Particulate matter chemical component concentrations and sources in settings of household solid fuel use.

Particulate matter (PM) air pollution derives from combustion and non-combustion sources and consists of various chemical species that may differentially impact human health and climate. Previous reviews of PM chemical component concentrations and sources focus on high-income urban settings, which likely differ from the low- and middle-income settings where solid fuel (ie, coal, biomass) is commonly burned for cooking and heating. We aimed to summarize the concentrations of PM chemical components and their contributing sources in settings where solid fuel is burned. We searched the literature for studies that reported PM component concentrations from homes, personal exposures, and direct stove emissions under uncontrolled, real-world conditions. We calculated weighted mean daily concentrations for select PM components and compared sources of PM determined by source apportionment. Our search criteria yielded 48 studies conducted in 12 countries. Weighted mean daily cooking area concentrations of elemental carbon, organic carbon, and benzo(a)pyrene were 18.8 µg m-3 , 74.0 µg m-3 , and 155 ng m-3 , respectively. Solid fuel combustion explained 29%-48% of principal component/factor analysis variance and 41%-87% of PM mass determined by positive matrix factorization. Multiple indoor and outdoor sources impacted PM concentrations and composition in these settings, including solid fuel burning, mobile emissions, dust, and solid waste burning.

Empirical Bayes estimation for combinations of multivariate bioassays.

This article presents a new empirical Bayes estimator (EBE) and a shrinkage estimator for determining the relative potency from several multivariate bioassays by incorporating prior information on the model parameters based on Jeffreys' rules. The EBE can account for any extra variability among the bioassays, and if this extra variability is 0, then the EBE reduces to the maximum likelihood estimator for combinations of multivariate bioassays. The shrinkage estimator turns out to be a compromise of the prior information and the estimator from each multivariate bioassay, with the weights depending on the prior variance.

A reduced-rank multivariate regression approach to aquatic joint toxicity experiments.

Classical multivariate regression techniques can lead to an asymptotically efficient restricted estimator of the regression matrix if there is evidence that the regression matrix is of reduced rank. A sublethal joint toxicity experiment involving two agents is used to illustrate this approach.

General estimation of relative potency using prior information.

A general point and interval estimator of the log relative potency is exhibited in closed form, which incorporates prior information on the log relative potency for symmetrical as well as asymmetrical parallel line bioassay. The point estimator turns out to be a weighted average of the usual estimator and the prior mean where the weights are determined by the prior variance.

Estimating relative potency using prior information.

This paper exhibits a closed-form point and interval estimator of the log relative potency that incorporates prior information on the log relative potency in a symmetric parallel line bioassay. The point estimator turns out to be a weighted average of the usual estimator and the prior mean where the weights are determined by the prior variance.