-lined wet-only rainfall collector and a high-volume filter/adsorption trap air sampler, respectively. Analysis of atmospheric deposition revealed the presence of several hundred compounds, many of which were of anthropogenic origin, e.g.: PAH, phenols and alkylbenzenes. Amounts of compounds varied from low nanograms to tens of micrograms per litre in rainwater samples and from low picograms to high nanograms per cubic metre in aerosols. Phenolic compounds were the most abundant group of organics identified in rainwater and were present at total concentrations of >20μg1^?1 in some of the samples analysed. In the high-volume air samples most anthropogenic compounds were detected in the adsorbent rather than the filter extract. Seasonal variations in the PAH content of the adsorbent extracts were observed. The presence of siloxanes in the air samples was thought to be the result of contamination.     

Seasonal characteristics of biomass burning contribution to Beijing aerosol

Along with the rapid economic growth and urbani-zation, a number of cities in China are facing the problem of severe air pollution with airborne particles (particulate matter) as the major pollutant identified most frequently. Urban airborne particles are…     

Assessment of the sequential principal component analysis chemometric tool to identify the soluble atmospheric pollutants in rainwater

In this study a new method of principal component (PC) analysis, sequential PC analysis (SPCA), is proposed and assessed on real samples. The aim was to identify the atmospheric emission sources of soluble compounds in rainwater samples, and the sample collection was performed with an automatic sampler. Anions and cations were separated and quantified by ion chromatography, whereas trace metals and metalloids were determined by inductively coupled plasma mass spectrometry. SPCA results showed eight interfering PCs and ten significant PCs. The interfering cases originated from different atmospheric sources, such as resuspended crustal particles, marine aerosols, urban traffic and a fertilizer factory. The significant PCs explained 84.6% of the total variance; 28.1% accounted for the main contribution, which was resuspended industrial soil from a fertilizer factory containing NO₂^-, NH₄⁺, NO₃^-, SO₄^2-, F^-, Al, K⁺, Mn, Sb and Ca²⁺ as indicators of the fertilizer factory. Another important source (15.0%) was found for Na⁺, Mg²⁺, K⁺, Cl^- and SO₄^2-, which represents the marine influence from south and southwest directions. Emissions of Ba²⁺, Pb, Sr²⁺, Sb and Mo, which represent a traffic source deposited in soils, were identified as another abundant contribution (12.1%) to the rainwater composition. Other important contributions to the rainwater samples that were identified through SPCA included the following: different urban emissions (Cu, As, Cd, Zn, Mo and Co, 18.1%), emissions from vegetation (HCOO^-, 7.7%) and emissions from industrial combustion processes (Ni, V 15.6%). The application of SPCA proved to be a useful tool to identify the complete information on rainwater samples as indicators of urban air pollution in a city influenced mainly by vehicle traffic emissions and resuspended polluted soils.     

Multivariate statistical analysis of coastal sediment data

Multivariate statistical analysis of sediment data (information matrix 123 × 16) from the Gulf of Mexico, USA shows that the data structure is defined by four latent factors conditionally called “inorganic natural”, “inorganic anthropogenic”, “bioorganic” and “organic anthropogenic” explaining 39.24%, 23.17%, 10.77% and 10.67% of the total variance of the data system, respectively. The receptor model obtained by the application of the PCR approach makes it possible to apportion the contribution of each chemical component for the latent factor formation. A separation of the contribution of each chemical parameter is achieved within the frames of “natural” and “anthropogenic” origin of the respective heavy metal or organic matter to the sediment formation process. This is a new approach as compared to the traditional “one dimensional” search with a limited number of preliminary selected tracer components. The model suggested divides natural from anthropogenic influences and allows in this way each participant in the sediment formation process to be used as marker of either natural or anthropogenic effects. Received: 20 March 1999 / Revised: 1 June 1999 / Accepted: 3 June 1999     

Multivariate statistical analysis of coastal sediment data

Multivariate statistical analysis of sediment data (information matrix 123 × 16) from the Gulf of Mexico, USA shows that the data structure is defined by four latent factors conditionally called “inorganic natural”, “inorganic anthropogenic”, “bioorganic” and “organic anthropogenic” explaining 39.24%, 23.17%, 10.77% and 10.67% of the total variance of the data system, respectively. The receptor model obtained by the application of the PCR approach makes it possible to apportion the contribution of each chemical component for the latent factor formation. A separation of the contribution of each chemical parameter is achieved within the frames of “natural” and “anthropogenic” origin of the respective heavy metal or organic matter to the sediment formation process. This is a new approach as compared to the traditional “one dimensional” search with a limited number of preliminary selected tracer components. The model suggested divides natural from anthropogenic influences and allows in this way each participant in the sediment formation process to be used as marker of either natural or anthropogenic effects. Received: 20 March 1999 / Revised: 1 June 1999 / Accepted: 3 June 1999     

Characterization of atmospheric aerosol in Buenos Aires, Argentina

PM10 and PM2.5 samples were taken using a Gent sampler to characterize the atmospheric aerosol of Buenos Aires metropolitan area. A total of 114 samples were collected from October 2005 to October 2006 at one urban site, every third day, for 24 h. Samples were analyzed by neutron activation, and black carbon and mass concentration were determined. In both fractions, elemental and gravimetric mass concentrations were compared with historical data. Enrichment factors, backward trajectories and factor analysis were calculated. The attribution of pollution sources is discussed.     

Annual variations of the elemental concentrations of PM10 in ambient air of Nagoya City as determined by ICP-AES and ICP-MS.

PM10 samples were collected at an urban site of Nagoya City during September, 2003, to August, 2004, and annual variations of the concentrations of the elements in PM10 samples were examined by inductively coupled plasma atomic emission spectrometry (ICP-AES) and inductively coupled plasma mass spectrometry (ICP-MS). The annual concentration variations of ca. 30 elements in ambient air were in the range from sub-ng m(-3) to several microg m(-3). From an evaluation by the enrichment factors of the elements, elements such as Al, Ca, Fe, Mg, Ti, Mn, Ba, Sr, Ce, La, Nd, Co, Cs, and Pr, in PM10 samples were found to have originated mostly from natural sources, while the elements such as S, Zn, Pb, Cu, Ni, Sb, Sn, Cd, Bi, W, Tl, and In originated from anthropogenic emission sources. Furthermore, in seasonal variations of the elemental concentrations of PM10 samples in ambient air, the elements originated mostly from natural sources provided significantly high concentrations in spring during the "Kosa" period (the dust season from March to May). On the other hand, the elements mainly from anthropogenic emission sources provided relatively higher concentrations in autumn and winter, which may be explained by the fact that the urban atmospheric structure is stabilized by the temperature-inversion layer formed over the city in those seasons. In addition, all of the elements provided significantly low concentrations in the summer, due to the dilution effect of the oceanic winds as well as due to the convection of air mass up to the high levels.     

Analytical investigations and chemometrical characterization of polluted soils

Summary Soil samples were taken in the surroundings of an industrial plant with heavy metal emission. A convenient digestion method for the determination of the mobile anthropogenic part of the heavy metal contents of soils was selected. This heavy metal contents were determined by atomic absorption spectrometry. The application of different multivariate statistical methods such as cluster analysis, multi-dimensional variance and discriminant analysis and factor analysis enables the objective characterization of polluted areas and of the degree of pollution as well as the identification of emission sources.     

Speciation of copper and zinc in size-fractionated atmospheric particulate matter using total reflection mode X-ray absorption near-edge structure spectrometry

The health effects of aerosol depend on the size distribution and the chemical composition of the particles. Heavy metals of anthropogenic origin are bound to the fine aerosol fraction (PM_2.5). The composition and speciation of aerosol particles can be variable in time, due to the time-dependence of anthropogenic sources as well as meteorological conditions. Synchrotron-radiation total reflection X-ray fluorescence (SR-TXRF) provides very high sensitivity for characterization of atmospheric particulate matter. X-ray absorption near-edge structure (XANES) spectrometry in conjunction with TXRF detection can deliver speciation information on heavy metals in aerosol particles collected directly on the reflector surface. The suitability of TXRF-XANES for copper and zinc speciation in size-fractionated atmospheric particulate matter from a short sampling period is presented. For high size resolution analysis, atmospheric aerosol particles were collected at different urban and rural locations using a 7-stage May cascade impactor having adapted for sampling on Si wafers. The thin stripe geometry formed by the particulate matter deposited on the May-impactor plates is ideally suited to SR-TXRF. Capabilities of the combination of the May-impactor sampling and TXRF-XANES measurements at HASYLAB Beamline L to Cu and Zn speciation in size-fractionated atmospheric particulate matter are demonstrated. Information on Cu and Zn speciation could be performed for elemental concentrations as low as 140 pg/m³. The Cu and Zn speciation in the different size fraction was found to be very distinctive for samples of different origin. Zn and Cu chemical state typical for soils was detected only in the largest particles studied (2–4 μm fraction). The fine particles, however, contained the metals of interest in the sulfate and nitrate forms.     

塔克拉玛干沙漠黑碳气溶胶的特性及来源

长期监测并采集塔克拉玛干沙漠腹地的大气颗粒物及黑碳气溶胶样品.在塔克拉玛干沙漠腹地,沙尘气溶胶中的黑碳在PM10中的年平均含量高达1.14%,这说明在人迹罕至的塔克拉玛干沙漠地区,其上空的沙尘气溶胶也已经受到人为活动的影响.黑碳气溶胶具有明显的季节变化和日变化,冬季最高,平均达2261.7ngm-3,依次为冬季春季秋季夏季.黑碳气溶胶日变化特征与城市地区恰好相反,夜间高于白天,午夜0:00～2:00出现峰值,而在上午8:00～11:00出现低值.非沙尘暴期间黑碳对PM10的贡献是沙尘暴时期的11倍.塔里木盆地周边绿洲带人为活动,尤其是新疆南北部地区跨越春秋冬三季的居民采暖所产生的黑碳经由局地、区域或长途传输是塔克拉玛干沙漠腹地黑碳气溶胶的主要来源,也是沙漠黑碳气溶胶具有明显的季节特征和日变化的主要原因.随着沙尘气溶胶的长途传输,沙漠每年大约输出6.3×104吨黑碳气溶胶,这势必会对全球的气候与环境变化产生一定影响.