Investigating Cyclicality and Seasonality in Black Carbon Pollution Sources via Linear Regression

Abstract

The focus of this project was to investigate possible daily and yearly patterns in black carbon emissions throughout Europe based on both actual data obtained from 2018 to 2019 and a transport model created to identify the sources of said black carbon. Previous work with the data had shown large gaps between the observed and predicted values, and our hope was to identify cyclical or seasonal patterns that would explain these gaps.

By stratifying the data separately according to time of day and month of the year, wecreated individual linear regression models for each subset of data. From here, we ran two parameter reduction methods to help identify possible significant covariates, checking for both common and unique covariates between each model. Finally, we used the reduced parameters and the original transport model predicted values to create one more set of models to investigate.

Analysis by time of day showed little in the way of clear patterns, but did help to confirm key covariates suggested by previous work. The eastern European regions which had been identified as areas with underreported black carbon emissions appeared consistently across models.

Monthly analysis provided somewhat more interesting results. Regions suspected of contributing more black carbon than predicted were most often identified by our models during the summer months. This was in contrast to our assumption that their overcontributions would be higher in winter, where the gaps between observed and predicted values were greater.

Popular Abstract

Understanding the emissions and transportation of pollution is critical to helping limit the havoc which it wreaks on our planet. If we do not know where it is coming from and where it is going, we can hardly work to effectively curtail it. To that end, Lund University has worked to create a model for the transportation of black carbon, a byproduct of burning fuel, across Europe. By combining the model with reported emissions in the European emissions database we can predict the expected concentration of black carbon at environmental monitoring stations.'

However, measurements taken at stations across Europe were consistently found to be
significantly higher than these predictions. The goal of this project was to investigate the causes of these discrepancies.

After looking at several stratifications of the data, it appeared that two plausible patterns of deviation came from time of day and time of year. By dividing the data along these lines, we were able to generate linear models specific to each subset in the hope of identifying clear patterns in the gap between observed and predicted black carbon measurements.

By looking at the data divided in multiple ways and fitted with multiple models, we were able to both strengthen some of the findings of previous work on the subject, as well as identify some new patterns as well as potential issues within the data itself. While we did find further evidence that the transport model appears to underaccount for several areas across eastern Europe, we also found that it seems to be overestimating the contributions of regions closest to the measurement stations.

• author: Cagle, Christian
• supervisor: Johan Lindström
• organization: Mathematical Statistics
• course: MASK11 20261
• year: 2026
• type: M2 - Bachelor Degree
• subject: Mathematics and Statistics
• publication/series: Bachelor’s Theses in Mathematical Sciences
• report number: LUNFMS-4089-2026
• ISSN: 1654-6229
• other publication id: 2026:K23
• language: English
• id: 9239813
• date added to LUP: 2026-06-18 15:38:59
• date last changed: 2026-06-18 15:38:59