Methodologies for the collection of parameters to estimate dust/soil ingestion for young children.

Background: Heavy metals, pesticides and a host of contaminants found in dust and soil pose a health risk to young children through ingestion. Dust/soil ingestion rates for young children can be estimated using micro-level activity time series (MLATS) as model inputs. MLATS allow for the generation of frequency and duration of children's contact activities, along with sequential contact patterns. Models using MLATS consider contact types, and transfer dynamics to assign mechanisms of contact and appropriate exposure factors for cumulative estimates of ingestion rates. Objective: The objective of this study is to describe field implementation, data needs, advanced field collection, laboratory methodologies, and challenges for integrating into and updating a previously validated physical-stochastic MLATS-based model framework called the Child-Specific Aggregate Cumulative Human Exposure and Dose (CACHED) model. The manuscript focuses on describing the methods implemented in the current study. Methods: This current multidisciplinary study (Dust Ingestion childRen sTudy [DIRT]) was implemented across three US regions: Tucson, Arizona; Miami, Florida and Greensboro, North Carolina. Four hundred and fifty participants were recruited between August 2021 to June 2023 to complete a 4-part household survey, of which 100 also participated in a field study. Discussion: The field study focused on videotaping children's natural play using advanced unattended 360° cameras mounted for participants' tracking and ultimately conversion to MLATS. Additionally, children's hand rinses were collected before and after recording, along with indoor dust and outdoor soil, followed by advanced mass analysis. The gathered data will be used to quantify dust/soil ingestion by region, sociodemographic variables, age groups (from 6 months to 6 years), and other variables for indoor/outdoor settings within an adapted version of the CACHED model framework. Significance: New innovative approaches for the estimation of dust/soil ingestion rates can potentially improve modeling and quantification of children's risks to contaminants from dust exposure.

Quantitative microbial risk assessment (QMRA) tool for modelling pathogen infection risk to wastewater treatment plant workers.

Wastewater treatment plants (WWTPs) provide vital services to the public by removing contaminants from wastewater prior to environmental discharge or reuse for beneficial purposes. WWTP workers occupationally exposed to wastewater can be at risk of respiratory or gastrointestinal diseases. The study objectives were to: (1) quantify pathogens and pathogen indicators in wastewater aerosols near different WWTP processes/unit operations, (2) develop a QMRA model for multi-pathogen and multi-exposure pathway risks, and (3) create a web-based application to perform and communicate risk calculations for wastewater workers. Case studies for seven different WWTP job tasks were performed investigating infection risk across nine different enteric and respiratory pathogens. It was observed that the ingestion risk among job tasks was highest for "walking the WWTP," which involved exposure from splashing, bioaerosols, and hand-to-mouth contact from touching contaminated surfaces. There was also a notable difference in exposure risk during peak (5:00am-9:00am) and non-peak hours (9:00am- 5:00am), with risks during the peak flow hours of the early morning assumed to be 5 times greater than non-peak hours. N95 respirator usage reduced median respiratory risks by 77 %. The developed tool performs multiple QMRA calculations to estimate WWTP workers' infection risks from accidental ingestion or inhalation of wastewater from multiple pathogens and exposure scenarios, which can inform risk management strategies to protect occupational health. However, more data are needed to reduce uncertainty in model estimates, including comparative data for pathogen concentrations in wastewater during peak and non-peak hours. QMRA tools will increase accessibility of risk models for utilization in decision-making.

Barriers to COVID-19 Intervention Implementation in K-5 Classrooms: A Survey of Teachers from a District with Mask Mandates despite a Statewide Mask Mandate Ban.

The study objective was to characterize K-5 teachers' risk perceptions and experiences with CDC COVID-19 classroom guidance in an Arizona school district with a mask mandate, conflicting with a statewide mask mandate ban. METHODS: Public school teachers (n = 111) were recruited between 14 December 2021, and 31 January 2022, for an anonymous online survey with questions on seven important topics related to: (1) population demographics, (2) teachers' perceptions of COVID-19 in the workplace, (3) masks, (4) physical distancing, (5) surface transmission routes, (6) air flow, and (7) contact tracing protocols. Descriptive statistics were calculated, and statistically significant differences in categorical responses by grade level taught were investigated with Fisher's exact test. RESULTS: There were 76 complete responses. No significant differences across grade levels were found. More than half (53%, 43/81) reported not feeling protected from occupational COVID-19 exposure. Lack of mask usage/enforcement was the most frequently listed reason (40%, 17/42). Physical distancing barriers included large student-teacher ratios. CONCLUSIONS: Consistent mask guidance at state and local levels, increased financial support, and lower student-teacher ratios may improve the implementation of CDC guidance for classrooms. Conflicting statewide and district-level school mask policies may negatively impact teachers' risk perceptions.