Marc L Serre

University of North Carolina at Chapel Hill, North Carolina, United States

Are you Marc L Serre?

Claim your profile

Publications (71)160 Total impact

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In recognition that intra-urban exposure gradients may be as large as between-city variations, recent air pollution epidemiologic studies have become increasingly interested in capturing within-city exposure gradients. In addition, because of the rapidly accumulating health data, recent studies also need to handle large study populations distributed over large geographic domains. Even though several modeling approaches have been introduced, a consistent modeling framework capturing within-city exposure variability and applicable to large geographic domains is still missing. To address these needs, we proposed a modeling framework based on the Bayesian Maximum Entropy (BME) method that integrates monitoring data and outputs from existing air quality models based on Land Use Regression (LUR) and Chemical Transport Models (CTM). The framework was applied to estimate the yearly average NO2 concentrations over the region of Catalunya in Spain. By jointly accounting for the global scale variability in the concentration from the output of CTM and the intra-urban scale variability through LUR model output, the proposed framework outperformed more conventional approaches.
    Environmental Science & Technology 03/2014; · 5.26 Impact Factor
  • Jeanette Reyes, Marc L Serre
    [Show abstract] [Hide abstract]
    ABSTRACT: Knowledge of Particulate Matter concentrations < 2.5 microns in diameter (PM2.5) across the United States is limited due to sparse monitoring across space and time. Epidemiological studies need accurate exposure estimates in order to properly investigate potential morbidity and mortality. Previous works have used geostatistics and Land Use Regression (LUR) separately to quantify exposure. This work combines both methods by incorporating a large area variability LUR model that accounts for on road mobile emissions and stationary source emissions along with data that take into account incompleteness of PM2.5 monitors into the modern geostatistical Bayesian Maximum Entropy (BME) framework to estimate PM2.5 across the United States from 1999-2009. A cross-validation was done to determine the improvement of the estimate due to the LUR incorporation into BME. These results were applied to known diseases to determine predicted mortality coming from total PM2.5 as well as PM2.5 explained by major contributing sources. This method showed a mean squared error reduction of over 21.89% over simple kriging. PM2.5 explained by on road mobile emissions and stationary emissions contributed to nearly 568,090 and 306,316 deaths, respectively, across the United States from 1999-2007.
    Environmental Science & Technology 01/2014; · 5.26 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created an model to predict ambient particulate matter less than 2.5 microns in aerodynamic diameter (PM2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM2.5 dataset included 104,172 monthly observations at 1,464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM2.5, land use and traffic indicators. Normalized cross-validated R2 values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated R2 were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM2.5 at multiple scales over the contiguous U.S.
    Environmental Science & Technology 05/2013; · 5.26 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Our objective was to determine the extent to which geographical core areas for gonorrhea and syphilis are located in rural areas as compared with urban areas. Incident gonorrhea (January 1, 2005-December 31, 2010) and syphilis (January 1, 1999-December 31, 2010) rates were estimated and mapped by census tract and quarter. Rurality was measured using percent rural and rural-urban commuting area (rural, small town, micropolitan, or urban). SaTScan was used to identify spatiotemporal clusters of significantly elevated rates of infection. Clusters lasting 5 years or longer were considered core areas; clusters of shorter duration were considered outbreaks. Clusters were overlaid on maps of rurality and qualitatively assessed for correlation. Twenty gonorrhea core areas were identified: 65% were in urban centers, 25% were in micropolitan areas, and the remaining 10% were geographically large capturing combinations of urban, micropolitan, small town, and rural environments. Ten syphilis core areas were identified with 80% in urban centers and 20% capturing 2 or more rural-urban commuting areas. All 10 (100%) of the syphilis core areas overlapped with gonorrhea core areas. Gonorrhea and syphilis rates were high for rural parts of North Carolina; however, no core areas were identified exclusively for small towns or rural areas. The main pathway of rural sexually transmitted disease (STI) transmission may be through the interconnectedness of urban, micropolitan, small town, and rural areas. Directly addressing STIs in urban and micropolitan communities may also indirectly help address STI rates in rural and small town communities.
    Sexually transmitted diseases 01/2013; 40(1):32-40. · 2.58 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Bangladesh is underlain by shallow aquifers in which millions of drinking water wells are emplaced without annular seals. Fecal contamination has been widely detected in private tubewells. To evaluate the impact of well construction on microbial water quality 35 private tubewells (11 with intact cement platforms, 19 without) and 17 monitoring wells (11 with the annulus sealed with cement, six unsealed) were monitored for culturable Escherichia coli over 18 months. Additionally, two 'snapshot' sampling events were performed on a subset of wells during late-dry and early-wet seasons, wherein the fecal indicator bacteria (FIB) E. coli, Bacteroidales and the pathogenicity genes eltA (enterotoxigenic E. coli; ETEC), ipaH (Shigella) and 40/41 hexon (adenovirus) were detected using quantitative polymerase chain reaction (qPCR). No difference in E. coli detection frequency was found between tubewells with and without platforms. Unsealed private wells, however, contained culturable E. coli more frequently and higher concentrations of FIB than sealed monitoring wells (p < 0.05), suggestive of rapid downward flow along unsealed annuli. As a group the pathogens ETEC, Shigella and adenovirus were detected more frequently (10/22) during the wet season than the dry season (2/20). This suggests proper sealing of private tubewell annuli may lead to substantial improvements in microbial drinking water quality.
    Journal of Water and Health 12/2012; 10(4):565-78. · 1.22 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Background: To better understand adverse health effects from chronic exposure to fine particulate matter (PM2.5) a need exists to derive accurate estimates of PM2.5 variation at fine spatial scales. Remote sensing has emerged as an important means of estimating PM2.5 exposures, but there are relatively few studies that compare remote-sensing estimates to those derived from monitor-based data. Objective: The purpose of this paper is to evaluate and compare the predictive capabilities of remote sensing and geostatistical interpolation. Methods: We develop a space-time geostatistical kriging model to predict PM2.5 over the continental United States and compare resulting predictions to estimates derived from satellite retrievals. Results: Within about 100 km of a monitoring station, the kriging estimate was more accurate, while the remote sensing estimate was more accurate for locations >100 km from a monitoring station. Based on this finding we developed a hybrid map that combines the kriging and satellite-based PM2.5 estimates. Conclusions: This study is part of a larger investigation aimed at improving the assessment of exposure to ambient air pollution for chronic health effects studies. We evaluated the estimation capability of monitor-based interpolation to monitor-free remote sensing and found that for most of the populated areas of the continental United States, geostatistical interpolation supplied more accurate estimates than remote sensing. The differences between the estimates from the two methods, however, were relatively small. We conclude that in areas with extensive monitoring networks, the interpolation may provide more accurate estimates, but in the many areas of the world without such monitoring, remote sensing can provide useful exposure estimates that perform nearly as well.
    Environmental Health Perspectives 10/2012; · 7.26 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Geostatistical methods are widely used in estimating long-term exposures for epidemiological studies on air pollution, despite their limited capabilities to handle spatial non-stationarity over large geographic domains and the uncertainty associated with missing monitoring data. We developed a moving-window (MW) Bayesian maximum entropy (BME) method and applied this framework to estimate fine particulate matter (PM(2.5)) yearly average concentrations over the contiguous US. The MW approach accounts for the spatial non-stationarity, while the BME method rigorously processes the uncertainty associated with data missingness in the air-monitoring system. In the cross-validation analyses conducted on a set of randomly selected complete PM(2.5) data in 2003 and on simulated data with different degrees of missing data, we demonstrate that the MW approach alone leads to at least 17.8% reduction in mean square error (MSE) in estimating the yearly PM(2.5). Moreover, the MWBME method further reduces the MSE by 8.4-43.7%, with the proportion of incomplete data increased from 18.3% to 82.0%. The MWBME approach leads to significant reductions in estimation error and thus is recommended for epidemiological studies investigating the effect of long-term exposure to PM(2.5) across large geographical domains with expected spatial non-stationarity.
    Journal of Exposure Science and Environmental Epidemiology 06/2012; 22(5):496-501. · 3.19 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Geographic information systems (GIS) based techniques are cost-effective and efficient methods used by state agencies and epidemiology researchers for estimating concentration and exposure. However, budget limitations have made statewide assessments of contamination difficult, especially in groundwater media. Many studies have implemented address geocoding, land use regression, and geostatistics independently, but this is the first to examine the benefits of integrating these GIS techniques to address the need of statewide exposure assessments. A novel framework for concentration exposure is introduced that integrates address geocoding, land use regression (LUR), below detect data modeling, and Bayesian Maximum Entropy (BME). A LUR model was developed for tetrachloroethylene that accounts for point sources and flow direction. We then integrate the LUR model into the BME method as a mean trend while also modeling below detects data as a truncated Gaussian probability distribution function. We increase available PCE data 4.7 times from previously available databases through multistage geocoding. The LUR model shows significant influence of dry cleaners at short ranges. The integration of the LUR model as mean trend in BME results in a 7.5% decrease in cross validation mean square error compared to BME with a constant mean trend.
    Environmental Science & Technology 03/2012; 46(5):2772-80. · 5.26 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Beach sand can harbor fecal indicator organisms and pathogens, but enteric illness risk associated with sand contact remains unclear. In 2007, visitors at 2 recreational marine beaches were asked on the day of their visit about sand contact. Ten to 12 days later, participants answered questions about health symptoms since the visit. F+ coliphage, Enterococcus, Bacteroidales, fecal Bacteroides, and Clostridium spp. in wet sand were measured using culture and molecular methods. We analyzed 144 wet sand samples and completed 4999 interviews. Adjusted odds ratios (aORs) were computed, comparing those in the highest tertile of fecal indicator exposure with those who reported no sand contact. Among those digging in sand compared with those not digging in sand, a molecular measure of Enterococcus spp. (calibrator cell equivalents/g) in sand was positively associated with gastrointestinal (GI) illness (aOR = 2.0 [95% confidence interval (CI) = 1.2-3.2]) and diarrhea (2.4 [1.4-4.2]). Among those buried in sand, point estimates were greater for GI illness (3.3 [1.3-7.9]) and diarrhea (4.9 [1.8-13]). Positive associations were also observed for culture-based Enterococcus (colony-forming units/g) with GI illness (aOR digging = 1.7 [1.1-2.7]) and diarrhea (2.1 [1.3-3.4]). Associations were not found among nonswimmers with sand exposure. We observed a positive relationship between sand-contact activities and enteric illness as a function of concentrations of fecal microbial pollution in beach sand.
    Epidemiology (Cambridge, Mass.) 01/2012; 23(1):95-106. · 5.51 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Background: Sexually transmitted infections (STIs) spread along sexual networks whose structural characteristics promote transmission that routine surveillance ihay not capture. Cases who have partners from multiple localities may operate as spatial network bridges, thereby facilitating geographical dissemination. We investigated how surveillance, sexual networks, and spatial bridges relate to each other for syphilis outbreaks in rural counties of North Carolina. Methods: We selected from the state health department's surveillance database cases diagnosed with primary, secondary, or early latent syphilis during October 1998 to December 2002 and who resided in central and southeastern North Carolina, along with their sex partners and their social contacts irrespective of infection status. We applied matching algorithms to eliminate duplicate names and create a unique roster of partnerships from which networks were compiled and graphed. Network members were differentiated by disease status and county of residence. Results: In the county most affected by the outbreak, densely connected networks indicative of STI outbreaks were consistent with increased incidence and a large case load. In other counties, the case loads were low with fluctuating incidence, but network structures suggested the presence of outbreaks. In a county with stable, low incidence and a high number of cases, the networks were sparse and dendritic, indicative of endemic spread. Outbreak counties exhibited densely connected networks within well-defined geographic boundaries and low connectivity between counties; spatial bridges did not seem to facilitate transmission. Conclusions: Simple visualization of sexual networks can provide key information to identify communities most in need of resources for outbreak investigation and disease control.
    Epidemiology 01/2012; 23(6):845-851. · 5.74 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Arsenic is a known human carcinogen and relevant environmental contaminant in drinking water systems. We set out to comprehensively examine statewide arsenic trends and identify areas of public health concern. Specifically, arsenic trends in North Carolina private wells were evaluated over an eleven-year period using the North Carolina Department of Health and Human Services database for private domestic well waters. We geocoded over 63,000 domestic well measurements by applying a novel geocoding algorithm and error validation scheme. Arsenic measurements and geographical coordinates for database entries were mapped using Geographic Information System techniques. Furthermore, we employed a Bayesian Maximum Entropy (BME) geostatistical framework, which accounts for geocoding error to better estimate arsenic values across the state and identify trends for unmonitored locations. Of the approximately 63,000 monitored wells, 7712 showed detectable arsenic concentrations that ranged between 1 and 806μg/L. Additionally, 1436 well samples exceeded the EPA drinking water standard. We reveal counties of concern and demonstrate a historical pattern of elevated arsenic in some counties, particularly those located along the Carolina terrane (Carolina slate belt). We analyzed these data in the context of populations using private well water and identify counties for targeted monitoring, such as Stanly and Union Counties. By spatiotemporally mapping these data, our BME estimate revealed arsenic trends at unmonitored locations within counties and better predicted well concentrations when compared to the classical kriging method. This study reveals relevant information on the location of arsenic-contaminated private domestic wells in North Carolina and indicates potential areas at increased risk for adverse health outcomes.
    Environment international 01/2012; 38(1):10-6. · 6.25 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: [This corrects the article on p. e29593 in vol. 6.].
    PLoS ONE 01/2012; 7(1). · 3.73 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: During the past three decades in Bangladesh, millions of tubewells have been installed to reduce the prevalence of diarrheal disease. This study evaluates the impacts of tubewell access and tubewell depth on childhood diarrhea in rural Bangladesh. A total of 59,796 cases of diarrhea in children under 5 were recorded in 142 villages of Matlab, Bangladesh during monthly community health surveys between 2000 and 2006. The location and depth of 12,018 tubewells were surveyed in 2002-04 and integrated with diarrhea and other data in a geographic information system. A proxy for tubewell access was developed by calculating the local density of tubewells around households. Logistic regression models were built to examine the relationship between childhood diarrhea, tubewell density and tubewell depth. Wealth, adult female education, flood control, population density and the child's age were considered as potential confounders. Baris (patrilineally-related clusters of households) with greater tubewell density were associated with significantly less diarrhea (OR (odds ratio) = 0.87, 95% confidence interval (CI): 0.85-0.89). Tubewell density had a greater influence on childhood diarrhea in areas that were not protected from flooding. Baris using intermediate depth tubewells (140-300 feet) were associated with more childhood diarrhea (OR = 1.24, 95% CI: 1.19-1.29) than those using shallow wells (10-140 feet). Baris using deep wells (300-990 feet) had less diarrheal disease than those using shallow wells, however, the difference was significant only when population density was low (< 1000 person/km(2)) or children were at the age of 13-24 months. Increased access to tubewells is associated with a lower risk of childhood diarrhea. Intermediate- depth wells are associated with more childhood diarrhea compared to shallower or deeper wells. These findings may have implications for on-going efforts to reduce exposure to elevated levels of arsenic contained in groundwater that is pumped in this study area primarily from shallow tubewells.
    Environmental Health 12/2011; 10:109. · 2.71 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Mercury in fish tissue is a major human health concern. Consumption of mercury-contaminated fish poses risks to the general population, including potentially serious developmental defects and neurological damage in young children. Therefore, it is important to accurately identify areas that have the potential for high levels of bioaccumulated mercury. However, due to time and resource constraints, it is difficult to adequately assess fish tissue mercury on a basin wide scale. We hypothesized that, given the nature of fish movement along streams, an analytical approach that takes into account distance traveled along these streams would improve the estimation accuracy for fish tissue mercury in unsampled streams. Therefore, we used a river-based Bayesian Maximum Entropy framework (river-BME) for modern space/time geostatistics to estimate fish tissue mercury at unsampled locations in the Cape Fear and Lumber Basins in eastern North Carolina. We also compared the space/time geostatistical estimation using river-BME to the more traditional Euclidean-based BME approach, with and without the inclusion of a secondary variable. Results showed that this river-based approach reduced the estimation error of fish tissue mercury by more than 13% and that the median estimate of fish tissue mercury exceeded the EPA action level of 0.3 ppm in more than 90% of river miles for the study domain.
    Environmental Science & Technology 08/2011; 45(18):7746-53. · 5.26 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: To determine if the spatial pattern of gonorrhea observed for North Carolina was influenced by neighborhood-level sociocultural determinants of health, including race/ethnicity. A generalized linear mixed model with spatially correlated random effects was fit to measure the influence of socio-cultural factors on the spatial pattern of gonorrhea reported to the North Carolina State Health Department (January 1, 2005 to March 31, 2008). Neighborhood gonorrhea rates increased as the percent single mothers increased (25th to 75th neighborhood percentile Relative Rate 1.18, 95% CI 1.12, 1.25), and decreased as socioeconomic status increased (Relative Rate 0.89, 95% CI 0.84, 0.95). Increasing numbers of men in neighborhoods with more women than men did not change the gonorrhea rate, but was associated with decreased rates in neighborhoods with more men than women. Living in the mountains was protective for all race/ethnicities. Rurality was associated with decreased rates for Blacks and increased rates for Native Americans outside the mountains. Neighborhood-level sociocultural factors, primarily those indicative of neighborhood deprivation, explained a significant proportion of the spatial pattern of gonorrhea in both urban and rural communities. Race/ethnicity was an important proxy for social and cultural factors not captured by measures of socioeconomic status.
    Annals of epidemiology 04/2011; 21(4):245-52. · 2.95 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Disease maps of crude rates from routinely collected health data indexed at a small geographical resolution pose specific statistical problems due to the sparse nature of the data. Spatial smoothers allow areas to borrow strength from neighboring regions to produce a more stable estimate of the areal value. Geostatistical smoothers are able to quantify the uncertainty in smoothed rate estimates without a high computational burden. In this paper, we introduce a uniform model extension of Bayesian Maximum Entropy (UMBME) and compare its performance to that of Poisson kriging in measures of smoothing strength and estimation accuracy as applied to simulated data and the real data example of HIV infection in North Carolina. The aim is to produce more reliable maps of disease rates in small areas to improve identification of spatial trends at the local level. In all data environments, Poisson kriging exhibited greater smoothing strength than UMBME. With the simulated data where the true latent rate of infection was known, Poisson kriging resulted in greater estimation accuracy with data that displayed low spatial autocorrelation, while UMBME provided more accurate estimators with data that displayed higher spatial autocorrelation. With the HIV data, UMBME performed slightly better than Poisson kriging in cross-validatory predictive checks, with both models performing better than the observed data model with no smoothing. Smoothing methods have different advantages depending upon both internal model assumptions that affect smoothing strength and external data environments, such as spatial correlation of the observed data. Further model comparisons in different data environments are required to provide public health practitioners with guidelines needed in choosing the most appropriate smoothing method for their particular health dataset.
    International Journal of Health Geographics 01/2011; 10:54. · 2.62 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Millions of households throughout Bangladesh have been exposed to high levels of arsenic (As) causing various deadly diseases by drinking groundwater from shallow tubewells for the past 30 years. Well testing has been the most effective form of mitigation because it has induced massive switching from tubewells that are high (>50 µg/L) in As to neighboring wells that are low in As. A recent study has shown, however, that shallow low-As wells are more likely to be contaminated with the fecal indicator E. coli than shallow high-As wells, suggesting that well switching might lead to an increase in diarrheal disease. Approximately 60,000 episodes of childhood diarrhea were collected monthly by community health workers between 2000 and 2006 in 142 villages of Matlab, Bangladesh. In this cross-sectional study, associations between childhood diarrhea and As levels in tubewell water were evaluated using logistic regression models. Adjusting for wealth, population density, and flood control by multivariate logistic regression, the model indicates an 11% (95% confidence intervals (CIs) of 4-19%) increase in the likelihood of diarrhea in children drinking from shallow wells with 10-50 µg/L As compared to shallow wells with >50 µg/L As. The same model indicates a 26% (95%CI: 9-42%) increase in diarrhea for children drinking from shallow wells with ≤10 µg/L As compared to shallow wells with >50 µg/L As. Children drinking water from shallow low As wells had a higher prevalence of diarrhea than children drinking water from high As wells. This suggests that the health benefits of reducing As exposure may to some extent be countered by an increase in childhood diarrhea.
    PLoS ONE 01/2011; 6(12):e29593. · 3.73 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: In a largely rural region of North Carolina during 1998-2002, outbreaks of heterosexually transmitted syphilis occurred, tied to crack cocaine use and exchange of sex for drugs and money. Sexual partnership mixing patterns are an important characteristic of sexual networks that relate to transmission dynamics of sexually transmitted infections (STIs). Using contact tracing data collected by disease intervention specialists, we estimated Newman assortativity coefficients and compared values in counties experiencing syphilis outbreaks to nonoutbreak counties, with respect to race/ethnicity, race/ethnicity and age, and the cases' number of social/sexual contacts, infected contacts, sex partners, and infected sex partners, and syphilis disease stage (primary, secondary, early latent). Individuals in the outbreak counties had more contacts and mixing by the number of sex partners was disassortative in outbreak counties and assortative nonoutbreak counties. Although mixing by syphilis disease stage was minimally assortative in outbreak counties, it was disassortative in nonoutbreak areas. Partnerships were relatively discordant by age, especially among older white men, who often chose considerably younger female partners. Whether assortative mixing exacerbates or attenuates the reach of STIs into different populations depends on the characteristic/attribute and epidemiologic phase. Examination of sexual partnership characteristics and mixing patterns offers insights into the growth of STI outbreaks that complement other research methods.
    Sexually transmitted diseases 01/2011; 38(5):378-84. · 2.58 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The health risks of As exposure due to the installation of millions of shallow tubewells in the Bengal Basin are known, but fecal contamination of shallow aquifers has not systematically been examined. This could be a source of concern in densely populated areas with poor sanitation because the hydraulic travel time from surface water bodies to shallow wells that are low in As was previously shown to be considerably shorter than for shallow wells that are high in As. In this study, 125 tubewells 6-36 m deep were sampled in duplicate for 18 months to quantify the presence of the fecal indicator Escherichia coli. On any given month, E. coli was detected at levels exceeding 1 most probable number per 100 mL in 19-64% of all shallow tubewells, with a higher proportion typically following periods of heavy rainfall. The frequency of E. coli detection averaged over a year was found to increase with population surrounding a well and decrease with the As content of a well, most likely because of downward transport of E. coli associated with local recharge. The health implications of higher fecal contamination of shallow tubewells, to which millions of households in Bangladesh have switched in order to reduce their exposure to As, need to be evaluated.
    Environmental Science & Technology 01/2011; · 5.26 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The goal of this study was to test hollow-fiber ultrafiltration as a method for concentrating in situ bacteria and viruses in groundwater samples. Water samples from nine wells tapping a shallow sandy aquifer in a densely populated village in Bangladesh were reduced in volume approximately 400-fold using ultrafiltration. Culture-based assays for total coliforms and Escherichia coli, as well as molecular-based assays for E. coli, Bacteroides, and adenovirus, were used as microbial markers before and after ultrafiltration to evaluate performance. Ultrafiltration increased the concentration of the microbial markers in 99% of cases. However, concentration factors (CF = post-filtration concentration/pre-filtration concentration) for each marker calculated from geometric means ranged from 52 to 1018 compared to the expected value of 400. The efficiency was difficult to quantify because concentrations of some of the markers, especially E. coli and total coliforms, in the well water (WW) collected before ultrafiltration varied by several orders of magnitude during the period of sampling. The potential influence of colloidal iron oxide precipitates in the groundwater was tested by adding EDTA to the pre-filtration water in half of the samples to prevent the formation of precipitates. The use of EDTA had no significant effect on the measurement of culturable or molecular markers across the 0.5 to 10 mg/L range of dissolved Fe2+ concentrations observed in the groundwater, indicating that colloidal iron did not hinder or enhance recovery or detection of the microbial markers. Ultrafiltration appears to be effective for concentrating microorganisms in environmental water samples, but additional research is needed to quantify losses during filtration.
    Ground Water 12/2010; 49(1):53 - 65. · 2.13 Impact Factor

Publication Stats

524 Citations
160.00 Total Impact Points

Institutions

  • 1999–2013
    • University of North Carolina at Chapel Hill
      • • Department of Medicine
      • • Department of Environmental Sciences and Engineering
      North Carolina, United States
  • 2011
    • University of Toronto
      Toronto, Ontario, Canada
  • 2009
    • National Renewable Energy Laboratory
      Golden, Colorado, United States
  • 2005
    • Heriot-Watt University
      Edinburgh, Scotland, United Kingdom
  • 2004
    • Institut National de Recherche Agronomique de Rabat
      Rabat, Rabat-Salé-Zemmour-Zaër, Morocco