Principal Component Analysis/absolute Principal Component Scores Research Articles

Spatial variability and source analysis of soil heavy metals: A case study of the key planting area of special agricultural products in Cangxi County, China.

Heavy metal pollution in farmland soil represents a considerable risk to ecosystems and human health, constituting a global concern. Focusing on a key area for the cultivation of special agricultural products in Cangxi County, we collected 228 surface soil samples. We analyzed the concentration, spatial distribution, and pollution levels of six heavy metals (Cr, Cu, Pb, Ni, Zn, and Hg) in the soil. Moreover, we investigated the sources and contribution rates of these heavy metals using Principal Component Analysis/Absolute Principal Component Scores (PCA/APCS) and Positive Matrix Factorization (PMF) models. Our findings indicate that none of the six metals exceeded the pollution thresholds for farmland soils. However, the mean concentrations of Cr and Ni surpassed the background levels of Sichuan Province. A moderate spatial correlation existed between Pb and Ni, attributable to both natural and anthropogenic factors, whereas Zn, Cu, Hg, and Cr displayed a strong spatial correlation, mainly due to natural factors. The spatial patterns of Cr, Cu, Zn, Pb, and Ni were similar, with higher concentrations in the northern and eastern regions and lower concentrations centrally. Hg's spatial distribution differed, exhibiting a broader range of lower values. The single pollution index evaluation showed that Cr and Ni were low pollution, and the other elements were no pollution. The average value of comprehensive pollution index is 0.994, and the degree of pollution is close to light pollution. Predominantly, higher pollution levels in the northern and eastern regions, lower around reservoirs. The PCA/APCS model identified two main pollution sources: agricultural traffic mixed source (65.2%) and natural parent source (17.2%). The PMF model delineated three sources: agricultural activities (32.59%), transportation (30.64%), and natural parent sources (36.77%). Comparatively, the PMF model proved more accurate and reliable, yielding findings more aligned with the study area's actual conditions.

Comparative Receptor Models Using Principal Component Analysis/Absolute Principal Component Scores and Positive Matrix Factorization to Assess Source Apportionment of PM2.5-10 and PM2.5 in Urban Cities

b Source apportionment of PM2.5-10 and PM2.5 was conducted using two receptor models: the principal component analysis/absolute principal component scores (PCA/APCS) and the positive matrix factorization (PMF). Methods: The PCA/APCS model resolved four sources, namely, mineral dust, sea salt/secondary sulfur, a mixed source, and traffic and industry emissions. PMF model also resolved four sources of the same origin based on the proportion of each component from the analysis. All models identified the main sources that contribute to PM2.5-10 and PM2.5 emissions and reconfirmed that the potential sources were the dominants in particulate matter in the Capital city. Findings: The first four extracted component accounted for nearly 88.3% of the variability of the data set. The running matrix elements used for data processing within collected air particulate matter was BC, Na, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Br, Rb, Sr, Ba, and Pb. This discrimination of the area of Dakar cites according to the PCA/APCS results confirms the impact that the major activities have on the environment in the most appropriate way, and it verifies the results of previous studies about the influence of Industry emissions, mineral dust, traffic emissions and sea salt/secondary sulfur on the surrounding urban areas. Novelty: Continued collection of speciation data at the urban areas will enhance the understanding of local versus regional source contributions for air quality index in sub-Sahara region. Keywords: Aerosol, Particulate Matter, Source apportionment, receptor Modelling, XRF, heavy metals

The spatial distribution and source apportionment of heavy metals in soil of Shizuishan, China

Environmental pollution of heavy metals in the typical coal industrial city should be paid more attentions nowadays. The spatial distribution and source apportionment of 8 heavy metals (i.e., Cd, Cr, Co, Zn, Ni, Cu, Pb and Mn) from topsoil samples (158) of Shizuishan city in Ningxia Hui Autonomous Region of China were investigated using principal component analysis/absolute principal component scores (PCA/APCS) receptor model and geographic information system (GIS). These results showed that the mean concentrations of Cd, Cr, Co, Zn, Ni, Cu and Pb were higher than their soil background values in Ningxia. 99.36% of soil samples were heavily polluted according to analysis of integrated Nemerow pollution index (PN), whereas 81.65% of soil samples exhibited the highly strong potential ecological risk by ERI (the comprehensive of potential ecological risk index) values. The source apportionment results showed that eight heavy metals in soil were mainly from natural (32.39%), industrial (26.56%), traffic emission/coal consumption (20.18%) and atmospheric deposition source (12.73%). Typically, Zn, Mn and Ni were derived from natural source, whereas Cr and Co were mainly derived from industrial sources. Cu was from the multiple sources, whereas Pb and Cd were weighted primarily from traffic emission/coal consumption source and atmospheric deposition source, respectively. These findings were crucial for the prevention and control of heavy metals pollution in Shizuishan city.

Vehicular influence on atmospheric concentrations and source apportionment of polycyclic aromatic hydrocarbons in some major cities in three regions of Ghana using epiphytic lichens

The present study employed epiphytic lichens as biomonitor and passive air sampler for the assessment of fifteen (15) atmospheric polycyclic aromatic hydrocarbons (PAHs) in some major cities in three regions of Ghana. A total of 36 composite lichen samples were collected and analysed using Gas Chromatography - Tandem Mass Spectrometry (GC-MS-MS). The total PAH recorded ranged between 1909.9 ng/kg (A36) and 250,091.4 ng/kg (W15). Due to the inherent deficiencies in using a single source apportionment tool, multiple source apportionment methods including diagnostic ratios, principal component analysis/absolute principal component scores (PCA-APCS) and APCS with automatic linear model (APCS–ALM) were used to ascertain the source of PAHs in the lichens. The diagnostic ratios revealed a mix source of wood/grass and petrol/petroleum fuel combustion, with the major source ascribing to wood/grass combustion. The source apportionment confirmatory statistical test conducted with the PCA-APCS and APCS–ALM, were in good agreement with the diagnostic ratio. Both PCA-APCS and APCS–ALM suggested two significant sources (p < 0.0), with wood/grass combustion as the major (contributing 77.8%) and mix petroleum related sources being the other with 22.2% contribution of PAHs to the receptor sites. The study found PCA-APCS and especially APCS–ALM to be an effective statistical tool for PAH source apportionment in passive air samplers. To our knowledge, this is the first use of lichens for PAH monitoring in the country. Therefore, this study could serve as an inexpensive and real time bio-monitoring tool for air quality assessment in the African sub-region and the world at large.

Soil metal(loid)s pollution around a lead/zinc smelter and source apportionment using isotope fingerprints and receptor models

Soil pollution from metal(loid)s has become a severe environmental issue. Source identification of soil metal(loid)s is the basis for pollution prevention and control. In this study, a total of 31 surface soil samples were collected from farmland near a large lead/zinc (Pb/Zn) smelter in Zhuzhou City, central China. The sources of soil metal(loid)s pollution were identified with principal component analysis/absolute principal component scores (PCA/APCS), positive matrix factorization model (PMF), and Pb isotope fingerprints. Results showed significant accumulation of metal(loid)s in agricultural soils. The averages of soil Cd, Pb, and Zn concentrations were 7.90, 273, and 675 mg kg−1, respectively. Metal(loid)s pollution was mainly distributed in the vicinity of the Pb/Zn smelter and the soil metal(loid)s contamination near the surface was quite high. The sources of metal(loid)s included Pb/Zn smelting, waste incineration, and natural sources. Smelting was the major source of metal(loid)s pollution. The Pb binary isotope mixing model indicated that smelting contributed 74.4% of soil Pb. PCA/APCS showed that smelting contributed 70.3%, 80.6%, and 86.5% to soil Cd, Pb, and Zn, respectively, while PMF revealed that it accounted for 73.1%, 72.1%, and 70.6%, respectively. Our study demonstrated that the combination of receptor models and isotope methods was comprehensive and effective for source identification, which provided new insights into determining the sources of soil metal(loid)s pollution.

Spatial-temporal Variation and Source Change of Heavy Metals in the Cropland Soil in the Industrial City

Environmental policy regulation affects the accumulation of heavy metals in soil. Two soil surveys were conducted in farmland in industrial cities. Classical statistical and geostatistical methods were applied to examine the content changes and spatial-temporal distribution characteristics of Cd, Pb, Cu, and Zn in soil. The pollution sources and their contribution rates were further analyzed by a principal component analysis/absolute principal component scores (PCA/APCS) model. The results indicated that the concentrations of Cd, Pb, Cu, and Zn increased during the two periods. The content of Cd and Pb in the spatial distribution decreased from northwest to southeast in both periods. The content of Cu and Zn decreased from north to south in 2011, and gradually decreased from northwest to southeast in 2017. Compared with 2011, the contribution rate of industrial activities increased by 5.58%, 10.4%, and 20.4% for Cd, Cu, and Zn, respectively, but decreased by 19.7% for Pb. The contribution rates of the other factors to Cd and Pb increased by 3.76% and 24.83%, respectively, and decreased by 9.27% and 4.31% for Cu and Zn, respectively. Relevant measures have reduced the accumulation of Pb in regional soil caused by industrial activities, but not Cd, Cu, or Zn. In addition, new Cd and Pb pollution sources need to be paid attention to.

Observation and Source Apportionment of Trace Gases, Water-Soluble Ions and Carbonaceous Aerosol During a Haze Episode in Wuhan

As the new core region of the haze pollution, the terrain effect of sub-basin and water networks over the Twin-Hu Basin (THB) in the Yangtze River Middle-Reach (YRMR) had great impacts on the variations and distributions of air pollutants. In this study, trace gases (NH3, HNO3, and HCl), water-soluble ions (WSIs), organic carbon (OC), and elemental carbon (EC) were measured in PM2.5 from 9 January to 27 January 2018, in Wuhan using monitoring for aerosols and gases (MARGA) and a semi-continuous OC/EC analyzer (Model RT-4). The characteristics of air pollutants during a haze episode were discussed, and the PM2.5 sources were quantitatively analyzed on haze and non-haze days using the principal component analysis/absolute principal component scores (PCA/APCS) model. The average PM2.5 concentration was 122.61 μg·m−3 on haze days, which was 2.20 times greater than it was on non-haze days. The concentrations of secondary water soluble ions (WSIs) including NO3−, SO42−, and NH4+ increased sharply on haze days, which accounted for 91.61% of the total WSIs and were 2.43 times larger than the values on non-haze days. The heterogeneous oxidation reactions of NO2 and SO2 during haze episodes were proven to be the major sources of sulfate and nitrate in PM2.5. On haze days, the concentrations of EC, primary organic carbon (POC), and secondary organic carbon (SOC) were 1.68, 1.69, and 1.34 times larger than those on non-haze days, the CO, HNO3, and NH3 concentrations enhanced and relatively low SO2, O3, and HNO2 levels were observed on haze days. The diurnal variations of different pollutants distinctly varied on haze days. The PM2.5 in Wuhan primarily originated from the secondary formation, combustion, dust, industry, and vehicle exhaust sources. The source contributions of the secondary formation + combustion sources to PM2.5 on haze days were 2.79 times larger than the level on non-haze days. The contribution of the vehicle exhaust + combustion source on haze days were 0.59 times the value on non-haze days. This description is supported by a summary of how pollutant concentrations and patterns vary in the THB compared to the variations in other pollution regions in China, which have been more completely described.

Seasonal Variations, Source Apportionment, and Health Risk Assessment of Heavy Metals in PM2.5 in Ningbo, China

ABSTRACT In order to assess the seasonal variations, potential sources, and health risks of heavy metals in fine particulate matter (PM2.5), PM2.5 samples (n = 96) were collected between March 2015 and February 2016 in Ningbo, China. Twelve heavy metals (Sb, As, Cd, Cr, Pb, Mn, Ni, Se, Tl, Al, Be, and Hg) found in the PM2.5 were analyzed by inductively coupled plasma mass spectrometry (ICP-MS). We used enrichment factors and principal component analysis/absolute principal component scores (PCA/APCS) to determine the sources of these heavy metals, and models from the United States Environmental Protection Agency (EPA) to assess both the carcinogenic and non-carcinogenic risks to adults and children. Results showed that the average annual mass concentration of the PM2.5 was 62.7 µg m–3, which exceeded the limit specified in the Chinese National Ambient Air Quality Standards (NAAQS). The average annual concentrations of the Pb, Cd, and As were 57.2 ng m–3, 1.5 ng m–3, and 4.7 ng m–3, respectively, which were below the NAAQS limits. The highest total concentrations for the heavy metals occurred in winter, whereas the lowest concentrations were observed in summer. Enrichment factor analysis indicated that the Sb, Cd, Pb, Se, As, and Tl were mainly from anthropogenic sources. Additionally, source apportionment by PCA/APCS identified four major sources for the studied metals: coal combustion and motor vehicles (46.3%), soil and construction dust (37.1%), steelworks (6.9%), and other smelting industries (6.8%). The carcinogenic risk of heavy metals in Ningbo fell within the safe level of exposure for both children and adults. However, the total non-carcinogenic risk exceeded the safe level (HI = 1.38), which warrants further research on sources of air pollution and measures for controlling pollutants in Ningbo, China.

Source apportionment and risk assessment of PAHs in Brisbane River sediment, Australia

Sediment samples collected over a 3-year period from Brisbane River, Australia, were analysed for fifteen (15) polycyclic aromatic hydrocarbons (PAHs). The total PAH concentrations varied from 148 to 3079ng/g with a mean concentration of 849±646ng/g. The study revealed that PAH input into the river was primarily dominated by pyrogenic sources as evidenced by the predominance of the high molecular weight (HMW) PAHs. Temporal variations of PAHs can be linked to the level of urbanization, with continuous input of combustion related PAHs in the commercial area of the river. Inherent deficiencies in using a single source identification/apportionment approach were overcome by using diagnostic ratios, principal component analysis/absolute principal component scores (PCA/APCS) and positive matrix factorization (PMF). Both, PCA/APCS and PMF resolved four (4) identical factors or sources of PAHs, namely: gasoline emissions, diesel emissions, biomass burning and natural gas combustion. Diagnostic ratios, PCA/APCS and PMF analysis indicated that vehicular emissions were the principal sources especially within the lower section of the river while biomass burning had moderate contribution. The distribution, temporal trend and source apportionment suggest the containment of industrial-derived sources of PAHs in the river. From an ecological point of view, the risk posed by PAHs in the Brisbane River sediment appears to be low. Nevertheless, when the investigated sites were ranked using multi-criteria decision making methods(MCDM) the commercial stratum was the most contaminated. Assessment of potential risks posed by incidental dermal exposure to PAHs revealed some degree of cancer risk, especially to children.

Effect of cloud-to-ground lightning and meteorological conditions on surface NOx and O3 in Hong Kong

Cloud-to-ground (CG) lightning, meteorological conditions and corresponding surface nitrogen oxides (NOx) and ozone (O3) variations in relation to thunderstorm and lightning activities over Hong Kong at Kwai Chung (urban), Tung Chung (new town) and Tap Mun (background) during active lightning seasons from 2009 to 2013 were studied by analyzing respective air quality monitoring station data along with CG lightning and meteorological data. We observed NOx enhancement and significant O3 decline on lightning days. Influences of land use types, lightning activities and meteorological conditions on surface NOx and O3 were examined. NOx and O3 concentrations shifted towards higher and lower levels, respectively, during lightning days especially in the dominant wind directions. Principal component analysis/absolute principal component scores (PCA/APCS) method and stepwise multiple linear regression (MLR) analysis were employed to examine the influence of thunderstorm related lightning and meteorological parameters on surface NOx and O3. Wind speed was supposed to be the most important meteorological parameter affecting the concentration of NOx, and lightning activities were observed to make a positive contribution to NOx. Negative contribution of hot, cloudy and wet weather and positive contribution of wind speed were found to affect the concentration of O3. Lightning parameters were also found to make a small positive contribution to O3 concentration at Tap Mun and Tung Chung, but the net effect of lightning activities and corresponding meteorological conditions was the decrease of O3 on lightning days. Reasonably good agreement between the predicted and observed NOx and O3 values indicates that PCA/APCS-MLR is a valuable method to study the thunderstorm induced NOx and O3 variations.

Determination and Source Apportionment of Aromatic Acids in PM2.5 from the Northern Suburb of Nanjing in Winter

Atmospheric particulate samples of PM2.5 were collected from the northern suburb of Nanjing in December, 2014, and a LC-MS method was optimized for the determination of aromatic acids in PM2.5; The concentrations of major water-soluble ions, organic carbon and elemental carbon were also determined. The quantification results showed that the average total concentration of five aromatic acids we have determined was (50.01±16.05) ng·m-3, and the average concentrations of terephthalic acid, phthalic acid, trimellitic acid, 4-methylphthalic acid and iso-phthalic acid were (34.54±12.79)、(8.14±3.34)、(2.27±1.39)、(1.68±0.77) and (1.08±0.43) ng·m-3, respectively. The different source apportionments of atmospheric particulate were analyzed by principal component analysis/absolute principal component scores (PCA/APCS) receptor model. The results of source apportionment showed that the main sources of Phthalic acid, Trimellitic acid and 4-methylphthalic acid were mainly secondary transformation, and primary emissions such as vehicle exhaust contributed less to Trimellitic acid; Secondary transformation and biomass burning made the most significant contributions to iso-Phthalic acid and vehicle exhaust contributed less; The sources of Terephthalic acid were primary emissions such as biomass burning and vehicle exhaust.

Sources of hydrocarbons in urban road dust: Identification, quantification and prediction

Among urban stormwater pollutants, hydrocarbons are a significant environmental concern due to their toxicity and relatively stable chemical structure. This study focused on the identification of hydrocarbon contributing sources to urban road dust and approaches for the quantification of pollutant loads to enhance the design of source control measures. The study confirmed the validity of the use of mathematical techniques of principal component analysis (PCA) and hierarchical cluster analysis (HCA) for source identification and principal component analysis/absolute principal component scores (PCA/APCS) receptor model for pollutant load quantification. Study outcomes identified non-combusted lubrication oils, non-combusted diesel fuels and tyre and asphalt wear as the three most critical urban hydrocarbon sources. The site specific variabilities of contributions from sources were replicated using three mathematical models. The models employed predictor variables of daily traffic volume (DTV), road surface texture depth (TD), slope of the road section (SLP), effective population (EPOP) and effective impervious fraction (EIF), which can be considered as the five governing parameters of pollutant generation, deposition and redistribution. Models were developed such that they can be applicable in determining hydrocarbon contributions from urban sites enabling effective design of source control measures.

Source apportionment and health risk assessment of PM10 in a naturally ventilated school in a tropical environment

This study aimed to investigate the chemical composition and potential sources of PM10 as well as assess the potential health hazards it posed to school children. PM10 samples were taken from classrooms at a school in Kuala Lumpur's city centre (S1) and one in the suburban city of Putrajaya (S2) over a period of eight hours using a low volume sampler (LVS). The composition of the major ions and trace metals in PM10 were then analysed using ion chromatography (IC) and inductively coupled plasma-mass spectrometry (ICP-MS), respectively. The results showed that the average PM10 concentration inside the classroom at the city centre school (82µg/m3) was higher than that from the suburban school (77µg/m3). Principal component analysis-absolute principal component scores (PCA-APCS) revealed that road dust was the major source of indoor PM10 at both school in the city centre (36%) and the suburban location (55%). The total hazard quotient (HQ) calculated, based on the formula suggested by the United States Environmental Protection Agency (USEPA), was found to be slightly higher than the acceptable level of 1, indicating that inhalation exposure to particle-bound non-carcinogenic metals of PM10, particularly Cr exposure by children and adults occupying the school environment, was far from negligible.

Atmospheric polycyclic aromatic hydrocarbons in the urban environment: Occurrence, toxicity and source apportionment

Polycyclic Aromatic Hydrocarbons (PAHs) represent a major class of toxic pollutants because of their carcinogenic and mutagenic characteristics. People living in urban areas are regularly exposed to PAHs because of abundance of their emission sources. Within this context, this study aimed to: (i) identify and quantify the levels of ambient PAHs in an urban environment; (ii) evaluate their toxicity; and (iii) identify their sources as well as the contribution of specific sources to measured concentrations. Sixteen PAHs were identified and quantified in air samples collected from Brisbane. Principal Component Analysis – Absolute Principal Component Scores (PCA-APCS) was used in order to conduct source apportionment of the measured PAHs. Vehicular emissions, natural gas combustion, petrol emissions and evaporative/unburned fuel were the sources identified; contributing 56%, 21%, 15% and 8% of the total PAHs emissions, respectively, all of which need to be considered for any pollution control measures implemented in urban areas.

Characteristics and source apportionment of VOCs measured in an industrial area of Nanjing, Yangtze River Delta, China

Based on the data of volatile organic compounds (VOCs) collected continuously in the Nanjing industrial area, the VOCs temporal variability, photochemical reactivity, differences in tracer ratios, and source apportionment by principal component analysis/absolute principal component scores (PCA/APCS) were analyzed. The results showed that the total VOC concentration was 43.5 ppbv, which was 45.1% alkanes, 25.3% alkenes, 7.3% alkynes and 22.3% aromatics. There was an obvious seasonal variation in the of VOCs, with a maximum in summer and minimum in winter, as well as in the VOC components, with alkanes and alkynes highest in winter, alkenes highest in summer, and aromatics highest in spring. The VOCs exhibited significant diurnal variations, i.e. high at night and low during the day. The concentrations of nighttime VOCs were highest to lowest as follows: summer > autumn > spring > winter. By contrast, the concentrations of daytime VOCs were highest to lowest as follows: winter > summer > spring > autumn. The diurnal variation of the VOCs was the largest in autumn and the smallest in winter. The largest diurnal variation of alkanes and alkenes occurred in autumn, whereas the largest diurnal variation of aromatics and alkynes occurred in spring. Using the propylene-equivalent method, alkenes had the highest concentration, followed by aromatics and alkanes. The ratios of T/B, E/B and X/B were 1.2, 1.0 and 0.8, respectively, possibly due to the aging air mass. Based on the specific pollutant ratio method, the observation site was greatly affected by the surrounding industrial areas. The source analysis of the VOCs in the PCA/APCS mode showed that the sources consisted of industrial production sources, automobile emission sources, combustion sources, industrial production volatilization sources, solvent use sources and biogenic emission sources; in addition, there were seasonal variations. Overall, the sources related to industrial production activities accounted for 45–63% of the VOCs, followed by automobile emission sources, which accounted for 34–50%.

Source Apportionment of Heavy Metals in Soils Using Multivariate Statistics and Geostatistics

The main objectives of this study were to introduce an integrated method for effectively identifying soil heavy metal pollution sources and apportioning their contributions, and apply it to a case study. The method combines the principal component analysis/absolute principal component scores (PCA/APCS) receptor model and geostatistics. The case study was conducted in an area of 31 km2 in the urban-rural transition zone of Wuhan, a metropolis of central China. 124 topsoil samples were collected for measuring the concentrations of eight heavy metal elements (Mn, Cu, Zn, Pb, Cd, Cr, Ni and Co). PCA results revealed that three major factors were responsible for soil heavy metal pollution, which were initially identified as “steel production”, “agronomic input” and “coal consumption”. The APCS technique, combined with multiple linear regression analysis, was then applied for source apportionment. Steel production appeared to be the main source for Ni, Co, Cd, Zn and Mn, agronomic input for Cu, and coal consumption for Pb and Cr. Geostatistical interpolation using ordinary kriging was finally used to map the spatial distributions of the contributions of pollution sources and further confirm the result interpretations. The introduced method appears to be an effective tool in soil pollution source apportionment and identification, and might provide valuable reference information for pollution control and environmental management.

Sources Affecting PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; Concentrations at a Rural Semi-Arid Coastal Site in South Texas

Principal component analysis/absolute principal component scores (PCA/APCS) and positive matrix factorization (PMF2), an advanced factor analysis technique were employed to apportion the sources influencing the PM2.5 levels measured during 2003 through 2005 at a rural coastal site located within the Corpus Christi urban airshed in South Texas. PCA/APCS identified five sources while PMF2 apportioned an optimal solution of eight sources. Both PCA/APCS and PMF2 quantified secondary sulfates to be the major contributor accounting for 47% and 45% of the apportioned PM2.5 levels. The other common sources apportioned by the models included crustal dust, fresh sea salt and traffic emissions. PMF2 successfully apportioned distinct sources of fresh and aged sea salt along with biomass burns while PCA/APCS was unsuccessful in identifying aged sea salt and biomass burns; however it successfully identified secondary organic aerosols from photochemical oxidations and also emitted by petrochemical refineries. The influence of long range transport was noted for sources such as secondary sulfates, biomass burns and crustal dust affecting the region. Continued collection of speciation data at the rural and urban sites will enhance the understanding of local versus regional source contributions for air quality policy makers and stakeholders.

Polychlorinated Biphenyl Congeners in River Sediments: Distribution and Source Identification Using Multivariate Factor Analysis

To assess the level of polychlorinated biphenyl (PCB) contamination and identify their sources, surface sediments were collected from selected locations along Nakdong River, Korea, and analyzed for 209 PCB congeners using high-resolution gas chromatography/high-resolution mass spectroscopy. PCB levels ranged from 0.124 to 79.2ng/g dry weight (coplanar PCBs 0.295 to 5720pg/g dry), which were similar to those of three other major rivers (Han, Geum, and Youngsan rivers) in Korea but slightly lower than those in neighboring countries. Regarding homologue composition, tetra-CBs were most abundant in most samples, but some samples with much higher PCBs concentrations had relatively lower proportions of tetra-CBs and higher proportions of penta- to hepta-CBs. To identify the sources of PCBs in sediment samples, principal component analysis/absolute principal component scores (PCA/APCS), positive matrix factorization (PMF), and multiple linear regression (MLR) were used with the congener composition of aroclors (1242, 1248, 1254, and 1260) and the flue gas of waste incinerators (data obtained from a previous article) as source profiles. Results showed that the three models showed similar source apportionments. Most sediment samples with lower PCB concentrations had higher proportions of incineration-derived materials, and some sediment samples with much higher PCB concentrations had higher proportions of aroclor 1260. This occurred because many industrial facilities, such as landfill leachate-treatment facilities, were gathered around sampling points with high PCB concentrations, and high-chlorinated PCBs are more stable in the elution process of landfill leachate than the incineration process. PCB concentrations estimated by APCS, PMF, and MLR were similar to the measured values with coefficients of determination ranging from 0.77 to 0.99.

Multi-criteria ranking and source apportionment of fine particulate matter in Brisbane, Australia

Environmental context. There are serious global concerns about the environmental and health effects of atmospheric air pollutants. However, estimates of pollutants from measurements made in the proximity of a source do not always represent the ultimate atmospheric concentrations. Therefore alternative methods of attributing pollutants to sources, and estimating their contributions to atmospheric concentrations, as demonstrated in the current work, will become an increasingly important area of environmental research. Abstract. This paper reports the application of multicriteria decision making techniques, Preference Ranking Organisation Methods for Enrichment Evaluation (PROMETHEE) and Graphical Analysis for Interactive Assistance (GAIA), and receptor models, principal component analysis/absolute principal component scores (PCA/APCS) and positive matrix factorisation (PMF), to data from an air monitoring site located on the campus of Queensland University of Technology in Brisbane, Australia and operated by Queensland Environmental Protection Agency (QEPA). The data consisted of the concentrations of 21 chemical species and meteorological data collected between 1995 and 2003. PROMETHEE/GAIA separated the samples into those collected when leaded and unleaded petrol were used to power vehicles in the region. The number and source profiles of the factors obtained from PCA/APCS and PMF analyses were compared. There are noticeable differences in the outcomes possibly because of the non-negative constraints imposed on the PMF analysis. Whereas PCA/APCS identified 6 sources, PMF reduced the data to 9 factors. Each factor had distinctive compositions that suggested that motor vehicle emissions, controlled burning of forests, secondary sulfate, sea salt and soil were the most important sources of fine particulate matter at the site. The most plausible locations of the sources were identified by combining the results obtained from the receptor models with meteorological data. The study demonstrated the potential benefits of combining results from multi-criteria decision making analysis with those from receptor models in order to gain insights into information that could enhance the development of air pollution control measures.

Source Characterization of Volatile Organic Compounds Affecting the Air Quality in a Coastal Urban Area of South Texas

Selected Volatile Organic Compounds (VOC) emitted from various anthropogenic sources including industries and motor vehicles act as primary precursors of ozone, while some VOC are classified as air toxic compounds. Significantly large VOC emission sources impact the air quality in Corpus Christi, Texas. This urban area is located in a semi-arid region of South Texas and is home to several large petrochemical refineries and industrial facilities along a busy ship-channel. The Texas Commission on Environmental Quality has setup two continuous ambient monitoring stations (CAMS 633 and 634) along the ship channel to monitor VOC concentrations in the urban atmosphere. The hourly concentrations of 46 VOC compounds were acquired from TCEQ for a comprehensive source apportionment study. The primary objective of this study was to identify and quantify the sources affecting the ambient air quality within this urban airshed. Principal Component Analysis/Absolute Principal Component Scores (PCA/APCS) was applied to the dataset. PCA identified five possible sources accounting for 69% of the total variance affecting the VOC levels measured at CAMS 633 and six possible sources affecting CAMS 634 accounting for 75% of the total variance. APCS identified natural gas emissions to be the major source contributor at CAMS 633 and it accounted for 70% of the measured VOC concentrations. The other major sources identified at CAMS 633 included flare emissions (12%), fugitive gasoline emissions (9%), refinery operations (7%), and vehicle exhaust (2%). At CAMS 634, natural gas sources were identified as the major source category contributing to 31% of the observed VOC. The other sources affecting this site included: refinery operations (24%), flare emissions (22%), secondary industrial processes (12%), fugitive gasoline emissions (8%) and vehicle exhaust (3%).