Harmonization of the Long-term PM2.5 Carbon Data from the CSN Sites in New York State
Abstract Harmonizing the particulate carbon data from the Chemical Speciation Network (CSN) is necessary to perform reliable long-term trend and seasonal variability analyses, clean air regulation assessments, and climate change studies. But it is challenging because the measurement of the carbonace...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2023-08-01
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| Series: | Aerosol and Air Quality Research |
| Subjects: | |
| Online Access: | https://doi.org/10.4209/aaqr.230077 |
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| Summary: | Abstract Harmonizing the particulate carbon data from the Chemical Speciation Network (CSN) is necessary to perform reliable long-term trend and seasonal variability analyses, clean air regulation assessments, and climate change studies. But it is challenging because the measurement of the carbonaceous fraction of PM2.5 (particulate matter with a diameter less than or equal to 2.5 µm) underwent several changes both in samplers and analysis protocols. To address the above issue, field blanks are used to remove artifacts from samples, an outlier filter is applied to remove anomalies from the dataset, and a regression between retired samplers and the current sampler data is used to establish the harmonization between two co-located urban sites in this study. A second comparison between the retired method and Interagency Monitoring of Protected Visual Environments (IMPROVE) network data was carried out at two sites (one urban and one rural) with co-located samplers. These results show no site dependence for organic carbon (OC) concentrations and small but non-negligible differences for elemental carbon (EC), which can be attributed to the relatively greater uncertainty of the low concentration rural EC measurements. An adjustment criterion that harmonizes the data from the beginning of the sampling period to the present is obtained. The harmonized data shows consistent trends and seasonal variability when compared to the reported data with these trends declining over the period 2001–2018. |
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| ISSN: | 1680-8584 2071-1409 |