Multimedia Modeling of the Fate for Emerging Pollutants

Abstract

Due to the complicacy of chemicals and the limited systematic monitoring data in the environment, simulating the multimedia fate of emerging pollutants has become a necessary step to link the emission to ecological and human health risks. The fugacity model (Mackay 1979) has been widely applied in analysis and prediction, especially for POPs. The concept of fugacity and principles of mass balance was used to describe the partitioning processes and predict the concentration, distribution, and persistence of the chemicals (Liu et al. 2011). The air and water flux between adjacent regions are introduced by spatially explicit fugacity models in model calculation and spatial transport of chemicals. The Berkeley-Trent (BETR) model was originally a spatially segmented multimedia fugacity model on a regional scale developed for North America (MacLeod et al. 2001; Woodfine et al. 2001). After that, the model was further developed to simulate the fate of chemicals in the European continent (Prevedouros et al. 2004) and on a global scale (Macleod et al. 2005). In this chapter, the structure of the BETR model was reconfigured, re-parameterized, and calibrated to be applied in China, which is located in the Bohai coastal region. The study region was divided into grid cells with an area of 1° × 1°. Each grid cell was then constructed with six connected compartments representing air, soil, vegetation, fresh water, freshwater sediment, and coastal water. The modified BETR model was named the BETR-Bohai model and was used to simulate the environmental fate and transfer processes of benzo[α]pyrene (BaP) (Liu et al. 2014) and PFOS (Liu et al. 2015). The reliability of the model estimation was evaluated through concentration validation and comparison, followed by sensitivity and uncertainty analysis. The application of the BETR model showed that the modeled concentrations of BaP generally agreed with field observations except for the soil concentration of urban areas. From the environmental concentrations and emissions of emerging pollutants studied in the above chapters, it was found that huge differences exist between urban and rural areas. This phenomenon was not considered in the simulation at global or continental scales. However, at a regional scale, it would lead to the simulated concentrations in urban areas being excessively undervalued due to the relatively high emissions in urban areas. The factors affecting the fate of pollutants in urban and rural areas include emissions, vegetation cover, soil properties, and atmospheric aerosols. Thus, based on the steady-state BETR-Bohai model, we developed a spatially resolved multi-media BETR-Urban–Rural (BETR-UR) model, which took land-use effects into account, dividing the soil and lower air compartments into urban soil and rural soil, lower urban air and lower rural air, respectively. The urban areas mainly indicated those areas with high population density, including industrial land use, commercial land use, urban residential land use, municipal land for public facilities, and their buffers. Rural land uses include rural villages, agricultural land, grassland, forest land, rural residential land, unused land, and so on. Moreover, the complex inter-media transport processes were optimized, effectively distinguishing the emissions from urban and rural areas. The BETR-UR model was further used to simulate the transport of PAHs (Song et al. 2016), the dynamic multimedia fate of PFOS from 1981 to 2050 (Su et al. 2018a), the multimedia fate of PFOS in the context of urbanization and climate change (Su et al. 2018b), and the fate and transport of PFOA/PFO (Su et al. 2018c). Based on the BETR-UR model, we further established a generalized fugacity model with a particular focus on simulating the urban–rural gradients of emerging pollutants in soil. Soil samples were taken for the analysis of PCBs and PAHs. The model and the measured concentrations were then combined to validate the urban–rural gradient, further explore the sources (Song et al. 2018), and make predictions (Song et al. 2019). Generally, the model approach is complementary to environmental monitoring efforts and estimations. It can not only make predictions based on existing data but also take more factors, such as urbanization and climate change, into consideration. The modeling results will be informative for policymakers to take action on emission control and ecological risk governance.