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.

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.

Neonicotinoids (Neonics) have become the most widely used insecticides around the world in recent years. Due to the hydrophilic character, neonics are emerging contaminants in drinking water. In this study, we aimed to characterize and quantify the fate and transport of neonics in the drinking water treatment system and their contributions to the overall dietary risks. Seven neonics in 97 surface and drinking water samples in the city of Hangzhou, China were analyzed. The relative potency factor method was adopted in order to calculate the total neonics concentrations. We then used the Monte Carlo simulation to calculate the chronic daily intake (CDI) of total neonics from water consumption. All 16 surface water samples collected from two rivers contained at least two neonics, and more than 93% of those contained 3 or more neonics. Imidacloprid was detected in all 16 surface water samples, followed by clothianidin and acetamiprid with average concentrations of 11.9, 7.6, 17.6 ng L-1, respectively. The drinking water treatment plants removed approximately 50% of neonics from surface water. However, 68 out of 71 tap water samples that we collected from the household faucets contained at least one neonic, with the highest average concentrations of 5.8 ng L-1 for acetamiprid. The maximum of CDIs of total neonics from water consumption for adult and children were 10.2 and 12.4 ng kg-1 d-1, respectively, which are significantly lower than the acceptable daily intake (ADI). The results presented here shown drinking water consumption only represented an insignificant portion of dietary risks of total neonics, mainly due to the modern drinking water treatment technologies that are capable of removing significant amount of neonics from drinking water. However, the ubiquity of neonics in the drinking water sources to kitchen faucets, should be a concern for public health.

Rice intake is a major route of oral exposure to inorganic arsenic (iAs), a known human carcinogen. The recent risk assessment on iAs in rice conducted by the US Food & Drug Administration (FDA) didn’t propose an action level for iAs in rice mainly because of the relatively low consumption rate in the general US population. However, this decision may not be applicable to high rice-intake populations, such as the Chinese population.The objective of this study was to probabilistically characterize the exposure of iAs via rice intake in Chinese population with a focus on the urban population.With the consideration of bioaccessibility of iAs in rice, iAs exposure is mainly determined by rice intake rate and iAs concentration in rice. To estimate the daily rice consumption, a dietary survey consisting of 1873 subjects was conducted in three major Chinese cities (Beijing, Guangzhou, and Hangzhou). Speciated As concentration in rice was measured using 480 rice samples collected from markets in these three cities. Monte Carlo simulation approach was applied to distributionally estimate the average daily dose (ADD) of iAs through rice intake.The estimated distribution of daily iAs exposure of the study population has a mean of 10.5 μg/day with a 95th percentile interval from 0.1 to 75.3 μg/day. The estimated distribution of ADD has a mean of 0.179 μg/kg/day with a 95th percentile interval from 0.001 to 1.224 μg/kg/day. Greater level of iAs exposure (due to higher rice intake) was observed in males than females, and in summer than other seasons with lower temperature. Geographically, the ADD of iAs exposure from rice intake reduces from south to north.About 13% of the Chinese urban population are exposed to iAs via rice intake higher than the Reference Dose (RfD) level.

From a health prospective, it is critical to provide a comprehensive model which integrates all the parameters involved in virus transmission and its consequences on human body. In order to estimate the health risks, for workers and residents, associated with an exposure airborne viruses emitted from a wastewater treatment (WWTP), the concentration levels of viruses in emitted bioaerosols over a twelve-month period were measured by real-time polymerase chain reaction (RT-PCR). A combined Gaussian plum dispersion model and quantitative microbial risk assessment (QMRA) with Monte-Carlo simulation served as suitable explanatory tools to estimate the risk of acquiring gastrointestinal illness (GI) due to exposure to air containing Rotavirus (RoV) and Norovirus (NoV) bioaerosols. Additionally, DALY metric was applied to quantify the disability and mortality for workers and residents. RoV and NoV were detected above aeration tank with annual mean concentration 27 and 3099 (Viruses/m³.h), respectively. The medium calculated DALY indicator based on viral loads in contaminant source (RoV:5.76 × 10⁻² and NoV:1.23 × 10⁻¹) and estimated in different distances away (300–1000 m) (RoV:2.87 × 10⁻²- 2.75 × 10⁻² and NoV:1.14 × 10⁻¹-1.13 × 10⁻¹) were markedly higher than the threshold values recommended by US EPA (10⁻⁴ DALY pppy) and WHO (10⁻⁶ DALY pppy). The sensitivity analysis highlighted dose exposure and disease burden per case (DBPC) as two most influential factors for both workers and residents following exposure to two pathogens of concern. Due to high resistance and high concentration in the environment, the presence of RoV and NoV can intensify the consequences of diarrhea especially for children under five years of age; A comprehensible and transparent presentation of DALYs and QMRA can help decision makers and responsibilities to justify the priorities of exposure to wastewater in comparison with other risks of daily life.

Heavy metal pollution is a major concern in China because of its serious effects on human health. To assess potential human health and ecological risks of heavy metal pollution, concentration data for seven heavy metals (As, Pb, Cd, Cr, Hg, Cu, Zn) from 14 sites spanning the rural-urban interface of the Wen-Rui Tang River watershed in southeast China were collected from 2000 to 2010. The heavy metal pollution index (HPI), hazard index (HI) and carcinogenic risk (CR) metrics were used to assess potential heavy metal risks. Further, we evaluated the uncertainty associated with the risk assessment indices using Monte Carlo analysis. Results indicated that all HPI values were lower than the critical level of 100 suggesting that heavy metal levels posed acceptable ecological risks; however, one site having an industrial point-source input reached levels of 80–97 on several occasions. Heavy metal concentrations fluctuated over time, and the decrease after 2007 is due to increased wastewater collection. The HI suggested low non-carcinogenic risk throughout the study period (HI < 1); however, nine sites showed CR values above the acceptable level of 10−4 for potential cancer risk from arsenic in the early 2000s. Uncertainty analysis revealed an exposure risk for As at all sites because some CR values exceeded the 10−4 level of concern; levels of Cd near an old industrial area also exceeded the Cd exposure standard (2.6% of CR values > 10−4). While most metrics for human health risk did not exceed critical values for heavy metals, there is still a potential human health risk from chronic exposure to low heavy metal concentrations due to long-term exposure and potential metal interactions. Results of this study inform water pollution remediation and management efforts designed to protect public health in polluted urban area waterways common in rapidly developing regions.

A nationwide emission estimate of perfluoroalkyl substances (PFASs) from wastewater treatment plants (WWTPs) is required to understand the source–receptor relationship of PFASs and to manage major types of WWTPs. In this study, the concentrations of 13 PFASs (8 perfluorocarboxylic acids, 3 perfluoroalkane sulfonates, and 2 intermediates) in wastewater and sludge from 81 WWTPs in South Korea were collected. The emission pathways of PFASs were redefined, and then the national emission of PFASs from WWTPs was rigorously updated. In addition to the direct calculations, Monte Carlo simulations were also used to calculate the likely range of PFAS emissions. The total (Σ13PFAS) emission (wastewater + sludge) calculated from the direct calculation with mean concentrations was 4.03 ton/y. The emissions of perfluorooctanoic acid (PFOA, 1.19 ton/y) and perfluorooctane sulfonate (PFOS, 1.01 ton/y) were dominant. The Monte Carlo simulations suggested that the realistic national emission of Σ13PFASs is between 2 ton/y and 20 ton/y. Combined WWTPs treating municipal wastewater from residential and commercial areas were identified as a major emission source, contributing 65% to the total PFAS emissions. The Han and Nakdong Rivers were the primary contaminated rivers, receiving 89% of the total PFAS discharge from WWTPs. The results and methodologies in this study can be useful to establish a management policy for PFASs.

As the consumption of electricity increases, air pollutants from power generation increase. In metropolitans such as Hong Kong and other Asian cities, the surge of electricity consumption has been phenomenal over the past decades. This paper presents a historical review about electricity consumption, population, and change in economic structure in Hong Kong. It is hypothesized that the growth of electricity consumption and change in gross domestic product can be modeled by 4-parameter logistic functions. The accuracy of the functions was assessed by Pearson's correlation coefficient, mean absolute percent error, and root mean squared percent error. The paper also applies the life cycle approach to determine carbon dioxide, methane, nitrous oxide, sulfur dioxide, and nitrogen oxide emissions for the electricity consumption of Hong Kong. Monte Carlo simulations were applied to determine the confidence intervals of pollutant emissions. The implications of importing more nuclear power are discussed.

A dynamic fugacity model was developed to simulate the spatial and seasonal variations of PAHs in Haihe Plain, China. The calculated and measured concentrations exhibited good consistency in magnitude with deviations within a factor of 4 in air and 2 in soil. The spatial distributions of PAHs in air were mainly controlled by emission while the seasonal variations were dominated by emission and gas–particle partition. In soil, the spatial distributions of PAHs were controlled by the soil organic carbon content while the seasonal variations were insignificant. The severest soil contamination was observed in Shanxi and followed by the southwest of Hebei province. Transfer fluxes of total PAHs between air and soil were calculated. The spatial distribution of air-to-soil flux was closely related to the landcover while the soil-to-air flux changed with soil organic matter content. Monte Carlo simulation was done to evaluate the uncertainty of the estimated results in air.

Spatial patterns and background ranges of naturally occurring radionuclides (NORs) (i.e. U-238, Th-232, K-40) and Cs-137 were studied in the urban soils of Yerevan, the capital city of Armenia. Multifractal Inverse Distance Weighting (MIDW) was used to generate and analyze distribution patterns of radionuclide activities. Based on Fourier transformation of radioactivity data, a spectral analysis was also applied to separate, where possible, background/baseline patterns from local anomalies: two ranges of background values were found to characterise the Yerevan territory. Specifically, in the south and south-east of Yerevan, the lower background ranges of U-238, Th-232 and K-40 comprised in the intervals 2.60–36.42 Bq/kg, 4.04–30.63 Bq/kg and 147.7–396.7 Bq/kg, respectively, were observed in association with the presence of sedimentary formations. In contrast, the higher ones were found, instead, in the central and northern parts of the city where andesite-basalt lavas and ignimbrite tuffs occur. Here, the background values rise to 142.4 Bq/kg, 138.76 Bq/kg and 1502 Bq/kg, respectively. As for the distribution of artificial Cs-137, its baseline levels in Yerevan seem to depend mostly on the global radioactive fallout and some local technogenic sources. Its distribution patterns partially differ from those of NORs. In the framework of this paper, Radium equivalent activity (RaEq), outdoor absorbed dose rate in air (ODRA) and annual effective dose equivalent (AEDEs) were also determined and mapped. They show a good coincidence of their spatial variations with those of NORs. The Monte Carlo simulation was used to assess excess lifetime cancer risk from a stochastic perspective. The related sensitivity analysis revealed that, among NORs, U-238 and Th-232 give the greatest contribution to the total variance (45.7% 42.8%, respectively). In comparison, K-40 has the lowest share (11.3%). Regarding Cs-137, a highly negligible contribution to the onset of health risks (accounting for 0.02%) was observed.