the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Feb 2025
Implementation of Real-Time Source Apportionment Approaches Using the ACSM-Xact-Aethalometer (AXA) Set-Up with SoFi RT: The Athens Case Study
Abstract. Air pollution, particularly from particulate matter (PM), poses serious public health and environmental risks, especially in urban areas. To address this, accurate source apportionment (SA) of PM is essential for effective air quality management. Traditional SA approaches often rely on offline data collection, limiting timely responses to pollution events. SA applied on data from online techniques, especially with high temporal resolution is advantageous over offline techniques, enabling the study of the diurnal variability of emission sources and also the study of specific events. Recent technological advancements now enable real-time SA, allowing continuous, detailed analysis of pollution sources. This study presents the first application of the ACSM-Xact-Aethalometer (AXA) setup combined with SoFi RT software for real-time source apportionment (RT-SA) of PM in Athens, Greece. The AXA setup integrates chemical, elemental, and black carbon data streams, covering a broad spectrum of PM components and capturing a comprehensive representation of PM sources in an urban environment. The results demonstrate that traffic-related emissions are the largest contributors to PM, with significant contributions from secondary species such as sulfate, nitrate, ammonium, and secondary organic aerosols, which together accounted for approximately 57 % of the PM mass. Primary sources such as biomass burning and cooking contributed around 10 % each, with natural sources like dust and sea salt comprising the remainder. The SoFi RT software is employed for continuous SA, offering automated analysis of PM sources in near real-time (minutes after the measurements). Our findings demonstrate that this setup effectively identifies major pollution sources. This work underscores the AXA system's potential for advancing urban air quality monitoring and informs targeted interventions to reduce PM pollution.
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Status: closed
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RC1: 'Comment on egusphere-2025-542', Anonymous Referee #1, 05 Apr 2025
Publisher’s note: a supplement was added to this comment on 8 April 2025.
Please see attached.
- AC1: 'Reply on RC1', Manousos Manousakas, 12 May 2025
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RC2: 'Comment on egusphere-2025-542', Anonymous Referee #2, 21 Apr 2025
The paper introduces a novel methodology for identifying the sources of both inorganic and organic components of particulate matter. By integrating non-refractory PM compounds with elemental and black carbon data, the authors evaluate model performance using optimal initial parameters compared to results obtained with minimal prior knowledge. This approach is innovative and merits publication in Egusphere.
The study aligns well with the journal’s scope and is generally well presented. Below, I offer several minor suggestions that I believe could enhance the clarity of the paper:
- Line 150: At this point in the manuscript, the definition of “generic conditions” is somewhat unclear. Although the meaning becomes clearer later in the text, providing a brief clarification earlier would greatly improve readability.
- Sofi Model: Line 70 mentions that in the previous publication (Chen, Canonaco, Slowik et al., 2022a), where ACSM data were analyzed, an earlier version of Sofi-RT was used. While section 2.3.1 describes the model’s operation in detail, it would be helpful to state early in the manuscript whether the version applied here differs from the earlier one used for evaluating ACSM and Aethalometer data.
- Line 77: I believe there is an inconsistency in this paragraph, which states: “In this study, data were collected from an Xact, an Aethalometer, a total carbon analyzer, and low-cost sensors.” To my understanding, data from neither a total carbon analyzer nor low-cost sensors were actually used. However, for the Sea Salt/Dust contribution assessment, measurements from a Grimm instrument were included (see Figure S4), yet this device is not mentioned here. Please clarify or correct this.
- Lines 127-132: The methodology for handling Aethalometer data lacks clarity. While prior publications by the authors detail their approach to this dataset, the current paper does not fully explain how the multiple scattering effect is accounted for. Please clarify the compensation method for this effect and specify the inlet size used (e.g., PM2.5).
- Line 235: The authors assert that high a-values are unsuitable for ACSM data. Include a reference or provide justification for this claim.
- Line 407: The construction of the constraint profiles remains unclear, particularly the criteria for identifying variables that can be classified as "irrelevant." Please elaborate on this process.
- Although the authors clearly state that ACSM analyzes PM1 while Xact measures PM2.5, the source apportionment percentages in Figures 4 and 5 are reported without distinction between these size fractions. Additionally, the abstract refers generically to contributions to "PM mass" without specifying size ranges. Given that each source inherently exhibits its own particle size distribution, we recommend elaborating on this concept and explicitly addressing the limitations of presenting combined results across different size fractions.
Citation: https://doi.org/10.5194/egusphere-2025-542-RC2 - AC2: 'Reply on RC2', Manousos Manousakas, 12 May 2025
Status: closed
-
RC1: 'Comment on egusphere-2025-542', Anonymous Referee #1, 05 Apr 2025
Publisher’s note: a supplement was added to this comment on 8 April 2025.
Please see attached.
- AC1: 'Reply on RC1', Manousos Manousakas, 12 May 2025
-
RC2: 'Comment on egusphere-2025-542', Anonymous Referee #2, 21 Apr 2025
The paper introduces a novel methodology for identifying the sources of both inorganic and organic components of particulate matter. By integrating non-refractory PM compounds with elemental and black carbon data, the authors evaluate model performance using optimal initial parameters compared to results obtained with minimal prior knowledge. This approach is innovative and merits publication in Egusphere.
The study aligns well with the journal’s scope and is generally well presented. Below, I offer several minor suggestions that I believe could enhance the clarity of the paper:
- Line 150: At this point in the manuscript, the definition of “generic conditions” is somewhat unclear. Although the meaning becomes clearer later in the text, providing a brief clarification earlier would greatly improve readability.
- Sofi Model: Line 70 mentions that in the previous publication (Chen, Canonaco, Slowik et al., 2022a), where ACSM data were analyzed, an earlier version of Sofi-RT was used. While section 2.3.1 describes the model’s operation in detail, it would be helpful to state early in the manuscript whether the version applied here differs from the earlier one used for evaluating ACSM and Aethalometer data.
- Line 77: I believe there is an inconsistency in this paragraph, which states: “In this study, data were collected from an Xact, an Aethalometer, a total carbon analyzer, and low-cost sensors.” To my understanding, data from neither a total carbon analyzer nor low-cost sensors were actually used. However, for the Sea Salt/Dust contribution assessment, measurements from a Grimm instrument were included (see Figure S4), yet this device is not mentioned here. Please clarify or correct this.
- Lines 127-132: The methodology for handling Aethalometer data lacks clarity. While prior publications by the authors detail their approach to this dataset, the current paper does not fully explain how the multiple scattering effect is accounted for. Please clarify the compensation method for this effect and specify the inlet size used (e.g., PM2.5).
- Line 235: The authors assert that high a-values are unsuitable for ACSM data. Include a reference or provide justification for this claim.
- Line 407: The construction of the constraint profiles remains unclear, particularly the criteria for identifying variables that can be classified as "irrelevant." Please elaborate on this process.
- Although the authors clearly state that ACSM analyzes PM1 while Xact measures PM2.5, the source apportionment percentages in Figures 4 and 5 are reported without distinction between these size fractions. Additionally, the abstract refers generically to contributions to "PM mass" without specifying size ranges. Given that each source inherently exhibits its own particle size distribution, we recommend elaborating on this concept and explicitly addressing the limitations of presenting combined results across different size fractions.
Citation: https://doi.org/10.5194/egusphere-2025-542-RC2 - AC2: 'Reply on RC2', Manousos Manousakas, 12 May 2025
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