Polycyclic aromatic hydrocarbons (PAHs) and certain ingredients in personal care products, such as parabens, bisphenols, triclosan and phthalate metabolites, have become ubiquitous in the world. Concerns of human exposure to these pollutants have increased during recent years because of various adverse health effects of these chemicals. Multiple compounds including parabens, bisphenols, triclosan, phthalate metabolites (mPAEs) and hydroxyl PAHs (OH-PAHs) in urine samples from Guangzhou were determined simultaneously to identify the human exposure pathways without external exposure data combined with data analysis, and the toxicants posed the highest risk to human health were screened in the present study. The detection frequencies for the chemicals exceeded 90%. Among the contaminants, mPAEs showed the highest concentrations, followed by OH-PAHs, with triclosan present at the lowest concentrations. Mono-n-butyl phthalate, methylparaben, bisphenol A, and hydroxynaphthalene represented the most abundant mPAE, parabens, bisphenol, and OH-PAH compounds, respectively. The present PAHs are mainly exposed to human through inhalation, while the chemicals added to personal care products are mainly exposed to human through oral intake and dermal contact. The urine samples from suburban subjects showed significantly higher OH-PAH levels than the urine samples from urban subjects, and females had lower OH-PAH levels than males. Urinary concentrations of the analyzed contaminants were significantly correlated with age, body mass index, residence time, as well as the frequencies of alcohol consumption and swimming. Risk assessments based on Monte Carlo simulation indicated that approximately 30% of the subjects suffered non-carcinogenic risks from mPAEs and OH-PAHs, with mPAEs accounting for 89% of the total risk.

Identification of pollution point source in rivers is strenuous due to accidental chemical spills or unmanaged wastewater discharges. It is crucial to take physical characteristics into account in the estimation of pollution sources. In this study, an integrated inverse modeling framework is developed to identify a point source of accidental water pollution based on the contaminant concentrations observed at monitoring sites in time series. The modeling approach includes a Markov chain Monte Carlo method based on Bayesian inference (Bayesian-MCMC) inverse model and a genetic algorithm (GA) inverse model. Both inverse models can estimate the pollution sources, including the emission mass quantity, release time, and release position in an accidental river pollution event. The developed model is first tested for a hypothetical case with field river conditions. The results show that the source parameters identified by the Bayesian-MCMC inverse model are very close to the true values with relative errors of 0.02% or less; the GA inverse model also works with relative errors in the range of 2%–7%. Additionally, the uncertainties associated with model parameters are analyzed based on global sensitive analysis (GSA) in this study. It is also found that the emission mass of pollution source positively correlates with the dispersion coefficient and the river cross-sectional area, whereas the flow velocity significantly affects release position and release time. A real case study in the Fen River is further conducted to test the applicability of the developed inverse modeling approach. Results confirm that the Bayesian-MCMC model performs better than the GA model in terms of accuracy and stability for the field application. The findings of this study would support decision-making during emergency responses to river pollution incidents.

To evaluate pesticide regulatory standards in agricultural crops, we introduced a regulatory modeling framework that can flexibly evaluate a population’s aggregate exposure risk via maximum residue levels (MRLs) under good agricultural practice (GAP). Based on the structure of the aggregate exposure model and the nature of variable distributions, we optimized the framework to achieve a simplified mathematical expression based on lognormal variables including the lognormal sum approximation and lognormal product theorem. The proposed model was validated using Monte Carlo simulation, which demonstrates a good match for both head and tail ends of the distribution (e.g., the maximum error = 2.01% at the 99th percentile). In comparison with the point estimate approach (i.e., theoretical maximum daily intake, TMDI), the proposed model produced higher simulated daily intake (SDI) values based on empirical and precautionary assumptions. For example, the values at the 75th percentile of the SDI distributions simulated from the European Union (EU) MRLs of 13 common pesticides in 12 common crops were equal to the estimated TMDI values, and the SDI values at the 99th percentile were over 1.6-times the corresponding TMDI values. Furthermore, the model was refined by incorporating the lognormal distributions of biometric variables (i.e., food intake rate, processing factor, and body weight) and varying the unit-to-unit variability factor (VF) of the pesticide residues in crops. This ensures that our proposed model is flexible across a broad spectrum of pesticide residues. Overall, our results show that the SDI is significantly reduced, which may better reflect reality. In addition, using a point estimate or lognormal PF distribution is effective as risk assessments typically focus on the upper end of the distribution.

Chinese children have been exposed to high level of lead due to polluted air, dust, contaminated foods and water, etc. In this research, we investigated published blood lead levels (BLLs) reflecting 1,057,832 Chinese children aged at 0–12 and teenagers aged at 13–18 in the past 30 years (1991–2020). The data mining and estimation were performed innovatively by Monte Carlo simulation to remedy the skewed distribution-induced bias. The temporal trend of Chinese children’s BLLs showed an obvious decrease in the past decades from 88.74 μg/L (Geometric SD = 4.09) during 1991–1995 to 27.32 μg/L (Geometric SD = 4.18) during 2016–2020. This study also indicated that children’s BLLs of Yunnan, Guizhou, Shanxi were at relatively high levels and most provinces showed a downward trend. Chinese boys aged at 1–18 years old had higher BLLs (GM: 44.03 μg/L) compared to girls (GM: 41.32 μg/L) (p < 0.001). At different age groups, Chinese children’s BLLs were 42.04 μg/L (1–3 years old), 52.88 μg/L (4–6 years old), 50.49 μg/L (7 and above years old), respectively. Although the BLLs of Chinese children exhibited a continuous declined trend in the past 30 years, it was still higher than that in developed countries, which indicated that more efforts are needed in children’s BLLs control.

Increasing attention has been devoted to the adverse effects of endocrine disrupting chemicals (EDCs) on aquatic environments, such as water, sediment and sludge. To date, few studies have investigated the bio-accumulative characteristics of EDCs in different tissues of diverse wild freshwater fish species and their combined impacts on human health. Five EDCs were investigated in the muscle, liver, gill and, especially, gonad of three fish species collected from the Xiangjiang River, southern China. Carnivorous Siniperca Chuatsi or omnivorous Cyprinus Carpio accumulated higher contents of bisphenol A (BPA) and estrone than herbivorous Parabramis Pekinensis in muscle. Furthermore, 4-n-nonylphenol and estrone were found at higher levels and more frequently in the liver, implying that the liver played an important role in basic metabolism for accumulation, biotransformation and excretion of EDCs. Highest concentrations of BPA found in the gonad revealed that the BPA may pose a serious threat to the reproductive system of aquatic organisms. The mean liver/muscle concentration ratios of 4-n-nonylphenol, BPA, estrone and 17α-ethynyl estradiol confirmed the prolonged exposure of the fish to these EDCs. In addition, the relationships between the fish sizes and the EDC concentrations analyzed by Pearson correlation analysis implied that the bioaccumulation of diethylstilbestrol and BPA increased with the growth of Parabramis Pekinensis, and there was a balance between the uptake rate and elimination rate of EDCs in Siniperca Chuatsi and Cyprinus Carpio. Most importantly, the cumulative impacts of combined EDCs on human health by fish consumption were evaluated. The total estradiol equivalent quantity of estrogens was higher than that of phenols. Also, based on the results of the Monte-Carlo simulation, the 95th percentile values of the total estimated daily intakes from consuming the three freshwater fish species from the Xiangjiang River were higher than the acceptable daily intake.

Short-chain chlorinated paraffins (SCCPs) are widely used chemicals in household products and might cause adverse human health effects. However, limited information is available on the occurrence of SCCPs in indoor environments and their exposure risks on humans. In this study the concentrations, profiles and human exposure of SCCPs in indoor dust from five different indoor environments, including commercial stores, residential apartments, dormitories, offices and laboratories were characterized. The SCCPs levels ranged from 10.1 to 173.0 μg/g, with the median and mean concentration of 47.2 and 53.6 μg/g, respectively. No significant difference was found on concentrations among the five microenvironments. The most abundant compounds in indoor dust samples were homologues of C13 group, Cl7 group and N20 (N is the total number of C and Cl) group. In the five microenvironments, commercial stores were more frequently exposed to shorter carbon chained and higher chlorinated homologues. Three potential sources for SCCPs were identified by the multiple linear regression of factor score model and correspondence analysis. The major sources of SCCPs in indoor dust were technical mixtures of CP–42 (42% chlorine, w/w) and CP–52 b (52% chlorine, w/w). The total daily exposure doses and hazard quotients (HQ) were calculated by the human exposure models, and they were all below the reference doses and threshold values, respectively. Monte Carlo simulation was applied to predict the human exposure risk of SCCPs. Infants and toddlers were at risk of SCCPs based on predicted HQ values, which were exceeded the threshold for neoplastic effects in the worst case. Our results on the occurrences, sources and human exposures of SCCPs will be useful to provide a better understanding of SCCPs behaviors in indoor environment in China, and to support environmental risk evaluation and regulation of SCCPs in the world.

Studies have yet to evaluate the effects of water improvement on fluoride concentrations in drinking water and the corresponding health risks to Chinese residents in endemic fluorosis areas (EFAs) at a national level. This paper summarized available data in the published literature (2008–2016) on water fluoride from the EFAs in China before and after water quality was improved. Based on these obtained data, health risk assessment of Chinese residents' exposure to fluoride in improved drinking water was performed by means of a probabilistic approach. The uncertainties in the risk estimates were quantified using Monte Carlo simulation and sensitivity analysis. Our results showed that in general, the average fluoride levels (0.10–2.24 mg/L) in the improved drinking water in the EFAs of China were lower than the pre-intervention levels (0.30–15.24 mg/L). The highest fluoride levels were detected in North and Southwest China. The mean non-carcinogenic risks associated with consumption of the improved drinking water for Chinese residents were mostly accepted (hazard quotient < 1), but the non-carcinogenic risk of children in most of the EFAs at the 95th percentile exceeded the safe level of 1, indicating the potential non-cancer-causing health effects on this fluoride-exposed population. Sensitivity analyses indicated that fluoride concentration in drinking water, ingestion rate of water, and the exposure time in the shower were the most relevant variables in the model, therefore, efforts should focus mainly on the definition of their probability distributions for a more accurate risk assessment.

Some cities in China are facing serious air pollution problems including high concentrations of particles, SO2 and NOx. Exposure to PM2.5, one of the primary air pollutants in many cities in China, is highly correlated with various adverse health impacts and ultimately represents a cost for society. The aim of this study is to assess health impacts and external costs related to PM2.5 pollution in Beijing, China with different baseline concentrations and valuation methods. The idea is to provide a reasonable estimate of the total health impacts and external cost due to PM2.5 pollution, as well as a quantification of the relevant uncertainty. PM2.5 concentrations were retrieved for the entire 2012 period in 16 districts of Beijing. The various PM2.5 related health impacts were identified and classified to avoid double counting. Exposure-response coefficients were then obtained from literature. Both the value of statistical life (VSL) and the amended human capital (AHC) approach were applied for external costs estimation, which could provide the upper and lower bound of the external costs due to PM2.5. To fully understand the uncertainty levels, the external cost distribution was determined via Monte Carlo simulation based on the uncertainty of the parameters such as PM2.5 concentration, exposure-response coefficients, and economic cost per case. The results showed that the external costs were equivalent to around 0.3% (AHC, China's guideline: C0 = 35 μg/m3) to 0.9% (VSL, WHO guideline: C0 = 10 μg/m3) of regional GDP depending on the valuation method and on the assumed baseline PM2.5 concentration (C0). Among all the health impacts, the economic loss due to premature deaths accounted for more than 80% of the overall external costs. The results of this study could help policymakers prioritizing the PM2.5 pollution control interventions and internalize the external costs through the application of economic policy instruments.

To better evaluate the potential risks of aquatic polycyclic aromatic compounds (PACs), multiple approaches have been implemented in this study to assess the human health and ecological risks of parent, nitrated and oxygenated polycyclic aromatic hydrocarbons (PAHs, NPAHs and OPAHs) in the surface water of Jialing and Yangtze Rivers in downtown Chongqing in southwestern China. The concentrations of ∑PAHs (334 ± 125 ng L⁻¹) were much higher than those of ∑OPAHs (20.2 ± 7.49 ng L⁻¹) in the two rivers, while NPAHs were barely detected. Concentrations of detected PACs were higher in wet season than dry season, probably resulted from the elevated particle input due to heavy rainfall in wet season. Concentrations of PAHs were higher in the particulate phase than dissolved phase, while OPAHs levels showed a reverse pattern. The partition coefficients (Kₚ) of PACs in the water-SPM (suspended particulate matter) system were mainly affected by SPM concentrations and octanol/water partition coefficients of specific PACs. Human health risks calculated from non-probabilistic risk assessment model and probabilistic risk assessment model based on Monte Carlo simulation showed similar data pattern with slight difference in absolute values. Both models revealed potential or even severe human health risks contributed mainly by dermal exposure to aquatic PACs in this study. Furthermore, these models also manifested that infant stage was highly sensitive for PAC exposure. Sensitivity analysis indicated that health risk results was most sensitive to Benzo[a]pyrene equivalent toxic concentration (BaPₑq), followed by showering time and daily water intake volume. Levels of ecological risks and contributions of individual PACs differed from models based on different quality values. The adequacy of toxicity data was crucial for the reliability of ecological risk assessment.

Smelting activities are considered as the primary cause of heavy metal (HM) accumulation in soils, and the human health around the smelter has been a great concern worldwide. In this study, a total of 242 agricultural soil samples were collected around a large scale Pb/Zn smelter in China, and eight HMs (As, Cd, Cr, Cu, Hg, Ni, Pb and Zn) were analyzed to assess HMs status, ecological -health risks, and identify source. Monte Carlo simulation was utilized to evaluate the probabilistic health risks, and positive matrix factorization (PMF) was employed to identify sources. The results revealed the average contents of five heavy metals (Cd 5.28 mg/kg, Pb 203.36 mg/kg, Hg 0.39 mg/kg, Zn 293.45 mg/kg, Cu 37.14 mg/kg) are higher than their background values in Hunan province. Cd had the highest mean pollution index (PI) of 41.8 and the greatest average ecological risk index (Eᵣ) of 1256.34, indicating that Cd was the primary enriched pollutant and had a higher ecological risk than other HMs. The mean hazard index (HI) through exposure to eight HMs was 2.95E-01 and 9.74E-01 for adults and children, respectively, with 35.94% of HI values for children exceeding the risk threshold of 1. Moreover, the mean total cancer risks (TCR) were 2.75E-05 and 2.37E-04 for adults and children, respectively, with 75.48% of TCR values for children exceeding the guideline value of 1E-04. In addition, the positive matrix factorization results showed smelting activities, natural sources, agricultural activities and atmospheric deposition were the three sources in soils, with the contribution rate of 48.62%, 22.35%, and 29.03%, respectively. The uncertainty analysis of the PMF indicated that the three-factor solution is reliable. This work will provide scientific reference for the comprehensive prevention of soil HM pollution adjacent to the large smelter.