Quantifying effects of long-range transport of air pollutants over Delhi using back-trajectories and satellite NO2 data
Abstract. Exposure to air pollution is a leading public health risk factor in India, especially over densely populated Delhi and the surrounding Indo-Gangetic Plain. During the post-monsoon months, the prevailing north-westerly winds are known to influence aerosol pollution events in Delhi, by advecting pollutants from agricultural fires as well as from local sources. Here we investigate the year-round impact of meteorology on gaseous nitrogen oxides (NOx = NO + NO2), a hazardous primary air pollutant for health, which can lead to the formation of secondary aerosols and ozone. We use bottom-up NOx emission inventories (anthropogenic and fire) and high-resolution satellite measurement based tropospheric column NO2 (TCNO2) data, from S5P on-board TROPOMI, alongside a back-trajectory model (ROTRAJ) to investigate the balance of local and external sources influencing air pollution changes in Delhi, with a focus on different emission sectors. Our analysis shows that accumulated emissions (i.e. integrated along the trajectory path, allowing for chemical loss) are highest under westerly, north-westerly and northerly flow during pre- (February–March) and post- (October–January) monsoon periods. During the pre-monsoon period, the residential and transport sectors together account for more than 50 % of the total accumulated emissions, which are dominated by local sources (90 %) under easterly winds and by non-local sources (> 70 %) under north-westerly winds. The high accumulated emissions estimated during the pre-monsoon season under north-westerly wind directions are likely to be driven by high NOx emissions locally and in nearby regions (since NOx lifetime is reduced and the boundary layer is relatively deeper in this period). During the post-monsoon period non-local (60 %) transport emissions are the largest contributor to the total accumulated emissions as high emissions, coupled with a relatively long NOx atmospheric lifetime and shallow boundary-layer aid the build-up of emissions along the trajectory path. Analysis of surface daily NO2 observations indicates that high pollution episodes (> 90th percentile) occur predominantly in the post-monsoon and more than 75 % of high pollution events are primarily caused by non-local sources. Overall, we find that in the post-monsoon period, there is a substantial import of NOx pollution into Delhi with a large contribution from the transport sector. This work indicates that the advection of highly polluted air originating from outside Delhi is of concern for the population and air quality mitigation strategies need to be adopted not only in Delhi but in the surrounding regions to successfully control this issue. In addition, our analysis suggests that the largest benefits to Delhi NOx air quality would be seen with targeted reductions in emissions from the transport sector, particularly during post-monsoon months.
Ailish Melissa Graham et al.
Status: open (extended)
- RC1: 'Comment on egusphere-2023-382', Anonymous Referee #2, 27 May 2023 reply
Ailish Melissa Graham et al.
Ailish Melissa Graham et al.
Viewed (geographical distribution)
The authors use back-trajectories and satellite data to investigate the long-range transport of NO2 over Delhi. The study is comprehensive and findings from this study will help promote targeted mitigation measures. Overall, the paper is well written but would benefit from a detailed discussion on the novelty and some aspects of the methodology. So, I can only recommend this paper for publication after major revisions.
In the abstract, the authors mention that nitrogen oxides are hazardous air pollutants for health and form secondary aerosols and ozone. There is no mention of this in the introduction or discussion on the implications of the study on health and secondary pollutants.
The authors mention a few studies in the introduction which have an overlap with this study and so the novelty of this study is not clear. The authors should describe how this study adds value to the existing literature.
There are two recently developed emission inventory datasets, one for road transport (Hakkim et al., 2021) and other for stubble burning (Kumar et al., 2021). The road transport dataset shows that existing emission inventories overestimate NOx emissions by a factor of 3 and this could potentially explain why the authors see large contributions from the transport sector. Could the authors do a comparison of the emission inventories or a sensitivity test to see how NOx emissions from this latest dataset would impact the study findings? Similarly, how does the stubble burning emission inventory compare to the fire emissions inventories. Could the role of agricultural fires be larger than what the authors have quantified?
There are minor grammatical errors in the manuscript, and I have pointed out some below.
Line 93. Just to confirm, it is ‘daily’ and not ‘hourly’ PM2.5 peaking at over 1000 ug/m3?
Lines 96-99. The authors should consider including additional literature to support the use of long-term satellite observations and focused on NO2 in Delhi (For example, Vohra et al., 2021).
Line 114. Stirling et al. (2020) and Graham et al. (2020) ‘extended this methodology’. This is not very clearly written and sounds like the only addition is that it now includes PM2.5 in addition to NO2.
Lines 119-127. Referring to the above comment, it is not exactly clear what the two studies do and so it is difficult to understand how the authors’ approach is similar and/or different. The authors should also refer to Bikkina et al. (2019) here or in lines 75-85 to explain clearly what has been done before and how does this study contributes to new knowledge.
Lines 126-127. Confusing. Please rephrase.
Section 2.1 on anthropogenic NOx emissions is quite difficult to follow. This should be restructured to have an introductory statement “We created a merged emissions data … (Lines 134-135) and then have sections on Delhi emissions and global emissions. If the authors want to have fire emissions as a separate section, then this should be made clear in the sections above, something like Delhi (except fire) emissions.
The authors do not mention natural sources such as lightning. Is their contribution negligible?
Section 2.1 should have a final section 2.1.4 on Merging of emissions to discuss the approach of combining these datasets. Currently, it is not clear what approach has been followed and what has been done to account for discontinuity in merging Delhi and global emissions datasets. The authors have included figures, but a quantitative assessment is also warranted.
Line 134. The authors say daily fire emissions but what is the temporal resolution of the other anthropogenic emissions.
Section 2.2 Can the authors briefly describe the approach of Pope et al. (2018) to derive the 0.05-degree dataset? Is it oversampling or error-weighted tessellation? Any quality flags used? Which months in 2018/2019 are selected? The resolution of TROPOMI changed around mid-2019 and this has not been stated. How do the authors account for that?
Line 202. The authors could consider using ‘and’ instead of either-or as both datasets are being used.
Lines 230-240. There are a few inconsistencies with the equation and description of the variables. Alpha should be alpha (subscript i). Variables such as N and E0 are described but are not in the equation. Also, not clear how E subscript N is linked to E subscript i.
Line 265. The authors can be more specific. Is it the 8 wind directions seen in Figures 6-7?
Line 271. Figure 3 shows more than 36 sites and some of them are outside Delhi too. Can the authors resolve this discrepancy?
Line 277. Is this local solar time or the local time? Should the authors consider a longer window (2, 3 or 4 hours) to ensure it includes the satellite overpass time?
Line 287. It is not exactly clear in the first instance where the BLH data is from. After reading the full paper, I know it is from ERA-Interim reanalysis dataset.
Lines 290-300. This section would benefit from more context for the range of values provided. Comparison to literature, perhaps?
Line 304. Do the authors mean ‘east Indian coastline’?
Lines 319-325. I get the reasons for post-monsoon season but not for pre-monsoon. Can the authors explicitly discuss how does pre-monsoon season conditions degrade NO2? Is this of similar magnitude to post-monsoon or just worse compared to monsoon conditions?
Line 344. Do the authors mean that the ‘results are in close agreement to those obtained with TCNO2 datasets’?
Lines 428-445. Are the surface observations used to identify the high pollution days from the satellite overpass time window? Are these representative of the daily mean NO2 in Delhi? Are the BLH also for the same time window?
Lines 450-461. The authors should mention high “NOx” pollution days to make it clear that poor AQ and high pollution are with reference to NO2.
Figure 3 caption should mention the number of sites in Delhi and why do the authors include sites outside of Delhi?
Figure 4. The top panel axis label should read NOx emissions. TROPOMI data was not used for 2017 but is mentioned in the caption.
Figures 7/10. Should the emissions currently shown as “India” be “Rest of India” or do they include “Delhi”?
All figure captions and text should read as “Feb-Apr” instead of “Feb_Mar_Apr”.
Bikina et al., 2019, doi: 10.1038/s41893-019-0219-0
Graham et al., 2020, doi: 10.1016/j.aeaoa.2019.100061
Hakkim et al., 2021, doi: 10.1016/j.aeaoa.2021.100118
Kumar et al., 2021, doi: 10.1016/j.scitotenv.2021.148064
Stirling et al., 2020, doi: 10.1002/asl.955.
Vohra et al., 2021, doi: 10.5194/acp-21-6275-2021