Research progress of the POP fugacity model: a bibliometrics-based analysis

Use of machine learning to identify key factors regulating volatilization of semi-volatile organic chemicals from soil to air

Volatilization from soil to air is a key process driving the distribution and fate of semi-volatile organic contaminants. However, quantifying this process and the key environmental governing factors remains difficult. To address this issue, the volatilization fluxes of polybrominated diphenyl ethers (PBDEs) and organophosphate esters (OPEs) from soil were determined in 16 batch experiments orthogonally with six variables (chemical property, soil concentration, air velocity, ambient temperature, soil porosity, and soil moisture) and analyzed with machine learning methods. The results showed that gradient-boosting regression tree models satisfactorily predicted the volatilization fluxes of PBDEs (r2 = 0.82 ± 0.07) and OPEs (r2 = 0.62 ± 0.13). Permutation importance analysis showed that partitioning potential of chemicals between soil and air was the most important factor regulating the volatilization of the target compounds from soil. Temperature and soil porosity played a secondary role in controlling the migration of PBDEs and OPEs, respectively, due to higher volatilization enthalpies of PBDEs than those of OPEs and dominant adsorption of OPEs on mineral surface. The effect of soil moisture was negative and positive for the volatilization fluxes of PBDEs and OPEs, respectively. These results suggested different responses in the soil-air diffusive transport of PBDEs and OPEs to high temperature and rainstorm induced by climate change.

The multi-media environmental behavior of heavy metals around tailings under the influence of precipitation

Precipitation can lead to significant leaching of heavy metals from abandoned tailings，resulting in a decline in the quality of the surrounding environment. This study aimed to simulate and quantify the migration patterns and fate of heavy metals in tailings caused by precipitation in various environmental media (tailings, air, water, soil, and sediments) using leaching tests, source apportionment, and a fugacity model. Results revealed that the average contents of Cd, Cu, As, Pb, Zn, and Cr in the un-weathered tailings were 3.43, 495.56, 160.70, 138.94, 536.57, and 69.52 mg/kg, respectively. The ecological risk factors in the tailings as well as in sediments and soils, were in the following order: Cd >Cu >As >Pb >Zn >Cr. A fugacity model based on the mass-balance methods was established, achieving a good agreement between simulation and measured values. The total amounts of Cd, Cu, As, Pb, and Zn leached from abandoned tailings over the 30-year evaluation period were estimated to be 1.09, 62.44, 0.16, 0.94, and 102.12 t, respectively. Soil and sediments are important reservoirs for heavy metals. The sum of the As, Cd, Cu, Pb, and Zn storage capacities in the soil and sediment accounted for 77.28%, 75.63%, 73.94%, 69.39%, and 57.80% of the total storage capacity, respectively. This study could provide the means for the establishment of a targeted pollution control plan, a guide for restoration projects, and will aid in controlling pollution risk and improving the surrounding environment.

Analysis of the multi-media environmental behavior of polycyclic aromatic hydrocarbons (PAHs) within Haizhou Bay using a fugacity model

In this study, we aimed to quantify the transport and fate of PAHs in different environmental phases (air, seawater, soil, sediment and fish), verify application of the Level III fugacity model in a bay simulation, and understand the transport and fate of PAHs in the bay environment on a macroscopic scale. The simulated average concentrations of ∑₁₆PAH in the air and soil (23.8 ng/m³ and 1080.91 ng/g, respectively), which is as a background reference data for the Haizhou Bay. In addition, the soil (307 t), fish (29.4 t), and sediment (9.72 t) phases were found to be important reservoirs in the Haizhou Bay. Emissions from road vehicles (658 t) accounted for the largest share of PAH emissions in the area, and atmospheric deposition contributed most to the input of PAHs to the polluted area in the region. Whereas the contribution of river runoff input was small, and degradation loss was the main output pathway.