A temperature-based approach to predicting lost data from highly seasonal pollutant data sets

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Air Quality Monitoring

Background:

• Benzo[a]pyrene (BaP) is a key polycyclic aromatic hydrocarbon (PAH) pollutant.
• Accurate monitoring of BaP concentrations is crucial for assessing air quality and health risks.
• Data gaps in monitoring networks can hinder comprehensive air quality assessments.

Purpose of the Study:

• To develop and validate a new technique for predicting benzo[a]pyrene (BaP) concentrations in ambient air.
• To address challenges posed by missing data in long-term air quality monitoring.
• To improve the accuracy of annual average BaP concentration calculations.

Main Methods:

• The technique utilizes the established relationship between ambient temperature and BaP concentration at individual monitoring stations.
• It was tested using monthly BaP concentration data in PM10 from the UK PAH Monitoring Network.
• Predicted data was incorporated to calculate annual average concentrations and compared with actual values.

Main Results:

• The new prediction technique significantly improved the accuracy of annual average BaP concentrations compared to methods without data prediction.
• It outperformed previous strategies for predicting intra-year trends in BaP concentrations.
• The method demonstrated suitability for predicting extended periods of missing data.

Conclusions:

• The developed technique offers a robust solution for filling data gaps in ambient air monitoring for benzo[a]pyrene.
• It provides a significant advancement over existing methods, particularly for long-term data imputation.
• This approach enhances the reliability of air quality assessments and trend analysis.