Fine particulate matter (PM2.5) serves as a significant carrier of toxic heavy metals and poses potential health risks, particularly for children in school environments. However, limited studies have systematically compared school settings across cities with differing industrial profiles, resulting in a critical gap in understanding spatial variations in exposure risks. This study characterized the concentrations, seasonal variability, sources, and inhalation health risks of five PM2.5-bound metals, namely Cu, Cd, Ni, Pb, and Zn, in schools from two contrasting cities in Guangdong Province, China: Guangzhou and Maoming. PM2.5 samples were collected from 20 schools during summer and winter of 2018 and analyzed by inductively coupled plasma mass spectrometry. Pollution status, assessed using the geoaccumulation index (Igeo) and enrichment factor (EF), indicated that PM2.5-bound metals in both Guangzhou and Maoming were predominantly of anthropogenic origin, with Cu and Cd exhibiting the highest enrichment and contamination levels, whereas Ni and Pb were largely associated with natural background sources and Zn reflected mixed origins. Principal component analysis (PCA) was used to identify dominant sources of heavy metals and support source apportionment. The noncarcinogenic and carcinogenic risks were assessed for children and adults. Across both cities, Cu (471.19) showed the highest mean concentrations, followed by Zn, Pb, Ni, and Cd, with winter values generally exceeding summer values. Guangzhou exhibited stronger anthropogenic enrichment than Maoming, particularly for Cu (27.72) and Cd (27.32), indicating greater influence from traffic, industrial combustion, and urban emissions. Correlation analysis suggested common and seasonally enhanced sources in winter. Health risk assessment showed that all hazard quotients and carcinogenic risk values were below accepted thresholds, indicating low immediate inhalation risk. However, children experienced higher exposure than adults, and Pb (4.85E-03) and Cu (3.27E-03) contributed most to noncarcinogenic risk, while Cd (7.30E-06) and Ni (2.21E-06) posed the main carcinogenic concern. Overall, the results highlight the need for continued source control in school environments, with special attention to nonexhaust traffic emissions and industrial combustion.
Haseeb Tufail Moryani, Fu Wang, Yang Zhou et al.· Journal of Applied Toxicolog...· 0 citations
Emerging studies indicate exposure to heavy metals is linked with uric acid levels and hyperuricemia risk. However, the combined effects of heavy metals and the underlying mechanisms remain poorly characterized.
In this community-based cross-sectional study, plasma concentrations of 20 heavy metals and serum uric acid were measured in 1,312 adults in China. Triglyceride-glucose (TyG) index, a biomarker of insulin resistance, was also calculated. Generalized linear regression model, bayesian kernel machine regression, weighted quantile sum, and quantile G-computation were used to explore the associations between single or co-exposure to heavy metals and serum uric acid levels (SUA), as well as hyperuricemia (HUA). We also explored the mediating role of TyG in these associations. And then, benchmark dose lower bound (BMDL) of selenium (Se) was calculated using benchmark dose model.
In the single pollutant model, Se was positively associated with SUA (β = 0.18, 95% CI: 0.04, 0.32) and HUA (OR = 1.51, 95% CI: 1.08, 2.11). Three multi-pollutant models all revealed positive associations between metal mixtures and SUA as well as HUA, with Se being the primary contributor. Furthermore, TyG significantly mediated the associations of Se with SUA and HUA, with the mediated proportions of 47.85% and 34.72%, respectively. BMDL of Se was 118.70–233.12 μg/d based on HUA.
In conclusion, metal mixtures had positive associations with SUA and HUA. Insulin resistance might be a crucial mediated pathway between Se and outcomes. Our estimated point of departure (POD) for Se was lower than those recommended by other studies.
Jing-Fu Lai, Ya-qin Zhang, Yun-Ting Zhang et al.· Frontiers in Public Health· 0 citations
China has invested heavily in air pollution control in recent years, yet comprehensive assessments of the associated health economic benefits remain limited. Prior research on the health economic benefits of air pollution control has mainly focused on mortality outcomes and PM2.5, which may substantially underestimates the true benefits of clean air policies. This study aims to evaluate the health economic benefits and losses associated with air pollution control by incorporating multiple health outcomes, population groups and air pollutants. We integrate daily mortality, hospital admission and outpatient visits and air pollutant (nitrogen dioxide, ozone [O3] and fine particulate matter) data from 18 cities from 2015 to 2022 to assess the health economic benefits. A generalized additive model was used to estimate concentration-response relationships. Health economic benefits were calculated based on the value of statistical life (VSL) and direct medical costs. Between 2015 and 2022, controlling PM2.5 and NO2 yielded significant health economic benefits across all health outcomes. Air pollution control generated significant health economic benefits across multiple outcomes in Guangdong Province. The total health economic benefits associated with PM2.5 and NO2 were 10,305.35 and 1017.00 million CNY, respectively. In contrast, O3 control yielded negative net benefits across all outcomes. Between 2015 and 2022, the multi-outcome health benefits of controlling PM2.5 exceeded mortality-only estimates by 31.79%, respectively For PM2.5 control, the elderly accounted for over 85% of the mortality-related benefits. These findings highlight the need for coordinated PM2.5-O3 control, the integration of multi-outcome evaluations into policy design, and targeted protection for vulnerable populations particularly the elderly.
