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Private wells often lack centralized oversight, drinking water quality standards, and consistent testing methodologies. For lead in well water, the lack of standardized data collection methods can impact reported measurements, which can misinform health risks. Here, we conducted a targeted community science testing of 1143 wells across 17 counties in North Carolina (USA) and compared results to state testing data primarily associated with new well construction compiled in the NCWELL database. The goal of our study was to explore the impacts of sampling methodology and household representation on estimated lead exposures and subsequent health risks. At the household scale, we illustrated how sampling and analytical techniques impact lead measurements. The community science testing first draw samples (characterizing drinking water) had a 90th percentile lead value of 12.8μg l⁻¹while the NCWELL database flushed samples (characterizing groundwater) had a value below the reporting level of 5μg l⁻¹. As lead was associated with the corrosion of premise plumbing, flushing prior to collection substantially reduced lead concentrations. At the community scale, we examined how the lack of representation based on household demographics and well construction characteristics impacted the knowledge of lead and blood lead level (BLL) occurrence. When simulating representative demographics of the well populations, we observed that the 90th percentile lead level could differ by up to 6μg l⁻¹, resulting in communities being above the USEPA action level. This translated to a 1.0–1.3μg dl⁻¹difference in predicted geometric mean BLL among infants consuming reconstituted formula. Further, inclusion of less common well construction types also increased lead in water occurrence. Overall, under- and overestimations of lead concentrations associated with differences in sampling techniques and sample representation can misinform conclusions about risks of elevated BLLs associated with drinking water from private wells which may hinder investigations of waterborne lead exposure.

 

We propose a new measurement of the ratio of positron-proton to electron-proton elastic scattering at DESY. The purpose is to determine the contributions beyond single-photon exchange, which are essential for the Quantum Electrodynamic (QED) description of the most fundamental process in hadronic physics. By utilizing a 20 cm long liquid hydrogen target in conjunction with the extracted beam from the DESY synchrotron, we can achieve an average luminosity of$$2.12\times 10^{35}$$$2.12\times {10}^{35}$ cm$$^{-2}\cdot $$${}^{-2}·$s$$^{-1}$$${}^{-1}$ ($$\approx 200$$$\approx 200$times the luminosity achieved by OLYMPUS). The proposed two-photon exchange experiment (TPEX) entails a commissioning run at a beam energy of 2 GeV, followed by measurements at 3 GeV, thereby providing new data up to$$Q^2=4.6$$$Q^2=4.6$ (GeV/c)$$^2$$${}^2$(twice the range of current measurements). We present and discuss the proposed experimental setup, run plan, and expectations.

 

The Association for the Sciences of Limnology and Oceanography (ASLO) sponsors Eco-DAS, which is now in its 30th year. The program aims to unite aquatic scientists, develop diverse collaborations, and provide professional development training opportunities with guests from federal agencies, nonprofits, academia, tribal groups, and other workplaces (a previous iteration is summarized in Ghosh et al. 2022). Eco-DAS XV was one of the largest and most nationally diverse cohorts, including 37 early career aquatic scientists, 15 of whom were originally from 9 different countries outside the United States (Fig. 2). As the first cohort to meet in-person since the COVID-19 pandemic, Eco-DAS participants convened from 5 to 11 March 2023 to expand professional networks, create shared projects, and discuss areas of priority for the aquatic sciences. During the weeklong meeting, participants developed 46 proposal ideas, 16 of which will be further developed into projects and peer-reviewed manuscripts. 

Evidence from victim service providers suggests the COVID-19 pandemic led to an increase in family violence. However, empirical evidence has been limited. This study uses novel survey data to investigate the occurrence of family violence during the early months of the COVID-19 pandemic in the United States. Data come from the second wave of the Assessing the Social Consequences of COVID-19 study, an online non-probability sample collected in April and May 2020. Family violence is measured using four variables: any violence, physical violence, verbal abuse, and restricted access. The authors use logistic regression and KHB decomposition to examine the prevalence of family violence during the COVID-19 pandemic. We find that sexual minorities, in particular bisexual people, experienced higher rates of family violence than heterosexual respondents. Women were the only group to report an increase in the frequency of family violence. Household income loss is associated with the incidence of verbal violence. Our findings demonstrate the importance of expanding victim services to address the additional barriers victims face within the pandemic context and beyond, including broad contexts of social isolation and financial precarity experienced by individuals at risk of family violence. 

Using primary data from the Assessing the Social Consequences of COVID-19 study, the authors examined how the pandemic affected the stress levels of women with and without coresiding minor children (mothers vs. nonmothers), paying special attention to the moderating role of employment status. The ordinary least squares regression results show that following the pandemic outbreak, among full-time working women, mothers reported smaller stress increases than nonmothers. In contrast, among part-time and nonemployed women, mothers and nonmothers experienced similar stress increases. Also, full-time working mothers reported smaller stress increases than women with most other mothering and employment statuses. Changes in women’s employment status, following pandemic onset, had limited impacts on the patterns of stress change. This study contributes to research on parenting and health by showing that during times of crisis, full-time employment may be protective of mothers’ mental health but may not buffer the mental health deterioration of women not raising children. 

Understanding models is important for engineering students, but not often taught explicitly in first-year courses. Although there are many types of models in engineering, studies have shown that engineering students most commonly identify prototyping or physical models when asked about modeling. In order to evaluate students’ understanding of different types of models used in engineering and the effectiveness of interventions designed to teach modeling, a survey was developed. This paper describes development of a framework to categorize the types of engineering models that first-year engineering students discuss based on both previous literature and students’ responses to survey questions about models. In Fall 2019, the survey was administered to first-year engineering students to investigate their awareness of types of models and understanding of how to apply different types of models in solving engineering problems. Students’ responses to three questions from the survey were analyzed in this study: 1. What is a model in science, technology, engineering, and mathematics (STEM) fields?, 2. List different types of models that you can think of., and 3. Describe each different type of model you listed. Responses were categorized by model type and the framework was updated through an iterative coding process. After four rounds of analysis of 30 different students’ responses, an acceptable percentage agreement was reached between independent researchers coding the data. Resulting frequencies of the various model types identified by students are presented along with representative student responses to provide insight into students’ understanding of models in STEM. This study is part of a larger project to understand the impact of modeling interventions on students’ awareness of models and their ability to build and apply models. 

Recent advances are revealing mechanisms mediating CO2-regulated stomatal movements in Arabidopsis, stomatal architecture and stomatal movements in grasses, and the long-term impact of CO2 on growth.