A new multimedia contaminant fate model for China: How important are environmental parameters in influencing chemical persistence and long-range transport potential?

We present a new multimedia chemical fate model (SESAMe) which was developed to assess chemical fate and behaviour across China. We apply the model to quantify the influence of environmental parameters on chemical overall persistence (P_OV) and long-range transport potential (LRTP) in China, which has extreme diversity in environmental conditions. Sobol sensitivity analysis was used to identify the relative importance of input parameters. Physicochemical properties were identified as more influential than environmental parameters on model output. Interactive effects of environmental parameters on P_OV and LRTP occur mainly in combination with chemical properties. Hypothetical chemicals and emission data were used to model P_OV and LRTP for neutral and acidic chemicals with different K_OW/D_OW, vapour pressure and pK_a under different precipitation, wind speed, temperature and soil organic carbon contents (f_OC). Generally for P_OV, precipitation was more influential than the other environmental parameters, whilst temperature and wind speed did not contribute significantly to P_OV variation; for LRTP, wind speed was more influential than the other environmental parameters, whilst the effects of other environmental parameters relied on specific chemical properties. f_OC had a slight effect on P_OV and LRTP, and higher f_OC always increased P_OV and decreased LRTP. Example case studies were performed on real test chemicals using SESAMe to explore the spatial variability of model output and how environmental properties affect P_OV and LRTP. Dibenzofuran released to multiple media had higher P_OV in northwest of Xinjiang, part of Gansu, northeast of Inner Mongolia, Heilongjiang and Jilin. Benzoa]pyrene released to the air had higher LRTP in south Xinjiang and west Inner Mongolia, whilst acenaphthene had higher LRTP in Tibet and west Inner Mongolia. TCS released into water had higher LRTP in Yellow River and Yangtze River catchments. The initial case studies demonstrated that SESAMe performed well on comparing P_OV and LRTP of chemicals in different regions across China in order to potentially identify the most sensitive regions. This model should not only be used to estimate P_OV and LRTP for screening and risk assessments of chemicals, but could potentially be used to help design chemical monitoring programmes across China in the future.