Black carbon (BC) exposure in China continues to be relatively high, prompting researchers to assess BC exposure levels using data from monitoring sites, satellite remote sensing, and models. However, data regarding the application of a combined strategy comprising the analysis of monitoring data and various types of data to simulate BC exposure levels are lacking. Hence, the current study seeks to estimate short- and long-term BC exposure levels by combining national monitoring data with data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2). Furthermore, this study attempts to improve the spatio-temporal resolution of BC exposure levels using Bayesian maximum entropy (BME). The BME model performed well in terms of estimating short- (R² = 0.74 and RMSE = 1.76 μg/m³) and long-term (R² = 0.76 and RMSE = 1.3 μg/m³) exposure. Premature mortalities and economic losses were also assessed by applying localised concentration–response coefficients simulated in China. A total of 74,500 (95% confidence interval (CI): 23,900–124,500) and 538,400 (95% CI: 495,000–581,300) all-cause premature mortality cases were found to be associated with short- and long-term BC exposure, respectively. Meanwhile, short-term BC exposure was associated with economic losses ranging from 7.5 to 13.2 billion US dollars (USD) (1 USD = 6.36 RMB on January 19, 2022) based on amended human capital (AHC) and willingness to pay (WTP), accounting for 0.06%–0.1% of China's total gross domestic product (GDP) in 2017 (1.2 × 10⁴ billion USD), respectively. The economic losses for long-term exposure varied from 53 to 93.2 billion USD based on AHC and WTP, accounting for 0.4%–0.8% of China's total GDP in 2017, respectively.

Environmental heavy metal exposure has been considered to be the risk factor for neurodevelopmental disorders in children. However, the available data on the associations between multiple metals exposure and the risk of dyslexia in China are limited. The purpose of our study was to examine the associations between urinary metal concentrations and Chinese dyslexia risk. A total of 56 Chinese dyslexics and 60 typically developing children were recruited. The urinary concentration of 13 metals were measured by inductively coupled plasma-mass spectrometer (ICP-MS). Binary logistic regression and the Probit extension of Bayesian kernel machine regression (BKMR-P) were used to explore the associations between multiple metal exposure and the risk of Chinese dyslexia. Our results indicated that Co, Zn and Pb were significantly associated with Chinese dyslexia in the multiple-metal exposure model. After adjusting the covariates, a positive association was observed between Pb and the risk of Chinese dyslexia, with the odds ratio (OR) in the highest quartiles of 6.81 (95%CI: 1.07–43.19; p–trend = 0.024). Co and Zn were negatively associated with the risk of Chinese dyslexia. Compared to the lowest quartile, the ORs of Co and Zn in the highest quartile are 0.13 (95%CI: 0.02–0.72; p–trend = 0.026) and 0.18 (95%CI: 0.04–0.88; p–trend = 0.038), respectively. In addition, BKMR-P analysis indicated that with the cumulative level across Co, Zn and Pb increased, the risk of Chinese dyslexia gradually declined and then rebounded, albeit non-significantly, and Pb was the major contributor in this association. In general, the urinary concentrations of Co, Zn and Pb were significantly associated with Chinese dyslexia. More prospective studies are needed to confirm the health effects of multiple metals exposure in children with Chinese dyslexia.

The association of co-exposure to polycyclic aromatic hydrocarbons (PAHs) and phthalates (PAEs) with blood cell-based inflammatory biomarkers is largely unknown. We conducted a panel study of 144 children aged 4–12 years, with up to 3 repeated visits across 3 seasons. For each visit, we collected the first-morning urine for 4 consecutive days and fasting blood on the day of physical examination. We developed a gas chromatography/tandem mass spectrometry method to detect the metabolites of 10 PAHs (OH-PAHs) and 10 PAEs (mPAEs) in urine samples. We employed linear mixed-effects models to evaluate the individual associations of each OH-PAH and mPAE with blood cell-based inflammatory biomarkers over different lag times. Bayesian kernel machine regression (BKMR) and quantile g-computation were used to evaluate the overall associations of OH-PAHs and mPAEs mixtures with blood cell-based inflammatory biomarkers. After multiple adjustments, we found positive associations of summed hydroxylphenanthrene (∑OHPHE), summed OH-PAHs, and mono-n-butyl phthalate with inflammatory biomarkers such as neutrophil count, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and the systemic immune-inflammation index (SII) at lag 0 (the day of physical examination). Each 1% increase in ∑OHPHE was related to a 0.18% (95% confidence interval: 0.10%, 0.25%) increase in SII, which was the strongest among the above associations. The results of BKMR and quantile g-computation suggested that co-exposure to PAHs and PAEs mixture was associated with an elevated white blood cell count, neutrophil count, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and SII, to which ∑OHPHE and 1-hydroxypyrene (1-OHPYR) might be the major contributors. In addition, gender and age modified the associations of ∑OHPHE and 1-OHPYR with inflammatory biomarkers, where girls and younger children were more susceptible. In conclusion, co-exposure to PAHs and PAEs was associated with elevated inflammation in children, in which ∑OHPHE and 1-OHPYR might play important roles.

Nitrate pollution in oxygenated karst aquifers is common due to nitrification and anthropogenic inputs. However, the shift of nitrogen sources influenced by enhanced rural tourism activities and land use changes are not well understood. In this study, hydrochemistry and dual nitrate isotopes of water samples from a rural karst basin in Chongqing, southwestern China were employed to investigate the nitrate fate and its decadal change during the periods from 2007–2008 and 2017–2019. The results showed that δ¹⁵N–NO₃ and δ¹⁸O–NO₃ values at the groundwater basin resurgence averaged 9 ± 3.4‰ and 2.5 ± 3.4‰, respectively, with a mean NO₃⁻ concentration of 19.7 ± 5.4 mg/L in 2017–2019, clearly exceeding natural background levels. The dual isotope results suggested that nitrification occurred at the sampled sites. From 2007–2008 to 2017–2019, the mean δ¹⁵N–NO₃ values from the primary sink point and the resurgence of the underground river water samples increased from −0.2 ± 2.1 to 11.2 ± 4.8‰, 4.2 ± 0.9 to 9.0 ± 3.4‰, respectively. A Bayesian mixing model in R (MixSIAR) based on the isotopes revealed that soil organic nitrogen, and manure and sewage proportions for the groundwater increased by 34% and 23%, respectively, while chemical fertilizer and atmospheric precipitation proportions decreased by 32% and 25%, respectively. These decadal changes resulted from reforestation practices and enhanced rural tourism activities in the basin, which were evidenced by the change of land use patterns. The elevated nitrogen load from the rapid development of rural tourism is likely to increase this contamination in the near future if the infrastructure cannot meet the demands. The results from this study could contribute to minimizing environmental health risks in drinking water when rural tourism activities are increasing.

Exposure to metals has been linked with the risk of hypertensive disorders of pregnancy (HDP), but little is known about the potential effects of exposure to metal mixtures. Thus, our study aimed to investigated the impact of a complex mixture of metals on HDP, especially the interactions among metal mixtures. We did a population-based nested case-control study from October 2013 to October 2016 in Wuhan, China, including 146 HDP cases and 292 controls. Plasma concentrations of Aluminum (Al), Barium (Ba), Cobalt (Co), Copper (Cu), Lead (Pb), Mercury (Hg), Molybdenum (Mo), Nickel (Ni), Selenium (Se), Strontium (Sr), Thallium (Tl), and Vanadium (V) were measured and collected between 10 and 16 gestational weeks. We employed quantile g-computation, conditional logistic regression models, and Bayesian Kernel Machine Regression (BKMR) to assess the association of individual metals and metal mixtures with HDP risk. In the quantile g-computation, the OR for a joint tertile increase in plasma concentrations was 3.67 (95% CI: 1.70, 7.91). Hg contributed the largest positive weights and followed by Al, Ni, and V. In conditional logistic regression models, concentrations of Hg, Al, Ni, and V were significantly associated with the risk of HDP (p-FDR < 0.05). Compared to the lowest tertiles, the ORs (95% CI) for the highest tertiles of these four metals were 2.67 (1.44, 4.95), 3.09 (1.70, 5.64), 5.31 (2.68, 10.53), and 4.52 (2.26, 9.01), respectively. In the BKMR analysis, we observed a linear positive association between Hg, Al, V, and HDP, and a nonlinear relationship between Ni and HDP. A potential interaction between Al and V was also identified. We found that exposure to metal mixtures in early pregnancy, both individually and as a mixture, was associated with the risk of HDP. Potential interaction effects of Al and V on the risk of HDP may exist.

Adipokines, cytokines secreted by adipose tissue, may contribute to obesity-related metabolic disease. The role of environmental phenols and parabens in racial difference in metabolic disease burden has been suggested, but there is limited evidence. We examined the cross-sectional associations of urinary phenols and parabens with adipokines and effect modification by race. Urinary concentrations of 6 phenols (bisphenol-A, bisphenol-F, 2,4-diclorophenol, 2,5-diclorophenol, triclosan, benzophenone-3) and 4 parabens (methyl-paraben, ethyl-paraben, propyl-paraben, butyl-paraben) were measured in 2002–2003 among 1200 women (mean age = 52.6) enrolled in the Study of Women's Health Across the Nation Multi-Pollutant Study. Serum adipokines included adiponectin, high molecular weight (HMW)-adiponectin, leptin, soluble leptin receptor (sOB-R). Linear regression models were used to estimate the adjusted percentage change in adipokines per inter-quantile range (IQR) increase in standardized and log-transformed levels of individual urinary phenols and parabens. Bayesian kernel machine regression (BKMR) was used to evaluate the joint effect of urinary phenols and parabens as mixtures. Participants included white (52.5%), black (19.3%), and Asian (28.1%) women. Urinary 2,4-dichlorophenol was associated with 6.02% (95% CI: 1.20%, 10.83%) higher HMW-adiponectin and urinary bisphenol-F was associated with 2.60% (0.48%, 4.71%) higher sOB-R. Urinary methyl-paraben was associated with lower leptin in all women but this association differed by race: 8.58% (−13.99%, −3.18%) lower leptin in white women but 11.68% (3.52%, 19.84%) higher leptin in black women (P interaction = 0.001). No significant associations were observed in Asian women. Additionally, we observed a significant positive overall effect of urinary phenols and parabens mixtures in relation to leptin levels in black, but not in white or Asian women. Urinary bisphenol-F, 2,4-dichlorophenol and methyl-paraben may be associated with favorable profiles of adipokines, but methyl-paraben, widely used in hair and personal care products, was associated with unfavorable leptin levels in black women. Future studies are needed to confirm this racial difference.

The fetus is prenatally exposed to a mixture of organochlorine pesticides (OCPs), mercury (Hg), docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA) and selenium (Se) through maternal seafood consumption in real-life scenario. Prenatal exposure to these contaminants and nutrients has been suggested to affect thyroid hormone (TH) status in newborns, but the potential relationships between them are unclear and the joint effects of the mixture are seldom analyzed. The aim of the study is to investigate the associations of prenatal exposure to a mixture of OCPs, Hg, DHA, EPA and Se with TH parameters in newborns. 228 mother-infant pairs in Shanghai, China were included. We measured 20 OCPs, total Hg, DHA, EPA and Se in cord blood samples as exposure variables. The total thyroxine (TT4), free thyroxine (FT4), total triiodothyronine (TT3), free triiodothyronine (FT3), and thyroid-stimulating hormone (TSH) levels and the FT3/FT4 ratio in cord serum were determined as outcomes. Using linear regression models, generalized additive models and Bayesian kernel machine regression, we found dose-response relationships of the mixture component with outcomes: among the contaminants, p,p'-DDE was the most important positive predictor of TT3, while HCB was predominantly positively associated with FT3 and the FT3/FT4 ratio, indicating different mechanisms underlying these relationships; among the nutrients, EPA was first found to be positively related to the FT3/FT4 ratio. Additionally, we found suggestive evidence of interactions between p,p'-DDE and HCB on both TT3 and FT3, and EPA by HCB interactions for TT3, FT3 and FT3/FT4 ratio. However, the overall effects of the mixture on thyroid hormone parameters were not significant. Our result suggests that prenatal exposure to p,p’-DDE, HCB and EPA as part of a mixture might affect thyroid function of newborns in independent and interactive ways. The potential biological mechanisms merit further investigation.

Black carbon (BC) aerosol negatively affects air quality and contributes to climate warming globally. However, little is known about the relative contributions of different source control measures to BC reduction owing to the lack of powerful source-diagnostic tools. We combine the fingerprints of dual-carbon isotope using an optimized Bayesian Markov chain Monte Carlo (MCMC) scheme and for the first time to study the key sources of BC in megacity Guangzhou of the Pearl River Delta (PRD) region, China in 2018 autumn season. The MCMC model-derived source apportionment of BC shows that the dominant contributor is petroleum combustion (39%), followed by coal combustion (34%) and biomass burning (27%). It should be noted that the BC source pattern is highly sensitive to the variations of air masses transported with an enhanced contribution of fossil source from the eastern area, suggesting the important impact of regional atmospheric transportation on the BC source profile in the PRD region. Also, we further found that fossil fuel combustion BC contributed 84% to the total BC reduction during 2013–2018. The response of PM₂.₅ concentration to the ¹⁴C-derived BC source apportionment is successfully fitted (r = 0.90) and the results predicted that it would take ∼6 years to reach the WHO PM₂.₅ guideline value (10 μg m⁻³) for the PRD region if the emission control measures keep same as they are at present. Taken together, our findings suggest that dual-carbon isotope is a powerful tool in constraining the source apportionment of BC for the evaluations of air pollution control and carbon emission measures.

Karst ecosystems make an important contribution to the global carbon cycle, in which carbon-fixing microorganisms play a vital role. However, the healthy functioning of karst ecosystems is threatened because pollutants easily diffuse and spread through them due to their strong hydraulic connectivity. The microbiome of a karst river contaminated with antibiotics was studied. Through co-occurrence network analysis, six ecological clusters (MOD 1–MOD 6) with different distribution characteristics were determined, of which four were significantly correlated with antibiotics. The carbon fixation pathways in different ecological clusters were varied, and the dominant hydroxypropionate-hydroxybutyrate cycle and reductive acetyl-CoA pathway were negatively and positively correlated with antibiotics, respectively. Long-term antibiotic contamination altered the selection of carbonic anhydrase (CA) encoding genes in some of the CA-producing mineralization microorganisms. The selection of different carbon fixation pathways is a possible strategy for the microbial community to compensate for the adaptation costs associated with the pressure of antibiotics contamination and emergence of antibiotics resistance. Bayesian network analysis revealed that some carbon sequestration functions (such as β-CA and reductive acetyl-CoA pathway) surpassed certain antibiotic resistance genes in the regulation of environmental factors and microbial networks. An ecological cluster (MOD5) that possibly homologous to antibiotic contamination was the final node of the microbial community in karst river, which indicated that ecological clusters were not only selected by antibiotics, but were also regulated by multiple environmental factors in the karst river system. The carbon sequestration pathway was more directly reflected in the abundance of ecological groups than in the influence of CA. This study provides new insights into the feedback effect of karst system on typical pollutants generated from human activities.