Principal Component Analysis-multiple Linear Regression Research Articles

Source apportionment of perfluoroalkyl substances in surface sediments from lakes in Jiangsu Province, China: Comparison of three receptor models

Receptor models have been proved as useful tools to identify source categories and quantitatively calculate the contributions of extracted sources. In this study, sixty surface sediment samples were collected from fourteen lakes in Jiangsu Province, China. The total concentrations of C4–C14-perfluoroalkyl carboxylic acids and perfluorooctane sulfonic acid (∑12PFASs) in sediments ranged from 0.264 to 4.44ng/gdw (dry weight), with an average of 1.76ng/gdw. Three commonly-applied receptor models, namely principal component analysis-multiple linear regression (PCA-MLR), positive matrix factorization (PMF) and Unmix models, were employed to apportion PFAS sources in sediments. Overall, these three models all could well track the ∑12PFASs concentrations as well as the concentrations explained in sediments. These three models identified consistently four PFAS sources: the textile treatment sources, the fluoropolymer processing aid/fluororesin coating sources, the textile treatment/metal plating sources and the precious metal sources, contributing 28.1%, 37.0%, 29.7% and 5.3% by PCA-MLR model, 30.60%, 39.3%, 22.4% and 7.7% by PMF model, and 20.6%, 52.4%, 20.2% and 6.8% by Unmix model to the ∑12PFASs, respectively. Comparative statistics of multiple analytical methods could minimize individual-method weaknesses and provide convergent results to enhance the persuasiveness of the conclusions. The findings could give us a better knowledge of PFAS sources in aquatic environments.

Study on Airborne Heavy Metals in Industrialized Urban Area of Delhi, India.

This study assessed the concentrations of airborne heavy metals (HMs) in particulate matter with a cutoff size of 10µm (i.e., PM10) in an industrialized urban area (Naraina Industrial Area) of New Delhi, India. The samples were collected from January to December, 2011. The annual mean concentrations of selected HMs were as follows As (0.002 ± 0.002), Cd (0.030 ± 0.020), Co (0.003 ± 0.002), Cr (0.170 ± 0.081), Cu (0.183 ± 0.120), Fe (4.774 ± 1.889), Mn (0.258 ± 0.145), Ni (0.170 ± 0.146), Pb (0.345 ± 0.207) and Zn (1.806 ± 1.042)µg/m3. The seasonal trend for HMs followed the order postmonsoon > winter > premonsoon > monsoon. Principal component analysis-multiple linear regression (PCA-MLR) suggested the three major emission sources: industrial emission (70 %), mobile and stationary combustion sources (16 %), and suspended/re-suspended dust (14 %). Mean seasonal concentrations of PM10 exceeded both the 24-hour and annual Indian National Ambient Air Quality Standards (NAAQS) of 60 and 100µg/m3, respectively, in all four seasons. Mean seasonal Ni concentrations in Delhi ambient air also exceeded the 24-h annual NAAQS of 0.020µg/m3 during all four seasons. Mean Pb concentrations exceeded the annual NAAQS of 0.50µg/m3 only during the post monsoon season. The high levels of Ni- and Pb-contaminated PM10 would appear to present the possibility of significant health risks.

Source contributions of Polycyclic Aromatic Hydrocarbons in soils around oilfield in the Brahmaputra Valley

Surface soils from Borholla oilfield in the upper Brahmaputra Valley in India were studied for the USEPA's 16 priority Polycyclic Aromatic Hydrocarbons (PAHs). Analysis of PAHs was carried out by high performance liquid chromatography (HPLC) system equipped with an UV detector. Seasonality in PAHs concentrations was evident and the concentrations were found to be greater in post-monsoon season. There has been a dominance of low molecular weight PAHs (80–90% of total PAHs) indicating recent deposition from combustion sources. The concentration profiles appeared in the decreasing order of 3-ring >2-ring >4-ring >5-ring >6 ring PAHs. The sources of PAHs were identified using isomer pair ratios and Principal Component Analysis-Multiple Linear Regression (PCA-MLR) and Positive Matrix Factorisation (PMF). The ratios of diagnostic pairs indicated for both pyrogenic and petrogenic input of PAHs. The PCA-MLR modelling revealed that the <16% of contribution came from petrogenic origin and the rest 85% was found to be from pyrogenic sources. The PMF model also shown that <19% of PAHs source were petrogenic origin whereas rest from pyrogenic origin. The correlations of black carbon (BC) with PAHs also supported the pyrogenic contribution. The analysis of air mass back trajectories revealed that there has been contribution of both local and distant sources, through long range transport of pollutants, which were deposited to the site.

Evaluating levels and health risk of heavy metals in exposed workers from surgical instrument manufacturing industries of Sialkot, Pakistan

The study aimed to monitor heavy metal (chromium, Cr; cadmium, Cd; nickel, Ni; copper, Cu; lead, Pb; iron, Fe; manganese, Mn; and zinc, Zn) footprints in biological matrices (urine, whole blood, saliva, and hair), as well as in indoor industrial dust samples, and their toxic effects on oxidative stress and health risks in exposed workers. Overall, blood, urine, and saliva samples exhibited significantly higher concentrations of toxic metals in exposed workers (Cr; blood 16.30μg/L, urine 58.15μg/L, saliva 5.28μg/L) than the control samples (Cr; blood 5.48μg/L, urine 4.47μg/L, saliva 2.46μg/L). Indoor industrial dust samples also reported to have elevated heavy metal concentrations, as an example, Cr quantified with concentration of 299mg/kg of dust, i.e., more than twice the level of Cr in household dust (136mg/kg). Superoxide dismutase (SOD) level presented significant positive correlation (p ≤ 0.01) with Cr, Zn, and Cd (Cr > Zn > Cd) which is an indication of heavy metal's associated raised oxidative stress in exposed workers. Elevated average daily intake (ADI) of heavy metals resulted in cumulative hazard quotient (HQ) range of 2.97-18.88 in workers of different surgical units; this is an alarming situation of health risk implications. Principal component analysis-multiple linear regression (PCA-MLR)-based pie charts represent that polishing and cutting sections exhibited highest metal inputs to the biological and environmental matrices than other sources. Heavy metal concentrations in biological matrices and dust samples showed a significant positive correlation between Cr in dust, urine, and saliva samples. Current study will help to generate comprehensive base line data of heavy metal status in biomatrices and dust from scientifically ignored industrial sector. Our findings can play vital role for health departments and industrial environmental management system (EMS) authorities in policy making and implementation.

Metallic species in PM10 and source apportionment using PCA-MLR modeling over mid-Brahmaputra Valley

This study was conducted to understand the attributes of metallic species associated with PM10 over Mid-Brahmaputra Valley. PM10 was sampled for a yearlong period (December 2008 to November 2009) at a rural receptor site by a high volume sampler, which was chemically analyzed for 10 metallic species viz. Mg, K, Ca, Cr, Mn, Fe, Co, Ni, Cu, Cd, and Pb by acid digestion followed by estimation by ICP-OES. The mean PM10 for the study period was found to be 147.10 ± 153 μg m−3, and marked seasonality of the mass concentrations of PM10 was observed. However, the concentrations of the metallic species did not show seasonality that was statistically significant. High PM10 levels during monsoon were therefore attributed to delay and deficit rainfall, secondary formation of aerosols, and favorable atmospheric conditions viz. subsidence and thermal inversion. This was as evident from analyses of vertical temperature profile and HYSPLIT air mass trajectories. The enrichment factors revealed that K, Cr, Ni, and Pb were moderately, and Cd highly enriched in PM10 indicating anthropogenic contribution. Inter-species correlations and principal component analysis-multiple linear regression (PCA-MLR) of elements associated in PM10 indicates biomass burning, crustal dust, soil, and fossil fuel burning as the possible sources. The source contributions were validated and the contributions of total elements by the sources to the predicted concentrations by PCA-MLR showed good agreement.

Identification, characterization, and human health risk assessment of perfluorinated compounds in groundwater from a suburb of Tianjin, China

This study investigated the occurrence of perfluorinated compounds (PFCs) in groundwater from 16 sampled wells for the public drinking water supply in a suburb of Tianjin, China. Thirteen PFC analytes were quantified by ultra-high performance liquid chromatography/electrospray tandem mass spectrometry. Concentrations of the total PFCs in groundwater ranged from 0.32 to 8.3 ng L−1 with an average of 2.4 ng L−1. The dominant PFCs were perfluorobutane sulfonic acid (PFBS) and perfluorooctanoic acid (PFOA) with averages of 1.1 and 0.81 ng L−1, respectively. Levels of perfluorocarboxylic acids containing 6–12 carbon atoms (except PFOA) in groundwater decreased with the increase of their carbon chain lengths. Strong positive correlations were observed between perfluoroheptanoic acid (PFHpA) and PFOA, PFHpA and perfluorononanoic acid (PFNA), PFHpA and PFBS, and PFOA and PFNA (Spearman Correlation test, p < 0.01), suggesting that these chemicals might originate from the same or similar pollution sources. The principal component analysis–multiple linear regression model analysis showed that PFCs mainly came from the fluorotelomer production and emission, followed by the introduction of the substitute compounds. PFC concentrations were positively correlated to the contents of dissolved organic carbon and Mn2+, and negatively correlated to the contents of total nitrogen and SO4 2− in groundwater. These indicated that PFC distributions and pollution levels in groundwater were related to organic carbon, nutrients and inorganic salts. The preliminary health risk assessment suggested that there was no significantly immediate harm of exposure to PFCs in drinking water for the villagers in the suburb of Tianjin, China.

Occurrence, source identification and ecological risk evaluation of metal elements in surface sediment: toward a comprehensive understanding of heavy metal pollution in Chaohu Lake, Eastern China.

In the present study, surface sediment samples from 48 sites covering the whole water area and three main estuaries of Chaohu Lake were collected to determine the concentrations of 25 metal elements using microwave-assisted digestion combined with ICP-MS. Spatial variation, source appointments, and contamination evaluation were examined using multivariate statistical techniques and pollution indices. The results show that for the elements Cd, Pb, Zr, Hf, U, Sr, Zn, Th, Rb, Sn, Cs, Tl, Bi, and Ba, which had higher coefficients of variation (CV), the concentrations were significantly higher in the eastern lake than in the western lake, but other elements with low CV values did not show spatial differences. The accumulation of Cu, Zn, Rb, Sr, Zr, Cd, Sn, Cs, Ba, Hf, Ta, Tl, Pb, Bi, U, and Th in the surface sediments was inferred as long-term agricultural cultivation impact, but that of Ti, V, Cr, Mn, Co, and Ni may have been a natural occurrence. The contribution from industrial and municipal impact was negligible, despite the rapid urbanization around the studied area. Principal component analysis-multiple linear regression (PCA-MLR) predicted the contribution from agricultural activities to range from 0.45 ± 1.31% for Co to 92.7 ± 17.7% for Cd. The results of the pollution indices indicate that Chaohu Lake was weakly to moderately affected by Ti, V, Cr, Mn, Co, and Ni but was severely contaminated by Hf and Cd. The overall pollution level in the eastern lake was higher than that in the western lake with respect to the pollution level index (PLI). Therefore, our results can help comprehensively understand the sediment contamination by metals in Chaohu Lake.

Sources and ecological risk assessment of PAHs in surface sediments from Bohai Sea and northern part of the Yellow Sea, China

Sources and ecological risk of sixteen priority PAHs in surface sediments from Bohai Sea (BS) and northern part of the Yellow Sea (NPYS) were investigated. The total concentrations of PAHs varied in ranges 149.24–1211.81 and 148.28–907.47ng/g for BS and NPYS, respectively. Principal component analysis-multiple linear regression (PCA–MLR) suggested that coal combustion, vehicular emission and coke oven could be the primary PAH contributors, accounting for 56.6%, 29.2% and 14.2% of PAH concentrations, respectively. Analysis with sediment quality guidelines (SQGs) indicated that acenaphthylene, fluorene, phenanthrene, fluoranthene, pyrene and dibenzo(a,h)anthracene may occasionally cause adverse biological effects in some stations. Organic carbon (OC)-normalized analysis and mean effects range-median quotient (M-ERM-Q) suggested that the combined ecological risk of PAHs was generally low. The toxic equivalent concentrations of carcinogenic PAHs were 12.87–64.6ng/g-BaPeq and 5.95–68.80ng/g-BaPeq in BS and NPYS, respectively, suggesting low carcinogenic risk for both BS and NPYS.

Composition and source apportionment of dust fall around a natural lake

The aim of this study was to determine the source apportionment of dust fall around Lake Chini, Malaysia. Samples were collected monthly between December 2012 and March 2013 at seven sampling stations located around Lake Chini. The samples were filtered to separate the dissolved and undissolved solids. The ionic compositions (NO3−, SO42−, Cl− and NH4+) were determined using ion chromatography (IC) while major elements (K, Na, Ca and Mg) and trace metals (Zn, Fe, Al, Ni, Mn, Cr, Pb and Cd) were determined using inductively coupled plasma mass spectrometry (ICP-MS). The results showed that the average concentration of total solids around Lake Chini was 93.49±16.16mg/(m2·day). SO42−, Na and Zn dominated the dissolved portion of the dust fall. The enrichment factors (EF) revealed that the source of the trace metals and major elements in the rain water was anthropogenic, except for Fe. Hierarchical agglomerative cluster analysis (HACA) classified the seven monitoring stations and 16 variables into five groups and three groups respectively. A coupled receptor model, principal component analysis multiple linear regression (PCA-MLR), revealed that the sources of dust fall in Lake Chini were dominated by agricultural and biomass burning (42%), followed by the earth's crust (28%), sea spray (16%) and a mixture of soil dust and vehicle emissions (14%).

Source apportionment and risk assessment of emerging contaminants: an approach of pharmaco-signature in water systems.

This paper presents a methodology based on multivariate data analysis for characterizing potential source contributions of emerging contaminants (ECs) detected in 26 river water samples across multi-scape regions during dry and wet seasons. Based on this methodology, we unveil an approach toward potential source contributions of ECs, a concept we refer to as the “Pharmaco-signature.” Exploratory analysis of data points has been carried out by unsupervised pattern recognition (hierarchical cluster analysis, HCA) and receptor model (principal component analysis-multiple linear regression, PCA-MLR) in an attempt to demonstrate significant source contributions of ECs in different land-use zone. Robust cluster solutions grouped the database according to different EC profiles. PCA-MLR identified that 58.9% of the mean summed ECs were contributed by domestic impact, 9.7% by antibiotics application, and 31.4% by drug abuse. Diclofenac, ibuprofen, codeine, ampicillin, tetracycline, and erythromycin-H2O have significant pollution risk quotients (RQ>1), indicating potentially high risk to aquatic organisms in Taiwan.

Excited States and photodebromination of selected polybrominated diphenyl ethers: computational and quantitative structure--property relationship studies.

This paper presents a density functional theory (DFT)/time-dependent DFT (TD-DFT) study on the lowest lying singlet and triplet excited states of 20 selected polybrominateddiphenyl ether (PBDE) congeners, with the solvation effect included in the calculations using the polarized continuum model (PCM). The results obtained showed that for most of the brominated diphenyl ether (BDE) congeners, the lowest singlet excited state was initiated by the electron transfer from HOMO to LUMO, involving a π–σ* excitation. In triplet excited states, structure of the BDE congeners differed notably from that of the BDE ground states with one of the specific C–Br bonds bending off the aromatic plane. In addition, the partial least squares regression (PLSR), principal component analysis-multiple linear regression analysis (PCA-MLR), and back propagation artificial neural network (BP-ANN) approaches were employed for a quantitative structure-property relationship (QSPR) study. Based on the previously reported kinetic data for the debromination by ultraviolet (UV) and sunlight, obtained QSPR models exhibited a reasonable evaluation of the photodebromination reactivity even when the BDE congeners had same degree of bromination, albeit different patterns of bromination.

Seasonal attributes of urban soil PAHs of the Brahmaputra Valley

Polycyclic Aromatic Hydrocarbons (PAHs) are ubiquitous organic pollutants, which are both toxic and carcinogenic. In the present study seasonally collected composite soil samples of Guwahati city of the Brahmaputra Valley were analysed for of PAHs by HPLC column. Black carbon (BC) and organic carbon (OC) of soil samples were analysed by thermochemical oxidation method in a TOC analyzer. Mean concentrations of ∑PAHs (USEPA 16) were found to be 5570168±7003, 9052±1292 and 19294±17827ngg−1 during monsoon, pre- and post-monsoon seasons, respectively. Two- and 4-ring PAHs dominated and the 2-ring PAHs were particularly abundant during post-monsoon period. The carcinogenic potentials of PAHs were calculated as BaP equivalents, which was found to be maximum (1167.064ngBaPqg−1) at industrial site. Diagnostic ratios of marker species indicated for pyrogenic origins of PAHs. Sources were indentified and contribution of individual sources was quantified by multivariate hyphenated model – Principal Component Analysis–Multiple Linear Regression (PCA–MLR). Mobile sources like the vehicular traffic were found to have contributed ∼63% to the PAHs load. The correlations of individual PAHs with BC or OC showed seasonal variations. High dependencies of PAHs on BC/OC ratios were found indicating that BC could be interfering with the association of PAHs and OC. However, such relationships showed seasonal bias and high positive dependencies were found during pre-monsoon period only. Strong relationships were found between PAHs and BC/OC during monsoon and post-monsoon seasons.

A Study on Pathway and QSPR Models for Debromination of PBDEs with Pseudopotential Method

Neutral PBDEs congeners and their corresponding radical anions were studied with the pseudopotential method of stuttgart group (SDD) effective-core potentials basis set for the bromine atoms and the all-electron basis set for all other atoms. The pseudopotential method can be used for compounds containing heavy elements with relativistic effects and can reduce the computational time. The quantitative structure property relationship (QSPR) study was also performed in this work to develop models to predict the normolized reaction rate constants for the reductive debromination of polybrominated diphenyl ethers (PBDEs) by zero-valent iron (ZVI). The partial least squares regression (PLSR), principal component analysis-multiple linear regression analysis (PCA-MLR), and back propagation artificial neural network (BP-ANN) approaches were employed for the QSPR study between the molecular descriptors and the logarithm of normalized reaction rate constants of fourteen selected BDE congeners. The results show that the ANN models could be more satisfactorily to predict the rate constants than the PLSR and PCA-MLR models.

A QSPR Study on Debromination of PBDEs with CPCM Solvation Model

The quantitative structure property relationship (QSPR) study was performed in this work to develop models to predict the normalized reaction rate constants for the reductive debromination of polybrominated diphenyl ethers (PBDEs) by zero-valent iron (ZVI). In order to consider the solvent effect, conductor-like polarizable continuum model (CPCM) was applied to optimize the geometries and obtain the molecular descriptors using the pseudopotential basis set. The prediction results with the inclusion of solvent effect are slightly better than that of the corresponding gas-phase calculations. The artificial neural network (ANN) model could be more satisfactory to predict the rate constants than the partial least squares regression (PLSR) and principal component analysis-multiple linear regression analysis (PCA-MLR) models.

A multi-molecular marker assessment of organic pollution in shore sediments from the Río de la Plata Estuary, SW Atlantic

Organic pollution was evaluated in surface sediments along the middle portion of the Río de la Plata Estuary, SW Atlantic. A multi-molecular marker approach was performed to identify major sources of organic compounds using diagnostic indices. The relative contribution of different sources of hydrocarbons was quantified by source apportionment employing Principal Component Analysis/Multiple Linear Regression (PCA/MLR) as chemometric technique. All molecular markers indicated high chronic organic pollution in the stations of Montevideo Bay. Main sources of aliphatic and polycyclic aromatic hydrocarbons were petroleum inputs and combustion, due to oil transport and refinement, harbour activities and vehicular emissions. Major sources of linear alkylbenzenes and steroids were industrial and domestic sewage. Although, significant anthropogenic inputs, a natural footprint of terrestrial higher plants contribution was recognized. Multi-molecular marker and comprehensive assessments can improve the establishment of more precise regulation actions to reduce pollution levels.

Spatial distribution, potential risk assessment, and source apportionment of polycyclic aromatic hydrocarbons (PAHs) in sediments of Lake Chaohu, China.

Twenty-nine sediment samples were collected from Lake Chaohu, a shallow eutrophic lake in Eastern China, and were analyzed for 15 priority polycyclic aromatic hydrocarbons (PAHs) to determine the spatial distribution and exposure risks of PAHs. Three receptor models, the principal component analysis-multiple linear regression (PCA-MLR) model, the positive matrix factorization (PMF) model, and the Unmix model, were used in combination with the PAHs diagnostic ratios to investigate the potential source apportionment of PAHs. A clear gradient in the spatial distribution and the potential toxicity of PAHs was observed from west to east in the sediments of Lake Chaohu. ∑15PAH concentrations and the TEQ were in the range of 80.82-30 365.01 ng g(-1) d.w. and 40.77-614.03, respectively. The highest values of the aforementioned variables were attributed to urban-industrial pollution sources in the west lake region, and the levels decreased away from the river inlets. The three different models yielded excellent correlation coefficients between the predicted and measured levels of the 15 PAH compounds. Similarly, source apportionment results were derived from the three receptor models and the PAH diagnostic ratios, suggesting that the highest contribution to the PAHs was from coal combustion and wood combustion, followed by vehicular emissions. The PMF model yielded the following contributions to the PAHs from gasoline combustion, diesel combustion, unburned petroleum emissions, and wood combustion: 34.49, 24.61, 16.11, 13.01, and 11.78 %, respectively. The PMF model produced more detailed source apportionment results for the PAHs than the PCA-MLR and Unmix models.

Potential source apportionment of polycyclic aromatic hydrocarbons in surface sediments from the middle and lower reaches of the Yellow River, China.

In this work, principal component analysis/multiple linear regression (PCA/MLR), positive matrix factorization (PMF), and UNMIX model were employed to apportion potential sources of polycyclic aromatic hydrocarbons (PAHs) in surface sediments from middle and lower reaches of the Yellow River, based on the measured PAHs concentrations in sediments collected from 22 sites in November 2005. The results suggested that pyrogenic sources were major sources of PAHs. Further analysis indicated that source contributions of PAHs compared well among PCA/MLR, PMF, and UNMIX. Vehicles contributed 25.1-36.7 %, coal 34.0-41.6 %, and biomass burning and coke oven 29.2-33.2 % of the total PAHs, respectively. Coal combustion and traffic-related pollution contributed approximately 70 % of anthropogenic PAHs to sediments, which demonstrated that energy consumption was a predominant factor of PAH pollution in middle and lower reaches of the Yellow River. In addition, the distributions of contribution for each identified source category were studied, which showed similar distributed patterns for each source category among the sampling sites.

Investigating sources and pathways of perfluoroalkyl acids (PFAAs) in aquifers in Tokyo using multiple tracers

We employed a multi-tracer approach to investigate sources and pathways of perfluoroalkyl acids (PFAAs) in urban groundwater, based on 53 groundwater samples taken from confined aquifers and unconfined aquifers in Tokyo. While the median concentrations of groundwater PFAAs were several ng/L, the maximum concentrations of perfluorooctane sulfonate (PFOS, 990ng/L), perfluorooctanoate (PFOA, 1800ng/L) and perfluorononanoate (PFNA, 620ng/L) in groundwater were several times higher than those of wastewater and street runoff reported in the literature. PFAAs were more frequently detected than sewage tracers (carbamazepine and crotamiton), presumably owing to the higher persistence of PFAAs, the multiple sources of PFAAs beyond sewage (e.g., surface runoff, point sources) and the formation of PFAAs from their precursors. Use of multiple methods of source apportionment including principal component analysis–multiple linear regression (PCA–MLR) and perfluoroalkyl carboxylic acid ratio analysis highlighted sewage and point sources as the primary sources of PFAAs in the most severely polluted groundwater samples, with street runoff being a minor source (44.6% sewage, 45.7% point sources and 9.7% street runoff, by PCA–MLR). Tritium analysis indicated that, while young groundwater (recharged during or after the 1970s, when PFAAs were already in commercial use) in shallow aquifers (<50m depth) was naturally highly vulnerable to PFAA pollution, PFAAs were also found in old groundwater (recharged before the 1950s, when PFAAs were not in use) in deep aquifers (50–500m depth). This study demonstrated the utility of multiple uses of tracers (pharmaceuticals and personal care products; PPCPs, tritium) and source apportionment methods in investigating sources and pathways of PFAAs in multiple aquifer systems.

Source Apportionment of Polycyclic Aromatic Hydrocarbons in Agricultural Soils of Yanqing County in Beijing, China

Guanting Reservoir in Yanqing County is a strategic drinking-water resource for Beijing, China. The agricultural soil samples (0 cm to 20 cm) were collected to determine the concentration, compositional profiles, and sources of polycyclic aromatic hydrocarbons (PAHs) surrounding the reservoir. The average measured PAH concentration was 140 ± 85.7 μg kg−1 (CV = 0.45) (dry weight or dw; n = 48; shown as arithmetic mean and standard deviation), which is lower than most of the results reported in literature. The PAH compositional profiles were dominated by 3- and 4-ring compounds, particularly phenanthrene (PHE), fluoranthene (FLA), fluorene (FLO), and pyrene (PYR). The PAH sources were estimated by analyzing the corrected ratios of isomeric PAH species derived using the multimedia fugacity model and principal component analysis/multiple linear regression (PCA/MLR). The corrected isomeric ratios of FLA/(FLA+PYR) and anthracene (ANT)/(ANT+PHE) indicate that combustion processes are the major sources of PAHs in the soil. Three principal components were identified as main sources, representing coal burning and traffic exhaust, biomass combustion and coking, and a source labeled “other,” which contribute 54%, 40%, and 6% of the total PAH, respectively (α = 0.05). Moreover, except those from local biomass burning, other PAHs were primarily deposited in the soil from distant sources via atmospheric transportation.

Vertical characteristics of levels and potential sources of water-soluble ions in PM10 in a Chinese megacity

To investigate the vertical characteristics of ions in PM10 as well as the contributions and possible locations of their sources, eight water-soluble ions were measured at four heights simultaneously along a meteorological tower in Tianjin, China. The total ion concentrations showed a general decreasing trend with increasing height, ranging from 64.94μgm−3 at 10m to 44.56μgm−3 at 220m. NH4+, SO42− and NO3− showed higher height-to-height correlations. In addition, relationships between ions are discussed using Pearson correlation coefficients and hierarchical clustering analysis (HCA), which implied that, for each height, the correlations among NH4+, SO42− and NO3− were higher. Finally, sources were identified qualitatively by the ratio of certain ions and quantitatively by principal component analysis/multiple linear regression (PCA/MLR) and positive matrix factorisation (PMF). Secondary sources played a dominant role for PM10 and water-soluble ions at four heights and became more important at greater heights (the percentage contributions were 43.04–66.41% for four heights by PCA/MLR and 46.93–67.62% by PMF). Then, the redistributed concentration field (RCF) combined with PCA/MLR and PMF was applied, which indicated the high potential source regions. The vertical characteristics of the levels, relationships, source contributions and locations would support the effective management of the water-soluble ions in particulate matter.