Review of receptor modeling methods for source apportionment

Area of Science:

• Environmental Science and Engineering
• Atmospheric Chemistry
• Air Quality Management

Background:

• For over 50 years, scientific efforts have focused on linking measured airborne constituent concentrations to their origins.
• Advancements in measurement technology and computational power have enabled sophisticated data analysis for atmospheric studies.
• Current capabilities offer valuable insights into pollutant sources and atmospheric processing, aiding air quality management.

Purpose of the Study:

• To review the evolution of receptor models in air quality data analysis.
• To assess the current state-of-the-art in source identification and apportionment from ambient air quality data.
• To highlight the capabilities and limitations of existing methods for understanding air pollution origins.

Main Methods:

• Integration of receptor and chemical transport models for improved source apportionment.
• Utilization of known source profiles with ambient data for accurate apportionment of targeted sources.
• Application of advanced models fitted to data based on conceptual source and sampling models.

Main Results:

• Significant progress in extracting information on pollutant sources and atmospheric processing from air quality data.
• Availability of tools and software for source identification and apportionment, making methods accessible.
• Demonstrated capability to provide useful insights for air quality management decisions.

Conclusions:

• Receptor modeling methods for source identification and apportionment are relatively mature.
• A critical limitation is the scarcity of measurements for source emission compositions, particularly for stationary sources.
• The lack of local source profiles in developing countries presents a significant challenge for effective source apportionment.