Comparison of methods for resolving the contributions of local emissions to measured concentrations
<p>To accurately study the characteristics of an air pollution emitter, it is necessary to isolate the contribution of that emitter to total measured pollution concentrations. A variety of published methods exist to complete this task, like placing measurements upwind the emitter, employing a...
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Copernicus Publications
2025-05-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/18/2201/2025/amt-18-2201-2025.pdf |
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| author | T. D. Edwards Y. K. Wong C.-H. Jeong J. M. Wang Y. Su G. J. Evans |
| author_facet | T. D. Edwards Y. K. Wong C.-H. Jeong J. M. Wang Y. Su G. J. Evans |
| author_sort | T. D. Edwards |
| collection | DOAJ |
| description | <p>To accurately study the characteristics of an air pollution emitter, it is necessary to isolate the contribution of that emitter to total measured pollution concentrations. A variety of published methods exist to complete this task, like placing measurements upwind the emitter, employing a distant background measurement station, or algorithmic methods that extract a background from the time series of measured concentrations (e.g. wavelet decomposition). In this study, we measured nitrogen oxides (NO<span class="inline-formula"><sub><i>x</i></sub></span>), carbon monoxide (CO), carbon dioxide (CO<span class="inline-formula"><sub>2</sub></span>), and fine particulate matter (PM<span class="inline-formula"><sub>2.5</sub></span>) at four sites spanning Toronto, Ontario, Canada. We first characterized the spatial variability of background concentrations across the city and then tested the accuracy of seven different algorithmic methods of estimating true measured upwind-of-emitter backgrounds near Toronto's Highway 401 by using the data collected at a downwind site. These methods included time-series and regression methods, including machine learning (XGBoost). We observed background concentrations had notable spatial variability, except for PM<span class="inline-formula"><sub>2.5</sub></span>. When predicting backgrounds upwind the highway, we found a distant measurement station provided an accurate background only during some times of day and was least accurate during rush hours. When testing algorithmic predictions of upwind-of-highway backgrounds, we found that regression models surpassed the performance of time-series methods, with best predictions having <span class="inline-formula"><i>R</i><sup>2</sup></span> exceeding 0.8 for all four pollutants. Despite the better performance of regression models, time-series methods still provided reasonable estimates. We also found that emitter-specific covariates (e.g. traffic counts, on-site dispersion modelling) did not play an important role in regressions, suggesting backgrounds can be well characterized by time of day, meteorology, and distant measurement stations. Based on our results, we provide ranked recommendations for choosing background estimation methods. We suggest future air pollution research characterizing individual emitters includes careful consideration of how background concentrations are estimated.</p> |
| format | Article |
| id | doaj-art-958c092ddb8a4dc1a79d4506e03f28be |
| institution | Kabale University |
| issn | 1867-1381 1867-8548 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj-art-958c092ddb8a4dc1a79d4506e03f28be2025-08-20T03:49:37ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482025-05-01182201224010.5194/amt-18-2201-2025Comparison of methods for resolving the contributions of local emissions to measured concentrationsT. D. Edwards0Y. K. Wong1C.-H. Jeong2J. M. Wang3Y. Su4G. J. Evans5Department of Chemical Engineering and Applied Chemistry, University of Toronto, Wallberg Memorial Building, 184 College St., Toronto, Ontario, CanadaDepartment of Chemical Engineering and Applied Chemistry, University of Toronto, Wallberg Memorial Building, 184 College St., Toronto, Ontario, CanadaDepartment of Chemical Engineering and Applied Chemistry, University of Toronto, Wallberg Memorial Building, 184 College St., Toronto, Ontario, CanadaDepartment of Chemical Engineering and Applied Chemistry, University of Toronto, Wallberg Memorial Building, 184 College St., Toronto, Ontario, CanadaEnvironmental Monitoring and Reporting Branch, Ontario Ministry of the Environment, Conservation and Parks, 125 Resources Road, Toronto, Ontario, CanadaDepartment of Chemical Engineering and Applied Chemistry, University of Toronto, Wallberg Memorial Building, 184 College St., Toronto, Ontario, Canada<p>To accurately study the characteristics of an air pollution emitter, it is necessary to isolate the contribution of that emitter to total measured pollution concentrations. A variety of published methods exist to complete this task, like placing measurements upwind the emitter, employing a distant background measurement station, or algorithmic methods that extract a background from the time series of measured concentrations (e.g. wavelet decomposition). In this study, we measured nitrogen oxides (NO<span class="inline-formula"><sub><i>x</i></sub></span>), carbon monoxide (CO), carbon dioxide (CO<span class="inline-formula"><sub>2</sub></span>), and fine particulate matter (PM<span class="inline-formula"><sub>2.5</sub></span>) at four sites spanning Toronto, Ontario, Canada. We first characterized the spatial variability of background concentrations across the city and then tested the accuracy of seven different algorithmic methods of estimating true measured upwind-of-emitter backgrounds near Toronto's Highway 401 by using the data collected at a downwind site. These methods included time-series and regression methods, including machine learning (XGBoost). We observed background concentrations had notable spatial variability, except for PM<span class="inline-formula"><sub>2.5</sub></span>. When predicting backgrounds upwind the highway, we found a distant measurement station provided an accurate background only during some times of day and was least accurate during rush hours. When testing algorithmic predictions of upwind-of-highway backgrounds, we found that regression models surpassed the performance of time-series methods, with best predictions having <span class="inline-formula"><i>R</i><sup>2</sup></span> exceeding 0.8 for all four pollutants. Despite the better performance of regression models, time-series methods still provided reasonable estimates. We also found that emitter-specific covariates (e.g. traffic counts, on-site dispersion modelling) did not play an important role in regressions, suggesting backgrounds can be well characterized by time of day, meteorology, and distant measurement stations. Based on our results, we provide ranked recommendations for choosing background estimation methods. We suggest future air pollution research characterizing individual emitters includes careful consideration of how background concentrations are estimated.</p>https://amt.copernicus.org/articles/18/2201/2025/amt-18-2201-2025.pdf |
| spellingShingle | T. D. Edwards Y. K. Wong C.-H. Jeong J. M. Wang Y. Su G. J. Evans Comparison of methods for resolving the contributions of local emissions to measured concentrations Atmospheric Measurement Techniques |
| title | Comparison of methods for resolving the contributions of local emissions to measured concentrations |
| title_full | Comparison of methods for resolving the contributions of local emissions to measured concentrations |
| title_fullStr | Comparison of methods for resolving the contributions of local emissions to measured concentrations |
| title_full_unstemmed | Comparison of methods for resolving the contributions of local emissions to measured concentrations |
| title_short | Comparison of methods for resolving the contributions of local emissions to measured concentrations |
| title_sort | comparison of methods for resolving the contributions of local emissions to measured concentrations |
| url | https://amt.copernicus.org/articles/18/2201/2025/amt-18-2201-2025.pdf |
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