Kriging Models Predicting Atrazine Concentrations in Surface Water Draining Agricultural Watersheds.

The performance of kriging methods in predicting maximum m-day (m = 1, 7, 14, or 30) rolling averages of atrazine concentrations in 42 site-years of Midwest Corn Belt watersheds under two systematic sampling designs (sampling every 7 or 14 d) was examined. Daily atrazine monitoring data obtained from the Atrazine Ecological Monitoring Program in the Corn Belt region (2009-2014) were used in the evaluation. Both ordinary and universal kriging methods were considered, with the covariate for universal kriging derived from the deterministic Pesticide Root Zone Model (PRZM). For the maximum 1-d rolling averages, prediction did not differ among methods. For rolling averages of longer duration (m > 1), predictions obtained by linear interpolation on a logarithmic scale were better (up to 15% lower for 7-d sampling and 22% lower for 14-d sampling in terms of the relative root mean squared prediction error) than those obtained by linear interpolation on the original linear scale and also less variable. For kriging methods, empirical semivariograms of daily atrazine time series suggest a negligible noise process, supported by replicate analysis of selected field samples; piecewise linear semivariogram models were found to perform best for predicting sampled data. We demonstrate that kriging prediction intervals offer close to nominal coverage for unsampled values.

Flow-covariate prediction of stream pesticide concentrations.

Potential peak functions (e.g., maximum rolling averages over a given duration) of annual pesticide concentrations in the aquatic environment are important exposure parameters (or target quantities) for ecological risk assessments. These target quantities require accurate concentration estimates on nonsampled days in a monitoring program. We examined stream flow as a covariate via universal kriging to improve predictions of maximum m-day (m = 1, 7, 14, 30, 60) rolling averages and the 95th percentiles of atrazine concentration in streams where data were collected every 7 or 14 d. The universal kriging predictions were evaluated against the target quantities calculated directly from the daily (or near daily) measured atrazine concentration at 32 sites (89 site-yr) as part of the Atrazine Ecological Monitoring Program in the US corn belt region (2008-2013) and 4 sites (62 site-yr) in Ohio by the National Center for Water Quality Research (1993-2008). Because stream flow data are strongly skewed to the right, 3 transformations of the flow covariate were considered: log transformation, short-term flow anomaly, and normalized Box-Cox transformation. The normalized Box-Cox transformation resulted in predictions of the target quantities that were comparable to those obtained from log-linear interpolation (i.e., linear interpolation on the log scale) for 7-d sampling. However, the predictions appeared to be negatively affected by variability in regression coefficient estimates across different sample realizations of the concentration time series. Therefore, revised models incorporating seasonal covariates and partially or fully constrained regression parameters were investigated, and they were found to provide much improved predictions in comparison with those from log-linear interpolation for all rolling average measures. Environ Toxicol Chem 2018;37:260-273. © 2017 SETAC.

Evaluation of the use of bias factors with water monitoring data.

Aquatic exposure assessments using surface water quality monitoring data are often challenged by missing extreme concentrations if sampling frequency is less than daily. A bias factor method has been previously proposed to address this concern for peak concentrations, where a bias factor is a multiplicative quantity to upwardly adjust estimates so that the true value is exceeded 95% of the time. In other words, bias factors are statistically protective adjustments. We evaluate this method using a research data set of 69 near-daily sampled site-years from the Atrazine Ecological Monitoring Program, dividing the data set into 23 reference and 46 validation site-years. Bias factors calculated from the reference data set are used to evaluate the method using the validation set for 1) point estimation, 2) interval estimation, and 3) decision-making. Sampling designs are every 7, 14, 28, and 90 d; and target quantities of assessment interest are the 90th and 95th percentiles and maximum m-day rolling averages (m = 1, 7, 21, 60, 90). We find that bias factors are poor point estimators in comparison with alternative methods. For interval estimation, average coverage is less than nominal, with coverage at individual site-years sometimes very low. Positive correlation of bias factors and target quantities, where present, adversely affects method performance. For decision rules or screening, the method typically shows very low false-negative rates but at the cost of extremely high false-positive rates. Environ Toxicol Chem 2018;37:1864-1876. © 2018 SETAC.

Reanalysis of Reported Associations of Beryllium and Lung Cancer in a Large Occupational Cohort.

OBJECTIVE: The aim of this study was to verify and extend reported associations of beryllium exposure and lung cancer by reanalyzing data from a large occupational cohort at three beryllium processing plants. METHODS: We used standardization and Poisson regression to evaluate the effect of cumulative and maximum exposure, unlagged, and lagged 10 years, adjusting for plant, employment tenure, and date of hire. Exposure was modeled either categorically or continuously using splines. RESULTS: Categorical analyses displayed previously reported effect patterns, but not the spline analysis, which provided a more consistent picture of risk across all analyzed groups. CONCLUSIONS: We found modestly but monotonically increasing risk in the full cohort, by duration of tenure, and within most subgroups defined by plant and date of hire. Regression-based point-wise confidence bands, however, did not clearly separate risk for low versus high exposure groups.

Sri Lanka Pilot Study to Examine Respiratory Health Effects and Personal PM2.5 Exposures from Cooking Indoors.

A pilot study of indoor air pollution produced by biomass cookstoves was conducted in 53 homes in Sri Lanka to assess respiratory conditions associated with stove type ("Anagi" or "Traditional"), kitchen characteristics (e.g., presence of a chimney in the home, indoor cooking area), and concentrations of personal and indoor particulate matter less than 2.5 micrometers in diameter (PM2.5). Each primary cook reported respiratory conditions for herself (cough, phlegm, wheeze, or asthma) and for children (wheeze or asthma) living in her household. For cooks, the presence of at least one respiratory condition was significantly associated with 48-h log-transformed mean personal PM2.5 concentration (PR = 1.35; p < 0.001). The prevalence ratio (PR) was significantly elevated for cooks with one or more respiratory conditions if they cooked without a chimney (PR = 1.51, p = 0.025) and non-significantly elevated if they cooked in a separate but poorly ventilated building (PR = 1.51, p = 0.093). The PRs were significantly elevated for children with wheeze or asthma if a traditional stove was used (PR = 2.08, p = 0.014) or if the cooking area was not partitioned from the rest of the home (PR = 2.46, p = 0.012). For the 13 children for whom the cooking area was not partitioned from the rest of the home, having a respiratory condition was significantly associated with log-transformed indoor PM2.5 concentration (PR = 1.51; p = 0.014).

Peak centiles of chlorpyrifos surface-water concentrations in the NAWQA and NASQAN programs.

We provide upper bound estimates for peak centiles of surface water chlorpyrifos concentration readings within spatial, temporal, and land-use domains of the United States Geological Survey (USGS) National Water-Quality Assessment (NAWQA) and National Stream Quality Accounting Network (NASQAN) programs. These datasets have large overall sample sizes but variable sampling frequencies and, for chlorpyrifos, extremely high levels of non-detections. Point and interval estimates are provided for the 90th, 95th, 99th, and the 99.9th centiles, given sufficient sample size. Overall upper bound estimates for the NAWQA program over the period 1992-2011 for the 90th, 95th, 99th, and 99.9th centiles are <0.005, 0.0066, 0.0214, and 0.0852 ug/L, respectively. The estimation method is based on a survey sampling approach, finding centiles of pooled data across aggregates of site-years. Although the population quantity estimated by a pooled data centile is not the easily interpretable average of population site-year centiles, we provide strong support that it bounds this average by a combination of theory, comparison of NAWQA aggregate and direct estimates, and using modeled populations.

Sparse-data bias accompanying overly fine stratification in an analysis of beryllium exposure and lung cancer risk.

PURPOSE: Beryllium's classification as a carcinogen is based on limited human data that show inconsistent associations with lung cancer. Therefore, a thorough examination of those data is warranted. We reanalyzed data from the largest study of occupational beryllium exposure, conducted by the National Institute of Occupational Safety and Health (NIOSH). METHODS: Data had been analyzed using stratification and standardization. We reviewed the strata in the original analysis, and reanalyzed using fewer strata. We also fit a Poisson regression, and analyzed simulated datasets that generated lung cancer cases randomly without regard to exposure. RESULTS: The strongest association reported in the NIOSH study, a standardized rate ratio for death from lung cancer of 3.68 for the highest versus lowest category of time since first employment, is affected by sparse-data bias, stemming from stratifying 545 lung cancer cases and their associated person-time into 1792 categories. For time since first employment, the measure of beryllium exposure with the strongest reported association with lung cancer, there were no strata without zeroes in at least one of the two contrasting exposure categories. Reanalysis using fewer strata or with regression models gave substantially smaller effect estimates. Simulations confirmed that the original stratified analysis was upwardly biased. Other metrics used in the NIOSH study found weaker associations and were less affected by sparse-data bias. CONCLUSIONS: The strongest association reported in the NIOSH study seems to be biased as a result of non-overlap of data across the numerous strata. Simulation results indicate that most of the effect reported in the NIOSH paper for time since first employment is attributable to sparse-data bias.

Confounding after risk-set sampling in the beryllium study of Sanderson et al.

PURPOSE: Beryllium is classified as carcinogenic on the basis largely of limited human data showing a modest increase in lung cancer from occupational exposure. With occupational exposure now curtailed, earlier results merit more scrutiny. We simulated data to understand the design implications of a landmark case-control study. METHODS: We generated datasets from the original occupational cohort by randomly assigning lung cancer events to workers independently of their exposure. We analyzed the simulated data on the basis of different modes of risk-set sampling, with risk sets defined by calendar time, age, or both, to assess how much bias existed using several exposure metrics. We controlled for several time related variables to assess confounding. Finally, we re-analyzed the data from the original study, controlling for time-related covariates. RESULTS: No bias occurred using any type of risk-set sampling with unlagged exposures. When exposure was lagged 10 or 20 years, however, there was considerable confounding by year of birth and year of hire, which remained uncontrolled in the original study. CONCLUSIONS: Simulations and reanalysis show that much of the reported association with lagged exposure is attributable to confounding by year of birth and year of hire. Lagging changes the exposure variable and can thus lead to changes in the amount of confounding.

Reconstructing exposures from small samples using physiologically based pharmacokinetic models and multiple biomarkers.

This study examines the use of physiologically based pharmacokinetic (PBPK) models for inferring exposure when the number of biomarker observations per individual is limited, as commonly occurs in population exposure surveys. The trade-off between sampling multiple biomarkers at a specific time versus fewer biomarkers at multiple time points was investigated, using a simulation-based approach based on a revised and updated chlorpyrifos PBPK model originally published. Two routes of exposure, oral and dermal, were studied as were varying levels of analytic measurement error. It is found that adding an additional biomarker at a given time point adds substantial additional information to the analysis, although not as much as the addition of another sampling time. Furthermore, the precision of the estimates of exposed dose scaled approximately with the analytic precision of the biomarker measurement. For acute exposure scenarios such as those considered here, the results of this study suggest that the number of biomarkers can be balanced against the number of sampling times to obtain the most efficient estimator after consideration of cost, intrusiveness, and other relevant factors.

Limitations on the uses of multimedia exposure measurements for multipathway exposure assessment--Part I: Handling observations below detection limits.

Multimedia data from two probability-based exposure studies were investigated in terms of how censoring of nondetects affected estimation of population parameters and associations. Appropriate methods for handling censored below-detection-limit(BDL)values in this context were unclear since sampling weights were involved and since bivariate associations/measures were of interest. Both simple substitution(e.g., using 1/2 or 2/3 of the detection limit(DL)for BDL values)and truncation-based strategies were investigated by creating some artificial DLs and comparing resultant estimates with the original studies'uncensored results. The substitution methods generally outperformed the truncation methods, with the(2/3)DL substitution generally performing best.