the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
How horizontal transport and turbulent mixing impacts aerosol particle and precursor concentrations at a background site in the UAE
Ewan James O'Connor
Anne Hirsikko
John Backman
Heikki Lihavainen
Hannele Korhonen
Eija Asmi
Abstract. Aerosol particle optical, physical and chemical properties have been previously studied in the United Arab Emirates (UAE), but there is still a gap in the knowledge of particle sources, and in the horizontal and vertical transport of aerosol particles and their precursors in the area. To investigate how aerosol particle and SO2 concentrations at the surface responded to changes in horizontal and vertical transport, we used data from a one-year measurement campaign at a background site where local sources of SO2 where expected to be minimal. The measurement campaign provided a combination of in-situ measurements at the surface, and the boundary layer evolution from vertical and horizontal wind profiles measured by a Doppler lidar. The diurnal structure of the boundary layer in the UAE was very similar from day to day, with deep well-mixed boundary layer during the day transitioning to a shallow nocturnal layer, with the maximum boundary layer height usually being reached around 1400 local time. Both SO2 and nucleation mode aerosol particle concentrations were elevated for surface winds coming from the east or western sectors. We attribute this to oil refineries located on the eastern and western coasts of the UAE. The concentrations of larger cloud condensation nuclei (CCN) sized particles and their activation fraction did not show any clear dependence on wind direction, but the CCN number concentration showed some dependence on wind speed, with higher concentrations coinciding with the weakest surface winds. Peaks in SO2 concentrations were also observed despite low surface wind speeds and wind directions unfavourable for transport. However, winds aloft were much stronger, with wind speeds of 10 m s-1 at 1 km common at night, and with wind directions favourable for transport, and surface-measured concentrations increased rapidly once these particular layers started to be entrained into the growing boundary layer, even if the surface wind direction was from a clean sector. These conditions also displayed higher nucleation mode aerosol particle concentrations, i.e. new particle formation events occurring due to the increase in the gaseous precursor.
Jutta Kesti et al.
Status: final response (author comments only)
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RC1: 'Comment on acp-2022-811', Anonymous Referee #1, 21 Feb 2023
This work is a companion to the publication of Kesti et al. (2022). It summarizes the wind patterns of a background site in UAE and tries to link CCN, SO2 and nucleation mode particles with the observed patterns to explain the variations of these parameters. The paper is fluently written but does not contain enough interesting results. These have already been reported by Kesti et al. (2022). The manuscript gives the impression of being the supplement of the first publication, with some of the data found in this work being already being presented.
The authors do a very good job in describing the meso/microscale wind circulation in the area. The problems arise when they try to link that with some measured variables.
General Comments
According to the authors the manuscript is focused on how air mass transport and boundary layer mixing conditions affect the Cloud Condensation Nuclei (CCN) activity measured in the surface. However, the analysis of their findings is rather incomplete and/or important information lacks from the manuscript. While they mention (cf. section 2 of the manuscript) that the CCN counter was coupled to a DMA in order to provide size resolved CCN activation fractions at different monodisperse aerosol sizes, this information is nowhere to be found neither in the text nor in the relevant figures. Accounting for the fact that in the supersaturation of 1.0 most of the accumulation mode particles will activate into droplets and therefore counted by the CCNc, reporting and depicting just the CCN concentration under various wind conditions (i.e., velocity and direction) does not contribute in understanding the implications of meteorological conditions, wind origin and path combined with the activity of different sources in the CCN activity. Note that when the number concentration of the accumulation mode particles increases, the CCN concentration is also expected to increase at the supersaturation of 1.0. Therefore figure 9 does not add anything to the discussion and It should be removed. Instead the authors should:
- i) Provide the activation fractions (i.e., ratio of CCN concentration over aerosol number concentration; similarly to figure 10) but for the different sizes they studied or at least for some representative sizes of the modes that are commonly observed in the atmosphere (i.e., nucleation, Aitken, accumulation).
- ii) Calculate the size resolved hygroscopicity of the sampled monodisperse aerosols (e.g., by calculating the hygroscopic parameter “kappa”; cf. Petters and Krenidenweis, 2007) for the different meteorological conditions.
The above would provide much more useful information and perhaps some insights on how the different meteorological conditions and boundary layer evolution affect the hygroscopicty/CCN activity of the studied aerosols. In addition, since the measured (i.e., by the CCNc) CCN activity (especially at the extreme supersaturation of 1.0) does not necessary reflect the effects of these aerosols on cloud formation the authors could use a simplified model for calculating the potential cloud droplet number concentration (cf. (Ghan et al., 2011; Morales Betancourt and Nenes, 2014). For instance, Kalkavouras et al. (2017) observed that during New Particle Formation (NPF) events over the Aegean Sea (Greece) the activation fraction of the sampled aerosols increased dramatically, however their effect on droplet formation was much lower. In addition to that, the sensitivity of the potential cloud droplet number concentrations on the type (i.e., chemical composition/hygroscopicity), size distribution of the sampled aerosols and on the meteorological conditions can be quantified by employing the abovementioned (or similar) models, which would significantly add value to the manuscript.
The authors show that when specific wind directions prevail, there is SO2 transport to the receptor site. This happens to occur during daytime when, conventionally, NPF occurs in most parts of the world. The authors provide no evidence that the transported SO2 is taking part or enhancing the NPF process. Therefore the discussion in lines 179-183 is highly speculative and should be removed. There is growing evidence that sulfuric acid alone cannot account for NPF formation rates and additional constituents are required. It has also been shown that acidic conditions may inhibit NPF (Pikridas et al., 2012, doi:10.1029/2012JD017570.) I understand that the authors try to link the three variables they are reporting, but this is not a valid way. The same holds for discussion in line 220.
Comments related to the presentation of the results
The authors should really use polar plots, or any other bivariate plot, to pass their message through. Polar plots can replace figures 7-10, and 12.
The authors needs to show a map of all the refineries in the area in combination with an elevation map. Satellite maps can be used to identify the refineries. This would assist in the discussion found in lines 172-175
Fig. 3 is too big and does not assist the reader. Can the authors put it in the supplement and replace it with a more concise figure. Eg by lumping several hours together with similar profiles.
Comments on analysis.
My view is that analysis presented in this work is very poor. It does not into deep on any front and leaves the reader with many questions. This is one of the big weaknesses of the manuscript.
The authors should discuss how the seasonal variation in wind direction is linked to synoptic conditions in the area. A trajectory analysis is a must. It was also requested to Kesti et al. (2022), but did not materialize.
When report high/low concentrations please also report absolute numbers along with an error metric (eg ±1std) throughout the manuscript.
The authors compare concentrations of SO2 in Section 3.3.1, but fail to report on any metric. What is elevated SO2 concentration, how much is low? Are these differences substantial? How would these bias a measurement done once per day, eg by TROPOMI? These are just some questions I would like to see answered.
Are the nucleation mode particles discussed in 3.3.1 part of NPF or just the background concentration. Is there any difference if NPF is involved with respect to wind direction and speed?
The disucussion in Section 3.3.2 is very problematic as already noted. Please see general comments above on how to improve. The discussion on figure 9 (strongly suggesting to be removed) does not add any valuable information. The rest of the discussion in section 3.3.2 is speculative and probably misleading, while - based on the available measurements – conclusions could be drawn by following a more detailed analysis (cf. general comments for suggestions).
Line 210. It is not clear why this explanation was chosen, even though I agree with the authors. A more detailed discussion is required.
References:
Ghan, S. J., Abdul-Razzak, H., Nenes, A., Ming, Y., Liu, X., Ovchinnikov, M., Shipway, B., Meskhidze, N., Xu, J., and Shi, X.: Droplet Nucleation: Physically-based Parameterization and Comparative Evaluation, J. Adv. Model. Earth Syst., 3, M10001, doi:10.1029/2011MS000074, 2011.
Kalkavouras, P., Bossioli, E., Bezantakos, S., Bougiatioti, A., Kalivitis, N., Stavroulas, I., Kouvarakis, G., Protonotariou, A. P., Dandou, A., Biskos, G., Mihalopoulos, N., Nenes, A., and Tombrou, M.: New Particle Formation in the South Aegean Sea during the Etesians: importance for CCN production and cloud droplet number, Atmos. Chem. Phys., 17, 175–192, 2017., https://doi.org/10.5194/acp-17-175-2017, 2017.
Morales Betancourt, R. and Nenes, A.: Droplet activation parameterization: the population-splitting concept revisited, Geosci. Model Dev., 7, 2345–2357, doi:10.5194/gmd-7-2345-2014, 2014.
Petters, M. D. and Kreidenweis, S. M.: A single parameter representation of hygroscopic growth and cloud condensation nucleus activity, Atmos. Chem. Phys., 7, 1961–1971, doi:10.5194/acp-7-1961-2007, 2007.
Citation: https://doi.org/10.5194/acp-2022-811-RC1 -
RC2: 'Comment on acp-2022-811', Anonymous Referee #2, 08 Mar 2023
The manuscript analyses wind data measured with a doppler lidar together with CCN-, SO2- and nucleation mode particles concentrations. The aim of the study is the change of concentrations measured near the surface and explain these changes by horizontal and vertical transport. The authors constraint the analysis their own data during a one-year measurement campaign. The manuscript seems to be a follow-up of the paper by Kesti et al. 2022 where already a seasonal analysis and some cases studies were presented. In this manuscript the authors wanted to tackle the boundary layer and transport more broadly which shows only limited novelty.
General comments
The authors aimed in describing and analysing the atmospheric transport of atmospheric constituents to a rural site in the UAE. And they tried it based on measurements of a single site (palm tree farm) and on few known sources of SO2 without using additional data or model simulations. For a comprehensive picture this is not enough. Many questions remain, especially how well the local wind directions are representative for the transport pattern.
I am missing a discussion about sources and sinks. For instance: How big is the background? Could satellite data contribute to the identification of the SO2 sources? Is the SO2 which comes from the oxidation of DMS in the ocean relevant to the observed SO2 level at the measurement site?
I am also missing that the discussion of the transport include land-sea-breezes. Eager et al. (2008, JGR) used a network of surface stations in the United Arab Emirates (UAE). They found that a sea breeze occurs during all seasons of the year and that the horizontal extend onshore can be a large as 80km. Hence the palm tree farm is well within reach of the sea breeze.
They authors did a good job in describing different transport regime together with vertical mixing (e.g. figure 11). However, they did not analyse sufficiently the data with the aim of supporting the transport hypotheses. Why the sector analysing is just applied to surface data and the vertical change of the wind direction is not considered? Figure 5 nicely shows how the wind changes with altitude.
There is also some doubts on the definition and naming of the sectors. Only four sectors are defined for the analysis although the wind roses exhibit much more sectors. However, under the assumption that the major refineries are the significant point sources for the measured SO2 levels it es expected that a refined sector analysis (more smaller sectors) could provide a clearer picture of the transport.
Figure 12 does not show clear differences between so-called clean and polluted sectors. The SO2 concentration values cover in both cases the range from 0.1 to 10 ppb. Only very few data points are larger for the polluted cases compared to the clean case. It’s similar for the nucleation mode concentration. Most data points – although less compared to SO2 - for both sectors are overlapping. It seems that the figure underlies the analysis of the wind direction independently from the pollution level. And hence the names of the sectors – clean and polluted – is confusing.
Furthermore I wonder whether wind direction data are reliable or can be used for transport analysis when the wind speed is very low. Calm conditions were excluded from the analysis?
Specific comments
* Line 213/214: the division of the data into sectors is based on the Vaisala weather station wind data or based on the Halo Doppler Lidar data near the surface? I assume it’s the weather station but I am not sure.
* Lines 163 ff and fig. 6: Although the distribution of the wind speed does not change, a normalization of the counts with respect the different length of day (5-20 LT = 16 hours) and night (21-4 LT = 8 hours) is recommended.
Minor Remarks
* Figure 1 and caption: as it is re-used from Kesti et al. (2022) in my opinion the authors should write “figure and caption from Kesti et al.” instead of “figure from Kesti et al.”
Citation: https://doi.org/10.5194/acp-2022-811-RC2
Jutta Kesti et al.
Jutta Kesti et al.
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