Particulate matter chemical component concentrations and sources in settings of household solid fuel use

Particulate matter (PM) air pollution derives from combustion and non‐combustion sources and consists of various chemical species that may differentially impact human health and climate. Previous reviews of PM chemical component concentrations and sources focus on high‐income urban settings, which likely differ from the low‐ and middle‐income settings where solid fuel (ie, coal, biomass) is commonly burned for cooking and heating. We aimed to summarize the concentrations of PM chemical components and their contributing sources in settings where solid fuel is burned. We searched the literature for studies that reported PM component concentrations from homes, personal exposures, and direct stove emissions under uncontrolled, real‐world conditions. We calculated weighted mean daily concentrations for select PM components and compared sources of PM determined by source apportionment. Our search criteria yielded 48 studies conducted in 12 countries. Weighted mean daily cooking area concentrations of elemental carbon, organic carbon, and benzo(a)pyrene were 18.8 μg m^?3, 74.0 μg m^?3, and 155 ng m^?3, respectively. Solid fuel combustion explained 29%‐48% of principal component/factor analysis variance and 41%‐87% of PM mass determined by positive matrix factorization. Multiple indoor and outdoor sources impacted PM concentrations and composition in these settings, including solid fuel burning, mobile emissions, dust, and solid waste burning.     

Characteristic of polycyclic aromatic hydrocarbon concentrations and source identification for fine and coarse particulates at Taichung Harbor near Taiwan Strait during 2004-2005

Fine (PM(2.5)) and coarse (PM(2.5-10)) particulate concentrations of ambient air polycyclic aromatic hydrocarbons (PAHs) were measured simultaneously from February 2004 to January 2005 at the Taichung Harbor (TH) sampling site near Taiwan of central Taiwan. Particle-bound polycyclic aromatic hydrocarbons (PAHs) were collected on quartz filters; the collected sample was Soxhlet extracted with a dichloromethane (DCM)/n-hexane mixture (50/50, v/v) for 24 h, and then the extracts were analyzed by gas chromatography-mass spectrometry (GC-MS). The results indicated that vehicle emissions, coal combustion, incomplete combustion and pyrolysis of fuel and oil burning were the main source of PAHs near Taiwan Strait of central Taiwan. Diagnostic ratios and principal component analysis (PCA) were also used to characterize and identify PAHs emission sources in this study.     

Sources and elemental composition of ambient PM(2.5) in three European cities

Source apportionment of urban fine particle mass (PM(2.5)) was performed from data collected during 1998-1999 in Amsterdam (The Netherlands), Erfurt (Germany) and Helsinki (Finland), using principal component analysis (PCA) and multiple linear regression. Six source categories of PM(2.5) were identified in Amsterdam. They were traffic-related particles (30% of the average PM(2.5)), secondary particles (34%), crustal material (7%), oil combustion (11%), industrial and incineration processes (9%), and sea salt (2%). The unidentified PM(2.5) fraction was 7% on the average. In Erfurt, four source categories were extracted with some difficulties in interpretation of source profiles. They were combustion emissions related to traffic (32%), secondary PM (32%), crustal material (21%) and industrial processes (8%). In Erfurt, 3% of PM(2.5) remained unidentified. Air pollution data and source apportionment results from the two Central European cities were compared to previously published results from Helsinki, where about 80% of average PM(2.5) was attributed to transboundary air pollution and particles from traffic and other regional combustion sources. Our results indicate that secondary particles and local combustion processes (mainly traffic) were the most important source categories in all cities; their impact on the average PM(2.5) was almost equal in Amsterdam and Erfurt whereas, in Helsinki, secondary particles made up for as much as half of the total average PM(2.5).     

Seasonal pollution characteristics of organic compounds in atmospheric fine particles in Beijing

Beijing is a rapidly developing city with severe and unique air pollution problems. Organic matter is the most abundant fraction in fine particles in Beijing, occupying 30-50% of the total mass, indicating its key role in air pollution control. However, detailed chemical characterization of particulate organic matter in Beijing has never been reported. In this study, fine particles in the urban atmosphere in Beijing were investigated for its organic components by GC/MS technique. Over 100 individual organic compounds were identified and quantified in 25 PM2.5 samples from the summer, autumn and winter of 2002-2003. Alkanes, fatty acids, dicarboxylic acids, polycyclic aromatic hydrocarbons and some important tracer compounds (hopanes, levoglucosan and steroids) were the major constituents with the sum of their concentrations of 502, 1471 and 1403 ng m(-3) in summer, autumn and winter, respectively. Different organic compounds presented apparently different seasonal characteristics, reflecting their different dominant emission sources, such as coal combustion, biomass burning and cooking emission. The abundance and origin of these organic compounds are discussed to reveal seasonal air pollution characteristics of Beijing.     

Impacts of stove/fuel use and outdoor air pollution on chemical composition of household particulate matter

Biomass combustion for cooking and heating releases particulate matter (PM_2.5) that contributes to household air pollution. Fuel and stove types affect the chemical composition of household PM, as does infiltration of outdoor PM. Characterization of these impacts can inform future exposure assessments and epidemiologic studies, but is currently limited. In this study, we measured chemical components of PM_2.5 (water-soluble organic matter [WSOM], ions, black carbon, elements, organic tracers) in rural Chinese households using traditional biomass stoves, semi-gasifier stoves with pelletized biomass, and/or non-biomass stoves. We distinguished households using one stove type (traditional, semi-gasifier, or LPG/electric) from those using multiple stoves/fuels. WSOM concentrations were higher in households using only semi-gasifier or traditional stoves (31%-33%) than in those with exclusive LPG/electric stove (13%) or mixed stove use (12%-22%). Inorganic ions comprised 14% of PM in exclusive LPG/electric households, compared to 1%-5% of PM in households using biomass. Total PAH content was much higher in households that used traditional stoves (0.8-2.8 mg/g PM) compared to those that did not (0.1-0.3 mg/g PM). Source apportionment revealed that biomass burning comprised 27%-84% of PM_2.5 in households using biomass. In all samples, identified outdoor sources (vehicles, dust, coal combustion, secondary aerosol) contributed 10%-20% of household PM_2.5.     

Particle-associated polycyclic aromatic hydrocarbons in the atmospheric environment of Zonguldak, Turkey

Airborne fine (PM(2.5)) and coarse (PM(2.5-10)) particulate matter was collected from January to December in 2007 in Zonguldak, Turkey using dichotomous Partisol 2025 sampler. Fourteen selected polycyclic aromatic hydrocarbons (PAHs) in particulate matter were determined simultaneously by high-performance liquid chromatography with fluorescence detection (HPLC-FL) and seasonal distributions were examined. The source identification of PAHs in airborne particulates was performed by principal component analysis (PCA) in combination with diagnostic ratios. The predominant PAHs determined in PM(2.5) were pyrene, fluoranthene, benzo[a]anthracene, chrysene, benzo[b]fluoranthene and benzo[a]pyrene. The total concentrations of PAHs were up to 464.0 ng m(-3) in fine and 28.0 ng m(-3) in coarse fraction in winter, whereas in summer times were up to 22.9 and 3.0 ng m(-3) respectively. Approximately 93.3% of total PAHs concentration was determined in PM(2.5) in winter and 84.0% in summer. The concentration levels of PAHs fluctuate significantly within a year with higher means and peak concentrations in the winter compared to that of summer times. Higher benzo(a)pyrene-equivalent (BaPE) concentrations of PAHs were obtained for PM(2.5) especially in winter. The results obtained from PCA in combination with diagnostic ratios revealed that coal combustion and vehicle emissions were the major pollutant sources for both PM(2.5) and PM(2.5-10) associated PAHs in studied area. Two principal components for PM(2.5) and three for PM(2.5-10) were identified and these accounted for 89.4 and 85.2% of the total variance respectively. The emissions from coal combustion were estimated to be the main source of PAHs in the ambient air particulates with contributions of 80.8% of total variance for PM(2.5) and 53.8% for PM(2.5-10).     

Multi-criteria ranking and receptor modelling of airborne fine particles at three sites in the Pearl River Delta region of China

The multi-criteria decision making methods, Preference Ranking Organization METHods for Enrichment Evaluation (PROMETHEE) and Graphical Analysis for Interactive Assistance (GAIA), and the two-way Positive Matrix Factorization (PMF) receptor model were applied to airborne fine particle compositional data collected at three sites in Hong Kong during two monitoring campaigns held from November 2000 to October 2001 and November 2004 to October 2005. PROMETHEE/GAIA indicated that the three sites were worse during the later monitoring campaign, and that the order of the air quality at the sites during each campaign was: rural site > urban site > roadside site. The PMF analysis on the other hand, identified 6 common sources at all of the sites (diesel vehicle, fresh sea salt, secondary sulphate, soil, aged sea salt and oil combustion) which accounted for approximately 68.8 ± 8.7% of the fine particle mass at the sites. In addition, road dust, gasoline vehicle, biomass burning, secondary nitrate, and metal processing were identified at some of the sites. Secondary sulphate was found to be the highest contributor to the fine particle mass at the rural and urban sites with vehicle emission as a high contributor to the roadside site. The PMF results are broadly similar to those obtained in a previous analysis by PCA/APCS. However, the PMF analysis resolved more factors at each site than the PCA/APCS. In addition, the study demonstrated that combined results from multi-criteria decision making analysis and receptor modelling can provide more detailed information that can be used to formulate the scientific basis for mitigating air pollution in the region.     

Techniques to assess cross-border air pollution and application to a US-Mexico border region

A year-long assessment of cross-border air pollution was conducted in the eastmost section of the US-Mexico border region, known as the Lower Rio Grande Valley, in South Texas. Measurements were conducted on the US side and included fine particle mass (PM2.5) and elemental composition, volatile organic compounds (VOCs) and meteorology. Wind sector analyses of chemical tracers and diagnostic ratios, in addition to principal component analysis (PCA), were initially applied to assess cross-border and overall air shed influences. Linear-angular correlation statistics [Biometrika, 63, (1976), 403-405] and nonparametric multiple comparisons between wind sectors were computed with the particle element data using principal component scores from PCA to determine the direction of source classes. Findings suggest crustal particles and salts carried or stirred by sea breeze winds from a southerly and southeasterly direction from the Gulf of Mexico heavily influenced the elemental composition of the particulate samples. Pair-wise comparisons of wind directions for the principal component scores suggest possible oil combustion influences from utilities or boilers coming from the south and possible coal combustion influences from the north and northwest. The techniques discussed can provide a methodology to assess future ambient levels and cross-border influences in the Valley as conditions change.     

Chemical compositions and source identification of PM₂.₅ aerosols for estimation of a diesel source surrogate

Exposure to traffic-related pollution during childhood has been associated with asthma exacerbation, and asthma incidence. The objective of the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS) is to determine if the development of allergic and respiratory disease is associated with exposure to diesel engine exhaust particles. A detailed receptor model analyses was undertaken by applying positive matrix factorization (PMF) and UNMIX receptor models to two PM_2.5 data sets: one consisting of two carbon fractions and the other of eight temperature-resolved carbon fractions. Based on the source profiles resolved from the analyses, markers of traffic-related air pollution were estimated: the elemental carbon attributed to traffic (ECAT) and elemental carbon attributed to diesel vehicle emission (ECAD).Application of UNMIX to the two data sets generated four source factors: combustion related sulfate, traffic, metal processing and soil/crustal. The PMF application generated six source factors derived from analyzing two carbon fractions and seven factors from temperature-resolved eight carbon fractions. The source factors (with source contribution estimates by mass concentrations in parentheses) are: combustion sulfate (46.8%), vegetative burning (15.8%), secondary sulfate (12.9%), diesel vehicle emission (10.9%), metal processing (7.5%), gasoline vehicle emission (5.6%) and soil/crustal (0.7%). Diesel and gasoline vehicle emission sources were separated using eight temperature-resolved organic and elemental carbon fractions. Application of PMF to both datasets also differentiated the sulfate rich source from the vegetative burning source, which are combined in a single factor by UNMIX modeling. Calculated ECAT and ECAD values at different locations indicated that traffic source impacts depend on factors such as traffic volumes, meteorological parameters, and the mode of vehicle operation apart from the proximity of the sites to highways. The difference in ECAT and ECAD, however, was less than one standard deviation. Thus, a cost benefit consideration should be used when deciding on the benefits of an eight or two carbon approach.     

The use of polar organic compounds to estimate the contribution of domestic solid fuel combustion and biogenic sources to ambient levels of organic carbon and PM2.5 in Cork Harbour, Ireland

PM_2.5 samples collected at Cork Harbour, Ireland during summer, autumn, late autumn and winter, 2008-2009 were analyzed for polar organic compounds that are useful markers for aerosol source characterization. The determined compounds include tracers for biomass burning primary particles, fungal spores, markers for secondary organic aerosol (SOA) from isoprene, α-/β-pinene, and d-limonene. Seasonal and temporal variations and other characteristic features of the detected tracers are discussed in terms of aerosol sources and processes. The biogenic species were detected only during the summer period where the contributions of isoprene SOA and fungal spores to the PM_2.5 organic carbon (OC) were estimated to be 1.6% and 1% respectively. The biomass burning markers, and in particular levoglucosan, were present in all samples and attributed to the combustion of cellulose-containing fuels including wood, peat, bituminous and smokeless coal. The contribution of domestic solid fuel (DSF) burning to the measured OC mass concentration was estimated at 10.8, 50, 66.4 and 74.9% for summer, autumn, late autumn and winter periods, respectively, based on factors derived from a series of burning experiments on locally available fuels. Application of an alternative approach, namely principal component analysis-multiple linear regression (PCA-MLR), to the measured concentrations of the polar organic marker compounds used in conjunction with real-time air quality data provided similar trends and estimates for DSF combustion during all seasons except summer. This study clearly demonstrates that, despite the ban on the sale of bituminous coal in Cork and other large urban areas in Ireland, DSF combustion is still the major source of OC during autumn and winter periods and also makes a significant contribution to PM_2.5 levels. The developed marker approach for estimating the contribution of DSF combustion to ambient OC concentrations can, in principle, also be applied to other locations.     

Receptor modeling of PM2.5, PM10 and TSP in different seasons and long-range transport analysis at a coastal site of Tianjin, China

Atmospheric particulate matter (PM_2.5, PM₁₀ and TSP) were sampled synchronously during three monitoring campaigns from June 2007 to February 2008 at a coastal site in TEDA of Tianjin, China. Chemical compositions including 19 elements, 6 water-solubility ions, organic and elemental carbon were determined. principle components analysis (PCA) and chemical mass balance modeling (CMB) were applied to determine the PM sources and their contributions with the assistance of NSS SO₄²⁻, the mass ratios of NO₃⁻ to SO₄²⁻ and OC to EC. Air mass backward trajectory model was compared with source apportionment results to evaluate the origin of PM. Results showed that NSS SO₄²⁻ values for PM_2.5 were 2147.38, 1701.26 and 239.80 ng/m³ in summer, autumn and winter, reflecting the influence of sources from local emissions. Most of it was below zero in summer for PM₁₀ indicating the influence of sea salt. The ratios of NO₃⁻ to SO₄²⁻ was 0.19 for PM_2.5, 0.18 for PM₁₀ and 0.19 for TSP in winter indicating high amounts of coal consumed for heating purpose. Higher OC/EC values (mostly larger than 2.5) demonstrated that secondary organic aerosol was abundant at this site. The major sources were construction activities, road dust, vehicle emissions, marine aerosol, metal manufacturing, secondary sulfate aerosols, soil dust, biomass burning, some pharmaceutics industries and fuel-oil combustion according to PCA. Coal combustion, marine aerosol, vehicular emission and soil dust explained 5-31%, 1-13%, 13-44% and 3-46% for PM_2.5, PM₁₀ and TSP, respectively. Backward trajectory analysis showed air parcels originating from sea accounted for 39% in summer, while in autumn and winter the air parcels were mainly related to continental origin.     

Approaching PM(2.5) and PM(2.5-10) source apportionment by mass balance analysis, principal component analysis and particle size distribution

A chemical characterization was carried out for PM(2.5) and PM(2.5-10) samples collected in a suburban area and the concentrations of 12 elements were determined in 8 size segregated fractions using a Berner Impactor. Two main objectives were proposed in this work: 1) to test for closure among chemical and gravimetric measurements of PM(2.5) and PM(2.5-10) and 2) evaluate the performance of Multilinear Regression Analysis (MLRA) and Mass Balance Analysis (MBA) in the determination of source contribution to Particulate Matter (PM) concentrations. The fraction unaccounted for by chemical analysis comprised on average 17% and 34% of gravimetric PM(2.5) and PM(2.5-10), respectively. The lack of closure in PM(2.5) and PM(2.5-10) mass (i.e., constituent concentrations not adding up to gravimetrically measured) could partly result from the presence of water associated with particles and errors in the estimation of unmeasured species. MLRA and MBA showed very similar results for the temporal variation of the source contributions. However, quantitatively important discrepancies could be observed, principally due to the lack of mass closure in PM(2.5) and PM(2.5-10). Both methods indicated that the major PM(2.5) aerosol mass contributors included secondary aerosol and vehicle exhaust. In the coarse fraction, marine and mineral aerosol contributions were predominant.     

Source apportionment of PM(2.5) and selected hazardous air pollutants in Seattle

The potential benefits of combining the speciated PM(2.5) and VOCs data in source apportionment analysis for identification of additional sources remain unclear. We analyzed the speciated PM(2.5) and VOCs data collected at the Beacon Hill in Seattle, WA between 2000 and 2004 with the Multilinear Engine (ME-2) to quantify source contributions to the mixture of hazardous air pollutants (HAPs). We used the 'missing mass', defined as the concentration of the measured total particle mass minus the sum of all analyzed species, as an additional variable and implemented an auxiliary equation to constrain the sum of all species mass fractions to be 100%. Regardless of whether the above constraint was implemented and/or the additional VOCs data were included with the PM(2.5) data, the models identified that wood burning (24%-31%), secondary sulfate (20%-24%) and secondary nitrate (15%-20%) were the main contributors to PM(2.5). Using only PM(2.5) data, the model distinguished two diesel features with the 100% constraint, but identified only one diesel feature without the constraint. When both PM(2.5) and VOCs data were used, one additional feature was identified as the major contributor (26%) to total VOC mass. Adding VOCs data to the speciated PM(2.5) data in source apportionment modeling resulted in more accurate source contribution estimates for combustion related sources as evidenced by the less 'missing mass' percentage in PM(2.5). Using the source contribution estimates, we evaluated the validity of using black carbon (BC) as a surrogate for diesel exhaust. We found that BC measured with an aethalometer at 370 nm and 880 nm had reasonable correlations with the estimated concentrations of diesel particulate matters (r>0.7), as well as with the estimated concentrations of wood burning particles during the heating seasons (r=0.56-0.66). This indicates that the BC is not a unique tracer for either source. The difference in BC between 370 and 880 nm, however, correlated well exclusively with the estimated wood smoke source (r=0.59) and may be used to separate wood smoke from diesel exhaust. Thus, when multiple BC related sources exist in the same monitoring environment, additional data processing or modeling of the BC measurements is needed before these measurements could be used to represent the diesel exhaust.     

Comparison of PM2.5 source apportionment using positive matrix factorization and molecular marker-based chemical mass balance

A comprehensive comparison of positive matrix factorization (PMF) and molecular marker-based chemical mass balance (CMB-MM) modeling on PM2.5 source contributions was conducted for particulate matter measurements taken at Jefferson Street, Atlanta, Georgia (JST). The datasets used in each type of receptor modeling were different: CMB-MM used data of primarily organic tracers plus a couple elements measured from 51 24-h PM2.5 samples collected in July 2001 and January 2002. While for PMF, with elements, ions, five gaseous components, and eight temperature-resolved carbon fractions as the input data, both source profiles and contributions were resolved from a total of 932 daily PM2.5 samples covering a 3-year period between January 2000 and December 2002. The model results for the overlapping periods (July 2001 and January 2002) were extracted for comparison. Seven primary sources and three secondary sources were resolved by CMB-MM, while a total of nine primary and secondary factors were resolved by PMF. On average, 107% and 85% of PM2.5 mass were explained by CMB-MM and PMF, respectively, with secondary aerosols handled differently in the above two methods. Four similar sources were resolved by both methods, with good correlation for road dust, but fair for gasoline exhaust and wood combustion. The CMB-MM diesel exhaust has very poor correlation with the PMF resolved diesel exhaust. However, the CMB-MM combined mobile source has improved correlation with the PMF result as compared with the diesel exhaust source. If only the winter data were included, the CMB-MM combined mobile source shows enhanced correlation with the PMF combined source, as compared with the single source of diesel exhaust or gasoline exhaust.     

Chemical composition, mass size distribution and source analysis of long-range transported wildfire smokes in Helsinki

Special episodes of long-range transported particulate (PM) air pollution were investigated in a one-month field campaign at an urban background site in Helsinki, Finland. A total of nine size-segregated PM samplings of 3- or 4-day duration were made between August 23 and September 23, 2002. During this warm and unusually dry period there were two (labelled P2 and P5) sampling periods when the PM2.5 mass concentration increased remarkably. According to the hourly-measured PM data and backward air mass trajectories, P2 (Aug 23-26) represented a single, 64-h episode of long-range transported aerosol, whereas P5 (Sept 5-9) was a mixture of two 16- and 14-h episodes and usual seasonal air quality. The large chemical data set, based on analyses made by ion chromatography, inductively coupled plasma mass spectrometry, X-ray fluorescence analysis and smoke stain reflectometry, demonstrated that the PM2.5 mass concentrations of biomass signatures (i.e. levoglucosan, oxalate and potassium) and of some other compounds associated with biomass combustion (succinate and malonate) increased remarkably in P2. Crustal elements (Fe, Al, Ca and Si) and unidentified matter, presumably consisting to a large extent of organic material, were also increased in P2. The PM2.5 composition in P5 was different from that in P2, as the inorganic secondary aerosols (NO3-, SO4(2-), NH4+) and many metals reached their highest concentration in this period. The water-soluble fraction of potassium, lead and manganese increased in both P2 and P5. Mass size distributions (0.035-10 microm) showed that a large accumulation mode mainly caused the episodically increased PM2.5 concentrations. An interesting observation was that the episodes had no obvious impact on the Aitken mode. Finally, the strongly increased concentrations of biomass signatures in accumulation mode proved that the episode in P2 was due to long-range transported biomass combustion aerosol.     

Source apportionment of population representative samples of PM(2.5) in three European cities using structural equation modelling

Apportionment of urban particulate matter (PM) to sources is central for air quality management and efficient reduction of the substantial public health risks associated with fine particles (PM(2.5)). Traffic is an important source combustion particles, but also a significant source of resuspended particles that chemically resemble Earth's crust and that are not affected by development of cleaner motor technologies. A substantial fraction of urban ambient PM originates from long-range transport outside the immediate urban environment including secondary particles formed from gaseous emissions of mainly sulphur, nitrogen oxides and ammonia. Most source apportionment studies are based on small number of fixed monitoring sites and capture well population exposures to regional and long-range transported particles. However, concentrations from local sources are very unevenly distributed and the results from such studies are therefore poorly representative of the actual exposures. The current study uses PM(2.5) data observed at population based random sampled residential locations in Athens, Basle and Helsinki with 17 elemental constituents, selected VOCs (xylenes, trimethylbenzenes, nonane and benzene) and light absorbance (black smoke). The major sources identified across the three cities included crustal, salt, long-range transported inorganic and traffic sources. Traffic was associated separately with source categories with crustal (especially Athens and Helsinki) and long-range transported chemical composition (all cities). Remarkably high fractions of the variability of elemental (R(2)>0.6 except for Ca in Basle 0.38) and chemical concentrations (R(2)>0.5 except benzene in Basle 0.22 and nonane in Athens 0.39) are explained by the source factors of an SEM model. The RAINS model that is currently used as the main tool in developing European air quality management policies seems to capture the local urban fraction (the city delta term) quite well, but underestimates crustal particle levels in the three cities of the current study. Utilizing structural equation modelling parallel with traditional principal component analysis (PCA) provides an objective method to determine the number of factors to be retained in a model and allows for formal hypotheses testing.     

Source apportionment of polycyclic aromatic hydrocarbons (PAHs) in surface sediments of the Huangpu River, Shanghai, China

We applied cluster analysis and principal component analysis (PCA) with multivariate linear regression (MLR) to apportion sources of polycyclic aromatic hydrocarbons (PAHs) in surface sediments of the Huangpu River in Shanghai, China, based on the measured PAH concentrations of 32 samples collected at eight sites in four seasons in 2006. The results indicate that petrogenic and pyrogenic sources are the important sources of PAHs. Further analysis shows that the contributions of coal combustion, traffic-related pollution and spills of oil products (petrogenic) are 40%, 36% and 24% using PCA/MLR, respectively. Pyrogenic sources (coal combustion and traffic related pollution) contribute 76% of anthropogenic PAHs to sediments, which indicates that energy consumption is a predominant factor of PAH pollution in Shanghai. Rainfall, the monsoon and temperature play important roles in the distinct seasonal variation of PAH pollution, such that the contamination level of PAHs in spring is significantly higher than in the other seasons.

Brief

We apportion PAHs in surface sediments of the Huangpu River and show that coal combustion, traffic-related pollution, and petroleum spillage are the major sources.     

Sources identification of the atmospheric aerosol at urban and suburban sites in Indonesia by positive matrix factorization

Samples of fine and coarse fractions of airborne particulate matter were collected in Indonesia (west central Java) at an urban site in Bandung and in suburban Lembang from January 2002 to December 2004. The samples were collected using a Gent stacked filter sampler in two size fractions of <2.5 microm (fine) and 2.5 to 10 microm (coarse). The samples were analyzed for elemental concentrations by instrumental neutron activation analysis (INAA) and proton-induced X-ray emission (PIXE). Black carbon was determined using an EEL Smoke Stain Reflectometer. The data sets were then analyzed using positive matrix factorization to identify the possible sources of fine and coarse atmospheric aerosols in both areas. The best solutions were found to be seven factors and five factors for elemental compositions of fine and coarse particulate matter in the urban area of Bandung and six factors and five factors for elemental compositions of fine and coarse particulate matter in the suburban area of Lembang, respectively. The sources are soil dust, motor vehicles, biomass burning, sea salt, and road dust. The PMF results showed that more than 50% of the PM2.5-10 mass at both sites comes from soil dust and road dust. The biomass burning factor contributes about 40% of the PM2.5 mass in case of suburban Lembang and about 20% in urban Bandung.     

Measuring the exposure of infants and children to indoor air pollution from biomass fuels in The Gambia

Indoor air pollution (IAP) from biomass fuels contains high concentrations of health damaging pollutants and is associated with an increased risk of childhood pneumonia. We aimed to design an exposure measurement component for a matched case-control study of IAP as a risk factor for pneumonia and severe pneumonia in infants and children in The Gambia. We conducted co-located simultaneous area measurement of carbon monoxide (CO) and particles with aerodynamic diameter <2.5 microm (PM(2.5)) in 13 households for 48 h each. CO was measured using a passive integrated monitor and PM(2.5) using a continuous monitor. In three of the 13 households, we also measured continuous PM(2.5) concentration for 2 weeks in the cooking, sleeping, and playing areas. We used gravimetric PM(2.5) samples as the reference to correct the continuous PM(2.5) for instrument measurement error. Forty-eight hour CO and PM(2.5) concentrations in the cooking area had a correlation coefficient of 0.80. Average 48-h CO and PM(2.5) concentrations in the cooking area were 3.8 +/- 3.9 ppm and 361 +/- 312 microg/m3, respectively. The average 48-h CO exposure was 1.5 +/- 1.6 ppm for children and 2.4 +/- 1.9 ppm for mothers. PM(2.5) exposure was an estimated 219 microg/m3 for children and 275 microg/m3 for their mothers. The continuous PM(2.5) concentration had peaks in all households representing the morning, midday, and evening cooking periods, with the largest peak corresponding to midday. The results are used to provide specific recommendations for measuring the exposure of infants and children in an epidemiological study. PRACTICAL IMPLICATIONS: Measuring personal particulate matter (PM) exposure of young children in epidemiological studies is hindered by the absence of small personal monitors. Simultaneous measurement of PM and carbon monoxide suggests that a combination of methods may be needed for measuring children's PM exposure in areas where household biomass combustion is the primary source of indoor air pollution. Children's PM exposure in biomass burning homes in The Gambia is substantially higher than concentrations in the world's most polluted cities.     

AHU sensor fault diagnosis using principal component analysis method

The paper presents a strategy based on the principal component analysis (PCA) method, which is developed to detect and diagnose the sensor faults in typical air-handling units. Sensor faults are detected using the Q-statistic or squared prediction error (SPE). They are isolated using the SPE and Q-contribution plot supplemented by a few simple expert rules. Two PCA models are built based on the heat balance and pressure–flow balance of the air-handling process, aiming at reducing the effects of the system non-linearity and enhancing the robustness of the strategy in different control modes. The fault isolation ability of the method is improved using the multiple models. Simulation tests and site data from the building management system (BMS) of a building are used to verify the PCA-based strategy for automatic validation of AHU monitoring instrumentations and detecting/isolating AHU sensor faults under typical operating conditions. The robustness of the PCA-based strategy in detecting/diagnosing AHU sensor faults is verified. Effects of sensor faults and the strategy energy efficiency of an automated AHU are evaluated using simulation tests.