Bisphenols (BPs) are widely used endocrine-disrupting chemicals that require accurate biological monitoring, but the optimal biological matrix for the detection of specific analogs remains unclear. This study performed the first comprehensive paired analysis of urine, whole blood, serum, and plasma samples from 38 individuals to systematically evaluate the matrix effects (ME), detection performance, and health risks of seven BPs (BPA, BPS, BPF, BPP, BPZ, BPAF, BPAP). Key findings show that urine is the best matrix for BPA due to minimal ME and highest sensitivity, confirming the reliability of its recent exposure assessment. Whole blood exhibits excellent stability and the highest concentration of ΣBPs, making it ideal for detecting BPF, BPAF, and BPAP and reflecting systemic exposure. Serum provides the best standardized data for BPS and BPP, supporting their use in chronic studies, whereas plasma exhibits specificity for BPZ but requires pretreatment optimization due to significant matrix inhibition. The health risk is negligible, although BPA exposure is skewed to the right in the high-risk subgroup, with surrogates (BPS/BPAP) accounting for less than 1% of the risk of BPA. These results underscore the need, within cost and design constraints, for multi-matrix biological monitoring of a large class of contaminants. Limitations included the small sample size and geographic specificity. Future studies should conduct more rigorous and in-depth health risk assessments, validate matrix-specific exposure windows over time, and extend monitoring to BPs in adipose tissue or breast milk.

Traditional electronic waste (E-waste) dismantling activities release substantial bisphenol analogues (BPs). In recent years, China has promoted the transformation of traditional E-waste dismantling parks into circular economy parks, yet the contamination characteristics of BPs under this new model remain poorly understood. In the present study, we systematically analyzed the pollution characteristics and health risks of 10 BPs in soils surrounding four circular economy E-waste parks [Ziya (ZY) in Tianjin, Fengjiang (FJ) and Binhai (BH) in Zhejiang, and Qingyuan (QY) in Guangdong]. The results showed that BPs were widely detected in soils surrounding the parks, with bisphenol A (BPA) accounting for 55%-77% of total concentrations, followed by tetrabromobisphenol A (TBBPA) (6%-33%) and bisphenol F (BPF) (5%-14%). Significant inter-park heterogeneity was observed: FJ exhibited higher median BPA, BPF, and TBBPA than ZY (P < 0.05), while its BPF concentrations surpassed those in BH and QY (P < 0.05). Further correlation and principal component analyses revealed strong positive associations among BPA, BPF, and TBBPA (r = 0.638-0.873), which collectively loaded highly on PC1 (33%-52%), and their co-release mechanisms were closely related to the E-waste dismantling and incineration process. In addition, the spatial distribution of BPs showed a radial decline with each park as the core, suggesting potential health risks to surrounding populations. Risk assessment using estimated daily intake showed that BPA exposures exceeded the European Food Safety Authority’s updated tolerable daily intake. Monte Carlo simulation indicated that 97%, 60%, 80%, 25%, and 15% of the population were at risk of non-carcinogenic effects from BPA, BPF, bisphenol AF, bisphenol S, and bisphenol AP, respectively.

Hairdressers are continually exposed to chemicals, including many endocrine-disrupting chemicals (EDCs), yet few studies have assessed these exposures among U.S. hairdressers despite the potential health risks. We quantified concentrations of five biomarkers - four EDC exposure biomarkers [2-naphthol, methylparaben (MeP), ethylparaben, and propylparaben] and capsaicin - in post-shift urine samples from 23 female hairdressers of color (Black/Latina) serving a primarily ethnic clientele in the Maryland/Washington DC metropolitan area. Results from hairdressers were compared to those from 17 female office workers of similar race/ethnicity and to a representative sample of 431 similarly aged women in the U.S. general population. We also assessed exposure determinants for highly detected biomarkers among hairdressers. Overall, hairdressers had higher biomarker concentrations than office workers and women in the U.S. general population. Geometric mean (GM) concentrations of 2-naphthol and MeP were 2-3 times higher in hairdressers than in office workers (2-naphthol:17.4 vs. 7.59 ng/mL; MeP: 150 vs. 48.9 ng/mL; both P < 0.01), and 1.5-2.5 times higher than in U.S. women (2-naphthol: 15.5 vs. 6.31 ng/mL; MeP: 134 vs. 87.0 ng/mL). Hairdressers serving predominantly Black clientele had higher biomarker concentrations than those serving a predominantly Latinx clientele. Select salon services and products (e.g., chemical straightening/relaxing, semipermanent hair color, hair extensions, hairspray) were associated with higher 2-naphthol and MeP concentrations, while hair bleach use and braiding were associated with lower concentrations. Mask use during chemical-intensive services was associated with reduced MeP concentrations (GM: 117 vs. 159 ng/mL). Larger studies are needed to comprehensively assess exposures and reduce health risks for this workforce.

This study examines the size-segregated particulate matter (PM) concentrations in blacksmith workshops (WSs) in West Java, Indonesia, using the Ambient Nano Sampler (ANS). PM was categorized into six size fractions: > 10 µm, 10-2.5 µm, 2.5-1 µm, 1-0.5 µm, 0.5-0.1 µm, and < 0.1 µm (ultrafine particles, UFPs). The results showed that WSs with intensive welding activities had the highest ultrafine PM concentrations. WS-A recorded the highest total suspended particle (TSP) concentration (3,504.58 µg/m³), with UFPs contributing 998.27 µg/m³ (37% of PM_2.5), while WS-B had the lowest TSP concentration (1,978.16 µg/m³). The ratio of PM < 0.1 to PM_2.5 ranged from 0.26 to 0.42, demonstrating the dominance of UFPs in fine PM fractions. Outdoor UFP levels (4.64 µg/m³) were significantly lower than indoor concentrations, confirming that blacksmithing activities are a major emission source. Heavy metal analysis revealed iron (Fe) as the dominant element (up to 284.775 µg/m³ in UFPs), with Cr, Pb, and Mn also detected, highlighting occupational exposure risks. These findings indicate that blacksmith WSs generate significantly higher UFP and metal emissions than ambient environments, posing potential health hazards for workers. This study underscores the need for effective air quality management in small-scale metal industries.

Superfund sites are polluted locations in the U.S. designated by the Environmental Protection Agency for cleanup due to hazardous waste contamination. The U.S. Government Accountability Office estimates that 50% of U.S. Superfund sites are at risk of flooding, potentially leading to mobility of contaminants into the environment. We investigate the relationships between rainfall, snowpack, and storms with levels of contaminants in well water beneath Dzus Fasteners Co. in West Islip, New York (NY, 2011-2019) and American Thermostat Co. in South Cairo, NY (2005-2011) using data shared by the New York State Department of Environmental Conservation. At Dzus, sampling was conducted approximately yearly. During Superstorm Sandy in October 2012, the water table beneath Dzus rose 1 to 2 meters due to storm surge, then receded into the Atlantic Ocean via Willetts Creek. In a Dzus monitoring well (MW), cadmium (Cd) increased from 64.4 µg/L in August 2012, to 120 µg/L in November 2013; lower levels of Cd were seen in other MWs. In April 2013, after Sandy, 14 out of 70 sediment samples collected along Willetts Creek were above 90 mg/kg Cd, with a maximum of 1,600 mg/kg; the previous maximum was 133 mg/kg. At American Thermostat, positive correlations were detected between volatile organic compounds and both rainfall and snowpack in three out of six observation wells. For example, in well observation well 12, snowpack showed strong correlations with the on-site main contaminants: trichloroethylene (r = 0.96) and tetrachloroethylene (r = 0.86). Despite limited sampling data, these case studies suggest that contaminant levels in groundwater and nearby sediment at Superfund sites can increase with rainfall, snowpack, and intense storms. Increased monitoring frequency is recommended to better understand the impacts of storms and precipitation at Superfund sites.

Due to its widespread occurrence in environments, the exposure risk to 6-PPD quinone (6-PPDQ) is receiving increasing attention. Considering the crucial role of retinoic acid in regulating physiological state, we investigated the effects of 6-PPDQ on retinoic acid synthesis and the underlying mechanisms in nematodes. Retinoic acid content was reduced by 6-PPDQ (0.1-10 μg/L). Expression of enzyme genes alh-3 governing retinoic acid synthesis and dhs-19 governing retinal synthesis was decreased by 6-PPDQ. Retinoic acid content was reduced by alh-3 and dhs-19 RNA interference (RNAi). Additionally, alh-3 and dhs-19 RNAi conferred susceptibility to 6-PPDQ toxicity, and these two genes functioned in intestine to modulate 6-PPDQ toxicity. In the intestine, alh-3 and dhs-19 expressions were decreased by intestinal pmk-1, bar-1, and daf-16 RNAi, and pmk-1, bar-1, and daf-16 RNAi reduced retinoic acid content and induced susceptibility to 6-PPDQ toxicity. Additionally, 6-PPDQ decreased expression of sex-1 encoding retinoic acid receptor. After 6-PPDQ exposure, sex-1 expression was decreased by alh-3 and dhs-19 RNAi, and sex-1 RNAi further inhibited pmk-1, bar-1, and daf-16 expressions. sex-1 RNAi caused susceptibility to 6-PPDQ toxicity. Moreover, 6-PPDQ-induced toxicity and decrease in sex-1 expression were suppressed by retinoic acid treatment. Therefore, 6-PPDQ disrupted retinoic acid synthesis, which was linked to toxicity induction through the formation of a feedback loop between the alh-3/dhs-19-sex-1 axis and intestinal signals.

Antimicrobial resistance poses a global public health crisis, yet environmental thresholds for antimicrobial-resistant bacteria remain underdeveloped. In this study, a quantitative microbial risk assessment (QMRA) approach was applied to derive environmental thresholds and evaluate the health risks of (i) third-generation cephalosporin-resistant Escherichia coli (3CEC); (ii) carbapenem-resistant Escherichia coli (CREC); (iii) methicillin-resistant Staphylococcus aureus (MRSA); (iv) fluoroquinolone-resistant Salmonella typhi (FRST), along with the corresponding general pathogens, i.e., Escherichia coli, Staphylococcus aureus and Salmonella typhi, in recreational waters at bathing beaches in the Yangtze Estuary. Using an acceptable disease burden of 1 × 10^-4 disability-adjusted life years (DALYs) per person per case, the derived environmental thresholds for 3CEC, CREC, MRSA and FRST were 6.95 × 10^-2, 3.95 × 10^-2, 4.10 × 10^-3, and 2.85 × 10^-1 copies/mL, respectively. Seasonal variations in health risks were evident: risk levels for 3CEC, CREC and FRST were higher in the low-flow season than in the high-flow season [median hazard quotients (HQs) = 3.80 × 10^-3, 6.70 × 10^-3, 0.629 in high-flow season and 1.00, 1.77, 4.38 in low-flow season], whereas the opposite trend was observed for MRSA (median HQ = 37.5 in high-flow season and 1.01 in low-flow season, P < 0.001). These patterns were consistently supported by infection probabilities and disease burden (DALYs). Sensitivity analysis revealed that pathogen abundance was the most influential part in the QMRA models, while other parameters showed low sensitivity. These results validated the thresholds derived and highlighted the necessity of frequent surveillance and accurate detection in practice.

Permeability coefficient (k_p) is routinely used to quantify the movement of chemicals across the skin. Log octanol-water partition coefficient (log K_ow) and molecular weight (MW) are often incorporated into skin permeation models to generate the k_p. Given that the same dataset is used to estimate skin permeation, novel approaches are required to achieve targeted and accurate results. The main goal of this study is to identify a prioritization scheme for quantitative structure-permeability relationships (QSPRs) when using two molecular descriptors, log K_ow and MW. A second goal is to determine whether classification based on functional groups and structural similarities enhances the existing QSPR models. Ten QSPR models using log K_ow and MW were reviewed to identify the predictive ability of k_p using a comprehensive dataset. The dataset was filtered to identify molecules with structural and functional group similarities, and the resulting subset was subjected to the QSPRs used in the preceding analysis to demonstrate improvements in predictive performance. By comparing the k_p predictions of the QSPRs to measured k_p values, we were able to devise a systematic approach to improve the predictive ability of QSPRs. Using the proposed hierarchical approach, researchers can select an appropriate QSPR model to accurately predict the dermal k_p of a given chemical compound. Such predictions can be a viable alternative to experimentation, which can be resource-intensive.