Spatial Variation of Endotoxin Concentrations Measured in Ambient PM10 in a Livestock-Dense Area: Implementation of a Land-Use Regression Approach.

ABSTRACT: Results from studies on residential health effects of livestock farming are inconsistent, potentially due to simple exposure proxies used (e.g., livestock density). Accuracy of these proxies compared with measured exposure concentrations is unknown.We aimed to assess spatial variation of endotoxin in PM10 (particulate matter ?10?m) at residential level in a livestock-dense area, compare simple livestock exposure proxies to measured endotoxin concentrations, and evaluate whether land-use regression (LUR) can be used to explain spatial variation of endotoxin.The study area (3,000?km2) was located in Netherlands. Ambient PM10 was collected at 61 residential sites representing a variety of surrounding livestock-related characteristics. Three to four 2-wk averaged samples were collected at each site. A local reference site was used for temporal variation adjustment. Samples were analyzed for PM10 mass by weighing and for endotoxin by using the limulus amebocyte lysate assay. Three LUR models were developed, first a model based on general livestock-related GIS predictors only, followed by models that also considered species-specific predictors and farm type-specific predictors.Variation in concentrations measured between sites was substantial for endotoxin and more limited for PM10 (coefficient of variation: 43%, 8%, respectively); spatial patterns differed considerably. Simple exposure proxies were associated with endotoxin concentrations although spatial variation explained was modest (R2<26%). LUR models using a combination of animal-specific livestock-related characteristics performed markedly better, with up to 64% explained spatial variation.The considerable spatial variation of ambient endotoxin concentrations measured in a livestock-dense area can largely be explained by LUR modeling based on livestock-related characteristics. Application of endotoxin LUR models seems promising for residential exposure estimation within health studies. https://doi.org/10.1289/EHP2252.

SUBMITTER: de Rooij MMT 

PROVIDER: S-EPMC6014694 | biostudies-other | 2018 Jan

REPOSITORIES: biostudies-other