Bayesian profile regression to study the ecologic associations of correlated environmental exposures with excess mortality risk during the first year of the Covid-19 epidemic in lombardy, Italy.

Many countries, including Italy, have experienced significant social and spatial inequalities in mortality during the Covid-19 pandemic. This study applies a multiple exposures framework to investigate how joint place-based factors influence spatial inequalities of excess mortality during the first year of the Covid -19 pandemic in the Lombardy region of Italy. For the Lombardy region, we integrated municipality-level data on all-cause mortality between 2015 and 2020 with 13 spatial covariates, including 5-year average concentrations of six air pollutants, the average temperature in 2020, and multiple socio-demographic factors, and health facilities per capita. Using the clustering algorithm Bayesian profile regression, we fit spatial covariates jointly to identify clusters of municipalities with similar exposure profiles and estimated associations between clusters and excess mortality in 2020. Cluster analysis resulted in 13 clusters. Controlling for spatial autocorrelation of excess mortality and health-protective agency, two clusters had significantly elevated excess mortality than the rest of Lombardy. Municipalities in these highest-risk clusters are in Bergamo, Brescia, and Cremona provinces. The highest risk cluster (C11) had the highest long-term particulate matter air pollution levels (PM2.5 and PM10) and significantly elevated NO2 and CO air pollutants, temperature, proportion ≤18 years, and male-to-female ratio. This cluster is significantly lower for income and ≥65 years. The other high-risk cluster, Cluster 10 (C10), is elevated significantly for ozone but significantly lower for other air pollutants. Covariates with elevated levels for C10 include proportion 65 years or older and a male-to-female ratio. Cluster 10 is significantly lower for income, temperature, per capita health facilities, ≤18 years, and population density. Our results suggest that joint built, natural, and socio-demographic factors influenced spatial inequalities of excess mortality in Lombardy in 2020. Studies must apply a multiple exposures framework to guide policy decisions addressing the complex and multi-dimensional nature of spatial inequalities of Covid-19-related mortality.

Control of Structural Hydrophobicity and Cation Solvation on Interlayer Water Transport during Clay Dehydration.

Swelling clay hydration/dehydration is important to many environmental and industrial processes. Experimental studies usually probe equilibrium hydration states in an averaged manner and thus cannot capture the fast water transport and structural change in interlayers during hydration/dehydration. Using molecular simulations and thermogravimetric analyses, we observe a two-stage dehydration process. The first stage is controlled by evaporation at the edges: water molecules near hydrophobic sites and the first few water molecules of the hydration shell of cations move fast to particle edges for evaporation. The second stage is controlled by slow desorption of the last 1-2 water molecules from the cations and slow transport through the interlayers. The two-stage dehydration is strongly coupled with interlayer collapse and the coordination number changes of cations, all of which depend on layer charge distribution. This mechanistic interpretation of clay dehydration can be key to the coupled chemomechanical behavior in natural/engineered barriers.

Association between lipid profiles and viral respiratory infections in human sputum samples.

BACKGROUND: Respiratory infections such as influenza account for significant global mortality each year. Generating lipid profiles is a novel and emerging research approach that may provide new insights regarding the development and progression of priority respiratory infections. We hypothesized that select clusters of lipids in human sputum would be associated with specific viral infections (Influenza (H1N1, H3N2) or Rhinovirus). METHODS: Lipid identification and semi-quantitation was determined with liquid chromatography and high-resolution mass spectrometry in induced sputum from individuals with confirmed respiratory infections (influenza (H1N1, H3N2) or rhinovirus). Clusters of lipid species and associations between lipid profiles and the type of respiratory viral agent was determined using Bayesian profile regression and multinomial logistic regression. RESULTS: More than 600 lipid compounds were identified across the sputum samples with the most abundant lipid classes identified as triglycerides (TG), phosphatidylethanolamines (PE), phosphatidylcholines (PC), Sphingomyelins (SM), ether-PC, and ether-PE. A total of 12 lipid species were significantly different when stratified by infection type and included acylcarnitine (AcCar) (10:1, 16:1, 18:2), diacylglycerols (DG) (16:0_18:0, 18:0_18:0), Lysophosphatidylcholine (LPC) (12:0, 20:5), PE (18:0_18:0), and TG (14:1_16:0_18:2, 15:0_17:0_19:0, 16:0_17:0_18:0, 19:0_19:0_19:0). Cluster analysis yielded three clusters of lipid profiles that were driven by just 10 lipid species (TGs and DGs). Cluster 1 had the highest levels of each lipid species and the highest prevalence of influenza A H3 infection (56%, n = 5) whereas cluster 3 had lower levels of each lipid species and the highest prevalence of rhinovirus (60%; n = 6). Using cluster 3 as the reference group, the crude odds of influenza A H3 infection compared to rhinovirus in cluster 1 was significantly (p = 0.047) higher (OR = 15.00 [95% CI: 1.03, 218.29]). After adjustment for confounders (smoking status and pulmonary comorbidities), the odds ratio (OR) became only marginally significant (p = 0.099), but the magnitude of the effect estimate was similar (OR = 16.00 [0.59, 433.03]). CONCLUSIONS: In this study, human sputum lipid profiles were shown to be associated with distinct types of viral infection. Better understanding the relationship between respiratory infections of global importance and lipids contributes to advancing knowledge of pathogenesis of infections including identifying populations with increased susceptibility and developing effective therapeutics and biomarkers of health status.

Formation of 6H-Ba3Ce0.75Mn2.25O9 during Thermochemical Reduction of 12R-Ba4CeMn3O12: Identification of a Polytype in the Ba(Ce,Mn)O3 Family.

The resurgence of interest in a hydrogen economy and the development of hydrogen-related technologies has initiated numerous research and development efforts aimed at making the generation, storage, and transportation of hydrogen more efficient and affordable. Solar thermochemical hydrogen production (STCH) is a process that potentially exhibits numerous benefits such as high reaction efficiencies, tunable thermodynamics, and continued performance over extended cycling. Although CeO2 has been the de facto standard STCH material for many years, more recently 12R-Ba4CeMn3O12 (BCM) has demonstrated enhanced hydrogen production at intermediate H2/H2O conditions compared to CeO2, making it a contender for large-scale hydrogen production. However, the thermo-reduction stability of 12R-BCM dictates the oxygen partial pressure (pO2) and temperature conditions optimal for cycling. In this study, we identify the formation of a 6H-BCM polytype at high temperature and reducing conditions, experimentally and computationally, as a mechanism and pathway for 12R-BCM decomposition. 12R-BCM was synthesized with high purity and then controllably reduced using thermogravimetric analysis (TGA). Synchrotron X-ray diffraction (XRD) data is used to identify the formation of a 6H-Ba3Ce0.75Mn2.25O9 (6H-BCM) polytype that is formed at 1350 °C under strongly reducing pO2. Density functional theory (DFT) total energy and defect calculations show a window of thermodynamic stability for the 6H-polytype consistent with the XRD results. These data provide the first evidence of the 6H-BCM polytype and could provide a mechanistic explanation for the superior water-splitting behaviors of 12R-BCM.

Association between PM2.5 and respiratory hospitalization in Rio Branco, Brazil: Demonstrating the potential of low-cost air quality sensor for epidemiologic research.

BACKGROUND: There is currently a scarcity of air pollution epidemiologic data from low- and middle-income countries (LMICs) due to the lack of air quality monitoring in these countries. Additionally, there is limited capacity to assess the health effects of wildfire smoke events in wildfire-prone regions like Brazil's Amazon Basin. Emerging low-cost air quality sensors may have the potential to address these gaps. OBJECTIVES: We investigated the potential of PurpleAir PM2.5 sensors for conducting air pollution epidemiologic research leveraging the United States Environmental Protection Agency's United States-wide correction formula for ambient PM2.5. METHODS: We obtained raw (uncorrected) PM2.5 concentration and humidity data from a PurpleAir sensor in Rio Branco, Brazil, between 2018 and 2019. Humidity measurements from the PurpleAir sensor were used to correct the PM2.5 concentrations. We established the relationship between ambient PM2.5 (corrected and uncorrected) and daily all-cause respiratory hospitalization in Rio Branco, Brazil, using generalized additive models (GAM) and distributed lag non-linear models (DLNM). We used linear regression to assess the relationship between daily PM2.5 concentrations and wildfire reports in Rio Branco during the wildfire seasons of 2018 and 2019. RESULTS: We observed increases in daily respiratory hospitalizations of 5.4% (95%CI: 0.8%, 10.1%) for a 2-day lag and 5.8% (1.5%, 10.2%) for 3-day lag, per 10 µg/m3 PM2.5 (corrected values). The effect estimates were attenuated when the uncorrected PM2.5 data was used. The number of reported wildfires explained 10% of daily PM2.5 concentrations during the wildfire season. DISCUSSION: Exposure-response relationships estimated using corrected low-cost air quality sensor data were comparable with relationships estimated using a validated air quality modeling approach. This suggests that correcting low-cost PM2.5 sensor data may mitigate bias attenuation in air pollution epidemiologic studies. Low-cost sensor PM2.5 data could also predict the air quality impacts of wildfires in Brazil's Amazon Basin.

Wastewater surveillance for SARS-CoV-2 in a small coastal community: Effects of tourism on viral presence and variant identification among low prevalence populations.

Wastewater-based epidemiology has been used to measure SARS-CoV-2 prevalence in cities worldwide as an indicator of community health, however, few longitudinal studies have followed SARS-CoV-2 in wastewater in small communities from the start of the pandemic or evaluated the influence of tourism on viral loads. Therefore the objective of this study was to use measurements of SARS-CoV-2 in wastewater to monitor viral trends and variants in a small island community over a twelve-month period beginning May 1, 2020, before the community re-opened to tourists. Wastewater samples were collected weekly and analyzed to detect and quantify SARS-CoV-2 genome copies. Sanger sequencing was used to determine genome sequences from total RNA extracted from wastewater samples positive for SARS-CoV-2. Visitor data was collected from the local Chamber of Commerce. We performed Poisson and linear regression to determine if visitors to the Cedar Key Chamber of Commerce were positively associated with SARS-CoV-2-positive wastewater samples and the concentration of SARS-CoV-2 RNA. Results indicated that weekly wastewater samples were negative for SARS-CoV-2 until mid-July when positive samples were recorded in four of five consecutive weeks. Additional positive results were recorded in November and December 2020, as well as January, March, and April 2021. Tourism data revealed that the SARS-CoV-2 RNA concentration in wastewater increased by 1.06 Log10 genomic copies/L per 100 tourists weekly. Sequencing from six positive wastewater samples yielded two complete sequences of SARS-CoV-2, two overlapping sequences, and two low yield sequences. They show arrival of a new variant SARS-CoV-2 in January 2021. Our results demonstrate the utility of wastewater surveillance for SARS-CoV-2 in a small community. Wastewater surveillance and viral genome sequencing suggest that population mobility likely plays an important role in the introduction and circulation of SARS-CoV-2 variants among communities experiencing high tourism and who have a small population size.

Vitamin D intake modifies the association of household air pollution exposure with maternal disorders of pregnancy.

To date, only three studies have investigated the association of household air pollution (HAP) exposure with pregnancy disorders. The ameliorating role of diet and nutrition in the association has never been explored. We conducted a cross-sectional study among 799 mothers who had recently given singleton birth in the Cape Coast Metropolis, Ghana. Structured questionnaire and semi-quantitative food frequency questionnaire were used to assess HAP exposure (from use of biomass fuels for cooking and garbage burning at home) and vitamin D (vitD) intake, respectively. Multivariable binary logistic regression was used to investigate the association between HAP exposure and pregnancy disorders. HAP exposure due to cooking with biomass fuels and garbage burning at home was associated with two fold (AOR = 2.15; 95% confidence interval [CI]: 1.05, 4.43) and six fold (AOR = 6.35; 95% CI: 2.43, 16.58) increased odds of hypertensive disorders of pregnancy (HDP). For gestational diabetes (GDM), the increased odds were two folds for both exposures but the 95% CI included the null value. Stove stacking was also associated with two folds increased odds of GDM (AOR = 1.83; 95% CI: 0.91, 3.68). In stratified analysis, the odds of HDP and GDM associated with biomass fuels use decreased with increasing vitD intake. All the interaction p values were, however, greater than 0.05. We provide the first evidence on the ameliorating role of vitD intake on the effect of HAP exposure on pregnancy disorders. In LMICs where solid fuel use and garbage burning at home is widespread, health workers should advise mothers during antenatal care visits to increase intake of vitamin D rich foods.

Use of biomass fuels predicts indoor particulate matter and carbon monoxide concentrations; evidence from an informal urban settlement in Fort Portal city, Uganda.

BACKGROUND: Poor indoor air quality (IAQ) is a leading cause of respiratory and cardiopulmonary illnesses. Particulate matter (PM2.5) and carbon monoxide (CO) are critical indicators of IAQ, yet there is limited evidence of their concentrations in informal urban settlements in low-income countries. OBJECTIVE: This study assessed household characteristics that predict the concentrations of PM2.5 and CO within households in an informal settlement in Fort Portal City, Uganda. METHODOLOGY: A cross-sectional study was conducted in 374 households. Concentrations of PM2.5 and CO were measured using a multi-purpose laser particle detector and a carbon monoxide IAQ meter, respectively. Data on household characteristics were collected using a structured questionnaire and an observational checklist. Data were analysed using STATA version 14.0. Linear regression was used to establish the relationship between PM2.5, CO concentrations and household cooking characteristics. RESULTS: The majority (89%, 332/374) of the households used charcoal for cooking. More than half (52%, 194/374) cooked outdoors. Cooking areas had significantly higher PM2.5 and CO concentrations (t = 18.14, p ≤ 0.05) and (t = 5.77 p ≤ 0.05), respectively. Cooking outdoors was associated with a 0.112 increase in the PM2.5 concentrations in the cooking area (0.112 [95% CI: -0.069, 1.614; p = 0.033]). Cooking with moderately polluting fuel was associated with a 0.718 increase in CO concentrations (0.718 [95% CI: 0.084, 1.352; p = 0.027]) in the living area. CONCLUSIONS: The cooking and the living areas had high concentrations of PM2.5 and CO during the cooking time. Cooking with charcoal resulted in higher CO in the living area. Furthermore, cooking outdoors did not have a protective effect against PM2.5, and ambient PM2.5 exceeded the WHO Air quality limits. Interventions to improve the indoor air quality in informal settlements should promote a switch to cleaner cooking energy and improvement in the ambient air quality.

Morphology and electrical properties of high-speed flexography-printed graphene.

Printed graphene electrodes have been demonstrated as a versatile platform for electrochemical sensing, with numerous examples of rapid sensor prototyping using laboratory-scale printing techniques such as inkjet and aerosol jet printing. To leverage these materials in a scalable production framework, higher-throughput printing methods are required with complementary advances in ink formulation. Flexography printing couples the attractive benefits of liquid-phase graphene printing with large-scale manufacturing. Here, we investigate graphene flexography for the fabrication of electrodes by analyzing the impacts of ink and process parameters on print quality and electrical properties. Characterization of the printed patterns reveals anisotropic structure due to striations along the print direction, which is related to viscous fingering of the ink. However, high-resolution imaging reveals a dense graphene network even in regions of sparse coverage, contributing to robust electrical properties even for the thinnest films (< 100 nm). Moreover, the mechanical and environmental sensitivity of the printed electrodes is characterized, with particular focus on atmospheric response and thermal hysteresis. Overall, this work reveals the conditions under which graphene inks can be employed for high-speed flexographic printing, which will facilitate the development of graphene-based sensors and related devices.

A land use regression model using machine learning and locally developed low cost particulate matter sensors in Uganda.

The application of land use regression (LUR) modeling for estimating air pollution exposure has been used only rarely in sub-Saharan Africa (SSA). This is generally due to a lack of air quality monitoring networks in the region. Low cost air quality sensors developed locally in sub-Saharan Africa presents a sustainable operating mechanism that may help generate the air monitoring data needed for exposure estimation of air pollution with LUR models. The primary objective of our study is to investigate whether a network of locally developed low-cost air quality sensors can be used in LUR modeling for accurately predicting monthly ambient fine particulate matter (PM2.5) air pollution in urban areas of central and eastern Uganda. Secondarily, we aimed to explore whether the application of machine learning (ML) can improve LUR predictions compared to ordinary least squares (OLS) regression. We used data for the entire year of 2020 from a network of 23 PM2.5 low-cost sensors located in urban municipalities of eastern and central Uganda. Between January 1, 2020 and December 31, 2020, these sensors collected highly time-resolved measurement data of PM2.5 air concentrations. We used monthly-averaged PM2.5 concentration data for LUR prediction modeling of monthly PM2.5 concentrations. We used eight different ML base-learner algorithms as well as ensemble modeling. We applied 5-fold cross validation (80% training/20% test random splits) to evaluate the models with resampling and Root mean squared error (RMSE). The relative explanatory power and accuracy of the ML algorithms were evaluated by comparing coefficient of determination (R2) and RMSE, using OLS as the reference approach. The overall average PM2.5 concentration during the study period was 52.22 µg/m3 (IQR: 38.11, 62.84 µg/m3)-well above World Health Organization PM2.5 ambient air guidelines. From the base-learner and ensemble models, RMSE and R2 values ranged between 7.65 µg/m3 - 16.85 µg/m3 and 0.24-0.84, respectively. Extreme gradient boosting (xgbTree) performed best out of the base learner algorithms (R2 = 0.84; RMSE = 7.65 µg/m3). Model performance from ensemble modeling with Lasso and Elastic-Net Regularized Generalized Linear Models (glmnet) did not outperform xgbTree, but prediction performance was comparable to that of xgbTree. The most important temporal and spatial predictors of monthly PM2.5 levels were monthly precipitation, percent of the population using solid fuels for cooking, distance to Lake Victoria, and greenspace (NDVI) within a 500-m buffer of air monitors. In conclusion, data from locally developed low-cost PM sensors provide evidence that they can be used for spatio-temporal prediction modeling of air pollution exposures in Uganda. Moreover, the non-parametric ML and ensemble approaches to LUR modeling clearly outperformed OLS regression algorithm for the prediction of monthly PM2.5 concentrations. Deploying low-cost air quality sensors in concert with implementation of data quality control measures, can help address the critical need for expanding and improving air quality monitoring in resource-constrained settings of sub-Saharan Africa. These low-cost sensors, in conjunction with non-parametric ML algorithms, may provide a rapid path forward for PM2.5 exposure assessment and to spur air pollution epidemiology research in the region.

A time series analysis of the ecologic relationship between acute and intermediate PM2.5 exposure duration on neonatal intensive care unit admissions in Florida.

Admissions of newborn infants into Neonatal Intensive Care Units (NICU) has increased in the US over the last decade yet the role of environmental exposures as a risk factor for NICU admissions is under studied. Our study aims to determine the ecologic association between acute and intermediate ambient PM2.5 exposure durations and rates of NICU admissions, and to explore whether this association differs by area-level social stressors and meteorological factors. We conducted an ecologic time-series analysis of singleton neonates (N = 1,027,797) born in Florida hospitals between December 26, 2011 to April 30, 2019. We used electronic medical records (EMRs) in the OneFlorida Data Trust and included infants with a ZIP code in a Metropolitan Statistical Areas (MSA) and excluded extreme preterm births (<24wks gestation). The study outcome is the number of daily NICU admission at 28 days old or younger for each ZIP code in the study area. The exposures of interest are average same day, 1- and 2-day lags, and 1-3 weeks ambient PM2.5 concentration at the ZIP code-level estimated using inverse distance weighting (IDW) for each day of the study period. We used a zero-inflated Poisson regression mixed effects models to estimate adjusted associations between acute and intermediate PM2.5 exposure durations and NICU admissions rates. NICU admissions rates increased over time during the study period. Ambient 7-day average PM2.5 concentrations was significantly associated with incidence of NICU admissions, with an interquartile range (IQR = 2.37 µg/m3) increase associated with a 1.4% (95% CI: 0.4%, 2.4%) higher adjusted incidence of daily NICU admissions. No other exposure duration metrics showed a significant association with daily NICU admission rates. The magnitude of the association between PM2.5 7-day average concentrations with NICU admissions was significantly (p < 0.05) higher among ZIP codes with higher proportions of non-Hispanic Blacks, ZIP codes with household incomes in the lowest quartile, and on days with higher relative humidity. Our data shows a positive relationship between acute (7-day average) PM2.5 concentrations and daily NICU admissions in Metropolitan Statistical Areas of Florida. The observed associations were stronger in socioeconomically disadvantaged areas, areas with higher proportions with non-Hispanic Blacks, and on days with higher relative humidity. Further research is warranted to study other air pollutants and multipollutant effects and identify health conditions that are driving these associations with NICU admissions.

The Effects of Air Pollution on COVID-19 Related Mortality in Northern Italy.

Long-term exposure to ambient air pollutant concentrations is known to cause chronic lung inflammation, a condition that may promote increased severity of COVID-19 syndrome caused by the novel coronavirus (SARS-CoV-2). In this paper, we empirically investigate the ecologic association between long-term concentrations of area-level fine particulate matter (PM2.5) and excess deaths in the first quarter of 2020 in municipalities of Northern Italy. The study accounts for potentially spatial confounding factors related to urbanization that may have influenced the spreading of SARS-CoV-2 and related COVID-19 mortality. Our epidemiological analysis uses geographical information (e.g., municipalities) and negative binomial regression to assess whether both ambient PM2.5 concentration and excess mortality have a similar spatial distribution. Our analysis suggests a positive association of ambient PM2.5 concentration on excess mortality in Northern Italy related to the COVID-19 epidemic. Our estimates suggest that a one-unit increase in PM2.5 concentration (µg/m3) is associated with a 9% (95% confidence interval: 6-12%) increase in COVID-19 related mortality.

Metal-Organic Framework Nanoparticle-Assisted Cryopreservation of Red Blood Cells.

The development of hybrid nanomaterials mimicking antifreeze proteins that can modulate/inhibit the growth of ice crystals for cell/tissue cryopreservation has attracted increasing interests. Herein, we describe the first utilization of zirconium (Zr)-based metal-organic framework (MOF) nanoparticles (NPs) with well-defined surface chemistries for the cryopreservation of red blood cells (RBCs) without the need of any (toxic) organic solvents. Distinguishing features of this cryoprotective approach include the exceptional water stability, low hemolytic activity, and the long periodic arrangement of organic linkers on the surface of MOF NPs, which provide a precise spacing of hydrogen donors to recognize and match the ice crystal planes. Five kinds of Zr-based MOF NPs, with different pore size, surface chemistry, and framework topologies, were used for the cryoprotection of RBCs. A "splat" assay confirmed that MOF NPs not only exhibited ice recrystallization inhibition activities but also acted as a "catalyst" to accelerate the melting of ice crystals. The human RBC cryopreservation tests displayed RBC recoveries of up to ∼40%, which is higher than that obtained via commonly used hydroxyethyl starch polymers. This cryopreservation approach will inspire the design and utilization of MOF-derived nanoarchitectures for the effective cryopreservation of various cell types as well as tissue samples.

Synthesis and Characterization of Structurally Diverse Alkaline-Earth Salen Compounds for Subterranean Fluid Flow Tracking.

A family of magnesium and calcium salen-derivatives was synthesized and characterized for use as subterranean fluid flow monitors. For the Mg complexes, di- n-butyl magnesium ([Mg(Bu n)2]) was reacted with N, N'-ethylene bis(salicylideneimine) (H2-salen), N, N'-bis(salicylidene)-1,2-phenylenediamine (H2-saloPh), N, N'-bis(3,5-di- t-butylsalicylidene)-ethylenediamine (H2-salo-Bu t), or N, N'-bis(3,5-di- t-butylsalicylidene)-1,2-phenylenediamine (H2-saloPh-Bu t), and the products were identified by single-crystal X-ray diffraction as [(κ3-(O,N,N'),µ-(O')saloPh)(µ-(O),(κ2-(N,N'),µ-(O')saloPh)2(µ-(O),κ3-(N,N',O')saloPh')Mg4]·2tol (1·2tol; saloPh' = an alkyl-modified saloPh derivative generated in situ), [(κ4-(O,N,N',O')saloPh)Mg(py)2]·py (2·py), [(κ4-(O,N,N',O')salo-Bu t)Mg(py)2] (3), [(κ4-(O,N,N',O')saloPh-Bu t)Mg(py)2]·tol (4·tol), and [(κ3-(O,N,N'),µ-(O')saloPh-Bu t)Mg]2 (5), where tol = toluene; py = pyridine. For the Ca species, a calcium amide was independently reacted with H2-salo-Bu t and H2-saloPh-Bu t to generate the crystallographcially characterized compounds: [(κ4-(O,N,N',O')salo-Bu t)Ca(py)3] (6), [(κ4-(O,N,N',O')saloPh-Bu t)Ca(py)3]·py (7·py). The bulk powders of these compounds were further characterized by a number of analytical tools, where 2-7 were found to be distinguishable by Fourier transform infrared and resonance Raman spectroscopies. Structural properties obtained from quantum calculations of gas-phase analogues are in good agreement with the single-crystal results. The potential utility of these compounds as taggants for monitoring subterranean fluid flows was demonstrated through a series of experiments to evaluate their stability to high temperature and pressure, interaction with mineral surfaces, and elution behavior from a loaded proppant pack.

Determinants of Exposure to Pyrethroid Insecticides in the VHEMBE Cohort, South Africa.

Exposure to pyrethroid insecticides has been linked to adverse health effects, and can originate from several sources, including indoor residual spraying (IRS) for malaria control, home pest control, food contamination, and occupational exposure. We aimed to explore the determinants of urinary pyrethroid metabolite concentrations in a rural population with high pesticide use. The Venda Health Examination of Mothers, Babies and their Environment (VHEMBE) is a birth cohort of 752 mother-child pairs in Limpopo, South Africa. We measured pyrethroid metabolites in maternal urine and collected information on several factors possibly related to pesticide exposure, including IRS, home pesticide use, and maternal factors (e.g., dietary habits and body composition). We performed statistical analysis using both conventional bivariate regressions and Bayesian variable selection methods. Urinary pyrethroid metabolites are consistently associated with pesticide factors around homes, including pesticide application in yards and food stocks, and IRS in the home during pregnancy, while more distant factors such as village spraying are not. High fat intake is associated with higher metabolite concentrations, and women from homes drawing water from wells or springs had marginally higher levels. Home pesticide use is the most consistent correlate of pyrethroid metabolite concentrations, but IRS, dietary habits, and household water source may also be important exposure determinants.

Part 3. Modeling of Multipollutant Profiles and Spatially Varying Health Effects with Applications to Indicators of Adverse Birth Outcomes.

The highly intercorrelated nature of air pollutants makes it difficult to examine their combined effects on health. As such, epidemiological studies have traditionally focused on single-pollutant models that use regression-based techniques to examine the marginal association between a pollutant and a health outcome. These relatively simple, additive models are useful for discerning the effect of a single pollutant on a health outcome with all other pollutants held to fixed values. However, pollutants occur in complex mixtures consisting of highly correlated combinations of individual exposures. For example, evidence for synergy among pollutants in causing health effects has been recently reviewed by Mauderly and Samet (2009). Also, studies cited in the Ozone Criteria Document (U.S. Environmental Protection Agency [U.S. EPA*] 2006) confirmed that synergisms between ozone and other pollutants have been demonstrated in laboratory studies involving humans and animals. Thus, the highly correlated nature of air pollution exposures makes marginal, single-pollutant models inadequate. This issue was raised in a report by the National Research Council (NRC 2004), which called for a multipollutant approach to air quality management. Here we present and apply a series of statistical approaches that treat patterns of covariates as a whole unit, stochastically grouping pollutant patterns into clusters and then using these cluster assignments as random effects in a regression model. Using this approach, the effect of a multipollutant pattern, or profile, is determined in a manner that takes into account the uncertainty in the clustering process. The models are set in a Bayesian framework, and in general, Markov chain Monte Carlo (MCMC) techniques (Gilks et al. 1998). For interpretation purposes, a best clustering is derived, and the uncertainty related to this best clustering is determined by utilizing model averaging techniques, in a manner such that consistent clustering obtained by the estimation process generally yields smaller standard errors while inconsistent clustering is generally associated with larger errors. These multivariate methods are applied to a range of different problems related to air pollution exposures, namely an association of multipollutant profiles with indicators of poverty and to an assessment of the association between measures of various air pollutants, patterns of socioeconomic status (SES), and birth outcomes. All of these studies involve an examination of regional-level exposures, at the census tract (CT) and census block group (CBG) levels, and individual-level outcomes throughout Los Angeles (LA) County. Results indicate that effects of pollutants vary spatially and vary in a complex interconnected manner that cannot be discerned using standard additive line ar models. Results obtaine d from these studies can be used to efficiently use limited resources to inform policies in targeting are as where air pollution reductions result in maximum health benefits.

Modeling spatial effects of PM(2.5) on term low birth weight in Los Angeles County.

Air pollution epidemiological studies suggest that elevated exposure to fine particulate matter (PM2.5) is associated with higher prevalence of term low birth weight (TLBW). Previous studies have generally assumed the exposure-response of PM2.5 on TLBW to be the same throughout a large geographical area. Health effects related to PM2.5 exposures, however, may not be uniformly distributed spatially, creating a need for studies that explicitly investigate the spatial distribution of the exposure-response relationship between individual-level exposure to PM2.5 and TLBW. Here, we examine the overall and spatially varying exposure-response relationship between PM2.5 and TLBW throughout urban Los Angeles (LA) County, California. We estimated PM2.5 from a combination of land use regression (LUR), aerosol optical depth from remote sensing, and atmospheric modeling techniques. Exposures were assigned to LA County individual pregnancies identified from electronic birth certificates between the years 1995-2006 (N=1,359,284) provided by the California Department of Public Health. We used a single pollutant multivariate logistic regression model, with multilevel spatially structured and unstructured random effects set in a Bayesian framework to estimate global and spatially varying pollutant effects on TLBW at the census tract level. Overall, increased PM2.5 level was associated with higher prevalence of TLBW county-wide. The spatial random effects model, however, demonstrated that the exposure-response for PM2.5 and TLBW was not uniform across urban LA County. Rather, the magnitude and certainty of the exposure-response estimates for PM2.5 on log odds of TLBW were greatest in the urban core of Central and Southern LA County census tracts. These results suggest that the effects may be spatially patterned, and that simply estimating global pollutant effects obscures disparities suggested by spatial patterns of effects. Studies that incorporate spatial multilevel modeling with random coefficients allow us to identify areas where air pollutant effects on adverse birth outcomes may be most severe and policies to further reduce air pollution might be most effective.

A cross sectional analysis of behaviors related to operating gas stoves and pneumonia in U.S. children under the age of 5.

BACKGROUND: Poorly ventilated combustion stoves and pollutants emitted from combustion stoves increase the risk of acute lower respiratory illnesses (ALRI) in children living in developing countries but few studies have examined these issues in developed countries. Our objective is to investigate behaviors related to gas stove use, namely using them for heat and without ventilation, on the odds of pneumonia and cough in U.S. children. METHODS: The National Health and Nutrition Examination Survey (1988-1994) was used to identify children < 5 years who lived in homes with a gas stove and whose parents provided information on their behaviors when operating their gas stoves and data on pneumonia (N = 3,289) and cough (N = 3,127). Multivariate logistic regression models were used to examine the association between each respiratory outcome and using a gas stove for heat or without ventilation, as well as, the joint effect of both behaviors. RESULTS: The adjusted odds of parental-reported pneumonia (adjusted odds ratio [aOR] = 2.08, 95% confidence interval [CI]: 1.08, 4.03) and cough (aOR = 1.66, 95% CI: 1.14, 2.43) were higher among children who lived in homes where gas stoves were used for heat compared to those who lived in homes where gas stoves were only used for cooking. The odds of pneumonia (aOR = 1.76, 95% CI: 1.04, 2.98), but not cough (aOR = 1.23, 95% CI: 0.87, 1.75), was higher among those children whose parents did not report using ventilation when operating gas stoves compared to those who did use ventilation. When considering the joint association of both stove operating conditions, only children whose parents reported using gas stoves for heat without ventilation had significantly higher odds of pneumonia (aOR = 3.06, 95% CI: 1.32, 7.09) and coughing (aOR = 2.07, 95% CI: 1.29, 3.30) after adjusting for other risk factors. CONCLUSIONS: Using gas stoves for heat without ventilation was associated with higher odds of pneumonia and cough among U.S. children less than five years old who live in homes with a gas stove. More research is needed to determine if emissions from gas stoves ventilation infrastructure, or modifiable behaviors contribute to respiratory infections in children.