Environmentally Just Futures: A Collection of Community-Driven African Environmental Education and Improvement Initiatives.

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.

Quantile regression for exposure data with repeated measures in the presence of non-detects.

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.

Flavoring exposure in food manufacturing.

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.

Comparison of immunoassay and HPLC-MS/MS used to measure urinary metabolites of atrazine, metolachlor, and chlorpyrifos from farmers and non-farmers in Iowa.

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.

Pesticide dose estimates for children of Iowa farmers and non-farmers.

Farm children have the potential to be exposed to pesticides. Biological monitoring is often employed to assess this exposure; however, the significance of the exposure is uncertain unless doses are estimated. In the spring and summer of 2001, 118 children (66 farm, 52 non-farm) of Iowa farm and non-farm households were recruited to participate in a study investigating potential take-home pesticide exposure. Each child provided an evening and morning urine sample at two visits spaced approximately 1 month apart, with the first sample collection taken within a few days after pesticide application. Estimated doses were calculated for atrazine, metolachlor, chlorpyrifos, and glyphosate from urinary metabolite concentrations derived from the spot urine samples and compared to EPA reference doses. For all pesticides except glyphosate, the doses from farm children were higher than doses from the non-farm children. The difference was statistically significant for atrazine (p<0.0001) but only marginally significant for chlorpyrifos and metolachlor (p = 0.07 and 0.1, respectively). Among farm children, geometric mean doses were higher for children on farms where a particular pesticide was applied compared to farms where that pesticide was not applied for all pesticides except glyphosate; results were significant for atrazine (p = 0.030) and metolachlor (p = 0.042), and marginally significant for chlorpyrifos (p = 0.057). The highest estimated doses for atrazine, chlorpyrifos, metolachlor, and glyphosate were 0.085, 1.96, 3.16, and 0.34 microg/kg/day, respectively. None of the doses exceeded any of the EPA reference values for atrazine, metolachlor, and glyphosate; however, all of the doses for chlorpyrifos exceeded the EPA chronic population adjusted reference value. Doses were similar for male and female children. A trend of decreasing dose with increasing age was observed for chlorpyrifos.

Urinary pesticide concentrations among children, mothers and fathers living in farm and non-farm households in iowa.

In the spring and summer of 2001, 47 fathers, 48 mothers and 117 children of Iowa farm and non-farm households were recruited to participate in a study investigating take-home pesticide exposure. On two occasions approximately 1 month apart, urine samples from each participant and dust samples from various rooms were collected from each household and were analyzed for atrazine, metolachlor, glyphosate and chlorpyrifos or their metabolites. The adjusted geometric mean (GM) level of the urine metabolite of atrazine was significantly higher in fathers, mothers and children from farm households compared with those from non-farm households (P < or = 0.0001). Urine metabolites of chlorpyrifos were significantly higher in farm fathers (P = 0.02) and marginally higher in farm mothers (P = 0.05) when compared with non-farm fathers and mothers, but metolachlor and glyphosate levels were similar between the two groups. GM levels of the urinary metabolites for chlorpyrifos, metolachlor and glyphosate were not significantly different between farm children and non-farm children. Farm children had significantly higher urinary atrazine and chlorpyrifos levels (P = 0.03 and P = 0.03 respectively) when these pesticides were applied by their fathers prior to sample collection than those of farm children where these pesticides were not recently applied. Urinary metabolite concentration was positively associated with pesticide dust concentration in the homes for all pesticides except atrazine in farm mothers; however, the associations were generally not significant. There were generally good correlations for urinary metabolite levels among members of the same family.

Pesticide contamination inside farm and nonfarm homes.

Twenty-five farm (F) households and 25 nonfarm (NF) households in Iowa were enrolled in a study investigating agricultural pesticide contamination inside homes. Air, surface wipe, and dust samples were collected. Samples from 39 homes (20 F and 19 NF) were analyzed for atrazine, metolachlor, acetochlor, alachlor, and chlorpyrifos. Samples from 11 homes (5 F and 6 NF) were analyzed for glyphosate and 2,4-Dichlorophenoxyac etic acid (2,4-D). Greater than 88% of the air and greater than 74% of the wipe samples were below the limit of detection (LOD). Among the air and wipe samples, chlorpyrifos was detected most frequently in homes. In the dust samples, all the pesticides were detected in greater than 50% of the samples except acetochlor and alachlor, which were detected in less than 30% of the samples. Pesticides in dust samples were detected more often in farm homes except 2,4-D, which was detected in 100% of the farm and nonfarm home samples. The average concentration in dust was higher in farm homes versus nonfarm homes for each pesticide. Further analysis of the data was limited to those pesticides with at least 50% of the dust samples above the LOD. All farms that sprayed a pesticide had higher levels of that pesticide in dust than both farms that did not spray that pesticide and nonfarms; however, only atrazine and metolachlor were significantly higher. The adjusted geometric mean pesticide concentration in dust for farms that sprayed a particular pesticide ranged from 94 to 1300 ng/g compared with 12 to 1000 ng/g for farms that did not spray a particular pesticide, and 2.4 to 320 ng/g for nonfarms. The distributions of the pesticides throughout the various rooms sampled suggest that the strictly agricultural herbicides atrazine and metolachlor are potentially being brought into the home on the farmer's shoes and clothing. These herbicides are not applied in or around the home but they appear to be getting into the home para-occupationally. For agricultural pesticides, take-home exposure may be an important source of home contamination.

Urinary and hand wipe pesticide levels among farmers and nonfarmers in Iowa.

In the spring and summer of 2001, as part of a larger study investigating farm family pesticide exposure and home contamination in Iowa, urine and hand wipe samples were collected from 24 male farmers and 23 male nonfarmer controls. On two occasions approximately 1 month apart, one hand wipe sample and an evening and morning urine sample were collected from each participant. The samples were analyzed for the parent compound or metabolites of six commonly used agricultural pesticides: alachlor, atrazine, acetochlor, metolachlor, 2,4-dichlorophenoxyacetic acid (2,4-D) and chlorpyrifos. For atrazine, acetochlor, metolachlor and 2,4-D, farmers who reported applying the pesticide had significantly higher urinary metabolite levels than nonfarmers, farmers who did not apply the pesticide, and farmers who had the pesticide commercially applied (P-value <0.05). Generally, there were no differences in urinary pesticide metabolite levels between nonfarmers, farmers who did not apply the pesticide, and farmers who had the pesticide commercially applied. Among farmers who reported applying 2,4-D themselves, time since application, amount of pesticide applied, and the number of acres to which the pesticide was applied were marginally associated with 2,4-D urine levels. Among farmers who reported applying atrazine themselves, time since application and farm size were marginally associated with atrazine mercapturate urine levels. Farmers who reported using a closed cab to apply these pesticides had higher urinary pesticide metabolite levels, although the difference was not statistically significant. Farmers who reported using closed cabs tended to use more pesticides. The majority of the hand wipe samples were nondetectable. However, detection of atrazine in the hand wipes was significantly associated with urinary levels of atrazine above the median (P-value <0.01).

Nicotine exposure and decontamination on tobacco harvesters' hands.

Green tobacco sickness is an illness associated with nicotine exposures among tobacco harvesters. Agricultural workers manually harvest tobacco and thus have the potential for skin exposure to nicotine, particularly on the hands. Often gloves are not worn as it hinders the harvesters' ability to harvest the tobacco leaves. The purposes of this study were to measure the concentration of nicotine residue on the hands of tobacco harvesters and the effectiveness of hand washing at removing the residue. Wipe samples from the hands of 12 tobacco harvesters were collected at the end of morning and afternoon work periods over two consecutive days. Each harvester had one hand wiped before washing his hands, and the other hand wiped after washing his hands with soap and water. Eight samples per worker were collected over the two days for a total of 96 samples collected. In addition to the hand-wipe samples, leaf-wipe samples were collected from 15 tobacco plants to estimate the amount of nicotine residue on the plants. The average nicotine level in leaf-wipe samples was 1.0 microg cm(-2). The geometric mean pre-wash and post-wash nicotine levels on the hands were 10 and 0.38 microg cm(-2), respectively. Nicotine leaf-wipe level, right or left hand and time of sampling did not significantly influence exposure. Job position-working on the bottom versus the top of the tobacco harvesting machine-was associated with nicotine levels. Pre-wash nicotine levels were higher for workers on the bottom of the harvester but not significantly higher (P = 0.17). Post-wash nicotine levels were significantly higher for workers on the bottom of the harvester (P = 0.012). A substantial amount of nicotine was transferred to the hands, but washing with soap and water in the field significantly reduced nicotine levels by an average of 96% (P < 0.0001).

Acephate exposure and decontamination on tobacco harvesters' hands.

Agricultural workers manually harvesting tobacco have the potential for high dermal fexposure to pesticides, particularly on the hands. Often gloves are not worn as it hinders the harvesters' ability to harvest the tobacco leaves. To enable harvesters to remove pesticide residue on the hands and decrease absorbed doses, the EPA Worker Protection Standard requires growers to have hand-wash stations available in the field. The purpose of this study was to measure the concentration of acephate residue on the hands of tobacco harvesters, and the effectiveness of hand washing in reducing the acephate residue. Hand-wipes from the hands of 12 tobacco harvesters were collected at the end of the morning and at the end of the afternoon over 2 consecutive days. Each harvester had one hand-wiped prior to washing his hands, and the other hand-wiped after washing his hands with soap and water. In addition to the hand-wipe samples, leaf-wipe samples were collected from 15 tobacco plants to determine the amount of acephate residue on the plants. The average acephate level in leaf-wipe samples was 1.4 ng/cm(2). The geometric mean prewash and postwash acephate levels on the hands were 10.5 and 0.4 ng/cm(2), respectively. Both prewash (P-value=0.0009) and postwash hand (P-value=0.01) samples were positively correlated with leaf-wipe concentrations. Tobacco harvester position tended to influence hand exposure. Hand washing significantly reduced acephate levels on the hand, after adjusting for sampling period, hand sampled, job position, and leaf-wipe concentration (P-value< or =0.0001) with levels reduced by 96%. A substantial amount of acephate was transferred to the hands, and while hand washing significantly reduced the amount of residue on the hands, not all residue was removed.

Surface sampling methods for Bacillus anthracis spore contamination.

During an investigation conducted December 17-20, 2001, we collected environmental samples from a U.S. postal facility in Washington, D.C., known to be extensively contaminated with Bacillus anthracis spores. Because methods for collecting and analyzing B. anthracis spores have not yet been validated, our objective was to compare the relative effectiveness of sampling methods used for collecting spores from contaminated surfaces. Comparison of wipe, wet and dry swab, and HEPA vacuum sock samples on nonporous surfaces indicated good agreement between results with HEPA vacuum and wipe samples. However, results from HEPA vacuum sock and wipe samples agreed poorly with the swab samples. Dry swabs failed to detect spores >75% of the time when they were detected by wipe and HEPA vacuum samples. Wipe samples collected after HEPA vacuum samples and HEPA vacuum samples collected after wipe samples indicated that neither method completely removed spores from the sampled surfaces.