Groundwater nitrate (NO₃⁻) pollution is a worldwide environmental problem. Therefore, identification and partitioning of its potential sources are of great importance for effective control of groundwater quality. The current study was carried out to identify the potential sources of groundwater NO₃⁻ pollution and determine their apportionment in different land use/land cover (LULC) types in a traditional agricultural area, Weining Plain, in Northwest China. Multiple hydrochemical indices, as well as dual NO₃⁻ isotopes (δ¹⁵N–NO₃ and δ¹⁸O–NO₃), were used to investigate the groundwater quality and its influencing factors. LULC patterns of the study area were first determined by interpreting remote sensing image data collected from the Sentinel-2 satellite, then the Bayesian stable isotope mixing model (MixSIAR) was used to estimate proportional contributions of the potential sources to groundwater NO₃⁻ concentrations. Groundwater quality in the study area was influenced by both natural and anthropogenic factors, with anthropological impact being more important. The results of LULC revealed that the irrigated land is the dominant LULC type in the plain, covering an area of 576.6 km² (57.18% of the total surface study area of the plain). On the other hand, the results of the NO₃⁻ isotopes suggested that manure and sewage (M&S), as well as soil nitrogen (SN), were the major contributors to groundwater NO₃⁻. Moreover, the results obtained from the MixSIAR model showed that the mean proportional contributions of M&S to groundwater NO₃⁻ were 55.5, 43.4, 21.4, and 78.7% in the forest, irrigated, paddy, and urban lands, respectively. While SN showed mean proportional contributions of 29.9, 43.4, 61.5, and 12.7% in the forest, irrigated, paddy, and urban lands, respectively. The current study provides valuable information for local authorities to support sustainable groundwater management in the study region.

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.

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.

Spatiotemporal variability complicates source apportionment of metals in urban lakes, especially when rainfall drives urban non-point source pollution. As, Cd, Cr, Pb, Hg, Ag, Co, Cu, Fe, Mn, Ni, Sb, Sr and Zn concentrations in 648 water samples collected before and after rain in 6 urban lakes of Beijing, China were determined during 2013–2015. The response of metals concentrations after rain to the interaction between rainfall and antecedent dry days was significant. Metals concentrations were normalized pursuant to the interaction effect as the input of positive matrix factorization (PMF) to develop the interaction normalized-PMF (IN-PMF). Four primary pollution sources were diagnosed. Sediment release was considered to be the main source of Fe, Co and Ni independent of rainfall. Hg, As and some Cr associated with pesticides and fertilizers were likely to come from soil erosion and runoff from green space. It is probable that road runoff was the dominant source for heavy metals related to traffic emissions, including Pb, Cd, Cu, Sb, Mn and Zn. Cr, Sr and some Cu and Zn as key elements of rooftops can be regarded as from roof runoff. The IN-PMF lowered roof and road runoff contributions and raised the contribution of soil erosion from green space, with Pb, Sb, Cu, Zn, Cd and Mn increasing by 15.9%, 10.7%, 13.1%, 12.2%, 13.3% and 16.8%. The results shed more light on the stormwater runoff pollution mitigation on impervious surfaces and metals enrichment problems in infiltration soil on green space in the low impact development (LID) setting. The Bayesian network revealed the spatial variability of transport and fate of metal elements from land surfaces to urban lakes, supplementing the secondary pollution sources from different land use. This study will provide new insights for source apportionment of non-point source pollution under the background of sponge city construction.

Metals may affect adversely cardiovascular system, but epidemiological evidence on the associations of priority-controlled metals including antimony (Sb), arsenic (As), cadmium, lead, and thallium with children's blood pressure (BP) was scarce and inconsistent. We conducted two panel studies with 3 surveys across 3 seasons among 144 and 142 children aged 4–12 years in Guangzhou and Weinan, respectively. During each seasonal survey, urine samples were collected for 4 consecutive days and BP was measured on the 4th day. We obtained 786 BP values and urinary metals measurements at least once within 4 days, while 773, 596, 612, and 754 urinary metals measurements were effective on the health examination day (Lag 0), and the 1ˢᵗ, 2ⁿᵈ, and 3ʳᵈ day preceding BP measurement (Lag 1, lag 2 and lag 3), respectively. We used linear mixed-effect models, generalized estimating equations and multiple informant models to assess the associations of individual metal at each lag day and accumulated lag day (4 days averaged, lag 0–3) with BP and hypertension, and Bayesian Kernel Machine Regression to evaluate the relations of metals mixture at lag 0–3 and BP outcomes. We found Sb was positively and consistently related to systolic BP (SBP), mean arterial pressure (MAP), and odds of having hypertension within 4 days, which were the strongest at lag 0 and declined over time. And such relationships at lag 0–3 showed in a dose-response manner. Meanwhile, Sb was the only contributor to the relations of mixture with SBP, MAP, and odds of having hypertension. Also, synergistic interaction between Sb and As was significant. In addition, modification effect of passive smoking status on the association of Sb and SBP was more evident in passive smokers. Accordingly, urinary Sb was consistently and dose-responsively associated with increased BP and hypertension, of which Sb was the major contributor among children.

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.

Dyslipidemia may be a potential mechanism linking air pollution to adverse cardiovascular outcomes and this may differ among obese and normal-weight populations. However, the joint effect of multiple air pollutants on lipid profiles and the role of each pollutant are still unclear. This panel study aims to investigate and compare the overall associations of major air pollutants with lipid parameters in obese and normal-weight adults, and assess the relative importance of each pollutant for lipid parameters. Forty-four obese and 53 normal-weight young adults were recruited from December 2017 to June 2018 in Beijing, China. Their fasting blood was collected and serum lipid levels were measured in three visits. Six major air pollutants were included in this study, which were PM₂.₅, PM₁₀, NO₂, SO₂, O₃ and CO. Bayesian kernel machine regression (BKMR) was implemented to estimate the joint effect of the six air pollutants on various lipid parameters. We found that decreased high-density lipoprotein cholesterol (HDL-C) in the obese group and increased low-density lipoprotein cholesterol (LDL-C) and non-HDL-C in the normal-weight group were associated with the exposure to the mixture of six air pollutants above. Significant increases in total cholesterol (TC)/HDL-C and non-HDL-C/HDL-C were observed in both groups, and the effect was stronger in obese group. Of the six air pollutants above, O₃ had the largest posterior inclusion probability in above lipid indices, ranging from 0.75 to 1.00. In the obese group, approximately linear exposure-response relationships were observed over the whole range of logarithmic O₃-8 h max concentration, while in the normal-weight group, these relationships existed when the logarithmic concentration exceeded about 2.8. Therefore, lipid profiles of obese adults may be more sensitive to air pollution and this study highlights the importance of strengthening emissions control efforts for O₃ in the future.

With increasingly stringent regulations on emission criteria and environment pollution concerns, marine fuel oils (particularly heavy fuel oils) that are commonly used today for powering ships will no longer be allowed in the future. Various maritime energy strategies are now needed for the long-term upgrade that might span decades, and quantitative predictions are necessary to assess the outcomes of their implementation for decision support purpose. To address the technical need, a novel approach is developed in this study that can incorporate the strategic implementation of fuel choices and quantify their adequacy in meeting future environmental pollution legislations for ship emissions. The core algorithm in this approach is based on probabilistic simulations with a large sample size of ship movement in the designated port area, derived using a Bayesian ship traffic generator from existing real activity data. Its usefulness with scenario modelling is demonstrated with application examples at five major ports, namely the Ports of Shanghai, Singapore, Tokyo, Long Beach, and Hamburg, for assessment at Years 2020, 2030, and 2050 with three economic scenarios. The included fuel choices in the application examples are comprehensive, including heavy fuel oils, distillates, low sulphur fuel oils, ultra-low sulphur fuel oils, liquefied natural gas, hydrogen, biofuel, methanol, and electricity (battery). Various features are fine-tuned to reflect micro-level changes on the fuel choices, terminal location, and/or ship technology. Future atmospheric pollutant emissions with various maritime energy strategies implemented at these ports are then discussed comprehensively in details to demonstrate the usefulness of the approach.

Goiter is one of common endocrine diseases, and its etiology has not been fully elucidated. The changes in trace elements' levels have an important impact on the thyroid. We designed a case-control study, which involved 383 goiter cases and 383 matched controls. We measured these elements in the urine of participants by inductively coupled plasma optical emission spectrometry (ICP-OES), graphite furnace atomic absorption spectrometry (GFAAS) and As³⁺-Ce⁴⁺ catalytic spectrophotometry. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select the elements into multi-element models, conditional logistic regression models were applied to analyze the association between elements and goiter risk. Bayesian kernel machine regression (BKMR) model was used to depict elements’ mixtures and evaluate their joint effects. Finally, 7 elements were included in the multi-element model. We found that the concentrations of lithium (Li), strontium (Sr) and barium (Ba) had a negative effect with goiter risk, and lead (Pb) and iodine (I) showed an extreme positive effect. Additionally, compared with the lowest levels, patients with highest quartiles of I and Pb were 6.49 and 1.94 times more likely to have goiter, respectively. On the contrary, in its second and third quartiles, arsenic (As) showed a negative effect (both OR<1). BKMR model showed a certain interaction among Pb, As, Sr and Li on goiter risk. Further large sample studies are needed to confirm these findings in the future.