Household Air Pollution and Child Lung Function: The Ghana Randomized Air Pollution and Health Study.

Rationale: The impact of a household air pollution (HAP) stove intervention on child lung function has been poorly described. Objectives: To assess the effect of a HAP stove intervention for infants prenatally to age 1 on, and exposure-response associations with, lung function at child age 4. Methods: The Ghana Randomized Air Pollution and Health Study randomized pregnant women to liquefied petroleum gas (LPG), improved biomass, or open-fire (control) stove conditions through child age 1. We quantified HAP exposure by repeated maternal and child personal carbon monoxide (CO) exposure measurements. Children performed oscillometry, an effort-independent lung function measurement, at age 4. We examined associations between Ghana Randomized Air Pollution and Health Study stove assignment and prenatal and infant CO measurements and oscillometry using generalized linear regression models. We used reverse distributed lag models to examine time-varying associations between prenatal CO and oscillometry. Measurements and Main Results: The primary oscillometry measure was reactance at 5 Hz, X5, a measure of elastic and inertial lung properties. Secondary measures included total, large airway, and small airway resistance at 5 Hz, 20 Hz, and the difference in resistance at 5 Hz and 20 Hz (R5, R20, and R5-20, respectively); area of reactance (AX); and resonant frequency. Of the 683 children who attended the lung function visit, 567 (83%) performed acceptable oscillometry. A total of 221, 106, and 240 children were from the LPG, improved biomass, and control arms, respectively. Compared with control, the improved biomass stove condition was associated with lower reactance at 5 Hz (X5 z-score: ß = -0.25; 95% confidence interval [CI] = -0.39, -0.11), higher large airway resistance (R20 z-score: ß = 0.34; 95% CI = 0.23, 0.44), and higher AX (AX z-score: ß = 0.16; 95% CI = 0.06, 0.26), which is suggestive of overall worse lung function. The LPG stove condition was associated with higher X5 (X5 score: ß = 0.16; 95% CI = 0.01, 0.31) and lower small airway resistance (R5-20 z-score: ß = -0.15; 95% CI = -0.30, 0.0), which is suggestive of better small airway function. Higher average prenatal CO exposure was associated with higher R5 and R20, and distributed lag models identified sensitive windows of exposure between CO and X5, R5, R20, and R5-20. Conclusions: These data support the importance of prenatal HAP exposure on child lung function. Clinical trial registered with www.clinicaltrials.gov (NCT01335490).

Association between malaria and household air pollution interventions in a predominantly rural area of Ghana.

BACKGROUND: Though anecdotal evidence suggests that smoke from HAP has a repellent effect on mosquitoes, very little work has been done to assess the effect of biomass smoke on malaria infection. The study, therefore, sought to investigate the hypothesis that interventions to reduce household biomass smoke may have an unintended consequence of increasing placental malaria or increase malaria infection in the first year of life. METHODS: This provides evidence from a randomized controlled trial among 1414 maternal-infant pairs in the Kintampo North and Kintampo South administrative areas of Ghana. Logistic regression was used to assess the association between study intervention assignment (LPG, Biolite or control) and placental malaria. Finally, an extended Cox model was used to assess the association between study interventions and all episodes of malaria parasitaemia in the first year of infant's life. RESULTS: The prevalence of placental malaria was 24.6%. Out of this, 20.8% were acute infections, 18.7% chronic infections and 60.5% past infections. The study found no statistical significant association between the study interventions and all types of placental malaria (OR = 0.88; 95% CI 0.59-1.30). Of the 1165 infants, 44.6% experienced at least one episode of malaria parasitaemia in the first year of life. The incidence of first and/or only episode of malaria parasitaemia was however found to be similar among the study arms. CONCLUSION: The findings suggest that cookstove interventions for pregnant women and infants, when combined with additional malaria prevention strategies, do not lead to an increased risk of malaria among pregnant women and infants.

Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM2.5 Monitoring in Accra, Ghana.

Particulate matter air pollution is a leading cause of global mortality, particularly in Asia and Africa. Addressing the high and wide-ranging air pollution levels requires ambient monitoring, but many low- and middle-income countries (LMICs) remain scarcely monitored. To address these data gaps, recent studies have utilized low-cost sensors. These sensors have varied performance, and little literature exists about sensor intercomparison in Africa. By colocating 2 QuantAQ Modulair-PM, 2 PurpleAir PA-II SD, and 16 Clarity Node-S Generation II monitors with a reference-grade Teledyne monitor in Accra, Ghana, we present the first intercomparisons of different brands of low-cost sensors in Africa, demonstrating that each type of low-cost sensor PM2.5 is strongly correlated with reference PM2.5, but biased high for ambient mixture of sources found in Accra. When compared to a reference monitor, the QuantAQ Modulair-PM has the lowest mean absolute error at 3.04 µg/m3, followed by PurpleAir PA-II (4.54 µg/m3) and Clarity Node-S (13.68 µg/m3). We also compare the usage of 4 statistical or machine learning models (Multiple Linear Regression, Random Forest, Gaussian Mixture Regression, and XGBoost) to correct low-cost sensors data, and find that XGBoost performs the best in testing (R2: 0.97, 0.94, 0.96; mean absolute error: 0.56, 0.80, and 0.68 µg/m3 for PurpleAir PA-II, Clarity Node-S, and Modulair-PM, respectively), but tree-based models do not perform well when correcting data outside the range of the colocation training. Therefore, we used Gaussian Mixture Regression to correct data from the network of 17 Clarity Node-S monitors deployed around Accra, Ghana, from 2018 to 2021. We find that the network daily average PM2.5 concentration in Accra is 23.4 µg/m3, which is 1.6 times the World Health Organization Daily PM2.5 guideline of 15 µg/m3. While this level is lower than those seen in some larger African cities (such as Kinshasa, Democratic Republic of the Congo), mitigation strategies should be developed soon to prevent further impairment to air quality as Accra, and Ghana as a whole, rapidly grow.

Cross-sectional associations between drinking water arsenic and urinary inorganic arsenic in the United States: NHANES 2003-2014.

BACKGROUND: Inorganic arsenic is a potent carcinogen and toxicant associated with numerous adverse health outcomes. The contribution of drinking water from private wells and regulated community water systems (CWSs) to total inorganic arsenic exposure is not clear. OBJECTIVES: To determine the association between drinking water arsenic estimates and urinary arsenic concentrations in the 2003-2014 National Health and Nutrition Examination Survey (NHANES). METHODS: We evaluated 11,088 participants from the 2003-2014 NHANES cycles. For each participant, we assigned private well and CWS arsenic levels according to county of residence using estimates previously derived by the U.S. Environmental Protection Agency and U.S. Geological Survey. We used recalibrated urinary dimethylarsinate (rDMA) to reflect the internal dose of estimated water arsenic by applying a previously validated, residual-based method that removes the contribution of dietary arsenic sources. We compared the adjusted geometric mean ratios and corresponding percent change of urinary rDMA across tertiles of private well and CWS arsenic levels, with the lowest tertile as the reference. Comparisons were made overall and stratified by census region and race/ethnicity. RESULTS: Overall, the geometric mean of urinary rDMA was 2.52 (2.30, 2.77) µg/L among private well users and 2.64 (2.57, 2.72) µg/L among CWS users. Urinary rDMA was highest among participants in the West and South, and among Mexican American, Other Hispanic, and Non-Hispanic Other participants. Urinary rDMA levels were 25% (95% confidence interval (CI): 17-34%) and 20% (95% CI: 12-29%) higher comparing the highest to the lowest tertile of CWS and private well arsenic, respectively. The strongest associations between water arsenic and urinary rDMA were observed among participants in the South, West, and among Mexican American and Non-Hispanic White and Black participants. DISCUSSION: Both private wells and regulated CWSs are associated with inorganic arsenic internal dose as reflected in urine in the general U.S.

Fine particulate matter composition in American Indian vs. Non-American Indian communities.

BACKGROUND: Fine particulate matter (PM2.5) exposure is a known risk factor for numerous adverse health outcomes, with varying estimates of component-specific effects. Populations with compromised health conditions such as diabetes can be more sensitive to the health impacts of air pollution exposure. Recent trends in PM2.5 in primarily American Indian- (AI-) populated areas examined in previous work declined more gradually compared to the declines observed in the rest of the US. To further investigate components contributing to these findings, we compared trends in concentrations of six PM2.5 components in AI- vs. non-AI-populated counties over time (2000-2017) in the contiguous US. METHODS: We implemented component-specific linear mixed models to estimate differences in annual county-level concentrations of sulfate, nitrate, ammonium, organic matter, black carbon, and mineral dust from well-validated surface PM2.5 models in AI- vs. non-AI-populated counties, using a multi-criteria approach to classify counties as AI- or non-AI-populated. Models adjusted for population density and median household income. We included interaction terms with calendar year to estimate whether concentration differences in AI- vs. non-AI-populated counties varied over time. RESULTS: Our final analysis included 3108 counties, with 199 (6.4%) classified as AI-populated. On average across the study period, adjusted concentrations of all six PM2.5 components in AI-populated counties were significantly lower than in non-AI-populated counties. However, component-specific levels in AI- vs. non-AI-populated counties varied over time: sulfate and ammonium levels were significantly lower in AI- vs. non-AI-populated counties before 2011 but higher after 2011 and nitrate levels were consistently lower in AI-populated counties. CONCLUSIONS: This study indicates time trend differences of specific components by AI-populated county type. Notably, decreases in sulfate and ammonium may contribute to steeper declines in total PM2.5 in non-AI vs. AI-populated counties. These findings provide potential directives for additional monitoring and regulations of key emissions sources impacting tribal lands.

Long-Term Air Pollution Exposure and COVID-19 Mortality: A Patient-Level Analysis from New York City.

Rationale: Risk factors for coronavirus disease (COVID-19) mortality may include environmental exposures such as air pollution. Objectives: To determine whether, among adults hospitalized with PCR-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), long-term air pollution exposure is associated with the risk of mortality, ICU admission, or intubation. Methods: We performed a retrospective analysis of SARS-CoV-2 PCR-positive patients admitted to seven New York City hospitals from March 8, 2020, to August 30, 2020. The primary outcome was mortality; secondary outcomes were ICU admission and intubation. We estimated the annual average fine particulate matter (particulate matter ⩽2.5 µm in aerodynamic diameter [PM2.5]), nitrogen dioxide (NO2), and black carbon (BC) concentrations at patients' residential address. We employed double robust Poisson regression to analyze associations between the annual average PM2.5, NO2, and BC exposure level and COVID-19 outcomes, adjusting for age, sex, race or ethnicity, hospital, insurance, and the time from the onset of the pandemic. Results: Among the 6,542 patients, 41% were female and the median age was 65 (interquartile range, 53-77) years. Over 50% self-identified as a person of color (n = 1,687 [26%] Hispanic patients; n = 1,659 [25%] Black patients). Air pollution exposure levels were generally low. Overall, 31% (n = 2,044) of the cohort died, 19% (n = 1,237) were admitted to the ICU, and 16% (n = 1,051) were intubated. In multivariable models, a higher level of long-term exposure to PM2.5 was associated with an increased risk of mortality (risk ratio, 1.11 [95% confidence interval, 1.02-1.21] per 1-µg/m3 increase in PM2.5) and ICU admission (risk ratio, 1.13 [95% confidence interval, 1.00-1.28] per 1-µg/m3 increase in PM2.5). In multivariable models, neither NO2 nor BC exposure was associated with COVID-19 mortality, ICU admission, or intubation. Conclusions: Among patients hospitalized with COVID-19, a higher long-term PM2.5 exposure level was associated with an increased risk of mortality and ICU admission.

Air Pollution in American Indian Versus Non-American Indian Communities, 2000-2018.

Objectives. To compare fine particulate matter (PM2.5) concentrations in American Indian (AI)-populated with those in non-AI-populated counties over time (2000-2018) in the contiguous United States. Methods. We used a multicriteria approach to classify counties as AI- or non--AI-populated. We ran linear mixed effects models to estimate the difference in countywide annual PM2.5 concentrations from well-validated prediction models and monitoring sites (modeled and measured PM2.5, respectively) in AI- versus non-AI-populated counties. Results. On average, adjusted modeled PM2.5 concentrations in AI-populated counties were 0.38 micrograms per cubic meter (95% confidence interval [CI] = 0.23, 0.54) lower than in non-AI-populated counties. However, this difference was not constant over time: in 2000, modeled concentrations in AI-populated counties were 1.46 micrograms per cubic meter (95% CI = 1.25, 1.68) lower, and by 2018, they were 0.66 micrograms per cubic meter (95% CI = 0.45, 0.87) higher. Over the study period, adjusted modeled PM2.5 mean concentrations decreased by 2.13 micrograms per cubic meter in AI-populated counties versus 4.26 micrograms per cubic meter in non-AI-populated counties. Results were similar for measured PM2.5. Conclusions. This study highlights disparities in PM2.5 trends between AI- and non-AI-populated counties over time, underscoring the need to strengthen air pollution regulations and prevention implementation in tribal territories and areas where AI populations live. (Am J Public Health. 2022;112(4): 615-623. https://doi.org/10.2105/AJPH.2021.306650).

Assessing Neighborhood-scale Traffic from Crowd-sensed Traffic Data: Findings from an Environmental Justice Community in New York City.

Background: The waterfront in the South Bronx in New York City is used industrially and harbors the Harlem River Yards (HRY). The HRY borders an environmental justice area, which includes a mixed-use area that is separated from a densely populated residential area by interstates. Recently, development of the HRY has expanded including the 2018 opening of a large online store warehouse. Objective: The goal of this study was to evaluate trends in traffic congestion nearby the HRY between 2017 to 2019. Methods: We analyzed one-hourly time series of crowd-sensed traffic congestion maps, both at the neighborhood scale and the road stretch level. Traffic radar measurements at two locations did not indicate bias in the crowd-sensed data over the study period, i.e., changed mappings between vehicle speed and the reported congestion. Results: In the mixed-use areas, traffic congestion increased significantly during all hours of the day, with greatest increases at night and in the morning. Congestion increased close to the entrances of the HRY and along routes used by pedestrians and bicyclists to access a nearby recreational area. In the residential area, congestion increased significantly from midnight to morning and was unchanged for the remainder of the day. On the interstates, congestion decreased during the daytime but increased at night. Conclusions: Neighborhood-scale traffic congestion increased in mixed-use and residential areas in an environmental justice community. Our methods can be applied globally as long as crowd-sensed traffic data can be acquired. The data enable communities to advocate for mitigating measures.

The association between short-term residential black carbon concentration on blood pressure in a general population sample.

BACKGROUND: Exposure to black carbon indoors may be associated with blood pressure; however, evidence is limited to vulnerable subpopulations and highly exposed individuals. Our objective was to explore the relationship between indoor black carbon at various exposure windows on resting blood pressure in a general population sample. METHODS: Black carbon was measured in the home of 76 individuals aged 10-71 in New Orleans, Louisiana. Exposure was measured every 1 minute for up to 120 hours using an AE51 microaethalometer. Systolic blood pressure and diastolic blood pressure were measured at the conclusion of exposure monitoring. RESULTS: In adjusted models, at all exposure windows, increasing black carbon was associated with increased systolic blood pressure. The period 0-72 hours prior to blood pressure measurement showed the strongest effect; a 1 µg/m3 increase in black carbon was associated with a 7.55 mm Hg (P = .02) increase in systolic blood pressure. The relationship was stronger in participants reporting doctor-diagnosed hypertension (ß = 6.47 vs ß = 3.27). Black carbon was not associated with diastolic blood pressure. CONCLUSION: Increasing black carbon concentration indoors is positively associated with increasing systolic blood pressure with the most relevant exposure window being 0-72 hours prior to blood pressure measurement. Individuals with hypertension may be a more susceptible population.

Toxicity of particles emitted by fireworks.

BACKGROUND: Particle matter (PM) has been associated with increased morbidity and mortality rates across the world. This study was designed to test the hypotheses that pyrotechnic firework displays introduce significant amounts of toxic metals into the atmosphere and are hazardous to human health. Size-selective emissions from 10 different fireworks displays were collected during particle generation in a dynamic, stainless steel chamber and tested for toxicity in cells. A subset of 2 particle types were tested in vivo in mice. At doses that did not produce cytotoxicity in an LDH assay, in vitro reactive oxygen species (ROS) formation was measured in bronchial epithelial airway (BEAS-2B) and human pulmonary microvascular endothelial (HPMEC-ST1.6R) cell lines treated with size-fractionated particles from the emissions of fireworks. RESULTS: Significant increases in ROS, in both cell types, were dependent upon the type of firework but not particle size. The in vitro ROS activity was correlated with lung inflammation produced in groups of mice treated by oropharyngeal aspiration with 0, 50, or 100 µg fireworks PM10/mouse. Trace metal analyses of the PM10 samples showed significant differences in metal content among fireworks type. Interestingly, the PM10 sample for the fireworks type producing the greatest in vitro ROS response in BEAS-2B cells contained ~ 40,000 and ~ 12,000 ppm of lead and copper, respectively. This sample also produced the greatest inflammatory response (i.e., increased neutrophils in bronchoalveolar lavage fluid) in mice. CONCLUSIONS: These findings demonstrate that pyrotechnic display particles can produce adverse effects in mammalian cells and lungs, thus suggesting that further research is needed to expand our understanding of the contribution of metal content to the adverse health effects of fireworks particles. This information will lead to the manufacture of safer fireworks.

Characterizing peak exposure of secondhand smoke using a real-time PM2.5 monitor.

Although short-duration elevated exposures (peak exposures) to pollutants may trigger adverse acute effects, epidemiological studies to understand their influence on different health effects are hampered by lack of methods for objectively identifying peaks. Secondhand smoke from cigarettes (SHS) in the residential environment can lead to peak exposures. The aim of this study was to explore whether peaks in continuous PM2.5 data can indicate SHS exposure. A total of 41 children (21 with and 20 without SHS exposure based on self-report) from 28 families in New York City (NY, USA) were recruited. Both personal and residential continuous PM2.5 monitoring were performed for five consecutive days using MicroPEM sensors (RTI International, USA). A threshold detection method based on cumulative distribution function was developed to identify peaks. When children were home, the mean accumulated peak area (APA) for peak exposures was 297 ± 325 hour*µg/m3 for children from smoking families and six times that of the APA from non-smoking families (~50 ± 54 hour*µg/m3 ). Average PM2.5 mass concentrations for SHS exposed and unexposed children were 24 ± 15 µg/m3 and 15 ± 9 µg/m3 , respectively. The average SHS exposure duration represents ~5% of total exposure time, but ~13% of children's total PM2.5 exposure dose, equivalent to an additional 2.6 µg/m3 per day. This study demonstrated the feasibility of peak analysis for quantifying SHS exposure. The developed method can be adopted more widely to support epidemiology studies on impacts of short-term exposures.

Application of land use regression to assess exposure and identify potential sources in PM2.5, BC, NO2 concentrations.

BACKGROUND: Understanding spatial variation of air pollution is critical for public health assessments. Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations. However, they have limited application in China due to the lack of spatially resolved data. OBJECTIVE: Based on purpose-designed monitoring networks, this study developed LUR models to predict fine particulate matter (PM2.5), black carbon (BC) and nitrogen dioxide (NO2) exposure and to identify their potential outdoor-origin sources within an urban/rural region, using Taizhou, China as a case study. METHOD: Two one-week integrated samples were collected at 30 PM2.5 (BC) sites and 45 NO2 sites in each two distinct seasons. Samples of 1/3 of the sites were collected simultaneously. Annual adjusted average was calculated and regressed against pre-selected GIS-derived predictor variables in a multivariate regression model. RESULTS: LUR explained 65% of the spatial variability in PM2.5, 78% in BC and 73% in NO2. Mean (±Standard Deviation) of predicted PM2.5, BC and NO2 exposure levels were 48.3 (±6.3) µg/m3, 7.5 (±1.4) µg/m3 and 27.3 (±8.2) µg/m3, respectively. Weak spatial corrections (Pearson r = 0.05-0.25) among three pollutants were observed, indicating the presence of different sources. Regression results showed that PM2.5, BC and NO2 levels were positively associated with traffic variables. The former two also increased with farm land use; and higher NO2 levels were associated with larger industrial land use. The three pollutants were correlated with sources at a scale of ≤5 km and even smaller scales (100-700m) were found for BC and NO2. CONCLUSION: We concluded that based on a purpose-designed monitoring network, LUR model can be applied to predict PM2.5, NO2 and BC concentrations in urban/rural settings of China. Our findings highlighted important contributors to within-city heterogeneity in outdoor-generated exposure, and indicated traffic, industry and agriculture may significantly contribute to PM2.5, NO2 and BC concentrations.

Prenatal Household Air Pollution Is Associated with Impaired Infant Lung Function with Sex-Specific Effects. Evidence from GRAPHS, a Cluster Randomized Cookstove Intervention Trial.

RATIONALE: Approximately 2.8 billion people are exposed daily to household air pollution from polluting cookstoves. The effects of prenatal household air pollution on lung development are unknown. OBJECTIVES: To prospectively examine associations between prenatal household air pollution and infant lung function and pneumonia in rural Ghana. METHODS: Prenatal household air pollution exposure was indexed by serial maternal carbon monoxide personal exposure measurements. Using linear regression, we examined associations between average prenatal carbon monoxide and infant lung function at age 30 days, first in the entire cohort (n = 384) and then stratified by sex. Quasi-Poisson generalized additive models explored associations between infant lung function and pneumonia. MEASUREMENTS AND MAIN RESULTS: Multivariable linear regression models showed that average prenatal carbon monoxide exposure was associated with reduced time to peak tidal expiratory flow to expiratory time (ß = -0.004; P = 0.01), increased respiratory rate (ß = 0.28; P = 0.01), and increased minute ventilation (ß = 7.21; P = 0.05), considered separately, per 1 ppm increase in average prenatal carbon monoxide. Sex-stratified analyses suggested that girls were particularly vulnerable (time to peak tidal expiratory flow to expiratory time: ß = -0.003, P = 0.05; respiratory rate: ß = 0.36, P = 0.01; minute ventilation: ß = 11.25, P = 0.01; passive respiratory compliance normalized for body weight: ß = 0.005, P = 0.01). Increased respiratory rate at age 30 days was associated with increased risk for physician-assessed pneumonia (relative risk, 1.02; 95% confidence interval, 1.00-1.04) and severe pneumonia (relative risk, 1.04; 95% confidence interval, 1.00-1.08) in the first year of life. CONCLUSIONS: Increased prenatal household air pollution exposure is associated with impaired infant lung function. Altered infant lung function may increase risk for pneumonia in the first year of life. These findings have implications for future respiratory health. Clinical trial registered with www.clinicaltrials.gov (NCT 01335490).

Air pollution, urgent asthma medical visits and the modifying effect of neighborhood asthma prevalence.

BACKGROUND: Social and environmental stressors may modify associations between environmental pollutants and asthma symptoms. We examined if neighborhood asthma prevalence (higher: HAPN vs. lower: LAPN), a surrogate for underlying risk factors for asthma, modified the relationship between pollutants and urgent asthma visits. METHODS: Through zip code, home addresses were linked to New York City Community Air Survey's land use regression model for street-level, annual average nitrogen dioxide (NO2), particulate matter (PM2.5), elemental carbon (EC), summer average ozone (O3), winter average sulfur dioxide (SO2) concentrations. Poisson regression models were fit to estimate the association (prevalence ratio, PR) between pollutant exposures and seeking urgent asthma care. RESULTS: All pollutants, except O3 were higher in HAPN than LAPN (P < 0.01). Neighborhood asthma prevalence modified the relationship between pollutants and urgent asthma (P-interaction < 0.01, for NO2 and SO3). Associations between pollutants and urgent asthma were observed only in LAPN (NO2: PR = 1.38, P = 0.01; SO3: PR = 1.85, P = 0.04). No association was observed between pollutants and urgent asthma among children in HAPN (P > 0.05). CONCLUSIONS: Relationships between modeled street-level pollutants and urgent asthma were stronger in LAPN compared to HAPN. Social stressors that may be more prevalent in HAPN than LAPN, could play a greater role in asthma exacerbations in HAPN vs. pollutant exposure alone.

Prenatal maternal stress and birth outcomes in rural Ghana: sex-specific associations.

BACKGROUND: In developed countries, prenatal maternal stress has been associated with poor fetal growth, however this has not been evaluated in rural sub-Saharan Africa. We evaluated the effect of prenatal maternal stress on fetal growth and birth outcomes in rural Ghana. METHODS: Leveraging a prospective, rural Ghanaian birth cohort, we ascertained prenatal maternal negative life events, categorized scores as 0-2 (low stress; referent), 3-5 (moderate), and > 5 (high) among 353 pregnant women in the Kintampo North Municipality and Kintampo South District located within the middle belt of Ghana. We employed linear regression to determine associations between prenatal maternal stress and infant birth weight, head circumference, and length. We additionally examined associations between prenatal maternal stress and adverse birth outcome, including low birth weight, small for gestational age, or stillbirth. Effect modification by infant sex was examined. RESULTS: In all children, high prenatal maternal stress was associated with reduced birth length (ß = - 0.91, p = 0.04; p-value for trend = 0.04). Among girls, moderate and high prenatal maternal stress was associated with reduced birth weight (ß = - 0.16, p = 0.02; ß = - 0.18, p = 0.04 respectively; p-value for trend = 0.04) and head circumference (ß = - 0.66, p = 0.05; ß = - 1.02, p = 0.01 respectively; p-value for trend = 0.01). In girls, high prenatal stress increased odds of any adverse birth outcome (OR 2.41, 95% CI 1.01-5.75; p for interaction = 0.04). Sex-specific analyses did not demonstrate significant effects in boys. CONCLUSIONS: All infants, but especially girls, were vulnerable to effects of prenatal maternal stress on birth outcomes. Understanding risk factors for impaired fetal growth may help develop preventative public health strategies. TRIAL REGISTRATION: NCT01335490 (prospective registration). Date of Registration: April 14, 2011. Status of Registration: Completed.

A custom-built low-cost chamber for exposing rodents to e-cigarette aerosol: practical considerations.

Objectives: To (1) design and build a low-cost exposure chamber system for whole-body exposure of rodents to electronic cigarette aerosol, (2) provide detailed instructions with particular focus on automated e-cigarette activation, and (3) develop a simple mathematical model for aerosol levels in the exposure chamber.Methods: We built the system with standard laboratory equipment and an open-source electronics platform (Arduino) for e-cigarette activation. Arduino is used to control a solenoid, which pushes the activation button of so-called "Mod" e-cigarettes, and a pump to move the aerosol from the mouthpiece of the e-cigarette into the chamber. For "Pods" and "Cigalikes," the solenoid is not used as they are activated by the vacuum created by the pump. Aerosol concentrations were measured with a light-scattering laser photometer.Results: The system allows varying the air exchange rate, monitoring aerosol levels, and programing arbitrary puff topography. Aerosol concentrations observed for different chamber operating conditions (puff time and period, e-cigarette power output, air exchange rate) were consistent with the mathematical model.Conclusions: Our low-cost exposure chamber can be used in animal experimental studies of the health effects of e-cigarettes. Our model allows estimating design parameters such as chamber volume, air exchange rate, and puff period.

Identification of Bicycling Periods Using the MicroPEM Personal Exposure Monitor.

Exposure assessment studies are the primary means for understanding links between exposure to chemical and physical agents and adverse health effects. Recently, researchers have proposed using wearable monitors during exposure assessment studies to obtain higher fidelity readings of exposures actually experienced by subjects. However, limited research has been conducted to link a wearer's actions to periods of exposure, a necessary step for estimating inhaled dosage. To aid researchers in these settings, we developed a machine learning model for identifying periods of bicycling activity using passively collected data from the RTI MicroPEM wearable exposure monitor, a lightweight device capable of continuously sampling both air pollution levels and accelerometry parameters. Our best performing model identifies biking activity with a mean leave-one-session-out (LOSO) cross-validation F1 score of 0.832 (unweighted) and 0.979 (weighted). Accelerometer derived features contributed greatly to the model performance, as well as temporal smoothing of the predicted activities. Additionally, we found competitive activity recognition can occur with even relatively low sampling rates, suggesting suitability for exposure assessment studies where continuous data collection for long periods (without recharge) are needed to capture realistic daily routines and exposures.

Model-Based Analysis of Arsenic Immobilization via Iron Mineral Transformation under Advective Flows.

Recent laboratory studies have demonstrated that coinjection of nitrate and Fe(II) (as ferrous sulfate) to As-bearing sediments can produce an Fe mineral assemblage containing magnetite capable of immobilizing advected As under a relatively wide range of aquifer conditions. This study combined laboratory findings with process-based numerical modeling approaches, to quantify the observed Fe mineral (trans)formation and concomitant As partitioning dynamics and to assess potential nitrate-Fe(II) remediation strategies for field implementation. The model development was guided by detailed solution and sediment data from our well-controlled column experiment. The modeling results demonstrated that the fate of As during the experiment was primarily driven by ferrihydrite formation and reductive transformation and that different site densities were identified for natural and neoformed ferrihydrite to explain the observations both before and after nitrate-Fe(II) injection. Our results also highlighted that when ferrihydrite was nearing depletion, As immobilization ultimately relied on the presence of magnetite. On the basis of the column model, field-scale predictive simulations were conducted to illustrate the feasibility of the nitrate-Fe(II) strategy for intercepting advected As from a plume. The predictive simulations, which suggested that long-term As immobilization was feasible, favored a scenario that maintains high dissolved Fe(II) concentration during injection periods and thereby converts ferrihydrite to magnetite.

Development of an approach to correcting MicroPEM baseline drift.

BACKGROUND: Fine particulate matter (PM2.5) is associated with various adverse health outcomes. The MicroPEM (RTI, NC), a miniaturized real-time portable particulate sensor with an integrated filter for collecting particles, has been widely used for personal PM2.5 exposure assessment. Five-day deployments were targeted on a total of 142 deployments (personal or residential) to obtain real-time PM2.5 levels from children living in New York City and Baltimore. Among these 142 deployments, 79 applied high-efficiency particulate air (HEPA) filters in the field at the beginning and end of each deployment to adjust the zero level of the nephelometer. However, unacceptable baseline drift was observed in a large fraction (> 40%) of acquisitions in this study even after HEPA correction. This drift issue has been observed in several other studies as well. The purpose of the present study is to develop an algorithm to correct the baseline drift in MicroPEM based on central site ambient data during inactive time periods. METHOD: A running baseline & gravimetric correction (RBGC) method was developed based on the comparison of MicroPEM readings during inactive periods to ambient PM2.5 levels provided by fixed monitoring sites and the gravimetric weight of PM2.5 collected on the MicroPEM filters. The results after RBGC correction were compared with those using HEPA approach and gravimetric correction alone. Seven pairs of duplicate acquisitions were used to validate the RBGC method. RESULTS: The percentages of acquisitions with baseline drift problems were 42%, 53% and 10% for raw, HEPA corrected, and RBGC corrected data, respectively. Pearson correlation analysis of duplicates showed an increase in the coefficient of determination from 0.75 for raw data to 0.97 after RBGC correction. In addition, the slope of the regression line increased from 0.60 for raw data to 1.00 after RBGC correction. CONCLUSIONS: The RBGC approach corrected the baseline drift issue associated with MicroPEM data. The algorithm developed has the potential for use with data generated from other types of PM sensors that contain a filter for weighing as well. In addition, this approach can be applied in many other regions, given widely available ambient PM data from monitoring networks, especially in urban areas.

Simultaneously Quantifying Ferrihydrite and Goethite in Natural Sediments Using the Method of Standard Additions with X-ray Absorption Spectroscopy.

The presence of ferrihydrite in sediments/soils is critical to the cycling of iron (Fe) and many other elements but difficult to quantify. Extended X-ray absorption fine structure (EXAFS) spectroscopy has been used to speciate Fe in the solid phase, but this method is thought to have difficulties in distinguishing ferrihydrite from goethite and other minerals. In this study, both conventional EXAFS linear combination fitting (LCF) and the method of standard-additions are applied to the same samples in attempt to quantify ferrihydrite and goethite more rigorously. Natural aquifer sediments from Bangladesh and the United States were spiked with known quantities of ferrihydrite, goethite and magnetite, and analyzed by EXAFS. Known mineral mixtures were also analyzed. Evaluations of EXAFS spectra of mineral references and EXAFS-LCF fits on various samples indicate that ferrihydrite and microcrystalline goethite can be distinguished and quantified by EXAFS-LCF but that the choice of mineral references is critical to yield consistent results. Conventional EXAFS-LCF and the method of standard-additions both identified appreciable amount of ferrihydrite in Bangladesh sediments that were obtained from a low-arsenic Pleistocene aquifer. Ferrihydrite was also independently detected by sequential extraction and 57Fe MÓ§ssbauer spectroscopy. These observations confirm the accuracy of conventional EXAFS-LCF and demonstrate that combining EXAFS with additions of reference materials provides a more robust means of quantifying short-range-ordered minerals in complex samples.