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Greywater is increasingly treated and reused for agricultural irrigation in off-grid communities in the Middle East and other water scarce regions of the world. However, there is a dearth of data regarding levels of antibiotics and herbicides in off-grid greywater treatment systems. To address this knowledge gap, we evaluated levels of these contaminants in two types of greywater treatment systems on four farms in the West Bank, Palestinian Territories. Samples of household greywater (influent, n = 23), treated greywater effluent intended for agricultural irrigation (n = 23) and pumped groundwater held in irrigation water ponds (n = 12) were collected from October 2017 to June 2018. Samples were analyzed using high performance liquid chromatography tandem mass spectrometry (LC-MS/MS) for the following antibiotics and herbicides: alachlor, ampicillin, atrazine, azithromycin, ciprofloxacin, erythromycin, linezolid, oxacillin, oxolinic acid, penicillin G, pipemidic acid, sulfamethoxazole, triclocarban, tetracycline, triflualin, and vancomycin. All tested antibiotics and herbicides were detected in greywater influent samples at concentrations ranging from 1.3 to 1592.9 ng/L and 3.1–22.4 ng/L, respectively. When comparing influent to effluent concentrations, removal was observed for azithromycin, alachlor, linezolid, oxacillin, penicillin G, pipemidic acid, sulfamethoxazole, triclocarban, and vancomycin. Removal was not observed for atrazine, ciprofloxacin, erythromycin, oxolinic acid, tetracycline, and trifluralin. Pond water also contained the majority of tested contaminants, but at generally lower concentrations. To our knowledge, this is the first description of an extensive array of antibiotics and herbicides detected in household greywater from off-grid treatment systems.

Mary Zaki and her colleagues published a working paper analyzing the effects of work requirements on SNAP participation, beneficiary composition, and labor supply

A major concern arising from ubiquitous tracking of individuals’ online activity is that algorithms may be trained to predict personal sensitive information, even for users who do not wish to reveal such information. Although previous research has shown that digital trace data can accurately predict sociodemographic characteristics, little is known about the potentials of such data to predict sensitive outcomes. Against this background, we investigate in this article whether we can accurately predict voting behavior, which is considered personal sensitive information in Germany and subject to strict privacy regulations. Using records of web browsing and mobile device usage of about 2,000 online users eligible to vote in the 2017 German federal election combined with survey data from the same individuals, we find that online activities do not predict (self-reported) voting well in this population. These findings add to the debate about users’ limited control over (inaccurate) personal information flows.

Sought-for freedoms require action in both domains, she says

Children exposed to "Sesame Street" were more likely to be enrolled in the correct grade level for their age at middle and high school

Gang membership is believed to impede success in the legitimate economic market while simultaneously supporting success in the illegal market. We extend the study of the economic effects of gang membership by using a within-and between-individual analytic design, decomposing gang membership into multiple statuses (i.e., entering a gang,continuously in a gang, leaving a gang, and inactive gang membership), examining legal and illegal earnings simultaneously, and accounting for factors endogenous to gang membership that may contribute to economic achievement.By using panel data from 1,213 individuals who participated in the Pathways to Desistance Study to conduct a multi level path analysis, we find that active gang membership status is unrelated to legal earnings. Alternatively,entering a gang is associated with increased illegal earnings, attributable to changes in delinquent peers and drug use, whereas leaving a gang has a direct relationship with decreased illegal earnings. Our results indicate that the positive economic effect of gang membership (i.e., illegal earnings and total earnings) is short-lived and that, on balance,the sum of the gang membership experience does not “pay”in terms of overall earnings.

Contextual Inequalities and Socioeconomic Outcomes among Adult Children of U.S. Immigrants

Bootstraps of Oppression: A Theoretical Framework of Structural Inequality in Policy Decision Making

Race differences in school attendance across the Jim Crow South and its implications for Black-White disparities in cognitive impairment risk among older adults