Characterizing Spatial Information Loss for Wastewater Surveillance Using crAssphage: Effect of Decay, Temperature, and Population Mobility

Populations contribute information about their health status to wastewater. Characterizing how that information degrades in transit to wastewater sampling locations (e.g., wastewater treatment plants and pumping stations) is critical to interpret wastewater responses. In this work, we statistically estimate the loss of information about fecal contributions to wastewater from spatially distributed populations at the census block group resolution. This was accomplished with a hydrologically and hydraulically influenced spatial statistical approach applied to crAssphage (Carjivirus communis) load measured from the influent of four wastewater treatment plants in Hamilton County, Ohio. We find that we would expect to observe a 90% loss of information about fecal contributions from a given census block group over a travel time of 10.3 h. This work demonstrates that a challenge to interpreting wastewater responses (e.g., during wastewater surveillance) is distinguishing between a distal but large cluster of contributions and a near but small contribution. This work demonstrates new modeling approaches to improve measurement interpretation depending on sewer network and wastewater characteristics (e.g., geospatial layout, temperature variability, population distribution, and mobility). This modeling can be integrated into standard wastewater surveillance methods and help to optimize sewer sampling locations to ensure that different populations (e.g., vulnerable and susceptible) are appropriately represented.

A hyperlocal hybrid data fusion near-road PM2.5 and NO2 annual risk and environmental justice assessment across the United States

Exposure to traffic-related air pollutants (TRAPs) has been associated with numerous adverse health effects. TRAP concentrations are highest meters away from major roads, and disproportionately affect minority (i.e., non-white) populations often considered the most vulnerable to TRAP exposure. To demonstrate an improved assessment of on-road emissions and to quantify exposure inequity in this population, we develop and apply a hybrid data fusion approach that utilizes the combined strength of air quality observations and regional/local scale models to estimate air pollution exposures at census block resolution for the entire U.S. We use the regional photochemical grid model CMAQ (Community Multiscale Air Quality) to predict the spatiotemporal impacts at local/regional scales, and the local scale dispersion model, R-LINE (Research LINE source) to estimate concentrations that capture the sharp TRAP gradients from roads. We further apply the Regionalized Air quality Model Performance (RAMP) Hybrid data fusion technique to consider the model's nonhomogeneous, nonlinear performance to not only improve exposure estimates, but also achieve significant model performance improvement. With a R2 of 0.51 for PM2.5 and 0.81 for NO2, the RAMP hybrid method improved R2 by ~0.2 for both pollutants (an increase of up to ~70% for PM2.5 and ~31% NO2). Using the RAMP Hybrid method, we estimate 264,516 [95% confidence interval [CI], 223,506-307,577] premature deaths attributable to PM2.5 from all sources, a ~1% overall decrease in CMAQ-estimated premature mortality compared to RAMP Hybrid, despite increases and decreases in some locations. For NO2, RAMP Hybrid estimates 138,550 [69,275-207,826] premature deaths, a ~19% increase (22,576 [11,288 - 33,864]) compared to CMAQ. Finally, using our RAMP hybrid method to estimate exposure inequity across the U.S., we estimate that Minorities within 100 m from major roads are exposed to up to 15% more PM2.5 and up to 35% more NO2 than their White counterparts.

Deep tubewell use is associated with increased household microbial contamination in rural Bangladesh: Results from a prospective cohort study among households in rural Bangladesh

Deep tubewells are important sources of arsenic mitigation in rural Bangladesh. Compared to commonly available shallow tubewells, deep tubewells tap into deeper low-arsenic aquifers and greatly reduce exposure to arsenic in drinking-water. However, benefits from these more distant and expensive sources may be compromised by higher levels of microbial contamination at point-of-use (POU). This paper examines differences in microbial contamination levels at source and POU among households using deep tubewells and shallow tubewells, and investigates factors associated with POU microbial contamination among deep tubewell users. We assessed a prospective longitudinal cohort of 500 rural households in Matlab, Bangladesh, across 135 villages. Concentration of Escherichia coli (E. coli) in water samples at source and POU using Compartment Bag Tests (CBTs) was measured across rainy and dry seasons. We employed linear mixed-effect regression models to measure the effect of different factors on log E. coli concentrations among deep tubewell users. CBT results show that log E. coli concentrations are similar at source and at POU during the first dry and rainy season, but are significantly higher at POU among deep tubewell users during the second dry season. Log E. coli at POU among deep tubewell users is positively associated with both presence (exponentiated beta exp(b) = 2.52, 95% Confidence Interval (CI) = 1.70, 3.73) and concentration of E. coli (exp(b) = 1.36, 95% CI = 1.19, 1.54) at source, and walking time to the tubewell source (exp(b) = 1.39, 95% CI = 1.15, 1.69). Drinking-water during the second dry season is associated with reduced log E. coli (exp(b) = 0.33, 95% CI = 0.23, 0.57) compared to the rainy season. These results suggest that while households that use deep tubewells have lower arsenic exposure, they may be at higher risk of consuming microbially contaminated water compared to households that use shallow tubewells.

Improving emissions inputs via mobile measurements to estimate fine-scale Black Carbon monthly concentrations through geostatistical space-time data fusion

Isolating air pollution sources in a complex transportation environment to quantify their contribution is challenging, particularly with sparse stationary measurements. Mobile measurements can add finer spatial resolution to support source apportionment, but they exhibit limitations when characterizing long term concentrations. Dispersion models can help overcome these limitations. However, they are only as reliable as their input emissions inventories. Herein, we developed an innovative method to revise emissions through inverse modeling and improve dispersion modeling predictions using stationary/mobile measurements. One specific revision estimated an adjustment factor of ~306 for warehouse emissions, indicating a significant underestimation of our initial estimates. This revised emission rate scaled up nationally would correspond to ~3.5% of the total Black Carbon emissions in the U.S. Nevertheless, domain-specific revisions only contribute to a 4% increase of area source emissions while improving R² from monthly estimates at fixed sites by 38%. After revising emissions through inverse dispersion modeling, we combine this model with stationary/mobile measurements through Bayesian Maximum Entropy (I-DISP BME) to produce temporally coarse yet spatially fine data fusion. We compare this novel data fusion approach to BME using only measurements (Flat BME). A 10-fold conventional cross-validation (representative of months with mobile measurements) shows that all BME methods have R² values that range from 0.787 to 0.798. A 2-fold cross-validation (representative of months with no mobile measurements) shows that the R² for I-DISP BME increases by a factor 90 when compared to Flat BME. Furthermore, not only is our novel I-DISP BME method more accurate than the classic Flat BME method, but the area it detects as highly exposed can be up to 5 times larger than that detected by the less accurate Flat BME method.

Spatialization of saturated hydraulic conductivity using the Bayesian Maximum Entropy method: Application to wastewater infiltration areas

Wastewater treatment, a major issue at the European level, focuses on improving surface water and groundwater quality, preserving the receiving environment, and ensuring a sustainable use of water. Soil infiltration is increasingly practiced downstream of wastewater treatment plants, particularly in rural areas without surface water bodies, as is the use of soil as an additional buffer and treatment step. However, the design of infiltration areas on heterogeneous soils remains an extremely complex task due to the costly and time-consuming spatial measurement of saturated hydraulic conductivity (Ks). This article proposes integrating 2D electrical resistivity tomography and infiltration tests into a Bayesian Maximum Entropy method, yielding a vertical mapping of soil heterogeneities at a metric scale. This updated method will facilitate infiltration area design in a heterogeneous soil setting.

Exposure to fine particulate matter and temporal dynamics of episodic memory and depressive symptoms in older women

BACKGROUND

Emerging data suggests PM2.5 (particulate matter with aerodynamic diameter <2.5 μm) may be associated with both earlier declines in episodic memory (EM) and increased depressive symptoms in older adults. Although late-life depressive symptoms are associated with EM, no longitudinal studies have examined the inter-relationship among PM2.5, depressive symptoms and EM.

METHODS

Older women (n = 2,202; aged 67-83 in 1999) enrolled in the Women's Health Initiative Study of Cognitive Aging completed up to eight annual assessments of depressive symptoms (15-item Geriatric Depression Scale) and EM (California Verbal Learning Test). A nationwide spatiotemporal model (1999-2010) was used to estimate ambient PM2.5 exposure at residential locations. Univariate and bivariate structural equation models (SEMs) for latent-change scores were used to examine how 3-year average PM2.5 preceding each assessment affects the temporal dynamics and bidirectional relations of annual changes in depressive symptoms and EM.

RESULTS

In univariate SEMs, one inter-quartile (4.04 μg/m3) increment of 3-year PM2.5 was significantly (p < 0.05) associated with accelerated declines in verbal learning (List A trials 1-3: β = -1.48) and free-recall memory (short-delay: β = -1.43; long-delay: β = -1.11), but not with change in depressive symptoms (β = 0.12; p = 0.71). In bivariate SEMs, significant associations were observed between PM2.5 and accelerated declines in EM measures (β = -1.44 to -0.99; p < 0.05) and between EM performance and changes in depressive symptoms (β = -0.08 to -0.05; p < 0.05), with significant indirect PM2.5 effects on changes in depressive symptoms (β = 0.08-0.10; p < 0.05). These findings were robust with adjustment for multiple demographic, lifestyle, and clinical factors, and remained after excluding subjects with dementia or mild cognitive impairment. No associations were found between PM2.5 and change in depressive symptoms or depressive symptoms and subsequent EM decline.

CONCLUSIONS

Findings suggest that PM2.5 neurotoxicity may damage brain areas implicated in EM, followed by manifestation of depressive symptoms. Our data did not support depressive symptoms as the neuropsychological mediator of accelerated brain aging associated with PM2.5 exposure.

Particulate Air Pollutants and Trajectories of Depressive Symptoms in Older Women

OBJECTIVES

Although several environmental factors contribute to the etiology of late-life depressive symptoms, the role of ambient air pollution has been understudied. Experimental data support the neurotoxicity of airborne particulate matter with aerodynamic diameter of ≤2.5 μm (PM_2.5), but it remains unclear whether long-term exposure is associated with late-life depressive symptoms. Our secondary aim was to explore whether the observed associations between exposure and depressive symptoms are explained by dementia risk.

DESIGN, SETTING, AND PARTICIPANTS

Prospective community-dwelling cohort study from the Women's Health Initiative Study of Cognitive Aging (1999-2010). Our analyses included 1,989 older women (baseline age 73.3 ± 3.75) with no prior depression or cognitive impairment.

MEASUREMENTS

Participants completed annual assessments of depressive symptoms (15-item Geriatric Depression Scale). Average ambient PM_2.5 exposure at the residential location was estimated by spatiotemporal modeling for the 3-years preceding each neuropsychological assessment. Participants underwent separate annual examinations for incident dementia defined by DSM-IV. Latent-class mixture models examined the association between PM_2.5 and identified trajectories of symptoms.

RESULTS

Six trajectories of depressive symptoms were identified. Across all women, PM_2.5 exposure was positively associated with depressive symptoms. The effect was especially strong in two clusters with sustained depressive symptoms (n = 625 sustained-mild [31%]; n = 125 sustained-moderate; [6%]). Among those with sustained-moderate symptoms, the estimated adverse effect of PM_2.5 exposure was greater than that of hypertension. Among women without dementia, associations were modestly attenuated.

CONCLUSION

Long-term exposure to ambient fine particles was associated with increased depressive symptoms among older women without prior depression or cognitive impairment.

Combining Dispersion Modeling and Monitoring Data for Community-Scale Air Quality Characterization

Spatially and temporally resolved air quality characterization is critical for community-scale exposure studies and for developing future air quality mitigation strategies. Monitoring-based assessments can characterize local air quality when enough monitors are deployed. However, modeling plays a vital role in furthering the understanding of the relative contributions of emissions sources impacting the community. In this study, we combine dispersion modeling and measurements from the Kansas City TRansportation local-scale Air Quality Study (KC-TRAQS) and use data fusion methods to characterize air quality. The KC-TRAQS study produced a rich dataset using both traditional and emerging measurement technologies. We used dispersion modeling to support field study design and analysis. In the study design phase, the presumptive placement of fixed monitoring sites and mobile monitoring routes have been corroborated using a research screening tool C-PORT to assess the spatial and temporal coverage relative to the entire study area extent. In the analysis phase, dispersion modeling was used in combination with observations to help interpret the KC-TRAQS data. We extended this work to use data fusion methods to combine observations from stationary, mobile measurements, and dispersion model estimates.

Finely Resolved On-Road PM2.5 and Estimated Premature Mortality in Central North Carolina

To quantify the on-road PM_2.5 -related premature mortality at a national scale, previous approaches to estimate concentrations at a 12-km × 12-km or larger grid cell resolution may not fully characterize concentration hotspots that occur near roadways and thus the areas of highest risk. Spatially resolved concentration estimates from on-road emissions to capture these hotspots may improve characterization of the associated risk, but are rarely used for estimating premature mortality. In this study, we compared the on-road PM_2.5 -related premature mortality in central North Carolina with two different concentration estimation approaches-(i) using the Community Multiscale Air Quality (CMAQ) model to model concentration at a coarser resolution of a 36-km × 36-km grid resolution, and (ii) using a hybrid of a Gaussian dispersion model, CMAQ, and a space-time interpolation technique to provide annual average PM_2.5 concentrations at a Census-block level (∼105,000 Census blocks). The hybrid modeling approach estimated 24% more on-road PM_2.5 -related premature mortality than CMAQ. The major difference is from the primary on-road PM_2.5 where the hybrid approach estimated 2.5 times more primary on-road PM_2.5 -related premature mortality than CMAQ due to predicted exposure hotspots near roadways that coincide with high population areas. The results show that 72% of primary on-road PM_2.5 premature mortality occurs within 1,000 m from roadways where 50% of the total population resides, highlighting the importance to characterize near-road primary PM_2.5 and suggesting that previous studies may have underestimated premature mortality due to PM_2.5 from traffic-related emissions.

Particulate Air Pollutants, Brain Structure, and Neurocognitive Disorders in Older Women

Introduction

An increasing number of studies have suggested that exposure to particulate matter (PM) may represent a novel - and potentially amendable - environmental determinant of brain aging. The current longitudinal environmental epidemiological study addressed some important knowledge gaps in this emerging field, which combines the study of air pollution and neuroepidemiology. The investigators hypothesized that long-term PM exposure adversely influences global brain volume and brain regions (e.g., frontal lobe or hippocampus) that are critical to memory and complex cognitive processing or that are affected by neuropathological changes in dementia. It was also hypothesized that long-term PM exposure results in neurovascular damage and may increase the risk of mild cognitive impairment (MCI) and -dementia.

Methods

The investigators selected a well-characterized and geographically diverse population of older women (N = 7,479; average age = 71.0 ± 3.8 years at baseline) in the Women's Health Initiative (WHI) Memory Study (WHIMS) cohort (1996-2007), which included a subcohort (n = 1,403) enrolled in the WHIMS-Magnetic Resonance Imaging (WHIMS-MRI) study (2005-2006). Residence-specific yearly exposures to PM ≤ 2.5 µm in aerodynamic diameter (PM₂.₅) were estimated using a Bayesian maximum entropy spatiotemporal model of annual monitoring data (1999-2007) recorded in the U.S. Environmental Protection Agency (U.S. EPA) Air Quality System (AQS). Annual exposures (1996-2005) to diesel PM (DPM) were assigned to each residential census tract in a nationwide spatiotemporal mapping, based on a generalized additive model (GAM), to conduct census tract-specific temporal interpolation of DPM on-road estimates given by the U.S. EPA National-Scale Air Toxics Assessment Program. Multiple linear regression and multicovariate-adjusted Cox models were used to examine the associations, with statistical adjustment for multiple potential confounders.

Results

The investigators found that participants had smaller brain volumes, especially in the normal-appearing white matter (WM), if they lived in locations with higher levels of cumulative exposure (1999-2006) to PM ₂.₅ before the brain MRI scans were performed. The associations were not explained by sociodemographic factors, socioeconomic status, lifestyle factors, or other clinical characteristics. Analyses showed that the adverse effect on brain structure in the participants was driven primarily by the smaller WM volumes associated with cumulative PM₂.₅ exposures, which were present in the WM divisions of the association brain area (frontal, parietal, and temporal lobes) and corpus callosum. Increased DPM exposures were associated with larger ventricular volume, suggesting an overall atrophic effect on the aging brains. The participants tended to have smaller gray matter (GM) volumes if they lived in areas with the highest (i.e., fourth quartile) estimated cumulative DPM exposure in the 10 years before the brain MRI scans, compared with women in the first to third quartiles. This observed association was present in the total brain GM and in the association brain cortices. The associations with normal-appearing WM varied by DPM exposure range. For women with estimated cumulative exposure below that of the fourth quartile, increased DPM estimates were associated with smaller WM volumes. However, for women with increased cumulative DPM exposures estimates in the fourth quartile, WM volumes were larger. This pattern of association was found consistently in the association brain area; no measurable difference was found in the volume of the corpus callosum. These observed adverse effects of cumulative exposure to PM₂.₅ (linking exposure with smaller WM volumes) and to DPM (linking exposure in the highest quartile with smaller GM volumes) were not significantly modified by existing cardiovascular diseases, diabetes mellitus, obesity, or measured white blood cell (WBC) count. MRI measurements of the structural brain showed no differences in small-vessel ischemic diseases (SVID) in participants with varying levels of cumulative exposure to PM₂.₅ (1999-2006) or DPM (1996-2005), and no associations between PM exposures and SVID volumes were noted for total brain, association brain area, GM, or WM. For neurocognitive outcomes followed until 2007, the investigators found no evidence for increased risk of MCI/dementia associated with long-term PM exposures. Although exploratory secondary analyses showed different patterns of associations linking PM exposures separately with MCI and dementia, none of the -results was statistically significant. A similar lack of associations between PM exposures and MCI/dementia was found across the subgroups, with no strong indications for effect modification by cardiovascular diseases, diabetes mellitus, obesity, or WBC count.

Conclusions

The investigators concluded that their study findings support the hypothesized brain-structure neurotoxicity associated with PM exposures, a result that is in line with emerging neurotoxicological data. However, the investigators found no evidence of increased risk of MCI/dementia associated with long-term PM exposures.

To better test the neurovascular effect hypothesis in PM-associated neurotoxic effects on the aging brain, the investigators recommend that future studies pay greater attention to selecting optimal populations with repeated measurements of cerebrovascular damage and address the possibility of selection biases accordingly. To further investigate the long-term consequence of brain-structure neurotoxicity on pathological brain aging, future researchers should take the pathobiologically heterogeneous neurocognitive outcomes into account and design adequately powered prospective cohort studies with improved exposure estimation and valid outcome ascertainment to assess whether PM-associated neurotoxicity increases the risks of pathological brain aging, including MCI and dementia.

Comparing the Health Effects of Ambient Particulate Matter Estimated Using Ground-Based versus Remote Sensing Exposure Estimates

BACKGROUND

Remote sensing (RS) is increasingly used for exposure assessment in epidemiological and burden of disease studies, including those investigating whether chronic exposure to ambient fine particulate matter (PM2.5) is associated with mortality.

OBJECTIVES

We compared relative risk estimates of mortality from diseases of the circulatory system for PM2.5 modeled from RS with that for PM2.5 modeled using ground-level information.

METHODS

We geocoded the baseline residence of 668,629 American Cancer Society Cancer Prevention Study II (CPS-II) cohort participants followed from 1982 to 2004 and assigned PM2.5 levels to all participants using seven different exposure models. Most of the exposure models were averaged for the years 2002-2004, and one RS estimate was for a longer, contemporaneous period. We used Cox proportional hazards regression to estimate relative risks (RRs) for the association of PM2.5 with circulatory mortality and ischemic heart disease.

RESULTS

Estimates of mortality risk differed among exposure models. The smallest relative risk was observed for the RS estimates that excluded ground-based monitors for circulatory deaths [RR = 1.02, 95% confidence interval (CI): 1.00, 1.04 per 10 μg/m3 increment in PM2.5]. The largest relative risk was observed for the land-use regression model that included traffic information (RR = 1.14, 95% CI: 1.11, 1.17 per 10 μg/m3 increment in PM2.5).

CONCLUSIONS

We found significant associations between PM2.5 and mortality in every model; however, relative risks estimated from exposure models using ground-based information were generally larger than those estimated using RS alone.

A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States

Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created a model to predict ambient particulate matter less than 2.5 μm 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 data set included 104,172 monthly observations at 1464 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 R(2) 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 R(2) 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.

Fecal indicators in sand, sand contact, and risk of enteric illness among beachgoers

BACKGROUND

Beach sand can harbor fecal indicator organisms and pathogens, but enteric illness risk associated with sand contact remains unclear.

METHODS

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.

RESULTS

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.

CONCLUSIONS

We observed a positive relationship between sand-contact activities and enteric illness as a function of concentrations of fecal microbial pollution in beach sand.

Impact of tubewell access and tubewell depth on childhood diarrhea in Matlab, Bangladesh

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Burden of disease attributed to anthropogenic air pollution in the United Arab Emirates: estimates based on observed air quality data

This study quantifies the national burden of disease attributed to particulate matter (PM) and ozone (O(3)) in ambient air in the United Arab Emirates (UAE), a rapidly growing nation in which economic development and climatic conditions pose important challenges for air quality management. Estimates of population exposure to these air pollutants are based on observed air quality data from fixed-site monitoring stations. We divide the UAE into small grid cells and use spatial-statistical methods to estimate the ambient pollutant concentrations in each cell based on the observed data. Premature deaths attributed to PM and O(3) are computed for each grid cell and then aggregated across grid cells and over a year to estimate the total number of excess deaths attributable to ambient air pollution. Our best estimate is that approximately 545 (95% CI: 132-1224) excess deaths in the UAE in the year 2007 are attributable to PM in ambient air. These excess deaths represent approximately 7% (95% CI: 2-17%) of the total deaths that year. We attribute approximately 62 premature deaths (95% CI: 17-127) to ground-level O(3) for the year 2007. Uncertainty in the natural background level of PM, due to the frequent dust storms occurring in the region, has significant impacts on the attributed mortality estimates. Despite the uncertainties associated with the integrated assessment framework, we conclude that anthropogenic ambient air pollution, in particular PM, causes a considerable public health impact in the UAE in terms of premature deaths. We discuss important uncertainties and scientific hypotheses to be investigated in future work that might help reduce the uncertainties in the burden of disease estimates.

[Epidemic of Trichophyton tonsurans tinea capitis in a nursery school in the Southern suburbs of Paris]

INTRODUCTION

In March 2001 a school and family survey was conducted in a nursery school in the Southern suburbs of Paris, during an epidemic of Trichophyton tondsurans tinea.

PATIENTS AND METHODS

One hundred twenty-nine children aged 3 to 6 were examined as well as 15 adults working in the school. A survey of the contaminated children or asymptomatic carriers was performed. All the children and adults concerned were treated at the same time, without eviction from school.

RESULTS

T. tonsurans was detected in 10 cases of tinea (7.7 p.cent of the persons examined), in 18 cases of cutaneous lesions (13.9 p.cent) and in 25 asymptomatic carriers on the scalp (19.4 p.cent). The majority of the positive cases came from the same school class as the original case: 23 of the 26 children (88 p.cent), with 6 tinea, 14 asymptomatic carriers and 13 cutaneous lesions. Only one of the 15 adults exhibited a T. tonsurans cutaneous lesion. Among the 13 families studied, 2 had several members involved, the first being that of the original case (3 tinea and 2 asymptomatic carriers).

DISCUSSION

Several important points are underlined by this study: 1) the high contagiousness of T. tonsurans; 2) the detection of 2 mechanisms of indirect contamination (rag doll mascot in the class and the family hair-clipper); 3) the one-year time lapse between the arrival of the contaminating child in the class and the survey, explaining the extent of the contamination; 4) the underestimation of the epidemic due to the lack of mycological examinations; 5) the identification of several dermatophytes in the same school:M. canis, T soudanense and T. tonsurans, and 7) the futility of eviction from school when all the children can be treated.

Ultrasonic non-destructive testing of pieces of complex geometry with a flexible phased array transducer

Ultrasonic non-destructive testing of components of complex geometry in the nuclear industry faces several difficulties: sensitivity variations due to unmatched contact, inaccurate localization of defects due to variations of transducer orientation, and uncovered area of the component. To improve the performances of such testing and defect characterization, we propose a new concept of ultrasonic contact phased array transducer. The phased array transducer has a flexible radiating surface able to fit the actual surface of the piece to optimize the contact and thus the sensitivity of the test. To control the transmitted field, and therefore to improve the defect characterization, a delay law optimizing algorithm is developed. To assess the capability of such a transducer, the Champ-Sons model, developed at the French Atomic Energy Commission for predicting field radiated by arbitrary transducers into pieces, has to be extended to sources directly in contact with pieces of complex geometry. The good behavior of this new type of probe predicted by computations is experimentally validated with a jointed transducer positioned on pieces of various profiles.

Hemodynamic response to SR 42128A in normal and sodium-depleted baboons

Hemodynamic changes following intravenous administration of SR 42128A (SR), a potent renin inhibitor, were evaluated in normal (N) and sodium-depleted (SD) anesthetized baboons. SR, at 9 mg/kg, decreased arterial pressure (AP) only in the SD group. This effect persisted for at least 2.30 h. At this dose, SR decreased the systemic vascular resistance (SVR) and increased the cardiac output in the SD group more than in the normal group. In both groups, heart rate was slightly increased. However, in the normal group, the highest dose (12 mg/kg) induced the same hemodynamic responses as the dose of 9 mg/kg in the SD group. Every time, plasma renin activity (PRA) was inhibited. Thus, in SD baboons, SR produced a hypotensive effect more pronounced than in the normal group. The dose-related effect on AP seemed to be correlated with the change in SVR. We can conclude that total inhibition of PRA is necessary but not sufficient, under normal conditions, to get an adequate lowering of SVR and a hypotensive effect.