Journal of Exposure Science and Environmental Epidemiology最新文献

Background: Elemental analysis of teeth allows for exposure assessment during critical windows of development and is increasingly used to link early life exposures and health. The measurement of inorganic elements in teeth is challenging; laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is the most widely used technique.

Objective: Both synchrotron x-ray fluorescence (SXRF) and LA-ICP-MS have the capability to measure elemental distributions in teeth with each having distinct advantages and disadvantages.

Methods: In our study, we compared these two methods for teeth elemental quantification. SXRF was able to achieve spatial resolutions of 0.3 µm and is non-destructive while giving similar elemental quantification results to LA-ICP-MS.

Results: For particular elements, SXRF can offer lower detection limits but depends on the specific beam intensity. The comparison between methods revealed less than 10% disagreement between quantification results from LA-ICP-MS and SXRF.

Impact: Synchrotron x-ray fluorescence can be used to effectively quantify elemental distributions in teeth at a nanoscale resolution and is comparable to current laser ablation inductively coupled plasma mass spectrometry. Both methods offer advantages and disadvantages with LA-ICP-MS offering in-lab analyses, whereas SXRF offers much finer spatial and temporal scales and better detection capabilities. For studies focused on fine scale changes in structure, SXRF is more appropriate than LA-ICP-MS.

Background: The Observed Individual Means (OIM) methodology, based on the non-parametric bootstrap, is usually employed to perform basic probabilistic dietary chronic exposure assessment, and assumes independence and identical distribution of occurrence data within food category. However, this assumption may not be valid if several expected distributions of occurrence can be a priori identified within food category. Moreover, OIM assumes each analysed food sample to equally contribute to mean occurrence, as information about relevance of each food item cannot be incorporated into exposure assessment.

Objective: In this paper we address the above-mentioned violations and develop two statistical methodologies to accommodate for them into OIM.

Methods: The stratified non-parametric bootstrap and weighted mean occurrence are employed to correct for such violations. As a case study, we compare the methodologies by estimating the exposure of the adult Italian population to the process contaminant 3-monochloropropane-1,2-diol.

Results: We propose strategies to interpret their results and show their relevance in conducting exposure assessment.

Impact statement: For the first time in the literature, we critically examine a widely used methodology for Probabilistic Dietary Exposure Assessment from a statistical perspective, focusing on the underlying assumptions and their potential violations in real-world scenarios. We then develop techniques to address these violations, providing a more accurate and robust approach to exposure assessment. This work is particularly relevant for risk assessors and managers, since it offers a refined toolset for more precise exposure assessments.

Background: Preterm birth (PTB) is a common pregnancy complication associated with significant neonatal morbidity. Prenatal exposure to environmental chemicals, including toxic and/or essential metal(loid)s, may contribute to PTB risk.

Objective: We aimed to summarize the epidemiologic evidence of the associations among levels of arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), manganese (Mn), lead (Pb), and zinc (Zn) assessed during the prenatal period and PTB or gestational age at delivery; to assess the quality of the literature and strength of evidence for an effect for each metal; and to provide recommendations for future research.

Methods: We adapted the Navigation Guide methodology and followed PRISMA guidelines. We searched the MEDLINE/PubMed database for epidemiologic studies from 1995 to 2023. We used a customized risk of bias protocol and evaluated the sufficiency of evidence for an effect of each metal(loid) on PTB risk.

Results: A total of 1206 studies were identified and screened. Of these, 139 were assessed for eligibility by reading the full-text, and 92 studies were ultimately included (arsenic: 40, cadmium: 30, chromium: 11, copper: 21, mercury: 27, manganese: 17, lead: 41, zinc: 18, metal(loid) mixtures: 12). We found sufficient evidence that lead increases the risk of PTB and, while the evidence was limited, suggestive evidence that cadmium and chromium increase the risk of PTB. The evidence was deemed inadequate to determine an effect for the other metal(loid)s.

Significance: Future research would benefit from more precise PTB clinical phenotyping, measuring exposure early and longitudinally throughout pregnancy, using an appropriate media for metal(loid)s under study, and evaluating metal mixtures. Given the strength of evidence linking lead exposure and PTB, active and comprehensive prenatal screening for lead exposure among pregnant individuals is warranted.

Impact: By summarizing 92 epidemiologic studies that investigated the associations between metal exposure and preterm birth using the rigorous Navigation Guide methodology, our review provides compelling evidence for a strong link between prenatal lead exposure and preterm birth. Additionally, it suggests potential associations between cadmium and chromium exposure and preterm birth. Given the robust nature of this evidence, there is an urgent need for prenatal screening for lead exposure during pregnancy, along with targeted interventions to reduce exposure. These actions are critical for advancing maternal and child health.

Background: Many chemical releases are first noticed by community members, but reporting these concerns often involves considerable hurdles. Artificial Intelligence (AI)-enabled technologies, especially large language models (LLMs), can potentially reduce these barriers.

Objective: We hypothesized that AI-powered chatbots can facilitate reporting of pollution incidents through text messaging.

Methods: We created an AI-powered chatbot, "MyEcoReporter," that enables communities to report environmental incidents to government authorities. Eschewing traditional web-based forms, users text concerns via SMS to the LLM-powered application, engaging in a natural conversation through which required information is collected. The application was built using Python, AWS Lambda, DynamoDB, and Twilio, and deployed via Serverless.

Results: This architecture allowed rapid customization for various use cases, which successfully facilitated conversations and stored structured data for formal submission.

Impact statement: MyEcoReporter showcases the potential of Artificial Intelligence/Large Language Models to create user-friendly tools that translate community environmental concerns into actionable information for reporting to government authorities.

Background: Accurate estimates of personal exposure to ambient air pollution are difficult to obtain and epidemiological studies generally rely on residence-based estimates, averaged spatially and temporally, derived from monitoring networks or models. Few epidemiological studies have compared the associated health effects of personal exposure and residence-based estimates.

Objective: To evaluate the association between exposure to air pollution and cognitive function using exposure estimates taking mobility and location into account.

Methods: Residence-based dispersion model estimates of ambient NO₂, PM₁₀ and PM_2.5 were assigned to 768 London-dwelling participants of the English Longitudinal Study of Ageing. The London Hybrid Exposure Model was implemented to adjust estimates per pollutant to reflect the estimated time-activity patterns of each participant based on age and residential location. Single pollutant linear mixed-effects models were fit for both exposure assessment methods to investigate the associations between assigned pollutant concentrations and cognitive function over a follow-up period of up to 15 years.

Results: Increased long-term exposures to residence-based ambient NO₂ (IQR: 11.10 µg/m³), PM₁₀ (2.35 µg/m³), and PM_2.5 (2.50 µg/m³) were associated with decreases of -0.10 [95% CI: -0.20, 0.00], -0.07 [-0.11, -0.02] and -0.14 [-0.21, -0.06], respectively, in composite memory score. Similar decreases were observed for executive function scores (-0.38 [-0.58, -0.18], -0.11 [-0.20, -0.02] and -0.14 [-0.29, 0.01], respectively). When applying personalised exposure estimates, which were substantially lower, similar decreases were observed for composite memory score per IQR, but a consistent pattern of slightly more adverse effects with executive function score was evident.

Impact statement: The present study constructed a framework through which time-activity information derived from a representative sample could be applied to estimates of ambient air pollution concentrations assigned to individuals in epidemiological cohort studies, with the intention of adjusting commonly used residence-based estimates to reflect population mobility and time spent in various microenvironments. Estimates of exposure were markedly lower when incorporating time-activity, likely because people in European populations spend a large proportion of their time indoors, where their exposure to ambient air pollution may be reduced through infiltration, which is not taken into account in residence-based ambient estimates. Further work into such methods could provide insights into the efficacy of personalising exposure estimates.

Background: Children living in communities with lower socioeconomic status and higher minority populations are often disproportionately exposed to particulate matter (PM) compared to children living in other communities.

Objective: We assessed whether adding HEPA filter air cleaners to classrooms with existing HVAC systems reduces indoor air pollution exposure.

Methods: From July 2022 to June 2023, using a block randomized crossover trial of 17 Los Angeles Unified School District elementary schools, classroom PM concentrations were monitored and compared for 99 classrooms with HEPA filter air cleaners and 87 classrooms with non-HEPA filter air cleaners.

Results: In HEPA classrooms, average school-year PM_2.5 was 39.9% lower (0.581 µg/m³; p < 0.001) and infiltration of outdoor PM_2.5 into classrooms was 13.8-82.4% lower than non-HEPA classrooms, depending on the school.

Impact: Few studies have examined HEPA filtration in a classroom environment, and this is one of the first studies since the COVID-19 pandemic to assess PM exposure in the classroom. Using a well powered block randomized crossover trial, we showed that adding portable HEPA air cleaners to classrooms that already had HVAC systems with MERV 13 air filters resulted in lower measurable PM concentrations and less infiltration of outdoor PM_2.5 compared to control classrooms with non-HEPA filters. This demonstrates that further improvements in classroom air quality, especially in environmentally burdened communities, can be achieved with additional filtration.

Background: Exposure to per- and polyfluoroalkyl substances (PFAS) has been linked with various cancers. Assessment of PFAS in drinking water and cancers can help inform biomonitoring and prevention efforts.

Objective: To screen for incident cancer (2016-2021) and assess associations with PFAS contamination in drinking water in the US.

Methods: We obtained county-level age-adjusted cancer incidence (2016-2021) from the Surveillance, Epidemiology, and End Results (SEER) Program. Data on PFAS levels in public drinking water systems were obtained from the Third (UCMR3; 2013-2015) and Fifth (UCMR5; 2023-2024) Unregulated Contaminant Monitoring Rule. UCMR3 measured PFOS, PFOA, PFNA, PFHxS, PFHpA, and PFBS. UCMR5 expanded measurements to include PFBA, PFHxA, PFPeA, and PFPeS. We created indicators of PFAS detection and, for UCMR5, concentrations above Maximum Contaminant Levels (MCLs). MCLs for PFOA and PFOS are 4 ng/L, and for PFNA and PFHxS are 10 ng/L. We used Poisson regression models to assess associations between PFAS detection or MCL violation and cancer incidence, adjusting for potential confounders. We estimated the number of attributable cancer cases.

Results: PFAS in drinking water was associated with increased cancer incidence in the digestive, endocrine, oral cavity/pharynx, and respiratory systems. Incidence rate ratios (IRRs) ranged from 1.02 to 1.33. The strongest association was observed between PFBS and oral cavity/pharynx cancers (IRR: 1.33 [1.04, 1.71]). Among males, PFAS was associated with cancers in the urinary, brain, leukemia, and soft tissues. Among females, PFAS was associated with cancers in the thyroid, oral cavity/pharynx, and soft tissue. PFAS in drinking water is estimated to contribute to 4626 [95% CI: 1,377, 8046] incident cancer cases per year based on UCMR3 data and 6864 [95% CI: 991, 12,804] based on UCMR5.

Impact statement: The ecological study examined the associations between PFAS in drinking water measured in two waves (2013-2015 and 2023-2024) and cancer incidence between 2016 and 2021. We found that PFAS in drinking water was associated with cancers in the organ system including the oral cavity/pharynx, lung, digestive system, brain, urinary system, soft tissue, and thyroid. Some cancers have not been widely studied for their associations with PFAS. We also observed sex differences in the associations between PFAS and cancer risks. This is the first ecological study that examined PFAS exposure in drinking water and various cancer risks.