The GREAT-ER model as a tool for chemical risk assessment and management for Chinese river catchments. Jackson, B. W., Sweetman, A., & Jones, K. Ph.D. Thesis, Lancaster University, 2018. ![The GREAT-ER model as a tool for chemical risk assessment and management for Chinese river catchments [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex 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.
@phdthesis{jackson_great-er_2018,
type = {phd},
title = {The {GREAT}-{ER} model as a tool for chemical risk assessment and management for {Chinese} river catchments},
url = {http://eprints.lancs.ac.uk/126264/},
abstract = {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.},
urldate = {2018-07-19TZ},
school = {Lancaster University},
author = {Jackson, Benjamin W. and Sweetman, Andrew and Jones, Kevin},
year = {2018}
}
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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. 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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. 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