Advocating for healthy environments is a matter of justice. Changes in environments have tremendous impacts on the health of communities, and oftentimes, individuals are unable to safeguard themselves through individual actions alone. Efforts frequently require collective action and are often most effective when led by the communities most impacted. In this spirit, we launched "Vibrations", an African environment photo essay contest. Through funding and publicity, we aimed to support community-led environmental improvement and education initiatives presently taking place on the continent. We received nearly two dozen submissions and selected eight winners. The winners come from five countries (Ghana, Kenya, Mozambique, Nigeria, and South Africa) and have taken on a range of projects aimed at improving environments across a variety of African regions. Projects included efforts to combat pollution, create environmentally conscious school curricula, utilize clean energy sources, and spread awareness about environmental justice concerns in local communities. It is our hope that this report highlights these transformative community-driven efforts, promotes continued conversations on environmental justice in Africa, and encourages meaningful action via policy changes and collaborations throughout the African continent and beyond.

BACKGROUND: Exposure data with repeated measures from occupational studies are frequently right-skewed and left-censored. To address right-skewed data, data are generally log-transformed and analyses modeling the geometric mean operate under the assumption the data are log-normally distributed. However, modeling the mean of exposure may lead to bias and loss of efficiency if the transformed data do not follow a known distribution. In addition, left censoring occurs when measurements are below the limit of detection (LOD). OBJECTIVE: To present a complete illustration of the entire conditional distribution of an exposure outcome by examining different quantiles, rather than modeling the mean. METHODS: We propose an approach combining the quantile regression model, which does not require any specified error distributions, with the substitution method for skewed data with repeated measurements and non-detects. RESULTS: In a simulation study and application example, we demonstrate that this method performs well, particularly for highly right-skewed data, as parameter estimates are consistent and have smaller mean squared error relative to existing approaches. SIGNIFICANCE: The proposed approach provides an alternative insight into the conditional distribution of an exposure outcome for repeated measures models.

Flavorings are substances that alter or enhance the taste of food. Workers in the food-manufacturing industry, where flavorings are added to many products, may be exposed to any number of flavoring compounds. Although thousands of flavoring substances are in use, little is known about most of these in terms of worker health effects, and few have occupational exposure guidelines. Exposure assessment surveys were conducted at nine food production facilities and one flavor manufacturer where a total of 105 area and 74 personal samples were collected for 13 flavoring compounds including five ketones, five aldehydes, and three acids. The majority of the samples were below the limit of detection (LOD) for most compounds. Diacetyl had eight area and four personal samples above the LOD, whereas 2,3-pentanedione had three area samples above the LOD. The detectable values ranged from 25-3124 ppb and 15-172 ppb for diacetyl and 2,3-pentanedione respectively. These values exceed the proposed National Institute for Occupational Safety and Health (NIOSH) recommended exposure limit for these compounds. The aldehydes had the most detectable samples, with each of them having >50% of the samples above the LOD. Acetaldehyde had all but two samples above the LOD, however, these samples were below the OSHA PEL. It appears that in the food-manufacturing facilities surveyed here, exposure to the ketones occurs infrequently, however levels above the proposed NIOSH REL were found. Conversely, aldehyde exposure appears to be ubiquitous.

Urine samples were collected from 51 participants in a study investigating pesticide exposure among farm families in Iowa. Aliquots from the samples were sent to two different labs and analyzed for metabolites of atrazine (atrazine mercapturate), metolachlor (metolachlor mercapturate) and chlorpyrifos (TCP) by two different analytical methods: immunoassay and high performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS). HPLC-MS/MS methods tend to be highly specific, but are costly and time consuming. Immunoassay methods are cheaper and faster, but can be less sensitive due to cross reactivity and matrix effects. Three statistical methods were employed to compare the two analytical methods. Each statistical method differed in how the samples that had results below the limit of detection (LOD) were treated. The first two methods involved an imputation procedure and the third method used maximum likelihood estimation (MLE). A fourth statistical method that modeled each lab separately using MLE was used for comparison. The immunoassay and HPLC-MS/MS methods were moderately correlated (correlation 0.40-0.49), but the immunoassay methods consistently had significantly higher geometric mean (GM) estimates for each pesticide metabolite. The GM estimates for atrazine mercapturate, metolachlor mercapturate, and TCP by immunoassay ranged from 0.16-0.98 microg l(-1), 0.24-0.45 microg l(-1) and 14-14 microg l(-1), respectively and by HPLC-MS/MS ranged from 0.0015-0.0039 microg l(-1), 0.12-0.16 microg l(-1), and 2.9-3.0 microg l(-1), respectively. Immunoassays tend to be cheaper and faster than HPLC-MS/MS, however, they may result in an upward bias of urinary pesticide metabolite levels.