the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Oct 2023
International airport emissions and their impact on local air quality: Chemical speciation of ambient aerosols at Madrid-Barajas Airport during AVIATOR Campaign
Saleh Alzahrani
Doğuşhan Kılıç
Michael Flynn
Paul Williams
James Allan
Abstract. Madrid-Barajas International Airport (MAD), located in Spanish Capital Madrid, is the fourth-busiest airport in Europe. As part of the AVIATOR campaign, chemical composition of particulate matter and other key pollutants were measured at the airport perimeter during October 2021, to assess the impact of airport emissions on local air quality. A high-fidelity ambient instrumentation system was deployed at Madrid Airport to measure: composition of ambient aerosol and concentrations of black carbon (eBC), carbon dioxide (CO2) carbon monoxide (CO), nitrogen dioxide (NOx), sulphur dioxide (SO2), particulate matter (PM2.5, PM10), total hydrocarbon (THC), and total particle number. The average concentration for the entire campaign of eBC, NOx, SO2, PM2.5, PM10, CO and THC at the airport were, 1.07 (µg/m³), 22.7 (µg/m³), 4.10 (µg/m³), 9.35 (µg/m³), 16.43 (µg/m³), 0.23 (mg/m³) and 2.30 (mg/m³) respectively. The source apportionment analysis of the non-refractory organic aerosol (OA) using positive matrix factorisation (PMF) allowed us to discriminate between different sources of pollution, namely: Semi Volatile Oxygenated Organic Aerosol (SV-OOA), Alkane Organic Aerosol (AlkOA), and More Oxidised Oxygenated Organic Aerosol (MO-OOA) source. The results showed that SVOOA and MO-OOA accounts for more than 80 % of the total organic particle mass that was measured near runway at the airport. Trace gases correlate better with AlkOA factor more than SVOOA and MO-OOA which indicate that AlkOA is mainly related to the primary emissions of combustion. Bivariate polar plots were used for the source identification. Significantly higher concentrations of the obtained factors were observed at low wind speeds < 3m/s from the southwest where two of runways, as well as all terminals are located. Higher SO2/NOx and CO/eBC ratios were observed when the winds originating from the northeast where the 18L/36R runways are located. This is attributed to the aircraft influence and the lack of a local road source in the northeast area.
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Saleh Alzahrani et al.
Status: final response (author comments only)
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RC1: 'Comment on egusphere-2023-2197', Anonymous Referee #1, 27 Oct 2023
This is an interesting and relevant study of aircraft emissions at a major airport. This topic is important as the contributions of aviation come under increasing scrutiny and new regulations are being considered. With some refinement of this manuscript, this could be a helpful contribution.
Major comments:
The interpretation and discussion of the results is lacking. There is explicit mention of source apportionment in the paper, but the authors do not carefully consider the distinction between background aerosol from the city (Madrid) versus the contributions from the airport and specifically from airplanes. The magnitude of the contribution that is labeled SV-OOA is large and its diurnal cycle both suggest urban contributions may dominate (young airplane emissions from the airport should not be that oxidized, the variation during the day of urban SV-OOA would be expected due to warming and boundary layer thickening, etc.). The wind direction also may provide support for this, as does comparing correlations with other, aviation-specific emissions. With mention of source apportionment, more attention should be paid to understanding the urban contributions. An attempt to quantify the relative contributions of urban vs aviation would be welcome but, if not possible, an explanation of what would be needed to do so would be needed.
In that vein, an illustrative figures of a time series of 1) an aircraft take-off and 2) an aircraft landing would provide insight into the detailed contribution of aviation compared to the more diffuse background contributions. Time series analysis would help understand immediate local contributions vs more distant background contributions. In fact, if the time series could be correlated with observation of the specific source airplane, information and statistics about the aircraft and engine types during the campaign could help interpret the data, as has been done in previous airport campaigns. Engine type might also provide more insights into the oil question also (see below).
Terminology: Use of SV-OOA—this should be rephrased to "less oxidized oxidized organic aerosol" (LO-OOA) as there is no specific volatility data presented (such a thermal denuder) to determine if it is semi volatile or more volatile than MO-OOA. The data in this paper is more specifically the oxidation state measured with the AMS
More detailed comments:
Line 59-60: Jet A1 (in Europe and Jet A in US) is a mix of BOTH aliphatic HCs and aromatic species (not just aliphatics). A minimum aromatic content is prescribed, in fact, which is relevant as sustainable fuels are considered. So the aromatic content cannot be ignored.
Line 91: "is the main international airport" , perhaps add "in Spain"? Otherwise it could be interpreted more broadly.
Section 2.3 Data Analysis: This section is poorly written and needs to be edited. The verb tense changes throughout ("was operating" vs "were analyzed", etc.) There is significant redundancy and 149-151 is an incomplete sentence or missing verb, etc. Line 148 should read SQUIRREL "operated within Igor Pro", not "supplied by Igor Pro".
Regarding the apparent lack of observation of oil emissions (lines187-202), it might be valuable to cite Ungeheuer et al., 2021 and Fushimi 2019 in line 187. But, more broadly, if the emissions in the AVIATOR campaign are actually dominated by urban aerosol, the oil in the aviation portion may be hard to measure due to the dominance of the urban background. Some insight on this might be gained by coloring the data stream in figure 3 with the wind direction. If the "little oil" region can be attributed to runway or terminal influenced air and the "unlikely" region is more attributable to urban background, it may well be that the oil (and aviation signal more generally) is swamped by urban background. Finally, the last sentence (line 202) might better say that the "organic mass may be under-represented in this study" unless the relative contributions of aviation and urban aerosol can be better quantified. But it may well be that the oil is just obscured by the larger urban background aerosol contribution and this dataset may not be at odds with earlier campaigns
line 227: "an earlier studies" singular/plural mismatch
line 248 "Aliphatic#1" is unclear. If this is referencing PMF analysis from a separate paper, this needs to be rephrased and referenced. (vs AlkOA already described in this manuscript).
line 287-288 discussion of SV-OOA and MO-OOA formation: might be worth emphasizing here that this processing occurs primarily for urban contributions in this study since there is too little time for significant photochemical oxidation of aviation emissions so close to the source.
Citation: https://doi.org/10.5194/egusphere-2023-2197-RC1 -
RC2: 'Comment on egusphere-2023-2197', Anonymous Referee #2, 03 Nov 2023
This manuscript presents a study focused on aircraft emissions at an airport. The authors conducted measurements at the airport and used a source apportionment model to investigate the sources of PM pollution. In addition, bivariate polar plots were employed to identify the sources of multiple pollutants.
Major comments:
Some of the discussions and conclusions in the "Results and Discussion" section lack sufficient evidence. Additionally, the authors discuss certain results based on information from previous literature, but these statements from the literature should be illustrated more clearly so that readers can easily connect the previous studies with this paper. This paper also contains some grammatical errors, please thoroughly review the manuscript for these issues.
The discussion overall lacks consideration of the effect of background urban pollution on the measurements performed in this work. Particularly, given the diurnal trend of the pollutants presented in Figures 5 and 6, it is evident that many pollutants show a strong influence from urban emissions. This strong influence from urban emissions could potentially alter some of the main conclusions in the current manuscript. If the authors consider the urban emission of organic aerosols, how would this affect the PMF factors selected in Section 2.3? Some sections in Section 3 need to be rewritten after considering urban aerosols.
Specific Comments:
- Section 2.1: Please note the distance from sampling location to the runways, terminals, and nearest highway in Fig.1 or text.
- Section 2.2: Please give a more detailed description of AMS, including the AMS inlet used here (PM1 or PM2.5?), and the time resolution of AMS.
- Section 2.3: Please add more details of how the authors selected the PMF factors and explain the technical terms used (e.g. FPEAK, Q/Qexp).
Rewrite sentence line 149-151.
Line 158: What are the “measured reference profiles” referred here?
- Section 3.1: are the signal at m/z 85 and m/z 71 used here all from AMS UMR analysis? If there are specific ions related to oil emission, does it make more sense to use the signal based on HR analysis?
- When comparing the results from this study to those presented by Yu et al. in 2012, it's important to consider other factors that may be contributing to the very low presence of oil-related aerosols in your findings. These factors could include the overall mass loading of aerosols, the influence of urban aerosol emissions, or the proximity of the sampling location to the nearest runways.
Furthermore, the values displayed in Fig 3 are noisy and show a broad range. It may be beneficial to further average or smooth these values for a clearer presentation of the data.
- Section 3.2: the main conclusions in this part are same as previous studies. Are there any new findings from this work? Please highlight them. The sources of MOOOA need to be discussed.
Line 248: the term “aliphatic #1” is from the reference, please avoid using it directly without explanation.
- Section 3.3: concentration of AlkOA is suggested to be influenced by flight activity, please include such information in main text or SI.
Line 279: is SVOOA used here referring to gas-phase organic compounds or particle-phase organic compounds?
Line 291: please further explain the method of normalization.
Line 298-300: the authors suggested the diurnal trend of the pollutants listed in Fig 6 were all similar, but it is very clear that BC, NOx, and CO have two peaks, total number concentration starts to increase mid-morning and maintains high during the daytime, while AlkOA only has one big peak around evening. Please provide further evidence for the conclusion. In Fig 6, at hour 23, the normalized level of AlkOA is about 1.9, but at hour 0, it’s about 0.8, can you please explain the big change here? I’m assuming the change from 1.9 to 0.8 happened within one hour since this plot describes diurnal cycle.
- Section 3.4 Fig.8 and Fig.9: Please explain why the authors selected AlkOA vs. eBC and SVOOA vs. THC for correlation analysis, and why selected SO2/NOx and CO/eBC ratios for analysis of aircraft activity? What do these ratios imply in terms of aircraft emission?
- The authors suggested CO was mainly related to road traffic emissions based on previous paper (line 379-380), then the authors related CO measured by the monitoring site with aircraft activities (line 390-392). Could you further explain your discussion and conclusion here?
Citation: https://doi.org/10.5194/egusphere-2023-2197-RC2
Saleh Alzahrani et al.
Saleh Alzahrani et al.
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