A quantitative assessment of source contributions to inhalable particulate matter pollution in metropolitan Boston

In this paper, source apportionment techniques are employed to identify and quantify the major particle pollution source classes affecting a monitoring site in metropolitan Boston, MA. A Principal Component Analysis (PCA) of paniculate elemental data allows the estimation of mass contributions for five fine mass panicle source classes (soil, motor vehicle, coal related, oil and salt aerosols), and six coarse panicle source classes (soil, motor vehicle, refuse incineration, residual oil, salt and sulfate aerosols). Also derived are the elemental characteristics of those source aerosols and their contributions to the total recorded elemental concentrations (i.e. an elemental mass balance). These are estimated by applying a new approach to apportioning mass among various PCA source components: the calculation of Absolute Principal Component Scores, and the subsequent regression of daily mass and elemental concentrations on these scores.One advantage of the PCA source apportionment approach developed is that it allows the estimation of mass and source particle characteristics for an unconventional source category: transported (coal combustion related) aerosols. This particle class is estimated to represent a major portion of the aerosol mass, averaging roughly 40 per cent of the fine mass and 25 per cent of the inhalable particle mass at the Watertown, MA site. About 45 per cent of the fine particle sulfur is ascribed to this one component, with only 20 per cent assigned to pollution from local sources. The composition of the coal related aerosol at this site is found to be quite different from particles measured in the stacks of coal-fired power plants. Sulfates were estimated to comprise a much larger percentage of the ambient coal related aerosol than has been measured in stacks, while crustal element percentages were much reduced. This is thought to be due to primary panicle deposition and secondary aerosol accretion experienced during transport. Overall, the results indicate that the application of further emission controls to local point sources of particles would have less influence on fine aerosol and sulfate concentrations than would the control of more distant emissions causing aerosols transported into the Boston vicinity.     

Assessing spatial variability of SO2 field as detected by an air quality network using Self-Organizing Maps,cluster, and Principal Component Analysis

In Bilbao (Spain), an air quality network measures sulphur dioxide levels at 4 locations. The objective of this paper is to develop a practical methodology to identify redundant sensors and evaluate a network's capability to correctly follow and represent SO₂ fields in Bilbao, in the frame of a continuous network optimization process.The methodology is developed and tested at this particular location, but it is general enough to be useable at other places as well, since it is not tied neither to the particular geographical characteristics of the place nor to the phenomenology of the air quality over the area.To assess the spatial variability of SO₂ measured at 4 locations in the area, three different techniques have been used: Self-Organizing Maps (SOMs), cluster analysis (CA) and Principal Component Analysis (PCA). The results show that the three techniques yield the same results, but the information obtained via PCA can be helpful not only for that purpose but also to throw light on the major mechanisms involved. This might be used in future network optimization stages. The main advantage of cluster analysis and SOMs is that they provide readily interpretable results. All the calculations have been carried out using the freely available software R.     

Polychlorinated biphenyls in two mollusc species from the coast of Tenerife (Canary Islands, Spain)

Levels of PCBs were determined in two marine organisms, limpets (Patella ullisiponensis aspera) and winkles (Ossilinus atratus), sampled in three stations in the south east coast of Tenerife during 1993. Winkles and limpets exhibited different PCB patterns. Principal Component Analysis (PCA) was used to study the patterns and the relationships among PCBs.     

Enhancement of source traceability of atmospheric PM by elemental chemical fractionation

During a 1-year study (“Fine dust” Project) funded by the Lazio regional government (Italy), about 1000 daily PM₁₀ and PM_2.5 samples collected from six sites in the region were subjected to chemical fractionation based on differences in elemental solubility. In this way, it was possible to achieve meaningful characterization of the elemental composition of individual samples. For most of the investigated elements, we found significant differences between the extracted and the mineralized residual fraction. In general, fine particulate was best characterized by the composition of the extracted fraction, while coarse particles from traffic-related sources were best characterized using residues. For several metals (Cd, Pb, Sn, Sb and V) having a critical environmental impact, this result was particularly clear.The application of Principal Component Analysis (PCA) and receptor modelling (PCR) to the data set allowed us to evidence the enhancement of selectivity towards different emission sources that is obtained when chemical fractionated data are considered instead of total element content. Chemical fractionation seems to generate very selective markers for specific emission sources and in particular for re-suspended road dusts, one of the main factors responsible for the increase of elemental concentrations in urban areas.     

One-year study of the elemental composition and source apportionment of PM10 aerosols in Florence, Italy

An extensive investigation was carried out for the characterisation of the air particulate composition in Florence. The aim was to determine the aerosol elemental concentrations, as well as to identify pollution sources. For our investigation, the external Particle-Induced X-Ray Emission-Particle-Induced gamma-Ray Emission beam facility of the Istituto Nazionale di Fisica Nucleare, Van de Graaff accelerator at the Physics Department of the Florence University was used. We report the results of the analysis of a long temporal series (approximately 1 yr) of PM10 particulate samples, collected on Millipore filters on a daily basis in three different sites (characterised by different urban settings). Daily concentrations of more than 20 elements were detected. The long sampling period (approximately 1 yr) allowed a comparison with the air quality recommended values and the identification of seasonal variations. Four main sources (traffic, oil-combustion, soil-dust, and wind transported sea-salt) were extracted with the help of Principal Component Analysis (PCA). An absolute PCA showed traffic to be the major source both in the high traffic site and in the urban background site.     

A neural network-based approach for the prediction of urban SO2 concentrations in the Istanbul metropolitan area

A three-layer Artificial Neural Network (ANN) model was developed to forecast air pollution levels. The subsequent SO₂ concentration (24-hour averaged) being the output parameter of this study was estimated by seven input parameters such as preceding SO₂ concentrations (24-hour averaged), average daily temperature, sea-level pressure, relative humidity, cloudiness, average daily wind speed and daily dominant wind direction. After Backpropagation training combined with Principal Component Analysis (PCA), the proposed model predicted subsequent SO₂ values based on measured data. ANN testing outputs were proven to be satisfactory with correlation coefficients of about 0.770, 0.744 and 0.751 for the winter, summer and overall data, respectively.     

Behavioural effects of endocrine disrupting chemicals on laboratory rodents: statistical methodologies and an application concerning developmental PCB exposure.

Appropriate behavioural tests and adequate statistical tools may help to establish the ED properties of a given compound by pointing out the alterations of selected behavioural endpoints. Frequently, laboratory collected data consist of frequencies and/or durations of specific items, and the analysis of variance (ANOVA) technique is performed to assess whether the investigated factors affect these behavioural endpoints. Moreover, when numerous aspects of behaviour are investigated simultaneously, Principal Component Analysis (PCA), a multivariate technique, may be very useful to reduce the overwhelming number of correlated original variables to a few orthogonal artificial variables (factors). Continuous Time Markov Chain (CTMC) models may be applied to analyse the time structure of a behavioural pattern when data consist of sequences of events and the time points at which they occur. Moreover, the Cox Proportional Hazard Model, a methodology originally developed for the analysis of failure time data, may help to evidence the effects of a given treatment on behavioural sequences when the assumptions of CTMC models are not fully satisfied. Analyses on data from mice of the outbred CD-1 strain (controls in a study of toxicity and exposed to PCB during development) are presented as examples to show how adequate statistical analyses and appropriate behavioural tests may reveal relevant effect of treatments otherwise not easily detected.     

One-Year Study of the Elemental Composition and Source Apportionment of PM10 Aerosols in Florence,Italy

Abstract

An extensive investigation was carried out for the characterisation of the air particulate composition in Florence. The aim was to determine the aerosol elemental concentrations, as well as to identify pollution sources. For our investigation, the external Particle-Induced X-Ray Emission–Particle-Induced γ-Ray Emission beam facility of the Istituto Nazionale di Fisica Nucleare, Van de Graaff accelerator at the Physics Department of the Florence University was used. We report the results of the analysis of a long temporal series (approximately 1 yr) of PM₁₀ particulate samples, collected on Millipore filters on a daily basis in three different sites (characterised by different urban settings). Daily concentrations of more than 20 elements were detected. The long sampling period (approximately 1 yr) allowed a comparison with the air quality recommended values and the identification of seasonal variations. Four main sources (traffic, oil-combustion, soil-dust, and wind transported sea-salt) were extracted with the help of Principal Component Analysis (PCA). An absolute PCA showed traffic to be the major source both in the high traffic site and in the urban background site.     

Formation of PCDD/PCDF

One option of recycling used contaminated packaging is to recover its high energy content. This can be performed in a normal multi-fuel power plant by co-combustion of packaging-derived fuel (PDF) or refuse-derived fuel (RDF) with fossil fuels, such as coal or peat. This work includes the results of 17 co-combustion tests and an evaluation of the results by the Principal Component Analysis (PCA) and the Partial Least Squares Projections to Latent Structures (PLS). PCA and PLS calculations showed that especially Pb, but also Cr, and Cu correlated with lower chlorinated furans (PCDFs) in the fly ash. Correlation between Sn and lower chlorinated dioxins (PCDDs) in the fly ash was also noticed. CO and PAH emission in the flue gas correlated with total PCDD/Fs in the flue gas. In a real full-scale combustion process, a single parameter in fuel, flue gas or a combustion parameter did not provide a guide to PCDD/F formation or to a level of the total PCDD/F emission, but correlations between different parameters and PCDD/Fs could be found. Although PDFs and RDF had catalytic heavy metals and chlorine, the co-combustion results showed that they can be co-combusted with peat and coal in a fluidized-bed boiler at least up to 26 % with very low total PCDD and PCDF emissions.     

Use of principal components analysis (PCA) on estuarine sediment datasets: The effect of data pre-treatment

Principal components analysis (PCA) is a multivariate statistical technique capable of discerning patterns in large environmental datasets. Although widely used, there is disparity in the literature with respect to data pre-treatment prior to PCA. This research examines the influence of commonly reported data pre-treatment methods on PCA outputs, and hence data interpretation, using a typical environmental dataset comprising sediment geochemical data from an estuary in SE England. This study demonstrated that applying the routinely used log (x + 1) transformation skewed the data and masked important trends. Removing outlying samples and correcting for the influence of grain size had the most significant effect on PCA outputs and data interpretation. Reducing the influence of grain size using granulometric normalisation meant that other factors affecting metal variability, including mineralogy, anthropogenic sources and distance along the salinity transect could be identified and interpreted more clearly.     

Source identification of personal exposure to fine particulate matter using organic tracers

Personal exposure to fine particulate matter (PM2.5) is due to both indoor and outdoor sources. Contributions of sources to personal exposure can be quite different from those observed at ambient sampling locations. The primary goal of this study was to investigate the effectiveness of using trace organic speciation data to help identify sources influencing PM2.5 exposure concentrations. Sixty-four 24-h PM2.5 samples were obtained on seven different subjects in and around Boulder, CO. The exposure samples were analyzed for PM2.5 mass, elemental and organic carbon, organic tracer compounds, water-soluble metals, ammonia, and nitrate. This study is the first to measure a broad distribution of organic tracer compounds in PM2.5 personal samples. PM2.5 mass exposure concentrations averaged 8.4 μg m^?3. Organic carbon was the dominant constituent of the PM2.5 mass. Forty-four organic species and 19 water-soluble metals were quantifiable in more than half of the samples. Fifty-four organic species and 16 water-soluble metals had measurement signal-to-noise ratios larger than two after blank subtraction.The dataset was analyzed by Principal Component Analysis (PCA) to determine the factors that account for the greatest variance. Eight significant factors were identified; each factor was matched to its likely source based primarily on the marker species that loaded the factor. The results were consistent with the expectation that multiple marker species for the same source loaded the same factor. Meat cooking was an important source of variability. The factor that represents meat cooking was highly correlated with organic carbon concentrations (r = 0.84). The correlation between ambient PM2.5 and PM2.5 exposure was relatively weak (r = 0.15). Time participants spent performing various activities was generally not well correlated with PCA factor scores, likely because activity duration does not measure emissions intensity. The PCA results demonstrate that organic tracers can aid in identifying factors that influence personal exposures to PM2.5.     

Interpretation of heavy metal data from mussel by use of multivariate classification techniques

Different strategies of multivariate analysis of metals concentrations (Mn, Fe, Ni, V, Co, Cu, Cd, Hg, Pb, Na, K) in mussel samples from different spanish markets are used to interpret a data base and identify differences between species and origin of the samples. Principal Component Analysis and Potential curves are applied to properly classify unknown samples from representative mussels samples (Mytilus edulis and Perna canaliculus). Also, Principal Components Analysis is used as display method to visualize the relation between the variables and objects of interest.     

Indication of airborne pollution by birch and spruce in the vicinity of copper smelter

The aim of the study was determination of air pollution impact of the copper smelter in Bor and its surroundings (Serbia) by assessing the suitability of birch (Betula pendula Roth.) and spruce (Picea abies L.) for the purposes of biomonitoring and comparing it with previously published data from the same study area. The concentrations of Cu, Zn, Pb and Mn in leaves/needles, branches, roots and soil were determined. Sampling was performed during 2009 in two zones with high load of air pollution due to copper mining and smelting activities, and one background zone. Metal accumulation and translocation was evaluated in terms of biological factors. In addition, plant enrichment factor was calculated. According to the results, plant foliage was not enriched through soil, which indicates absorption from the air, with both species acting as excluders of Cu, Pb, Zn and Mn. Leaves were more enriched with all the metals than needles, indicating a better response of birch to airborne pollution than spruce. Cluster analysis showed different level of pollution at the sites, while correlations between Cu and Pb obtained by Principal Component Analysis indicated their anthropogenic origin. Regarding previously published results, beside birch leaves, pine needles (which showed higher level of response to pollution compared to linden leaves) could be applied in air biomonitoring surveys near copper smelters.     

Persistent organic pollutant in a fish community of a sub-alpine lake

This study investigated the presence of organochlorine pollutants in abiotic and biotic samples from Lake Como (Italy). DDTs and PCBs were found to be the major contaminants, ranging from 0.04 to 4.25 and from 0.25 to 40.8 μg/g lipid respectively. Evidence of biomagnification according to the trophic role of the investigated organisms was highlighted by means of Stable Isotope Analysis. A Trophic Magnification Factor (TMF) was calculated for the chemicals of interest and the applicability of the method for global use was confirmed. Statistically significant correlation has been found between the calculated trophic level and the concentrations of more lipophilic compounds, while for the less lipophilic (e.g., HCH, 3CBs) the relationship is no statistically significant and the TMF is close to 1.The role of the foraging area in affecting PCB and DDT concentrations within aquatic ecosystems has been highlighted by a Principal Component Analysis (PCA).     

Classification and ordination of DDT and HCH in soil samples from the Guanting Reservoir, China

Organochlorine pesticides (OCPs) have been a major environmental issue, drawing much scientific and public attention due to their bioaccumulation potential, persistence and toxicity. Soil samples from three villages around the Guanting Reservoir, one of Beijing's five major water systems located to the northwest of the city, were collected in 2003 and analyzed to determine DDT and HCH-concentrations. The samples were also analyzed for soil texture, pH, and concentrations of total carbon, nitrogen and phosphorus to investigate their possible relationship to current OCP-concentrations. Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) were used to study the distribution and contamination levels of OCPs within the study area. Classification splits were made to divide the 30 samples into three groups. The first group contained samples in soils from village C; the second group contained all of the samples in village B and most of the samples in village A; and the third group contained just three samples from village A, and the three samples had a relatively high concentration of OCPs. Ordination plots of the first two axes from PCA (cumulative percentage 80.91%) were constructed to explore the HCH and DDT-distribution patterns as well as the degradation ratios between the parent substances and their isomers.     

Characterization of surface marine sediments from Ría de Arousa estuary according to extractable humic matter content

Surface marine sediments from Ría de Arousa estuary were analyzed for humic and fulvic acids by UV-visible spectrometry and have been characterized using elemental analysis (carbon, hydrogen and nitrogen elemental composition) and spectrometric data (A2/A4 ratio, absorbancies at 270 and 407 nm and E4/E6 ratio, absorbancies at 465 and 665 nm). These variables have been used as discriminating factors to distinguish of marine and terrestrial origin of humic and fulvic acids in Ría de Arousa surface marine sediments. Principal component analysis, PCA, and cluster analysis, CA, have been used as unsupervised pattern recognition procedures. The half-range central value transformation was used as data pre-treatment to homogenize data. After a Varimax rotation, PCA applied to humic acid data has reveled that spectrometric A2/A4 and E4/E6 ratios are the main dominating features in the first principal component (48.6% of total variance), the humic acid content is the feature with the highest weight in the second principal component (22.9% of the total variability) and the carbon elemental composition domain in the third principal component (13.3% of total variance). Results from PCA have revealed that surface sediments collected at inner-left part of the estuary and at the mouth of the river Ulla belong to the same group. Similarly, PCA has shown that surface sediments from the right mouth of the estuary form a compact group. Taking in account the water circulation in Ría de Arousa estuary, these findings mean that the organic matter in surface sediments from the inner-left part of the estuary derived mainly from terrestrial organic matter while the organic matter in surface sediments from the right mouth of the estuary is mainly derived from marine sources. Finally, it must be noticed that any classification of surface sediments was assessed when applying of PCA and CA from fulvic acids data.