We aimed to explore the effects of mixtures of lead and various metals on blood pressure (BP) and the odds of pre-hypertension (systolic blood pressure (SBP) 120–139 mmHg, and/or diastolic blood pressure (DBP) 80–89 mmHg) and hypertension (SBP/DBP ≥140/90 mmHg) among Chinese adults in a cross-sectional study. This study included 11,037 adults aged 18 years or older from the 2017–2018 China National Human Biomonitoring. Average BP and 13 metals (lead, antimony, arsenic, cadmium, mercury, thallium, chromium, cobalt, molybdenum, manganese, nickel, selenium, and tin) in blood and urine were measured and lifestyle and demographic data were collected. Weighted multiple linear regressions were used to estimate associations of metals with BP in both single and multiple metal models. Weighted quantile sum (WQS) regression was performed to assess the relationship between metal mixture levels and BP. In the single metal model, after adjusting for potential confounding factors, the blood lead levels in the highest quartile were associated with the greater odds of both pre-hypertension (odds ratio (OR): 1.56, 95% CI: 1.22–1.99) and hypertension (OR:1.75, 95% CI: 1.28–2.40) when compared with the lowest quartile. We also found that blood arsenic levels were associated with increased odds of pre-hypertension (OR:1.31, 95% CI:1.00–1.74), while urinary molybdenum levels were associated with lower odds of hypertension (OR:0.68, 95% CI:0.50–0.93). No significant associations were found for the other 10 metals. WQS regression analysis showed that metal mixture levels in blood were significantly associated with higher SBP (β = 1.56, P < 0.05) and DBP (β = 1.56, P < 0.05), with the largest contributor being lead (49.9% and 66.8%, respectively). The finding suggests that exposure to mixtures of metals as measured in blood were positively associated with BP, and that lead exposure may play a critical role in hypertension development.

Previous studies have indicated that maternal exposure to particles with aerodynamic diameter <2.5 μm (PM₂.₅) is associated with adverse birth outcomes. However, the critical exposure windows remain inconsistent. A retrospective cohort study was conducted in Huai River Basin, Henan, China during 2013–2018. Daily PM₂.₅ concentration was collected using Chinese Air Quality Reanalysis datasets. We calculated exposures for each participant based on the residential address during pregnancy. Binary logistic regression was used to examine the trimester-specific association of PM₂.₅ exposure with preterm birth (PTB), low birth weight (LBW) and term LBW (tLBW), and we further estimated monthly and weekly association using distributed lag models. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated for each 10 μg/m³ increase in PM₂.₅ exposure. Stratified analyses were performed by maternal age, infant gender, parity, and socioeconomic status (SES). In total, 196,780 eligible births were identified, including 4257 (2.2%) PTBs, 3483 (1.8%) LBWs and 1770 (0.9%) tLBWs. Maternal PM₂.₅ exposure during the second trimester were associated with the risk of PTB and LBW. At the monthly level, the PTB and LBW risks were associated with PM₂.₅ exposure mainly in the 4th -6th month. By estimating the weekly-specific association, we observed that critical exposure windows of PM₂.₅ exposure and PTB were in the 18th- 27th gestational weeks. Stronger associations were found in younger, multiparous mothers and those with a female baby and in low SES. In conclusion, the results indicate that maternal PM₂.₅ exposure during the second trimester was associated with PTB and LBW. Younger, multiparous mothers and those with female babies and in low SES were susceptible.

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.

Sea fish contain omega-3 polyunsaturated fatty acids (omega-3 PUFAs) which have been found to reduce triglyceride (TG) levels. However, sea fish may contain pollutants such as mercury which cause oxidative stress and increase TG levels. Therefore, the relationship between sea fish and TG remains unclear. We aimed to explore whether blood mercury (BHg) can affect the effect of sea fish consumption frequency on TG level among Chinese adults. A total of 10,780 participants were included in this study. BHg levels were measured using inductively coupled plasma mass spectrometry (ICP-MS). The associations of sea fish consumption frequency with BHg and TG levels as well as the association of BHg with TG levels were evaluated using multiple linear regression. Causal mediation analysis was used to evaluate the mediation effect of BHg levels on the association of sea fish consumption frequency with TG levels. The frequency of sea fish consumption showed a negative association with TG level. Compared with the participants who never ate sea fish, the TG level decreased by 0.193 mmol/L in those who ate sea fish once a week or more [β (95%CI): −0.193 (−0.370, −0.015)]. Significant positive associations were observed of BHg with TG levels. With one unit increase of log2-transformed BHg, the change of TG level was 0.030 mmol/L [0.030 (0.009, 0.051)]. The association between sea fish consumption and TG was mediated by log2-transformed BHg [total effect = −0.037 (−0.074, −0.001); indirect effect = 0.009 (0.004, 0.015)], and the proportion mediated by log2-transformed BHg was 24.25%. BHg may reduce the beneficial effect of sea fish consumption frequency on TG levels among Chinese adults. Overall, sea fish consumption has more benefits than harms to TG.

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.

Exposure to suspended particulate matter (PM), found in the air, is one of the most acute environmental problems that affect the health of modern society. Among the different airborne pollutants, heavy metals (HMs) are particularly relevant because they are bioaccumulated, impairing the functions of living beings. This study aimed to establish a method to predict heavy metal concentrations in leaves and road dust, through their magnetic properties measurements. For this purpose, machine learning, automatic linear regression (MLR), and support vector machine (SVM) were used to establish models for the prediction of airborne heavy metals based on leaves and road dust magnetic properties. Road dust samples and leaves of two common evergreen species (Cupressus lusitanica/Casuarina equisetifolia) were sampled simultaneously during two different years in the Great Metropolitan Area (GMA) of Costa Rica. MLR and SVM algorithms were used to establish the relationship between airborne heavy metal concentrations based on single (χlf) and multiple (χlf y χdf) leaf magnetic properties and road dust. Results showed that Fe, Cu, Cr, V, and Zn concentrations were well-simulated by SVM prediction models, with adjusted R² values ≥ 0.7 in both training and test stages. By contrast, the concentrations of Pb and Ni were not well-simulated, with adjusted R² values < 0.7 in both training and test stages. Heavy metal predicción models using magnetic properties of leaves from Casuarina equisetifolia, as collectors, yielded better prediction results than those based on the leaves of Cupressus lusitanica and road dust, showing relatively higher adjusted R² values and lower errors (MAE and RMSE) in both training and test stages. SVM proved to be the best prediction model with variations between single (χlf) and multiple (χlf y χdf) magnetic properties depending on the element studied.

The inorganic components of particulate matter (PM), especially transition metals, have been shown to contribute to PM toxicity. In this study, the spatial distribution of PM elements and their potential sources in the Greater Los Angeles area were studied. The mass concentration and detailed elemental composition of fine (PM₂.₅) and coarse (PM₂.₅₋₁₀) particles were assessed at 46 locations, including urban traffic, urban community, urban background, and desert locations. Crustal enrichment factors (EFs), roadside enrichments (REs), and bivariate correlation analysis revealed that Ba, Cr, Cu, Mo, Pd, Sb, Zn, and Zr were associated with traffic emissions in both PM₂.₅ and PM₂.₅₋₁₀, while Fe, Li, Mn, and Ti were affected by traffic emissions mostly in PM₂.₅. The concentrations of Ba, Cu, Mo, Sb, Zr (brake wear tracers), Pd (tailpipe tracer), and Zn (associated with tire wear) were higher at urban traffic sites than urban background locations by factors of 2.6–4.6. Both PM₂.₅ and PM₂.₅₋₁₀ elements showed large spatial variations, indicating the presence of diverse emission sources across sampling locations. Principal component analysis extracted four source factors that explained 88% of the variance in the PM₂.₅ elemental concentrations, and three sources that explained 86% of the variance in the PM₂.₅₋₁₀ elemental concentrations. Based on multiple linear regression analysis, the contribution of traffic emissions (27%) to PM₂.₅ was found to be higher than mineral dust (23%), marine aerosol (18%), and industrial emissions (8%). On the other hand, mineral dust was the dominant source of PM₂.₅₋₁₀ with 45% contribution, followed by marine aerosol (22%), and traffic emissions (19%). This study provides novel insight into the spatial variation of traffic-related elements in a large metropolitan area.

Household air pollution (HAP) arising from combustion of biomass fuel (BMF) is a leading cause of morbidity and mortality in low-income countries. Air pollution may stimulate pro-inflammatory responses by activating diverse immune cells and cyto/chemokine expression, thereby contributing to diseases. We aimed to study cellular immune responses among women chronically exposed to HAP through use of BMF for domestic cooking. Among 200 healthy, non-smoking women in rural Bangladesh, we assessed exposure to HAP by measuring particulate matter 2.5 (PM₂.₅), black carbon (BC) and carbon monoxide (CO), through use of personal monitors RTI MicroPEM™ and Lascar CO logger respectively, for 48 h. Blood samples were collected following HAP exposure assessment and were analyzed for immunoprofiling by flow cytometry, plasma IgE by immunoassay analyzer and cyto/chemokine response from monocyte-derived-macrophages (MDM) and -dendritic cells (MDDC) by multiplex immunoassay. In multivariate linear regression model, a doubling of PM₂.₅ was associated with small increments in immature/early B cells (CD19⁺CD38⁺) and plasmablasts (CD19⁺CD38⁺CD27⁺). In contrast, a doubling of CO was associated with 1.20% reduction in CD19⁺ B lymphocytes (95% confidence interval (CI) = -2.36, −0.01). A doubling of PM₂.₅ and BC each was associated with 3.12% (95%CI = −5.85, −0.38) and 4.07% (95%CI = −7.96, −0.17) decrements in memory B cells (CD19⁺CD27⁺), respectively. Exposure to CO was associated with increased plasma IgE levels (beta(β) = 240.4, 95%CI = 3.06, 477.8). PM₂.₅ and CO exposure was associated with increased MDM production of CXCL10 (β = 12287, 95%CI = 1038, 23536) and CCL5 (β = 835.7, 95%CI = 95.5, 1576), respectively. Conversely, BC exposure was associated with reduction in MDDC-produced CCL5 (β = −3583, 95%CI = −6358, −807.8) and TNF-α (β = −15521, 95%CI = −28968, −2074). Our findings suggest that chronic HAP exposure through BMF use adversely affects proportions of B lymphocytes, particularly memory B cells, plasma IgE levels and functions of antigen presenting cells in rural women.

High-density samples are usually a prerequisite for obtaining high-precision source apportionment results in large-scale areas. In-situ field portable X-ray fluorescence spectrometry (FPXRF) is a fast and cheap way to increase the sample size of soil heavy metals (HMs). Moreover, categorical land-use types may be closely associated with source contributions. However, the above information has rarely been incorporated into the source apportionment. In this study, robust geographically weighted regression (RGWR) was first used to correct the spatially varying effect of the related soil factors (e.g., soil water and soil organic matter) on in-situ FPXRF in an urban-rural fringe of Wuhan City, China, and the correction accuracy of RGWR was compared with those of the traditionally non-spatial multiple linear regression (MLR) and basic GWR. Then, the effect of land-use types on HM concentrations was partitioned using analysis of variance (ANOVA). Last, based on the robust spatial receptor model (i.e., robust absolute principal component scores/RGWR [RAPCS/RGWR]), this study proposed RAPCS/RGWR with categorical land-use types and RGWR-corrected in-situ FPXRF data (RAPCS/RGWR_LU&FPXRF), and its performance was compared with those of RAPCS/RGWR with none or one kind of auxiliary data. Results showed that (i) the performances of the correction models for in-situ FPXRF data were in the order of RGWR > GWR > MLR, and the relative improvement of RGWR over MLR ranged from 52.6% to 70.71% for each HM; (ii) categorical land-use types significantly affected the concentrations of soil Zn, Cu, As, and Pb; (iii) the highest estimation accuracy for source contributions was obtained by RAPCS/RGWR_LU&FPXRF, and the lowest estimation accuracy was obtained by basic RAPCS/RGWR. It is concluded that land-use types and RGWR-corrected in-situ FPXRF data are closely associated with the source contribution, and RAPCS/RGWR_LU&FPXRF is a cost-effective source apportionment method for soil HMs in large-scale areas.

Noise pollution is a pervasive factor that increasingly threatens natural resources and human health worldwide. In particular, large-scale changes in road networks have driven shifts in the acoustic environment of rural landscapes during the past few decades. Using sampling plots from the Spanish Landscape Monitoring System (SISPARES), 16 km² each, we modelled the spatio-temporal changes in road traffic noise pollution in Ecoregion 1 of Spain (approximately 66,000 km²). We selected a study period that was characterised by significant changes in the size of the road network and the vehicle fleet (i.e. between 1995 and 2014) and used standard and validated acoustic computation methods for environmental noise modelling (i.e. European Directive, 2002/49/EC) within sampling plots. We then applied a multiple linear regression to expand noise modelling throughout the whole of Ecoregion 1. Our results showed that the noise level increased by 1.7 dB(A) in average per decade in approximately 65% of the territory, decreased by 1.3 dB(A) per decade in about 33%, and remained unchanged in 2%. This suggests that road traffic noise pollution levels may not grow homogeneously in large geographical areas, maybe due to the concentration of large fast traffic flows on modern motorways connecting towns. Our research exemplifies how landscape monitoring systems such as cost-effective approaches may play an important role when assessing spatio-temporal patterns and the impact of anthropogenic noise pollution at large geographical scales, and even more so in a global context of constricted resources and limited availability of historical data on traffic and environmental noise monitoring.