Boknam Lee | USGS Science

USGS2013

Comprehensive assessment of hormones, phytoestrogens, and estrogenic activity in an anaerobic swine waste lagoon

In this study, the distribution of steroid hormones, phytoestrogens, and estrogenic activity was thoroughly characterized within the anaerobic waste lagoon of a typical commercial swine sow operation. Three independent rounds of sampling were conducted in June 2009, April 2010, and February 2011. Thirty-seven analytes in lagoon slurry and sludge were assessed using LC/MS-MS, and yeast estrogen screen was used to determine estrogenic activity. Of the hormone analytes, steroidal estrogens were more abundant than androgens or progesterone, with estrone being the predominant estrogen species. Conjugated hormones were detected only at low levels. The isoflavone metabolite equol was by far the predominant phytoestrogen species, with daidzein, genistein, formononetin, and coumestrol present at lower levels. Phytoestrogens were often more abundant than steroidal estrogens, but contributed minimally towards total estrogenic activity. Analytes were significantly elevated in the solid phases of the lagoon; although low observed log KOC values suggest enhanced solubility in the aqueous phase, perhaps due to dissolved or colloidal organic carbon. The association with the solid phase, as well as recalcitrance of analytes to anaerobic degradation, results in a markedly elevated load of analytes and estrogenic activity within lagoon sludge. Overall, findings emphasize the importance of adsorption and transformation processes in governing the fate of these compounds in lagoon waste, which is ultimately used for broadcast application as a fertilizer.

Environmental Science & Technology↗

USGS2010

Multilevel Hierarchical Modeling of Benthic Macroinvertebrate Responses to Urbanization in Nine Metropolitan Regions across the Conterminous United States

Multilevel hierarchical modeling methodology has been developed for use in ecological data analysis. The effect of urbanization on stream macroinvertebrate communities was measured across a gradient of basins in each of nine metropolitan regions across the conterminous United States. The hierarchical nature of this dataset was harnessed in a multi-tiered model structure, predicting both invertebrate response at the basin scale and differences in invertebrate response at the region scale. Ordination site scores, total taxa richness, Ephemeroptera, Plecoptera, Trichoptera (EPT) taxa richness, and richness-weighted mean tolerance of organisms at a site were used to describe invertebrate responses. Percentage of urban land cover was used as a basin-level predictor variable. Regional mean precipitation, air temperature, and antecedent agriculture were used as region-level predictor variables. Multilevel hierarchical models were fit to both levels of data simultaneously, borrowing statistical strength from the complete dataset to reduce uncertainty in regional coefficient estimates. Additionally, whereas non-hierarchical regressions were only able to show differing relations between invertebrate responses and urban intensity separately for each region, the multilevel hierarchical regressions were able to explain and quantify those differences within a single model. In this way, this modeling approach directly establishes the importance of antecedent agricultural conditions in masking the response of invertebrates to urbanization in metropolitan regions such as Milwaukee-Green Bay, Wisconsin; Denver, Colorado; and Dallas-Fort Worth, Texas. Also, these models show that regions with high precipitation, such as Atlanta, Georgia; Birmingham, Alabama; and Portland, Oregon, start out with better regional background conditions of invertebrates prior to urbanization but experience faster negative rates of change with urbanization. Ultimately, this urbanization-invertebrate response example is used to detail the multilevel hierarchical construction methodology, showing how the result is a set of models that are both statistically more rigorous and ecologically more interpretable than simple linear regression models.

Scientific Investigations Report↗