Reduced water collection time improves learning achievement among primary school children in India.

In low-and-middle-income countries, the provisioning of safe drinking water is a challenge that will likely worsen with climate change. Securing water will require more work and time, burdening women and children the most. Currently, the consequences of this time burden to children's development remain understudied. To address this gap, we examine the tradeoff between children's household water collection responsibilities and learning achievement. Using nationally representative data from India, we measure the effect of daily fetching time on primary school children's learning achievement in a two-stage regression model, with rainfall as the instrument. Our analyses indicate that higher fetching times predict lower mathematics (-1.23 standard deviations, 95CI[-2.32, -0.14]), reading (-1.13 standard deviations, 95CI[-2.07, -0.19]), and writing (-1.21 standard deviations, 95CI[-1.89, -0.51]) test scores. These effects are heterogeneous across sex and infrastructure type. For example, we find girls' mathematical and reading skills profit more from reductions in fetching time than boys' (score less affected for boys by ß amount: mathematics: ß=0.26 points, 95CI[0.095, 0.42]; reading: ß=0.27 points, 95CI[0.054, 0.49]). Children using hand pumps, open wells, or tube wells are hurt more academically in mathematics and writing by increases in fetching time than children with mostly off-premises piped access (e.g., writing scores more affected by ß amount: hand pump: ß=-0.18, 95CI[-0.29, -0.081]; open well: ß=-0.18, 95CI[-0.33, -0.040]; tube well: ß=-0.14, 95CI[-0.29, -0.00072]). Given these results, we recommend off-premises piped infrastructure in the absence of piped-to-premises water in water-insecure contexts and offer guidance for targeting infrastructure investments in India.

Association of Human Mobility Restrictions and Race/Ethnicity-Based, Sex-Based, and Income-Based Factors With Inequities in Well-being During the COVID-19 Pandemic in the United States.

Importance: An accurate understanding of the distributional implications of public health policies is critical for ensuring equitable responses to the COVID-19 pandemic and future public health threats. Objective: To identify and quantify the association of race/ethnicity-based, sex-based, and income-based inequities of state-specific lockdowns with 6 well-being dimensions in the United States. Design, Setting, and Participants: This pooled, repeated cross-sectional study used data from 14 187 762 households who participated in phase 1 of the population-representative US 2020 Household Pulse Survey (HPS). Households were invited to participate by email, text message, and/or telephone as many as 3 times. Data were collected via an online questionnaire from April 23 to July 21, 2020, and participants lived in all 50 US states and the District of Columbia. Exposures: Indicators of race/ethnicity, sex, and income and their intersections. Main Outcomes and Measures: Unemployment; food insufficiency; mental health problems; no medical care received for health problems; default on last month's rent or mortgage; and class cancellations with no distance learning. Race/ethnicity, sex, income, and their intersections were used to measure distributional implications across historically marginalized populations; state-specific, time-varying population mobility was used to measure lockdown intensity. Logistic regression models with pooled repeated cross-sections were used to estimate risk of dichotomous outcomes by social group, adjusted for confounding variables. Results: The 1 088 314 respondents (561 570 [51.6%; 95% CI, 51.4%-51.9%] women) were aged 18 to 88 years (mean [SD], 51.55 [15.74] years), and 826 039 (62.8%; 95% CI, 62.5%-63.1%) were non-Hispanic White individuals; 86 958 (12.5%; 95% CI, 12.4%-12.7%), African American individuals; 86 062 (15.2%; 95% CI, 15.0%-15.4%), Hispanic individuals; and 50 227 (5.6%; 95% CI, 5.5%-5.7%), Asian individuals. On average, every 10% reduction in mobility was associated with higher odds of unemployment (odds ratio [OR], 1.3; 95% CI, 1.2-1.4), food insufficiency (OR, 1.1; 95% CI, 1.1-1.2), mental health problems (OR, 1.04; 95% CI, 1.0-1.1), and class cancellations (OR, 1.1; 95% CI, 1.1-1.2). Across most dimensions compared with White men with high income, African American individuals with low income experienced the highest risks (eg, food insufficiency, men: OR, 3.3; 95% CI, 2.8-3.7; mental health problems, women: OR, 1.9; 95% CI, 1.8-2.1; medical care inaccessibility, women: OR, 1.7; 95% CI, 1.6-1.9; unemployment, men: OR, 2.8; 95% CI, 2.5-3.2; rent/mortgage defaults, men: OR, 5.7; 95% CI, 4.7-7.1). Other high-risk groups were Hispanic individuals (eg, unemployment, Hispanic men with low income: OR, 2.9; 95% CI, 2.5-3.4) and women with low income across all races/ethnicities (eg, medical care inaccessibility, non-Hispanic White women: OR, 1.8; 95% CI, 1.7-2.0). Conclusions and Relevance: In this cross-sectional study, African American and Hispanic individuals, women, and households with low income had higher odds of experiencing adverse outcomes associated with the COVID-19 pandemic and stay-at-home orders. Blanket public health policies ignoring existing distributions of risk to well-being may be associated with increased race/ethnicity-based, sex-based, and income-based inequities.

A field-deployable surface-enhanced Raman scattering (SERS) method for sensitive analysis of silver nanoparticles in environmental waters.

The proliferation of silver nanoparticles (AgNPs) in the environment and resultant transport into aquatic systems have raised concerns regarding their potential toxicity to various organisms. These environmental and ecological concerns demand reliable AgNP detection methods which can measure environmentally relevant quantities of AgNPs in real aquatic systems. This study developed a method that couples a rapid vacuum filtration technique with a portable Raman spectrometer to achieve on-site detection of ultra-low levels of AgNPs in typical and complex aquatic systems. To extract and detect AgNPs, aluminum chloride and ferbam were added for AgNP aggregation and labelling, respectively. The AgNP aggregates were filtered through a membrane, and their presence and quantity were determined based upon the surface-enhanced Raman scattering (SERS) peak intensity of ferbam. Under the optimized conditions, the extraction efficiencies are 99 ±â€¯0.001% in ultrapure water and 98 ±â€¯0.025% in marine water for 1 mg/L AgNPs. This method enables simple volume adjustment and improves the consistency of AgNP distribution on the membrane. The performance of the method was evaluated in different environmental waters, including marine water, fresh waters (pond water, river water, and reservoir outlet water) and drinking waters (municipal tap water and well water), with highest signal intensity in marine water and lowest signals in fresh waters. The signal intensity difference was suggested to be caused by the amount of natural organic matter (NOM) in these environmental waters. Using pond water as an example, the interference was minimized by changing the aggregating salt from AlCl3 to MgCl2, and AgNPs as low as 5 µg/L were reliably detected with a volume of 100 mL. At the same volume, the developed method was sensitive enough to detect 1 µg/L AgNPs in marine water and also holds promise for assessing the time-dependent transformation of AgNPs.

Surface-enhanced Raman scattering detection of silver nanoparticles in environmental and biological samples.

Growing concerns over the potential release and threat of silver nanoparticles (AgNPs) to environmental and biological systems urge researchers to investigate their fate and behavior. However, current analytical techniques cannot meet the requirements for rapidly, sensitively and reliably probing AgNPs in complex matrices. Surface-enhanced Raman spectroscopy (SERS) has shown great capability for rapid detection of AgNPs based on an indicator molecule that can bind on the AgNP surface. The objective of this study was to exploit SERS to detect AgNPs in environmental and biological samples through optimizing the Raman indicator for SERS. Seven indicator molecules were selected and determined to obtain their SERS signals at optimal concentrations. Among them, 1,2-di(4-pyridyl)ethylene (BPE), crystal violet and ferric dimethyl-dithiocarbamate (ferbam) produced the highest SERS intensities. Further experiments on binding competition between each two of the three candidates showed that ferbam had the highest AgNPs-binding ability. The underlying mechanism lies in the strong binding affinity of ferbam with AgNPs via multiple sulfur atoms. We further validated ferbam to be an effective indicator for SERS detection of as low as 0.1mg/L AgNPs in genuine surface water and 0.57 mg/L in spinach juice. Moreover, limited interference on SERS detection of AgNPs was found from environmentally relevant inorganic ions, organic matter, inorganic particles, as well as biologically relevant components, demonstrating the ferbam-assisted SERS is an effective and sensitive method to detect AgNPs in complex environmental and biological samples.