Although association between polycyclic aromatic hydrocarbons (PAHs) exposure and mitochondrial DNA copy number (mtDNAcn) was researched by traditional linear model extensively, most of these studies analyzed independent effect of each PAHs metabolite and adjust for the confounding other metabolites concomitantly, without considering others interactions. As a complex organic pollutant, a reasonable statistical method is needed to study toxic effects of PAHs.Therefore, we aimed to conduct a novel statistical approach, Bayesian Kernel Machine Regression (BKMR), to explore the effect of PAHs exposure on mtDNAcn among coke oven workers. In this cross-sectional study, the concentrations urinary of PAHs metabolites were measured using high performance liquid chromatography mass spectrometry (HPLC-MS). The mtDNAcn was measured using real-time quantitative polymerase chain reaction (RT-PCR) in peripheral blood of 696 Chinese coke oven workers. The relationship of urinary of PAHs metabolites and mtDNAcn were evaluated by BKMR model. And the results showed a significant negative effect of PAHs metabolites on mtDNAcn when PAHs metabolites concentrations were all above 35th percentile compared to the median and the statistically significant negative single-exposure effect of 2-OHNAP and 2-OHPHE on mtDNAcn when all of the other PAHs are fixed at a particular threshold (25th, 50th, 75th percentile). The changes in log 2-OHNAP and 2-OHPHE from the 25th to the 75th percentile when other PAHs metabolites were at the 50th percentile were associated with change in mtDNAcn of −0.082 (−0.021, −0.124) and −0.048 (−0.021, −0.090) respectively. And evidence of a linear effect of urinary 2-OHNAP and 2-OHPHE were found. Finally, our findings suggested that PAHs cumulative exposures and particularly single-exposure of 2-OHNAP and 2-OHPHE might compromise mitochondrial function by decreasing mtDNAcn in Chinese coke oven workers.

Evidence is accumulating about the impacts of plastics on marine life. The prevalence of plastics in seabird nests has been used as an indicator of levels of this pollutant in the ocean. However, the lack of a framework for defining sample sizes and errors associated with estimating the prevalence of plastic in nests prevents researchers from optimising time and reducing impacts of fieldwork. We present a method to determine the confidence intervals for the prevalence of debris in seabird nests and provide, for the first time, information on the prevalence of these items in nests of the Hartlaub’s gull Larus hartlaubii, the African penguin Spheniscus demersus, the great white pelican Pelecanus onocrotalus, and the white-breasted cormorant Phalacrocorax lucidus in South Africa. The method, based on observations and resampling simulations and tested here for nests of 12 seabird species from 15 locations worldwide, allows for straightforward hypothesis testing. Appropriate sample sizes can be defined by combining this method with a Bayesian approach. We show that precise estimates of prevalence of debris in nests can be obtained by sampling around 250 nests. Smaller sample sizes can be useful for obtaining rough estimates. For the Hartlaub’s gull, the African penguin, the great white pelican, and the white-breasted cormorant, debris were present in 0.75%, 3.00%, 6.41%, and 25.62% of the respective nests. Our approach will help researchers to determine errors associated with the prevalence of debris recorded in seabird nests and to optimise time and costs spent collecting data. It can also be applied to estimate confidence intervals and define sample sizes for assessing prevalence of plastic ingestion by any organism.

Evidence is available about the associations of phthalates or their metabolites with blood lipids, however, the mixture effects of multiple phthalate metabolites on blood lipid traits remain largely unknown. In this pilot study, 106 individuals at three age groups of <18, 18- and ≥60 years were recruited from the residents (n = 1240) who were randomly selected from two communities in Wuhan city, China. The participants completed the questionnaire survey and physical examination as well as provided urine samples in the winter of 2014 and the summer of 2015. We measured urinary levels of nine phthalate metabolites using a high-performance liquid chromatography-tandem mass spectrometry. We estimated the associations of individual phthalate metabolite with blood lipid traits by linear mixed effect (LME) models, and assessed the overall association of the mixture of nine phthalate metabolites with blood lipid traits using Bayesian kernel machine regression (BKMR) models. LME models revealed the negative association of urinary mono-2-ethylhexyl phthalate (MEHP) with total cholesterol (TC) as well as of urinary mono-benzyl phthalate or urinary MEHP with low density lipoprotein cholesterol (LDL-C). BKMR models revealed the negative overall association of the mixture of nine phthalate metabolites with TC or LDL-C, and DEHP metabolites (especially MEHP) had a greater contribution to TC or LDL-C levels than non-DEHP metabolites. The findings indicated the negative overall association of the mixture of nine phthalate metabolites with TC or LDL-C. Among nine phthalate metabolites, MEHP was the most important component for the changes of TC or LDL-C levels, implying that phthalates exposure may disrupt lipid metabolism in the body.

The human pathogenic bacteria (HPB) in animal feces may disseminate to agricultural soils with their land application as organic fertilizer. However, the knowledge about the impacts of different sources and rates of animal manures on the temporal changes of soil HPB remains limited, which hamper our ability to estimate the potential risks of their land application. Here, we constructed an HPB database including 565 bacterial strains. By blasting the 16 S rRNA gene sequences against the database we explored the occurrence and fate of HPB in soil microcosms treated with two rates of swine, poultry or cattle manures. A total of 30 HPB were detected in all of manure and soil samples. Poultry manure at the high level obviously improved the abundance of soil HPB. The application of swine manure could introduce concomitant HPB into the soils. Of which, Pseudomonas syringae pv. syringae B728a and Escherichia coli APEC O78 may deserve more attention because of their survival for a few days in manured soils and being possible hosts of diverse antibiotic resistance genes (ARGs) as revealed by co-occurrence pattern. Bayesian source tracking analysis showed that the HPB derived from swine manure had a higher contribution to soil pathogenic communities than those from poultry or cattle manures in early days of incubation. Mantel test together with variation partitioning analysis suggested that bacterial community and soil physicochemical properties were the dominant factors determining the profile of HPB and contributed 64.7% of the total variations. Overall, our results provided experimental evidence that application of animal manures could facilitate the potential dissemination of HPB in soil environment, which should arouse sufficient attention in agriculture practice and management to avoid the threat to human health.

Sulfate (SO₄²⁻) contamination in groundwater and surface water is an environmental problem of widespread concern. In this study, we combined stable isotope analyses of SO₄²⁻ (δ³⁴S and δ¹⁸O) and water (δ²H and δ¹⁸O) with a Bayesian mixing model (SIAR), for the first time, to identify sources and transformation of SO₄²⁻ in an area of northern China with multiple potential sources of pollution. The overall values of δ³⁴S and δ¹⁸O-SO₄²⁻ ranged from 1.3‰ to 16.3‰ and −3.8‰–8.8‰ in groundwater, and from −1.1‰ to 9.3‰ and 2.7‰–9.2‰ in surface waters, respectively. Analyses of SO₄²⁻ isotopes and water chemistry indicated that SO₄²⁻ in groundwater and surface water mainly originated from mixing of oxidation of sulfate, sewage, chemical fertilizers, dissolution of evaporite and precipitation. There was no significant correlation between δ³⁴S and δ¹⁸O and SO₄²⁻ concentration in groundwater, indicating that bacterial sulfate reduction did not affect the SO₄²⁻ isotopic composition. SIAR model showed the main sources of SO₄²⁻ in groundwater and surface water comprised oxidation of sulfide minerals and sewage. In groundwater, oxidation of sulfide minerals and sewage accounted for 37.5–44.5% and 35.5–42.7% of SO₄²⁻, respectively. In regard to surface waters, the contribution of oxidation of sulfide minerals to SO₄²⁻ was higher in the wet season (31.8 ± 9.9%) than in the intermediate (22.4 ± 7.8%) and dry (20.9 ± 8.2%) seasons, but the contribution proportion of sewage was slightly lower in the wet season (19.9 ± 8.5%) than in the intermediate (23.8 ± 8.7%) and dry (24.2 ± 8.5%) seasons. This study indicates that it is necessary for local government to improve the treatment infrastructure for domestic sewage and optimize methods of agricultural fertilization and irrigation to prevent SO₄²⁻ contamination of groundwater and surface water.

High concentration of fluoride (up to 20.9 mg/L) in groundwater with significant variation (p = 5.9E-128) among samples was reported from Birbhum district, an acknowledged fluoride endemic region in India. The groundwater samples (N = 368) were grouped based on their hydrochemical properties and aquifer geology for hydro-geochemical characterization. Friedman’s test showed p < 0.0001 confidence level which indicates that fluoride concentration among geological groups and water groups are independent. Bland-Altman plot was used to study the inter-relationships among the groups through bias value (∂) and limit of agreement (LoA). Among the geological groups, laterites and granite-gneiss groups exhibited statistically significantly difference in fluoride geochemistry; whereas the younger and older alluvium groups displayed similar characteristics. The fluoride concentration was found to be in the order Lateritic > Granite-gneiss > Older alluvium ≥ Younger alluvium. Dissolution of minerals (such as fluorite, biotite) in laterite sheeted basalt, and granite-gneiss is the main source of groundwater fluoride in the region. Fluoride concentration is also influenced by depth of water table. Hydrochemical study indicated that fluoride concentration was higher in Na–HCO₃ than in Ca–SO₄ and Ca–HCO₃ type of groundwater. The fluoride concentration were positively correlated with Na⁺ and pH and negatively correlated with the Ca²⁺ and Mg²⁺ signifying linkage with halite dissolution and calcite, dolomite precipitation. Geostatistical mapping of WQI through empirical bayesian kriging (EBK) with respect to regional optimal guideline value (0.73 mg/L) classified that groundwater in some parts of the district are unfit for drinking purpose. Health survey (N = 1767) based on Dean’s criteria for dental fluorosis indicated presence of slight to moderate dental hazard. Besides, providing baseline data for management of groundwater quality in the study area, the study demonstrated the applicability of Bland-Altman analysis and empirical bayesian kriging (EBK) in delineation and interpolation of fluoride contaminated region.

Uncertainty assessment of parameters associated with non-point source pollution mechanism modeling are crucial for improving the effectiveness of pollution controlling. In this study, an approach based on Bayesian inference and integrated Markov chain Monte Carlo and multilevel factorial analysis has been developed, and it can not only apply straightforward Bayesian inference to assess parameter uncertainties, but also quantitatively investigate the main and interactive effects of multiple parameters on the model response variables by measuring the specific variations of model outputs. Its applicability and advantages are presented through the application of the Soil and Water Assessment Tool to Shitoukoumen Reservoir Catchment in northeast China. This study investigated the uncertainties of a set of sensitive parameters and their multilevel effects on model response variables, including average annual runoff (AAR), average annual sediment (AAS) and average annual total nitrogen (AAN). Results revealed that (i) soil conservation service runoff curve number for moisture condition II (CN2) had a positive effect on all response variables; (ii) available water capacity of the soil layer (SOL_AWC) had a negative effect on all response variables; (iii) the universal soil loss equation support practice (USLE_P) had a positive effect on AAS and AAN, and little effect on AAR; while the nitrate percolation coefficient (NPERCO) had a positive effect on AAN, and little effect on AAS and AAR; and (iv) the interactions amongst parameters had obvious interdependent effects on the model response variables, for example, the interaction between CN2 and SOL_AWC had a major impact on AAR. The above findings can improve the simulating and predicting capabilities of non-point source pollution mechanism model. Overall, this study highlights that the proposed approach represents a promising solution for uncertainty assessment of model parameters in non-point source pollution mechanism modeling.

Current studies indicate that long-term exposure to ambient fine particulate matter (PM₂.₅) is related with global mortality, yet no studies have explored relationships of PM₂.₅ and its species with DNAm PhenoAge acceleration (DNAmPhenoAccel), a new epigenetic biomarker of phenotypic age. We identified which PM₂.₅ species had association with DNAmPhenoAccel in a one-year exposure window in a longitudinal cohort. We collected whole blood samples from 683 elderly men in the Normative Aging Study between 1999 and 2013 (n = 1254 visits). DNAm PhenoAge was calculated using 513 CpGs retrieved from the Illumina Infinium HumanMethylation450 BeadChip. Daily concentrations of PM₂.₅ species were measured at a fixed air-quality monitoring site and one-year moving averages were computed. Linear mixed-effect (LME) regression and Bayesian kernel machine (BKM) regression were used to estimate the associations. The covariates included chronological age, body mass index (BMI), cigarette pack years, smoking status, estimated cell types, batch effects etc. Benjamini-Hochberg false discovery rate at a 5% false positive threshold was used to adjust for multiple comparison. During the study period, the mean DNAm PhenoAge and chronological age in our subjects were 68 and 73 years old, respectively. Using LME model, only lead and calcium were significantly associated with DNAmPhenoAccel. For example, an interquartile range (IQR, 0.0011 μg/m³) increase in lead was associated with a 1.29-year [95% confidence interval (CI): 0.47, 2.11] increase in DNAmPhenoAccel. Using BKM model, we selected PM₂.₅, lead, and silicon to be predictors for DNAmPhenoAccel. A subsequent LME model showed that only lead had significant effect on DNAmPhenoAccel: 1.45-year (95% CI: 0.46, 2.46) increase in DNAmPhenoAccel following an IQR increase in one-year lead. This is the first study that investigates long-term effects of PM₂.₅ components on DNAmPhenoAccel. The results demonstrate that lead and calcium contained in PM₂.₅ was robustly associated with DNAmPhenoAccel.

As the most important gas-phase alkaline species, atmospheric ammonia (NH3) contributes considerably to the formation and development of fine-mode particles (PM2.5), which affect air quality and environmental health. Recent satellite-based observations suggest that the North China Plain is the largest agricultural NH3 emission source in China. However, our isotopic approach shows that the surface NH3 in the intraregional urban environment of Beijing-Tianjin-Shijiazhuang is contributed primarily by combustion-related processes (i.e., coal combustion, NH3 slip, and vehicle exhaust). Specifically, the Batch fractionation model was used to describe the partitioning of gaseous NH3 into particles and to trace the near-ground atmospheric NH3 sources. With the development of haze pollution, the dynamics of δ15N-NH4+ were generally consistent with the fractionation model. The simulated initial δ15N-NH3 values ranged from −22.6‰ to −2.1‰, suggesting the dominance of combustion-related sources for urban NH3. These emission sources contributed significantly (92% on hazy days and 67% on clean days) to the total ambient NH3 in urban cities, as indicated by a Bayesian mixing model. Based on the Batch fractionation model, we concluded the following: 1) δ15N-NH4+ can be used to model the evolution of fine-mode aerosols and 2) combustion-related sources dominate the near-ground atmospheric NH3 in urban cities. These findings highlight the need for regulatory controls on gaseous NH3 emissions transported from local and surrounding industrial sources.

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) are synthetically manufactured chemicals recognized to be toxic, bioaccumulative, and persistent. Previous studies on PFAS exposure and serum lipid levels have mainly focused on individual PFASs; however, the influence of multiple-PFAS exposure on the serum lipid profile remains unclear. This study was performed to evaluate the combined effects of multiple PFASs on serum lipid levels. Based on the National Health and Nutrition Examination Survey (NHANES) data (2011–2014), we first established a linear regression model to estimate the association between single-PFAS exposure and the serum lipid profile. Then, a weighted quantile sum (WQS) regression model and a Bayesian kernel machine regression (BKMR) model were used to evaluate the effects of multiple-PFAS exposure on the serum lipid profile. A mediating effect model was used to assess how albumin mediates these effects. We found that PFASs were significantly associated with the levels of serum lipids, including high-density lipoprotein (HDL), low-density lipoprotein (LDL) and total cholesterol (TC). The WQS index was significantly correlated with the levels of HDL (β: 2.03, 95% CI: 0.74–3.32, P-value = 0.002), LDL (β: 4.16, 95% CI: 1.07–7.24, P-value = 0.008) and TC (β: 6.54, 95% CI: 3.00–10.1, P-value < 0.001). In the BKMR analysis, our results demonstrated that the effect of PFASs on serum lipids increased significantly when the concentrations of the PFASs were at their 60th percentiles or above compared to those at their 50th percentile. Mediation analysis showed that albumin mediated the effects of selected PFASs on the levels of serum lipids except for triglycerides (TG). PFAS exposure was correlated with the levels of serum lipids, and this correlation was mediated by albumin. Our results suggest that a comprehensive evaluation of multi-PFAS exposure could better characterize real-life exposure compared with single-PFAS exposure.