The ISWA Plastic Pollution Calculator provides the most comprehensive local quantification of an areas plastic pollution to date. However, this detailed analysis requires relatively high levels of data inputs in comparison to other models such as the Waste Flow Diagram. This includes data related to waste generation by different activities and their plastic item composition, tourism levels by month, local socioeconomic conditions, and detailed descriptions of the solid waste management infrastructure and practices.
To try and simply the data needs of the Calculator, the inputs are separated into three distinct types as follows:
1) Primary data
Primary data relates to details about the local demographics, geography and waste management infrastructure of the district. There are 23 main input areas, of which 18 are mandatory and 5 are optional. These main areas are further broken down into a number of sub-input questions, giving a total of 208 data inputs or which 183 are mandatory. However, many of these data inputs are either:
Additionally, a number of the primary inputs have default values that can be used if no other available data exists. These defaults are typically related to country level data, or based on assumptions determined from previous user inputs. Whilst this can simplify the data requirements, we advocate primary data collection where possible to ensure the highest level of data quality and therefore results.
2) Secondary data
Secondary data relates to data inputs which are not as easily attainable as those in the primary data, and as such, default options are provided for all based on either literature data, country level data or income level data. Although these default values largely negate the need to complete the secondary data, attempts should still be made where possible to replace these default values with district specific data, particularly for critical inputs such as compositions which can show highly localised variations.
3) Tertiary data
Tertiary data refers to data inputs which are both typically site specific and which data generally does not exist. Therefore, as these cannot be replaced by any default data, assumptions are instead provided which relate to either expert opinions or conceptual models. Ideally, critical inputs such as the degree of dumping in water, and the levels of littering by land use, should be determined for the district where possible and used instead. Dr Velis Research Team specialises in data analytics and methodologies to measure such parameters and would be happy to be involved with any team looking to quantify these aspects in greater detail.
For a full list of data requirements and guidance on applying the Calculator, please contact Dr Costas Velis by email C.Velis@leeds.ac.uk who will be happy to provide more information.