Source apportionment of pollution in groundwater source area using factor analysis and positive matrix factorization methods

By means of factor analysis (FA) and positive matrix factorization (PMF) methods, groundwater pollution sources were identified in the Jinji groundwater source area, which is beside the Yellow River and is the only urban water supply source for the city of Wuzhong in Northwestern China. The sources of groundwater were quantified based on 16 samples of shallow groundwater from the source area. The source apportionment with the PMF model identified three dominant groundwater pollution sources. These were anthropogenic activities of agricultural and industrial pollution with a pollution contribution of 53.0%, water–rock interaction of 24.6%, and evaporation and concentration of 22.4%. The source apportionment with the FA model identified four sources which were evaporation and concentration, with the largest contribution (42.6%), followed by anthropogenic activities (29.2%), mineral dissolution and industrial pollution (22.4%), and natural effects (5.8%). Specific attention should be paid to these natural (fluoride, TH, etc.) and anthropogenic sources (NH₄⁺, NO₂^?, turbidity, total bacterial count, etc.), and pertinent measures should be taken to control local groundwater pollution. The most significant trait of the PMF is its scientific interpretation and physical explanation of the results, depending on non-negative restriction of the pollution source profiles and contributions.     

Estimating the acute health effects of coarse particulate matter accounting for exposure measurement error

In air pollution epidemiology, there is a growing interest in estimating the health effects of coarse particulate matter (PM) with aerodynamic diameter between 2.5 and 10 μm. Coarse PM concentrations can exhibit considerable spatial heterogeneity because the particles travel shorter distances and do not remain suspended in the atmosphere for an extended period of time. In this paper, we develop a modeling approach for estimating the short-term effects of air pollution in time series analysis when the ambient concentrations vary spatially within the study region. Specifically, our approach quantifies the error in the exposure variable by characterizing, on any given day, the disagreement in ambient concentrations measured across monitoring stations. This is accomplished by viewing monitor-level measurements as error-prone repeated measurements of the unobserved population average exposure. Inference is carried out in a Bayesian framework to fully account for uncertainty in the estimation of model parameters. Finally, by using different exposure indicators, we investigate the sensitivity of the association between coarse PM and daily hospital admissions based on a recent national multisite time series analysis. Among Medicare enrollees from 59 US counties between the period 1999 and 2005, we find a consistent positive association between coarse PM and same-day admission for cardiovascular diseases.     

A Conceptual Framework for the Interpretation of Biological Markers for Environmental Exposure Assessment

Human exposure to environmental contaminants occurs via air, water, soil, dust, food, and other environmental media. Given this multitude of sources, environmental exposure assessment is moving away from single route exposure assessment to more integrated measures of exposure. Biological markers are frequently advocated as appropriate exposure assessment tools since they provide a measure of internal dose integrated over all routes of exposure. However, contributing sources may be difficult to identify through use of biological markers, and thus, have had limited utility in the regulatory community. To explore the different perspectives on the use and application of biological markers for exposure assessors, epidemiologists, and regulatory personnel, we have developed a biological marker conceptual framework. This framework is developed as a paradigm for the interpretation of biological markers for environmental exposure assessment linking the exposure assessment and the health effects assessment perspectives regarding biological markers. Further, it incorporates issues of source-specific exposures, aggregate exposure assessment, route-specific contributions, and biological variation in response to exposure. This structure provides an approach to explore the current constraints in using biological markers to evaluate source-specific exposures. This framework is discussed in the context of currently available biological markers for lead, carbon monoxide, and toluene. Biological markers represent a complex tool to assess human exposures to environmental contaminants; the biological marker framework presents a structure for their interpretation recognizing that many of the determinants of exposure, bioavailablity, and toxicokinetics are still being evaluated. The conceptual framework presented here provides another tool for the researcher in assessing the utility of biological markers in exposure assessment and epidemiology.     

Spatial Modeling of Air Pollution in Studies of Its Short‐Term Health Effects

Summary In studies that estimate the short‐term effects of air pollution on health, daily measurements of pollution concentrations are often available from a number of monitoring locations within the study area. However, the health data are typically only available in the form of daily counts for the entire area, meaning that a corresponding single daily measure of pollution is required. The standard approach is to average the observed measurements at the monitoring locations, and use this in a log‐linear health model. However, as the pollution surface is spatially variable this simple summary is unlikely to be an accurate estimate of the average pollution concentration across the region, which may lead to bias in the resulting health effects. In this article, we propose an alternative approach that jointly models the pollution concentrations and their relationship with the health data using a Bayesian spatio‐temporal model. We compare this approach with the simple spatial average using a simulation study, by investigating the impact of spatial variation, monitor placement, and measurement error in the pollution data. An epidemiological study from Greater London is then presented, which estimates the relationship between respiratory mortality and four different pollutants.     

Air pollution combustion emissions: characterization of causative agents and mechanisms associated with cancer, reproductive, and cardiovascular effects

Combustion emissions account for over half of the fine particle (PM(2.5)) air pollution and most of the primary particulate organic matter. Human exposure to combustion emissions including the associated airborne fine particles and mutagenic and carcinogenic constituents (e.g., polycyclic aromatic compounds (PAC), nitro-PAC) have been studied in populations in Europe, America, Asia, and increasingly in third-world counties. Bioassay-directed fractionation studies of particulate organic air pollution have identified mutagenic and carcinogenic polycyclic aromatic hydrocarbons (PAH), nitrated PAH, nitro-lactones, and lower molecular weight compounds from cooking. A number of these components are significant sources of human exposure to mutagenic and carcinogenic chemicals that may also cause oxidative and DNA damage that can lead to reproductive and cardiovascular effects. Chemical and physical tracers have been used to apportion outdoor and indoor and personal exposures to airborne particles between various combustion emissions and other sources. These sources include vehicles (e.g., diesel and gasoline vehicles), heating and power sources (e.g., including coal, oil, and biomass), indoor sources (e.g., cooking, heating, and tobacco smoke), as well as secondary organic aerosols and pollutants derived from long-range transport. Biomarkers of exposure, dose and susceptibility have been measured in populations exposed to air pollution combustion emissions. Biomarkers have included metabolic genotype, DNA adducts, PAH metabolites, and urinary mutagenic activity. A number of studies have shown a significant correlation of exposure to PM(2.5) with these biomarkers. In addition, stratification by genotype increased this correlation. New multivariate receptor models, recently used to determine the sources of ambient particles, are now being explored in the analysis of human exposure and biomarker data. Human studies of both short- and long-term exposures to combustion emissions and ambient fine particulate air pollution have been associated with measures of genetic damage. Long-term epidemiologic studies have reported an increased risk of all causes of mortality, cardiopulmonary mortality, and lung cancer mortality associated with increasing exposures to air pollution. Adverse reproductive effects (e.g., risk for low birth weight) have also recently been reported in Eastern Europe and North America. Although there is substantial evidence that PAH or substituted PAH may be causative agents in cancer and reproductive effects, an increasing number of studies investigating cardiopulmonary and cardiovascular effects are investigating these and other potential causative agents from air pollution combustion sources.     

Microbial Source Tracking for Identification of Fecal Pollution

Fecal pollution is a serious environmental problem that affects many coastal and inland waters worldwide. Both human and animal fecal pollution impose risks to human health from exposure to pathogenic bacteria, viruses, and protozoa. To assist authorities with the implementation of the changes suggested by more restricted legislation concering water quality in Europe, methods are needed which can identify the sources of fecal pollution. Management of fecal contamination of water would be improved if the origin of the fecal pollution could be correctly identified since remediation efforts could then be allocated in a more effective manner. The concept that the origin of fecal pollution can be traced has been termed microbial source tracking. In microbial source tracking (MST) endogenous markers of fecal sources are used for identification of the fecal pollution in aquatic environments. Chemical MST-methods can be used to trace mainly sewage pollution, but the used chemical targets have no direct relationship with pathogenic bacteria. This is not the case in microbial MST-methods where source-specific bacteria or viruses are cultured to identify fecal pollution sources. However, sometimes these microbial targets can be present in too low numbers to be detected. This is circumvented by using molecular assays for host-specific marker detection. Phenotypic and genotypic library-based methods can be used to discriminate among different fecal sources. However, the isolation step makes this procedure very labour-intensive, and issues as temporal and geographical variability remain unresolved. The underlying assumptions will be discussed and the methods mostly used in microbial source tracking will be described in more detail.     

Ambient particulate matter exposure and cardiovascular diseases: a focus on progenitor and stem cells

Air pollution is a major challenge to public health. Ambient fine particulate matter (PM) is the key component for air pollution, and associated with significant mortality. The majority of the mortality following PM exposure is related to cardiovascular diseases. However, the mechanisms for the adverse effects of PM exposure on cardiovascular system remain largely unknown and under active investigation. Endothelial dysfunction or injury is considered one of the major factors that contribute to the development of cardiovascular diseases such as atherosclerosis and coronary heart disease. Endothelial progenitor cells (EPCs) play a critical role in maintaining the structural and functional integrity of vasculature. Particulate matter exposure significantly suppressed the number and function of EPCs in animals and humans. However, the mechanisms for the detrimental effects of PM on EPCs remain to be fully defined. One of the important mechanisms might be related to increased level of reactive oxygen species (ROS) and inflammation. Bone marrow (BM) is a major source of EPCs. Thus, the number and function of EPCs could be intimately associated with the population and functional status of stem cells (SCs) in the BM. Bone marrow stem cells and other SCs have the potential for cardiovascular regeneration and repair. The present review is focused on summarizing the detrimental effects of PM exposure on EPCs and SCs, and potential mechanisms including ROS formation as well as clinical implications.     

Toxicological assessment of ambient and traffic-related particulate matter: a review of recent studies

Particulate air pollution (PM) is an important environmental health risk factor for many different diseases. This is indicated by numerous epidemiological studies on associations between PM exposure and occurrence of acute respiratory infections, lung cancer and chronic respiratory and cardiovascular diseases. The biological mechanisms behind these associations are not fully understood, but the results of in vitro toxicological research have shown that PM induces several types of adverse cellular effects, including cytotoxicity, mutagenicity, DNA damage and stimulation of proinflammatory cytokine production. Because traffic is an important source of PM emission, it seems obvious that traffic intensity has an important impact on both quantitative and qualitative aspects of ambient PM, including its chemical, physical and toxicological characteristics. In this review, the results are summarized of the most recent studies investigating physical and chemical characteristics of ambient and traffic-related PM in relation to its toxicological activity. This evaluation shows that, in general, the smaller PM size fractions (相似文献   

Oxidative stress-induced DNA damage by particulate air pollution

Exposure to ambient air particulate matter (PM) is associated with pulmonary and cardiovascular diseases and cancer. The mechanisms of PM-induced health effects are believed to involve inflammation and oxidative stress. The oxidative stress mediated by PM may arise from direct generation of reactive oxygen species from the surface of particles, soluble compounds such as transition metals or organic compounds, altered function of mitochondria or NADPH-oxidase, and activation of inflammatory cells capable of generating ROS and reactive nitrogen species. Resulting oxidative DNA damage may be implicated in cancer risk and may serve as marker for oxidative stress relevant for other ailments caused by particulate air pollution. There is overwhelming evidence from animal experimental models, cell culture experiments, and cell free systems that exposure to diesel exhaust and diesel exhaust particles causes oxidative DNA damage. Similarly, various preparations of ambient air PM induce oxidative DNA damage in in vitro systems, whereas in vivo studies are scarce. Studies with various model/surrogate particle preparations, such as carbon black, suggest that the surface area is the most important determinant of effect for ultrafine particles (diameter less than 100 nm), whereas chemical composition may be more important for larger particles. The knowledge concerning mechanisms of action of PM has prompted the use of markers of oxidative stress and DNA damage for human biomonitoring in relation to ambient air. By means of personal monitoring and biomarkers a few studies have attempted to characterize individual exposure, explore mechanisms and identify significant sources to size fractions of ambient air PM with respect to relevant biological effects. In these studies guanine oxidation in DNA has been correlated with exposure to PM(2.5) and ultrafine particles outdoor and indoor. Oxidative stress-induced DNA damage appears to an important mechanism of action of urban particulate air pollution. Related biomarkers and personal monitoring may be useful tools for risk characterization.     

Bayesian distributed lag models: estimating effects of particulate matter air pollution on daily mortality

Summary .  A distributed lag model (DLagM) is a regression model that includes lagged exposure variables as covariates; its corresponding distributed lag (DL) function describes the relationship between the lag and the coefficient of the lagged exposure variable. DLagMs have recently been used in environmental epidemiology for quantifying the cumulative effects of weather and air pollution on mortality and morbidity. Standard methods for formulating DLagMs include unconstrained, polynomial, and penalized spline DLagMs. These methods may fail to take full advantage of prior information about the shape of the DL function for environmental exposures, or for any other exposure with effects that are believed to smoothly approach zero as lag increases, and are therefore at risk of producing suboptimal estimates. In this article, we propose a Bayesian DLagM (BDLagM) that incorporates prior knowledge about the shape of the DL function and also allows the degree of smoothness of the DL function to be estimated from the data. We apply our BDLagM to its motivating data from the National Morbidity, Mortality, and Air Pollution Study to estimate the short-term health effects of particulate matter air pollution on mortality from 1987 to 2000 for Chicago, Illinois. In a simulation study, we compare our Bayesian approach with alternative methods that use unconstrained, polynomial, and penalized spline DLagMs. We also illustrate the connection between BDLagMs and penalized spline DLagMs. Software for fitting BDLagM models and the data used in this article are available online.     

Oxidant mechanisms in response to ambient air particles

The toxic effects of air pollution are widely documented. In recent years, however, there has been an increasing interest in the study of the health effects of particulate matter (PM), a previously unexplored constituent of urban air pollution. Exposure to increased levels of PM of respirable size is strongly and consistently associated with increased cardiopulmonary morbidity and mortality. Conversely, improved air quality appears to correlate with decreased mortality. Particulate matter is a mixture of inorganic and organic components that vary in size, origin, and composition. The mechanisms of PM health effects are still poorly understood. However, studies in cellular and animal models suggest a variety of possible mechanisms including direct effects of particle components on the intracellular sources of reactive oxygen species (ROS), indirect effects due to pro-inflammatory mediators released from PM-stimulated macrophages, and neural stimulation after particle deposition in the lungs. The involvement of ROS in each one of these possible pathways is discussed.     

Basin-wide analysis of the dynamics of fecal contamination and fecal source identification in Tillamook Bay, Oregon

The objectives of this study were to elucidate spatial and temporal dynamics in source-specific Bacteroidales 16S rRNA genetic marker data across a watershed; to compare these dynamics to fecal indicator counts, general measurements of water quality, and climatic forces; and to identify geographic areas of intense exposure to specific sources of contamination. Samples were collected during a 2-year period in the Tillamook basin in Oregon at 30 sites along five river tributaries and in Tillamook Bay. We performed Bacteroidales PCR assays with general, ruminant-source-specific, and human-source-specific primers to identify fecal sources. We determined the Escherichia coli most probable number, temperature, turbidity, and 5-day precipitation. Climate and water quality data collectively supported a rainfall runoff pattern for microbial source input that mirrored the annual precipitation cycle. Fecal sources were statistically linked more closely to ruminants than to humans; there was a 40% greater probability of detecting a ruminant source marker than a human source marker across the basin. On a sample site basis, the addition of fecal source tracking data provided new information linking elevated fecal indicator bacterial loads to specific point and nonpoint sources of fecal pollution in the basin. Inconsistencies in E. coli and host-specific marker trends suggested that the factors that control the quantity of fecal indicators in the water column are different than the factors that influence the presence of Bacteroidales markers at specific times of the year. This may be important if fecal indicator counts are used as a criterion for source loading potential in receiving waters.     

Use of 16S-23S rRNA intergenic spacer region PCR and repetitive extragenic palindromic PCR analyses of Escherichia coli isolates to identify nonpoint fecal sources

Despite efforts to minimize fecal input into waterways, this kind of pollution continues to be a problem due to an inability to reliably identify nonpoint sources. Our objective was to find candidate source-specific Escherichia coli fingerprints as potential genotypic markers for raw sewage, horses, dogs, gulls, and cows. We evaluated 16S-23S rRNA intergenic spacer region (ISR)-PCR and repetitive extragenic palindromic (rep)-PCR analyses of E. coli isolates as tools to identify nonpoint fecal sources. The BOXA1R primer was used for rep-PCR analysis. A total of 267 E. coli isolates from different fecal sources were typed with both techniques. E. coli was found to be highly diverse. Only two candidate source-specific E. coli fingerprints, one for cow and one for raw sewage, were identified out of 87 ISR fingerprints. Similarly, there was only one candidate source-specific E. coli fingerprint for horse out of 59 BOX fingerprints. Jackknife analysis resulted in an average rate of correct classification (ARCC) of 83% for BOX-PCR analysis and 67% for ISR-PCR analysis for the five source categories of this study. When nonhuman sources were pooled so that each isolate was classified as animal or human derived (raw sewage), ARCCs of 82% for BOX-PCR analysis and 72% for ISR-PCR analysis were obtained. Critical factors affecting the utility of these methods, namely sample size and fingerprint stability, were also assessed. Chao1 estimation showed that generally 32 isolates per fecal source individual were sufficient to characterize the richness of the E. coli population of that source. The results of a fingerprint stability experiment indicated that BOX and ISR fingerprints were stable in natural waters at 4, 12, and 28 degrees C for 150 days. In conclusion, 16S-23S rRNA ISR-PCR and rep-PCR analyses of E. coli isolates have the potential to identify nonpoint fecal sources. A fairly small number of isolates was needed to find candidate source-specific E. coli fingerprints that were stable under the simulated environmental conditions.     

Feasibility of future epidemiological studies on possible health effects of mobile phone base stations

The increasing deployment of mobile communication base stations led to an increasing demand for epidemiological studies on possible health effects of radio frequency emissions. The methodological challenges of such studies have been critically evaluated by a panel of scientists in the fields of radiofrequency engineering/dosimetry and epidemiology. Strengths and weaknesses of previous studies have been identified. Dosimetric concepts and crucial aspects in exposure assessment were evaluated in terms of epidemiological studies on different types of outcomes. We conclude that in principle base station epidemiological studies are feasible. However, the exposure contributions from all relevant radio frequency sources have to be taken into account. The applied exposure assessment method should be piloted and validated. Short to medium term effects on physiology or health related quality of life are best investigated by cohort studies. For long term effects, groups with a potential for high exposure need to first be identified; for immediate effect, human laboratory studies are the preferred approach.     

A measurement error model for time-series studies of air pollution and mortality

One barrier to interpreting the observational evidence concerning the adverse health effects of air pollution for public policy purposes is the measurement error inherent in estimates of exposure based on ambient pollutant monitors. Exposure assessment studies have shown that data from monitors at central sites may not adequately represent personal exposure. Thus, the exposure error resulting from using centrally measured data as a surrogate for personal exposure can potentially lead to a bias in estimates of the health effects of air pollution. This paper develops a multi-stage Poisson regression model for evaluating the effects of exposure measurement error on estimates of effects of particulate air pollution on mortality in time-series studies. To implement the model, we have used five validation data sets on personal exposure to PM10. Our goal is to combine data on the associations between ambient concentrations of particulate matter and mortality for a specific location, with the validation data on the association between ambient and personal concentrations of particulate matter at the locations where data have been collected. We use these data in a model to estimate the relative risk of mortality associated with estimated personal-exposure concentrations and make a comparison with the risk of mortality estimated with measurements of ambient concentration alone. We apply this method to data comprising daily mortality counts, ambient concentrations of PM10measured at a central site, and temperature for Baltimore, Maryland from 1987 to 1994. We have selected our home city of Baltimore to illustrate the method; the measurement error correction model is general and can be applied to other appropriate locations.Our approach uses a combination of: (1) a generalized additive model with log link and Poisson error for the mortality-personal-exposure association; (2) a multi-stage linear model to estimate the variability across the five validation data sets in the personal-ambient-exposure association; (3) data augmentation methods to address the uncertainty resulting from the missing personal exposure time series in Baltimore. In the Poisson regression model, we account for smooth seasonal and annual trends in mortality using smoothing splines. Taking into account the heterogeneity across locations in the personal-ambient-exposure relationship, we quantify the degree to which the exposure measurement error biases the results toward the null hypothesis of no effect, and estimate the loss of precision in the estimated health effects due to indirectly estimating personal exposures from ambient measurements.     

Estimating Mortality Derived from Indoor Exposure to Particles of Outdoor Origin

Following an extensive review of the literature, we further analyze the published data to examine the health effects of indoor exposure to particulate matter (PM) of outdoor origin. We obtained data on all-cause, cardiovascular, and respiratory mortality per 10 μg/m³ increase in outdoor PM₁₀ or PM_2.5; the infiltration factors for buildings; and estimated time spent outdoors by individuals in the United States, Europe, China, and globally. These data were combined log-linear exposure–response model to estimate the all-cause, cardiovascular, and respiratory mortality of exposure to indoor PM pollution of outdoor origin. Indoor PM pollution of outdoor origin is a cause of considerable mortality, accounting for 81% to 89% of the total increase in mortality associated with exposure to outdoor PM pollution for the studied regions. The findings suggest that enhancing the capacity of buildings to protect occupants against exposure to outdoor PM pollution has significant potential to improve public health outcomes.     

Basin-Wide Analysis of the Dynamics of Fecal Contamination and Fecal Source Identification in Tillamook Bay, Oregon

The objectives of this study were to elucidate spatial and temporal dynamics in source-specific Bacteroidales 16S rRNA genetic marker data across a watershed; to compare these dynamics to fecal indicator counts, general measurements of water quality, and climatic forces; and to identify geographic areas of intense exposure to specific sources of contamination. Samples were collected during a 2-year period in the Tillamook basin in Oregon at 30 sites along five river tributaries and in Tillamook Bay. We performed Bacteroidales PCR assays with general, ruminant-source-specific, and human-source-specific primers to identify fecal sources. We determined the Escherichia coli most probable number, temperature, turbidity, and 5-day precipitation. Climate and water quality data collectively supported a rainfall runoff pattern for microbial source input that mirrored the annual precipitation cycle. Fecal sources were statistically linked more closely to ruminants than to humans; there was a 40% greater probability of detecting a ruminant source marker than a human source marker across the basin. On a sample site basis, the addition of fecal source tracking data provided new information linking elevated fecal indicator bacterial loads to specific point and nonpoint sources of fecal pollution in the basin. Inconsistencies in E. coli and host-specific marker trends suggested that the factors that control the quantity of fecal indicators in the water column are different than the factors that influence the presence of Bacteroidales markers at specific times of the year. This may be important if fecal indicator counts are used as a criterion for source loading potential in receiving waters.     

Health risk assessment of a reservoir-type water source in the northeastern region of China

Urban water sources are the major source of water resources for urban life, and its water quality affects the daily life and health of the local people. However, there were some reports on the poor water quality status of water sources in the past. In the present study, by applying fuzzy synthetic evaluation, the water quality was studied from a total of 24 water samples from a reservoir-type water source in the northeastern region of China. Health risks of eight trace elements in source water were assessed using health risk assessment model and Monte Carlo simulation, and source apportionment of eight trace elements in source water were also analyzed. The results indicated that the water quality of the source water was acceptable for category I surface water, while the concentrations of total phosphorus (TP) and manganese (Mn) were higher than the permissible level. The noncarcinogenic risks due to eight trace elements exposure were As > Mn > Pb > Cd ≈ Cr > Se > Zn > Cu and carcinogenic risk of Arsenic (As) was 3E?05 with a maximum probability. Furthermore, statistical analyses, such as correlation analysis, principal component analysis (PCA) and cluster analysis (CA) showed that the trace metals in the water source have a certain degree of anthropogenic contributions, especially Mn. Overall, both the contents of TP and Mn and the health risk of As require some attention of the relevant department, and further protected measures should be taken in the source water.     

Coal Fly Ash Impairs Airway Antimicrobial Peptides and Increases Bacterial Growth

Air pollution is a risk factor for respiratory infections, and one of its main components is particulate matter (PM), which is comprised of a number of particles that contain iron, such as coal fly ash (CFA). Since free iron concentrations are extremely low in airway surface liquid (ASL), we hypothesize that CFA impairs antimicrobial peptides (AMP) function and can be a source of iron to bacteria. We tested this hypothesis in vivo by instilling mice with Pseudomonas aeruginosa (PA01) and CFA and determine the percentage of bacterial clearance. In addition, we tested bacterial clearance in cell culture by exposing primary human airway epithelial cells to PA01 and CFA and determining the AMP activity and bacterial growth in vitro. We report that CFA is a bioavailable source of iron for bacteria. We show that CFA interferes with bacterial clearance in vivo and in primary human airway epithelial cultures. Also, we demonstrate that CFA inhibits AMP activity in vitro, which we propose as a mechanism of our cell culture and in vivo results. Furthermore, PA01 uses CFA as an iron source with a direct correlation between CFA iron dissolution and bacterial growth. CFA concentrations used are very relevant to human daily exposures, thus posing a potential public health risk for susceptible subjects. Although CFA provides a source of bioavailable iron for bacteria, not all CFA particles have the same biological effects, and their propensity for iron dissolution is an important factor. CFA impairs lung innate immune mechanisms of bacterial clearance, specifically AMP activity. We expect that identifying the PM mechanisms of respiratory infections will translate into public health policies aimed at controlling, not only concentration of PM exposure, but physicochemical characteristics that will potentially cause respiratory infections in susceptible individuals and populations.     

Environmental Risk Assessment: Comparison of Receptor and Air Quality Models for Source Apportionment

Source apportionment of particulate matter has been commonly performed using receptor models, but studies suggest that the assumptions in receptor models limit the accuracy of results. An alternative approach is the use of three-dimensional source-oriented air quality models. Here, a comparison is conducted between the PM_2.5 apportioned from the Chemical Mass Balance (CMB) receptor model using organic tracers as molecular markers with those from the source-based Community Multiscale Air Quality (CMAQ) model. Source apportionment was conducted at sites in the southeastern United States for July 2001 and January 2002. PM_2.5 source apportionment results had moderate discrepancies, which originate from different spatial scales, fundamental limitations, and uncertainties of the two models. Results from CMB fluctuated temporally more than real variation due to measurement and source profile errors and uncertainties, whereas those from CMAQ could not capture daily variation well. In addition, results from CMB are mass contributions for the monitoring location, whereas those from CMAQ represent the average mass contributions of the model's grid. It is difficult to assess which approach is “better.” Indeed, both models have strengths and limitations, and each model's strengths can be utilized to help overcome the other model's limitations.