Keywords
chemical regulation; classification; data mining; time series analysis
Start Date
17-9-2020 8:20 AM
End Date
17-9-2020 8:40 AM
Abstract
Modern industrial practices employ a large and diverse collection of chemicals. This can challenge regulators charged with environmental protection. Typically, insufficient data is available for risk assessments. Thus, chemicals may find widespread use until adequate evidence of adverse environmental effects prompts regulatory action. Globally, regulators have seen that such ‘reactive’ risk management has disadvantages. Recently in Australia (and elsewhere), relatively rapidly, certain unrestricted, longused perand polyfluoroalkyl substances (PFAS) became subjects of concern, then regulation. Such events motivate us to support regulators’ ‘proactive’ risk management efforts. We aim to assist regulators in anticipating the emergence of potentially risky chemicals, enabling their timely actions. We hypothesise that a time series of research interest mined from a scientific publication database may reveal ‘emerging interest’ in a chemical that foreshadows its progress towards regulation. We investigate this for six PFAS by determining the associated research interest in Web of Science. For each chemical, we use R code to apply queries to an application programming interface, and count annual positive results across a publication year range. Inspection of these time series suggests two tests, each of which determines the first year in which some condition is satisfied. We propose classification rules to interpret test outcomes, and compare results against PFAS regulatory histories. For the regulated PFAS, we anticipate the historical progression of Australian regulatory concern. We also judge some unrestricted PFAS as being of concern, and this is validated by interest from other jurisdictions. These results demonstrate our system’s predictive ability, and encourage further development.
On using 'Emerging Interest' in Scientific Literature to inform Chemical Risk Prioritisation
Modern industrial practices employ a large and diverse collection of chemicals. This can challenge regulators charged with environmental protection. Typically, insufficient data is available for risk assessments. Thus, chemicals may find widespread use until adequate evidence of adverse environmental effects prompts regulatory action. Globally, regulators have seen that such ‘reactive’ risk management has disadvantages. Recently in Australia (and elsewhere), relatively rapidly, certain unrestricted, longused perand polyfluoroalkyl substances (PFAS) became subjects of concern, then regulation. Such events motivate us to support regulators’ ‘proactive’ risk management efforts. We aim to assist regulators in anticipating the emergence of potentially risky chemicals, enabling their timely actions. We hypothesise that a time series of research interest mined from a scientific publication database may reveal ‘emerging interest’ in a chemical that foreshadows its progress towards regulation. We investigate this for six PFAS by determining the associated research interest in Web of Science. For each chemical, we use R code to apply queries to an application programming interface, and count annual positive results across a publication year range. Inspection of these time series suggests two tests, each of which determines the first year in which some condition is satisfied. We propose classification rules to interpret test outcomes, and compare results against PFAS regulatory histories. For the regulated PFAS, we anticipate the historical progression of Australian regulatory concern. We also judge some unrestricted PFAS as being of concern, and this is validated by interest from other jurisdictions. These results demonstrate our system’s predictive ability, and encourage further development.
Stream and Session
false