The use of source apportionment for air quality management and health assessments

Source apportionment of PM10 and health risk assessment related in a narrow tropical valley. Study case: Metropolitan area of Aburrá Valley (Colombia)

This study investigates spatio-temporal variations of PM10 mass concentrations and associated metal(oid)s, δ13C carbon isotope ratios, polycyclic aromatic hydrocarbons (PAHs), total organic carbon (TOC) and equivalent black carbon (eBC) concentrations over a half year period (from March 2017 to October 2017) in two residential areas of Medellín (MED-1 and MED-2) and Itagüí municipality (ITA-1 and ITA-2) at a tropical narrow valley (Aburrá Valley, Colombia), where few data are available. A total of 104 samples were analysed by using validated analytical methodologies, providing valuable data for PM10 chemical characterisation. Metal(oid)s concentrations were measured by inductively coupled plasma mass spectrometry (ICP-MS) after acid digestion, and PAHs concentrations were measured by Gas Chromatography-Mass Spectrometry (GC-MS) after Pressurised Hot Water Extraction (PHWE) and Membrane Assisted Solvent Extraction (MASE). Mean PM10 mass concentration ranged from 37.0 µg m-3 to 45.7 µg m-3 in ITA-2 and MED-2 sites, respectively. Al, Ca, Mg and Na (from 6249 ng m-3 for Mg at MED-1 site to 10,506 ng m-3 for Ca at MED-2 site) were the major elements in PM10 samples, whilst As, Be, Bi, Co, Cs, Li, Ni, Sb, Se, Tl and V were found at trace levels (< 5.4 ng m-3). Benzo[g,h,i] perylene (BghiP), benzo[b + j]fluoranthene (BbjF) and indene(1,2,3-c,d)pyrene (IcdP) were the most profuse PAHs in PM10 samples, with average concentrations of 0.82-0.86, 0.60-0.78 and 0.47-0.58 ng m-3, respectively. Results observed in the four sampling sites showed a similar dispersion pattern of pollutants, with temporal fluctuations which seems to be associated to the meteorology of the valley. A PM source apportionment study were carried out by using the positive matrix factorization (PMF) model, pointing to re-suspended dust, combustion processes, quarry activity and secondary aerosols as PM10 sources in the study area. Among them, combustion was the major PM10 contribution (accounting from 32.1 to 32.9% in ITA-1 and ITA-2, respectively), followed by secondary aerosols (accounting for 13.2% and 23.3% ITA-1 and MED-1, respectively). Finally, a moderate carcinogenic risk was observed for PM10-bound PAHs exposure via inhalation, whereas significant carcinogenic risk was estimated for carcinogenic metal(oid)s exposure in the area during the sampling period.

Understanding temporal and spatial changes of O3 or NO2 concentrations combining multivariate data analysis methods and air quality transport models

The application of the multivariate curve resolution method to the analysis of temporal and spatial data variability of hourly measured O₃ and NO₂ concentrations at nineteen air quality monitoring stations across Catalonia, Spain, during 2015 is shown. Data analyzed included ground-based experimental measurements and predicted concentrations by the CALIOPE air quality modelling system at three horizontal resolutions (Europe at 12 × 12 km², Iberian Peninsula at 4 × 4 km² and Catalonia at 1 × 1 km²). Results obtained in the analysis of these different data sets allowed a better understanding of O₃ and NO₂ concentration changes as a sum of a small number of different contributions related to daily sunlight radiation, seasonal dynamics, traffic emission patterns, and local station environments (urban, suburban and rural). The evaluation of O₃ and NO₂ concentrations predicted by the CALIOPE system revealed some differences among data sets at different spatial resolutions. NO₂ predictions, showed in general a better performance than O₃ predictions for the three model resolutions, specially at urban stations. Our results confirmed that the application of the trilinearity constraint during the multivariate curve resolution factor analysis decomposition of the analyzed data sets is a useful tool to facilitate the understanding of the resolved variability sources.

Estimating Occupational Exposure to VOCs, SVOCs, Particles and Participant Survey Reported Symptoms in Central Thailand Rice Farmers Using Multiple Sampling Techniques

Thailand is known for its agricultural productivity and rice exportation. Most farms use small machines and manual labor, creating potential exposure to multiple health hazards. A cross-sectional study was conducted to measure pollutants liberated during preparation, pesticide application, and harvesting. Thirty rice farmers, mostly males from 41 to 50 years old, participated. The participant survey data showed that 53.3% of the respondents spent >2 h per crop on preparation, <1 h on pesticide application, and about 1-2 h harvesting; 86.7% of the respondents maintained and stored mechanical applicators at home, suggesting possible after-work exposures. Gloves, fabric masks, boots, and hats were worn during all activities, and >90% wore long sleeved shirts and pants. VOCs and SVOCs were collected using charcoal tubes and solid phase micro sample extraction (SPME). An analysis of the charcoal and SPME samplers found that 30 compounds were detected overall and that 10 were in both the charcoal tubes and SPME samplers. The chemicals most often detected were 1, 1, 1 Trichloro ethane and xylene. Additionally, farmers experienced the highest exposure to particulates during harvesting. These results demonstrated that farmers experience multiple exposures while farming and that risk communication with education or training programs may mitigate exposure.

Characteristics and health effects of PM2.5 emissions from various sources in Gwangju, South Korea

Increasing evidence suggests that the toxicity of fine dust particles (PM_2.5) is linked to specific components rather than their mass. However, research on the chemical composition and health risk of PM_2.5 is insufficient. This study analyzed the metals, polycyclic aromatic hydrocarbon (PAHs), organochlorine pesticides (OCPs), and polychlorinated biphenyls (PCBs) present in PM_2.5 and evaluated their risk to health during outdoor activities. The concentration of metals was one order of magnitude higher than that of PAHs and the concentration and detection frequency of OCPs and PCBs were considerably lower than those of metals and PAHs. The lifetime excess cancer risk (LECR) for carcinogens in PM_2.5 exceeded de minimis risk (1 × 10^-6) as 1.33-3.44 × 10^-6 (at 5th-95th percentile) as Cr(VI), As, and Cd showed high contributions. Children in the 2 < years <18 age group had a high risk of cancer due to early-life susceptibility. The proportion of ∑Metals to LECR was approximately 95%, while ∑PAHs attributed to 5% of total LECR. The effects of ∑OCPs and 2,3',4,4',5'-Pentachlorobiphenyl (PCB-123) on LECR were negligible. The hazard quotient (HQ) for non-carcinogens was <1, and non-carcinogenic effects were not expected. Mn, BaP, Pb, As, and Cd were the key determinants of the HQ values and among the identified PM_2.5 sources they are closely related to industrial activities, oil combustion, and gasoline exhaust. Therefore, control strategies for these sources can effectively reduce PM_2.5 risk. This study measured the concentrations of toxic compounds in ambient PM_2.5 and considered only PM_2.5 exposure during outdoor activities. PM_2.5 health risk during the entire day would be higher than the PM_2.5 risk determined in this study, and further research is required for this evaluating this risk.

Association of emergency room visits for respiratory diseases with sources of ambient PM2.5

Previous studies have reported associations of short-term exposure to different sources of ambient fine particulate matter (PM_2.5) and increased mortality or hospitalizations for respiratory diseases. Few studies, however, have focused on the short-term effects of source-specific PM_2.5 on emergency room visits (ERVs) of respiratory diseases. Source apportionment for PM_2.5 was performed with Positive Matrix Factorization (PMF) and generalized additive model was applied to estimate associations between source-specific PM_2.5 and respiratory disease ERVs. The association of PM_2.5 and total respiratory ERVs was found on lag4 (RR = 1.011, 95%CI: 1.002, 1.020) per interquartile range (76 μg/m³) increase. We found PM_2.5 to be significantly associated with asthma, bronchitis and chronic obstructive pulmonary disease (COPD) ERVs, with the strongest effects on lag5 (RR = 1.072, 95%CI: 1.024, 1.119), lag4 (RR = 1.104, 95%CI: 1.032, 1.176) and lag3 (RR = 1.091, 95%CI: 1.047, 1.135), respectively. The estimated effects of PM_2.5 changed little after adjusting for different air pollutants. Six primary PM_2.5 sources were identified using PMF analysis, including dust/soil (6.7%), industry emission (4.5%), secondary aerosols (30.3%), metal processing (3.2%), coal combustion (37.5%) and traffic-related source (17.8%). Some of the sources were identified to have effects on ERVs of total respiratory diseases (dust/soil, secondary aerosols, metal processing, coal combustion and traffic-related source), bronchitis ERVs (dust/soil) and COPD ERVs (traffic-related source, industry emission and secondary aerosols). Different sources of PM_2.5 contribute to increased risk of respiratory ERVs to different extents, which may provide potential implications for the decision making of air quality related policies, rational emission control and public health welfare.

Identification of diverse air pollution sources in a complex urban area of Croatia

Pinpointing the contribution of sources in complex urban areas, affected by large point sources such as oil refineries, is important for developing emission control strategies. Receptor models based on the chemical composition of particulate matter (PM), such as chemical mass balance (CMB) and positive matrix factorization (PMF), are useful means for source apportionment, but the inclusion of other gaseous pollutants need further consideration. The results of the multipollutant analyses using temporal variations in pollutant concentrations, chemical PM speciation and receptor modeling, PMF and conditional bivariate polar plots (CBPF), were used for determination of major pollutant sources of fine particulate matter (PM_2.5) and less represented pollutants - hydrogen sulfide (H₂S), nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) in an urban area in Slavonski Brod, Croatia influenced by a large point source (an oil refinery) in Brod, Bosnia and Herzegovina. It is found that the composition of PM_2.5 is dominated by carbonaceous combustion particles, mainly organic carbon (OC), with maximum values appearing during winter. Summer PM_2.5 levels were dominated by sulfate and ammonium, which can be related to the industrial activities i.e., oil refinery. According to PMF analysis, the majority of OC is coming from biomass burning with ∼50% contribution to observed species concentration followed by ∼30% from industry/refinery and ∼10% from traffic. CPBF model showed that urban and highway traffic was the main source of NO₂ concentrations while oil refinery was identified as the dominant source of SO₂ and H₂S. The CBPF receptor model combines concentrations of pollutants and meteorological parameters and emerged as a reliable complementary tool for the identification of sources for considered gaseous pollutants. Limitations of the CBPF method are in the application in stable atmospheric boundary layer conditions (SABL) as wind direction is not representative. Also, larger uncertainty is related to the representation of peak concentrations transported with higher wind speeds (>8  m/s) due to the lower number of events. This work uses various source apportionment methods in the assessment of PM but also for gaseous pollutants, such as NO₂, SO₂ and H₂S that are less represented in the source apportionment studies and can be used for future scientific applications to assure more efficient air quality management.

Major Source Contributions to Ambient PM2.5 and Exposures within the New South Wales Greater Metropolitan Region

The coupled Conformal Cubic Atmospheric Model (CCAM) and Chemical Transport Model (CTM) (CCAM-CTM) was undertaken with eleven emission scenarios segregated from the 2008 New South Wales Greater Metropolitan Region (NSW GMR) Air Emission Inventory to predict major source contributions to ambient PM2.5 and exposure in the NSW GMR. Model results illustrate that populated areas in the NSW GMR are characterised with annual average PM2.5 of 6–7 µg/m3, while natural sources including biogenic emissions, sea salt and wind-blown dust contribute 2–4 µg/m3 to it. Summer and winter regional average PM2.5 ranges from 5.2–6.1 µg/m3 and 3.7–7.7 µg/m3 across Sydney East, Sydney Northwest, Sydney Southwest, Illawarra and Newcastle regions. Secondary inorganic aerosols (particulate nitrate, sulphate and ammonium) and sodium account for up to 23% and 18% of total PM2.5 mass in both summer and winter. The increase in elemental carbon (EC) mass from summer to winter is found across all regions but particularly remarkable in the Sydney East region. Among human-made sources, “wood heaters” is the first or second major source contributing to total PM2.5 and EC mass across Sydney in winter. “On-road mobile vehicles” is the top contributor to EC mass across regions, and it also has significant contributions to total PM2.5 mass, particulate nitrate and sulphate mass in the Sydney East region. “Power stations” is identified to be the third major contributor to the summer total PM2.5 mass across regions, and the first or second contributor to sulphate and ammonium mass in both summer and winter. “Non-road diesel and marine” plays a relatively important role in EC mass across regions except Illawarra. “Industry” is identified to be the first or second major contributor to sulphate and ammonium mass, and the second or third major contributor to total PM2.5 mass across regions. By multiplying modelled predictions with Australian Bureau of Statistics 1-km resolution gridded population data, the natural and human-made sources are found to contribute 60% (3.55 µg/m3) and 40% (2.41 µg/m3) to the population-weighted annual average PM2.5 (5.96 µg/m3). Major source groups “wood heaters”, “industry”, “on-road motor vehicles”, “power stations” and “non-road diesel and marine” accounts for 31%, 26%, 19%, 17% and 6% of the total human-made sources contribution, respectively. The results in this study enhance the quantitative understanding of major source contributions to ambient PM2.5 and its major chemical components. A greater understanding of the contribution of the major sources to PM2.5 exposures is the basis for air quality management interventions aiming to deliver improved public health outcomes.

Land use regression models for crustal and traffic-related PM2.5 constituents in four areas of the SAPALDIA study

Many studies have documented adverse health effects of long-term exposure to fine particulate matter (PM2.5), but there is still limited knowledge regarding the causal relationship between specific sources of PM2.5 and such health effects. The spatial variability of PM2.5 constituents and sources, as a exposure assessment strategy for investigating source contributions to health effects, has been little explored so far. Between 2011 and 2012, three measurement campaigns of PM and nitrogen dioxide (NO2) were performed in 80 sites across four areas of the Swiss Study on Air Pollution and Lung and heart Diseases in Adults (SAPALDIA). Reflectance analysis and energy dispersive X-ray fluorescence (XRF) were performed on PM2.5 filter samples to estimate light absorbance and trace element concentrations, respectively. Three air pollution source factors were identified using principal-component factor analysis: vehicular, crustal, and long-range transport. Land use regression (LUR) models were developed for temporally-adjusted scores of each factor, combining the four study areas. Model performance was assessed using two cross-validation methods. Model explained variance was high for the vehicular factor (R(2)=0.76), moderate for the crustal factor (R(2)=0.46), and low for the long-range transport factor (R(2)=0.19). The cross-validation methods suggested that models for the vehicular and crustal factors moderately accounted for both the between and within-area variability, and therefore can be applied to the four study areas to estimate long-term exposures within the SAPALDIA study population. The combination of source apportionment techniques and LUR modelling may help in identifying air pollution sources and disentangling their contribution to observed health effects in epidemiologic studies.

Methods for estimating uncertainty in PMF solutions: examples with ambient air and water quality data and guidance on reporting PMF results

The new version of EPA's positive matrix factorization (EPA PMF) software, 5.0, includes three error estimation (EE) methods for analyzing factor analytic solutions: classical bootstrap (BS), displacement of factor elements (DISP), and bootstrap enhanced by displacement (BS-DISP). These methods capture the uncertainty of PMF analyses due to random errors and rotational ambiguity. To demonstrate the utility of the EE methods, results are presented for three data sets: (1) speciated PM2.5 data from a chemical speciation network (CSN) site in Sacramento, California (2003-2009); (2) trace metal, ammonia, and other species in water quality samples taken at an inline storage system (ISS) in Milwaukee, Wisconsin (2006); and (3) an organic aerosol data set from high-resolution aerosol mass spectrometer (HR-AMS) measurements in Las Vegas, Nevada (January 2008). We present an interpretation of EE diagnostics for these data sets, results from sensitivity tests of EE diagnostics using additional and fewer factors, and recommendations for reporting PMF results. BS-DISP and BS are found useful in understanding the uncertainty of factor profiles; they also suggest if the data are over-fitted by specifying too many factors. DISP diagnostics were consistently robust, indicating its use for understanding rotational uncertainty and as a first step in assessing a solution's viability. The uncertainty of each factor's identifying species is shown to be a useful gauge for evaluating multiple solutions, e.g., with a different number of factors.

Differential distributed lag patterns of source-specific particulate matter on respiratory emergency hospitalizations

While different emission sources and formation processes generate mixtures of particulate matter (PM) with different physicochemical compositions that may differentially affect PM toxicity, evidence of associations between PM sources and respiratory events is scarce. We estimated PM10 sources contributed from 19 chemical constituents by positive matrix factorization, and examined association of short-term sources exposure with emergency respiratory hospitalizations using generalized additive models for single- and distributed lag periods. PM10 contributions from eight sources were identified. Respiratory risks over a consecutive 6-day exposure period were the highest for vehicle exhaust [2.01%; 95% confidence interval (CI): 1.04, 2.99], followed by secondary sulfate (1.59%; 95% CI: 0.82, 2.37). Vehicle exhaust, regional combustion, and secondary nitrate were significantly associated with 0.93%-2.04% increase in respiratory hospitalizations at cumulative lag2-5; significant associations of aged sea salt (1.2%; 95% CI: 0.63, 1.78) and soil/road dust (0.42%; 95% CI: 0.03, 0.82) were at lag0-1. Some effect estimates were no longer significant in two-pollutant models adjusting for PM10; however, a similar temporal pattern of associations remains. Differential lag associations of respiratory hospitalizations with PM10 sources were indicated, which may reflect the different particle size fractions that sources tend to emit. Findings may have potential biological and policy implications.

Synthesizing scientific progress: outcomes from U.S. EPA's carbonaceous aerosols and source apportionment STAR grants

In response to recommendations by the National Research Council in the late 1990 s and early 2000s for critical research into understanding sources and formation mechanisms of PM2.5, EPA created multiple funding opportunities through the Science to Achieve Results (STAR) program: "Measurement, Modeling, and Analysis Methods for Airborne Carbonaceous Fine Particulate Matter" (2003) and "Source Apportionment of Particulate Matter" (2004). The carbonaceous fine PM solicitation resulted in 16 different projects focusing on the measurement methods, source identification, and exploration of the chemical and physical processes important for PM2.5 carbon in the atmosphere. The source apportionment funding opportunity led to 11 projects improving tools and characterization of source-receptor relationships of PM2.5. Many funding mechanisms include a final synopsis of funded research and published manuscripts. Here, this evaluation is extended to include citations of research published as part of these solicitations. These solicitations resulted in 275 publications that included more than 850 unique authors in 37 different journals with a weighted average 2011 impact factor of 4.21. At the time of this assessment, these publications have been cited by 13,612 peer review journal articles with 31 (11%) of the manuscripts being cited over 100 times.

Properties of agricultural aerosol released during wheat harvest threshing, plowing and sowing

This study shows for the first time a chemical and morphological characterization of agricultural aerosols released during three important agricultural operations: threshing, plowing and sowing. The field campaigns were carried out in the eastern part of the Po Valley, Italy, in summer and autumn 2009. The aerosol particles were sampled on quartz fiber filters and polytetrafluoroethylene membranes in order to allow Inductively Coupled Plasma Mass Spectrometry (ICP-MS) analysis and Scanning Electron Microscopy equipped with an Energy Dispersive X-ray Spectrometer (SEM-EDS) investigations, respectively. The organic carbon mass concentrations were measured with a Sunset Laboratory Dual-Optical Organic Carbone/Elemental Carbon (OCEC) Aerosol analyzer. The morphological and chemical analyses by SEM-EDS allowed recognizing four main particle classes: organic, silica, calcite and clay minerals. The organic particles contribute to both fine and coarse aerosol fractions up to ca. 50% for all three agricultural activities. This was also confirmed by OCEC analysis for fine fraction. Most of the agricultural aerosols, about 60%, were single particles and the remaining 40% were agglomerations of particles. The ICP-MS results showed that threshing and plowing produce more aerosol particles than sowing, which was characterized by important amounts of clay minerals produced from land soils.

Differential effects of source-specific particulate matter on emergency hospitalizations for ischemic heart disease in Hong Kong

BACKGROUND

Ischemic heart disease (IHD) is a major public health concern. Although many epidemiologic studies have reported evidence of adverse effects of particulate matter (PM) mass on IHD, significant knowledge gaps remain regarding the potential impacts of different PM sources. Much the same as PM size, PM sources may influence toxicological characteristics.

OBJECTIVES

We identified contributing sources to PM10 mass and estimated the acute effects of PM10 sources on daily emergency IHD hospitalizations in Hong Kong.

METHODS

We analyzed the concentration data of 19 PM10 chemical components measured between 2001 and 2007 by positive matrix factorization to apportion PM10 mass, and used generalized additive models to estimate associations of interquartile range (IQR) increases in PM10 exposures with IHD hospitalization for different lag periods (up to 5 days), adjusted for potential confounders.

RESULTS

We identified 8 PM10 sources: vehicle exhaust, soil/road dust, regional combustion, residual oil, fresh sea salt, aged sea salt, secondary nitrate, and secondary sulfate. Vehicle exhaust, secondary nitrate, and secondary sulfate contributed more than half of the PM10 mass. Although associations with IQR increases in 2-day moving averages (lag01) were statistically significant for most sources based on single-source models, only PM10 from vehicle exhaust [1.87% (95% CI: 0.66, 3.10); IQR = 4.9 μg/m3], secondary nitrate [2.28% (95% CI: 1.15, 3.42); IQR = 8.6 μg/m3], and aged sea salt [1.19% (95% CI: 0.04, 2.36); IQR = 5.9 μg/m3] were significantly associated with IHD hospitalizations in the multisource model. Analysis using chemical components provided similar findings.

CONCLUSION

Emergency IHD hospitalization was significantly linked with PM10 from vehicle exhaust, nitrate-rich secondary PM, and sea salt-related PM. Findings may help prioritize toxicological research and guide future monitoring and emission-control polices.

Source apportionment of PM10, organic carbon and elemental carbon at Swiss sites: an intercomparison of different approaches

In this study, the results of source apportionment of particulate matter (PM10), organic carbon (OC), and elemental carbon (EC) - as obtained through different approaches at different types of sites (urban background, urban roadside, and two rural sites in Switzerland) - are compared. The methods included in this intercomparison are positive matrix factorisation modelling (PMF, applied to chemical composition data including trace elements, inorganic ions, OC, and EC), molecular marker chemical mass balance modelling (MM-CMB), and the aethalometer model (AeM). At all sites, the agreement of the obtained source contributions was reasonable for OC, EC, and PM10. Based on an annual average, and at most of the considered sites, secondary organic carbon (SOC) is the component with the largest contribution to total OC; the most important primary source of OC is wood combustion, followed by road traffic. Secondary aerosols predominate in PM10. All considered techniques identified road traffic as the dominant source of EC, while wood combustion emissions are of minor importance for this constituent. The intercomparison of different source apportionment approaches is helpful to identify the strengths and the weaknesses of the different methods. Application of PMF has limitations when source emissions have a strong temporal correlation, or when meteorology has a strong impact on PM variability. In these cases, the use of PMF can result in mixed source profiles and consequently in the under- or overestimation of the real-world sources. The application of CMB models can be hampered by the unavailability of source profiles and the non-representativeness of the available profiles for local source emissions. This study also underlines that chemical transformations of molecular markers in the atmosphere can lead to the underestimation of contributions from primary sources, in particular during the summer period or when emission sources are far away from the receptor sites.

In vitro toxicology of ambient particulate matter: correlation of cellular effects with particle size and components

High concentrations of airborne particulate matter (PM) have been associated with increased rates of morbidity and mortality among exposed populations. Although certain components of PM were suggested to influence these effects, no clear-cut correlation was determined thus far. One of the possible modes of action is the induction of oxidative stress by inhaled PM triggering inflammatory responses. Therefore, the in vitro formation of reactive oxygen species (ROS) in three cell lines in the presence of five subfractions of PM(10), collected in Münster, Germany was investigated. The PM components chloride, nitrate, ammonium, sulfate, 68 chemical elements, and endotoxin were quantified. The highest concentration of endotoxin was found in particles of 0.42-1.2 μm aerodynamic diameters, and therefore probably subject to long-range transport. Intracellular ROS formation in three well established mammalian cell lines (CaCo2, human; MDCK, canine; RAW264.7, mouse) only correlated positively with particle size. The two smallest PM size fractions provoked the highest rise in ROS. However, the latter did not correlate with the concentration of any PM components investigated. The smallest PM size fractions significantly dominated the number of particles. Therefore, the particle number may be most effective in inducing oxidative stress in vitro.

Use of X-ray absorption spectroscopy to speciate manganese in airborne particulate matter from five counties across the United States

The purpose of this study is to characterize manganese oxidation states and speciation in airborne particulate matter (PM) and describe how these potentially important determinants of PM toxicity vary by location. Ambient PM samples were collected from five counties across the US using a high volume sequential cyclone system that collects PM in dry bulk form segregated into "coarse" and "fine" size fractions. The fine fraction was analyzed for this study. Analyses included total Mn using ICP-MS and characterization of oxidation states and speciation using X-ray absorption spectroscopy (XAS). XAS spectra of all samples and ten standard compounds of Mn were obtained at the National Synchrotron Light Source. XAS data was analyzed using Linear Combination Fitting (LCF). Results of the LCF analysis describe differences in composition between samples. Mn(II) acetate and Mn(II) oxide are present in all samples, while Mn(II) carbonate and Mn(IV) oxide are absent. To the best of our knowledge, this is the first paper to characterize Mn composition of ambient PM and examine differences between urban sites in the US. Differences in oxidation state and composition indicate regional variations in sources and atmospheric chemistry that may help explain differences in health effects identified in epidemiological studies.

The Environmental Protection Agency's Community-Focused Exposure and Risk Screening Tool (C-FERST) and its potential use for environmental justice efforts

OBJECTIVES

Our primary objective was to provide higher quality, more accessible science to address challenges of characterizing local-scale exposures and risks for enhanced community-based assessments and environmental decision-making.

METHODS

After identifying community needs, priority environmental issues, and current tools, we designed and populated the Community-Focused Exposure and Risk Screening Tool (C-FERST) in collaboration with stakeholders, following a set of defined principles, and considered it in the context of environmental justice.

RESULTS

C-FERST is a geographic information system and resource access Web tool under development for supporting multimedia community assessments. Community-level exposure and risk research is being conducted to address specific local issues through case studies.

CONCLUSIONS

C-FERST can be applied to support environmental justice efforts. It incorporates research to develop community-level data and modeled estimates for priority environmental issues, and other relevant information identified by communities. Initial case studies are under way to refine and test the tool to expand its applicability and transferability. Opportunities exist for scientists to address the many research needs in characterizing local cumulative exposures and risks and for community partners to apply and refine C-FERST.

Source apportionment of indoor residential fine particulate matter using land use regression and constrained factor analysis

Source contributions to urban fine particulate matter (PM(2.5) ) have been modelled using land use regression (LUR) and factor analysis (FA). However, people spend more time indoors, where these methods are less explored. We collected 3-4- day samples of nitrogen dioxide and PM(2.5) inside and outside of 43 homes in summer and winter, 2003-2005, in and around Boston, Massachusetts. Particle filters were analysed for black carbon and trace element concentrations using reflectometry, X-ray fluorescence (XRF), and high-resolution inductively coupled mass spectrometry (ICP-MS). We regressed indoor against outdoor concentrations modified by ventilation, isolating the indoor-attributable fraction, and then applied constrained FA to identify source factors in indoor concentrations and residuals. Finally, we developed LUR predictive models using GIS-based outdoor source indicators and questionnaire data on indoor sources. FA using concentrations and residuals reasonably separated outdoor (long-range transport/meteorology, fuel oil/diesel, road dust) from indoor sources (combustion, smoking, cleaning). Multivariate LUR regression models for factors from concentrations and indoor residuals showed limited predictive power, but corroborated some indoor and outdoor factor interpretations. Our approach to validating source interpretations using LUR methods provides direction for studies characterizing indoor and outdoor source contributions to indoor cocentrations.

PRACTICAL IMPLICATIONS

By merging indoor-outdoor modeling, factor analysis, and LUR-style predictive regression modeling, we have added to previous source apportionment studies by attempting to corroborate factor interpretations. Our methods and results support the possibility that indoor exposures may be modeled for epidemiologic studies, provided adequate sample size and variability to identify indoor and outdoor source contributions. Using these techniques, epidemiologic studies can more clearly examine exposures to indoor sources and indoor penetration of source-specific components, reduce exposure misclassification, and improve the characterization of the relationship between particle constituents and health effects.

Environmental health indicators and a case study of air pollution in Latin American cities

Environmental health indicators (EHIs) are applied in a variety of research and decision-making settings to gauge the health consequences of environmental hazards, to summarize complex information, or to compare policy impacts across locations or time periods. While EHIs can provide a useful means of conveying information, they also can be misused. Additional research is needed to help researchers and policy-makers understand categories of indicators and their appropriate application. In this article, we review current frameworks for environmental health indicators and discuss the advantages and limitations of various forms. A case study EHI system was developed for air pollution and health for urban Latin American centers in order to explore how underlying assumptions affect indicator results. Sixteen cities were ranked according to five indicators that considered: population exposed, children exposed, comparison to health-based guidelines, and overall PM(10) levels. Results indicate that although some overall patterns in rankings were observed, cities' relative rankings were highly dependent on the indicator used. In fact, a city that was ranked best under one indicator was ranked worst with another. The sensitivity of rankings, even when considering a simple case of a single pollutant, highlights the need for clear understanding of EHIs and how they may be affected by underlying assumptions. Careful consideration should be given to the purpose, assumptions, and limitations of EHIs used individually or in combination in order to minimize misinterpretation of their implications and enhance their usefulness.