A new methodology for source apportionment of gaseous industrial emissions

• Published in: Journal of hazardous materials 2023-02, Vol.443, p.130335-130335, Article 130335
• Main Authors: Jorquera, Héctor, Villalobos, Ana María
• Format: Article
• Language: English
• Subjects: Air pollution episodes; Air quality modeling; Fuzzy clustering; Source apportionment; Sulfur dioxide
• ISSN: 0304-3894; 1873-3336
• DOI: 10.1016/j.jhazmat.2022.130335

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 emissions from the copper smelter. [Display omitted] •New methodology for source apportionment of industrial gaseous emissions (FUSTA).•Data requirements are similar as in air quality modeling (AQM).•FUSTA methodology handles outliers present in ambient data.•FUSTA methodology also resolves contributions from non-local sources.