A new methodology for source apportionment of gaseous industrial emissions

Air quality modeling (AQM) is often used to investigate gaseous pollution around industrial zones. However, this methodology requires accurate emission inventories, unbiased AQM algorithms and realistic boundary conditions.We introduce a new methodology for source apportionment of industrial gaseous emissions, which is based on a fuzzy clustering of ambient concentrations, along with a standard AQM approach. First, by applying fuzzy clustering, ambient concentration is expressed as a sum of non-negative contributions - each corresponding to a specific spatiotemporal pattern (STP); we denote this method as FUSTA (FUzzy SpatioTemporal Apportionment). Second, AQM of the major industrial emissions in the study zone generates another set of STP. By comparing both STP sets, all major source contributions resolved by FUSTA are identified, so a source apportionment is achieved. The uncertainty in FUSTA results may be estimated by comparing results for different numbers of clusters.We have applied FUSTA in an industrial zone in central Chile, obtaining the contributions from major sources of ambient SO2: a thermal power plant complex and a copper smelter, and other contributions from local and regional sources (outside the AQM domain). The methodology also identifies SO2 episodes associated to emis-sions from the copper smelter.

Automated summary

Air quality modeling is commonly used to study gaseous pollution around industrial areas. This study proposes a new methodology, FUSTA, which combines fuzzy clustering with standard AQM to apportion industrial gaseous emissions sources. The methodology is applied in a central Chilean industrial zone to identify major sources of ambient SO2 and episodes associated with emissions from a copper smelter.