The GREAT-ER model as a tool for chemical risk assessment and management
      for Chinese river catchments
Creators: name: Jackson, Benjamin W
Publisher: name: Lancaster University
Year: 2018
Subjects: GREAT-ER
water quality
Environmental chemistry
Hydrology
Chemical risk management
modelling
GIS

description: The Chinese government has introduced a range of policies with the aim to
      improve freshwater quality to safe levels for both humans and ecosystem
      function. These policies form an important part of sustainable economic
      development. An important component of the improvement in surface water
      quality is to assess and reduce the risk from organic chemicals. The
      development of reliable predictive tools is therefore required which can
      be used for the purpose of chemical risk assessment and catchment
      management. The catchment scale Geo-referenced Regional Exposure
      Assessment Tool for European Rivers (GREAT-ER) model was developed for
      this purpose. Its application in China would represent a valuable water
      quality management tool. However, the data requirements for the
      parameterization of GREAT-ER are difficult to meet, especially in
      countries with limited data accessibility such as China.
      
      A methodology has been developed to facilitate the use of the GREAT-ER
      model in any catchment in China. Key methodological contributions include
      an approach to locate sewage treatment works (STW), estimate population
      served and to estimate the distribution and magnitude of untreated
      emissions. Low-flow statistics were estimated by means of regional
      regression. The GREAT-ER model was applied to the East river catchment for
      the chemicals Triclosan (TCS), Triclocarban (TCC), Estrone (E1) and
      17β-estradiol (E2). As part of the study, a sampling campaign was
      conducted in January 2016 to collect water samples from sites within the
      East river catchment; samples were subsequently analysed to determine the
      concentration of target chemicals. These data, along with data obtained
      from collaborators collected in December 2008, were used to estimate the
      accuracy of the model. Overall, the model performed well for E1 and E2.
      However, there were some significant errors in the model’s estimation for
      the concentration of TCC and TCS. This included a number of remote rural
      subcatchments, which may be a reflection of the affordability of personal
      care products to the rural population. These, and other factors, were
      explored during validation of the model. A risk assessment was performed
      for the four chemicals for the years 2016 and 2020. In 2016, the model
      estimated that TCC would not exceed the predicted no effect concentration
      (PNEC) anywhere in the catchment, however, in 2020 the PNEC was exceeded
      for 3 stretches, each downstream of major STWs. The model estimated that
      the concentrations of E1 and E2 in 2016 would exceed PNEC values in
      stretches largely confined to the heavily urbanised Shenzhen catchment,
      but also isolated minor stretches located downstream of population
      centres. In 2020, the number of stretches exceeding the PNEC threshold
      reduced for areas with improved wastewater treatment infrastructure, but
      overall, the area that exceeded the PNEC for E1 and E2 was estimated to
      expand. TCS posed a high risk to the catchment in 2016, with the model
      predicting the PNEC to be exceeded throughout much of the catchment. In
      2020, it was estimated that the same stretches would exceed the PNEC for
      TCS, but concentrations would be considerably higher overall. A series of
      catchment management scenarios were then utilised, such as increasing STW
      removal efficiency and the expansion of STW connectivity. These
      infrastructure developments were found to be effective for E1 and E2, but
      not so for TCS.