|Review of Rupakheti et al.|
The authors present PM, BC, CO and O3 concentrations measured at Lumbini during April-June 2013 and explained meteorology, pollutant concentrations by conducting WRF-Stem model simulation. They also estimated the regional contributions of CO and aerosol composition to local air quality based only on the model simulation results. This reviewer full agree that the presented observational data set in this study are unique and very useful to understating the level of air pollution in the study area. However, in this revised manuscript, there are several important issues on model simulation and scientific discussion. Therefore, this revised manuscript cannot be accepted in its current form. Before publishing in ACP, several points should be clarified. Specific comments and suggestions are below:
L68, Fig 1: Information given in Figs 1, 2 and 5 are overlapped. This reviewer recommends to merge Fig 1 and Fig 5. That is, plot both monthly mean AOD and winds for separately in April, May and June. Those figures will give more direct insight on aerosol distribution and regional-scale circulation during the intensive measurement period. Fig 2 is not necessary in main body text, so please move it to the supplement.
L68, Fig 1: Which version of MODIS TERRA data have used? Why the authors are not used MODIS Aqua data?
L54-139: This reviewer recommends to reduce the length of INTRODUCTION section with deleting sentences are not closely related the topic of this paper. For example, the authors emphasize many times in the paper that Lumbini is a UNESCO world heritage. This is interested to the authors, but not to all readers. So please minimize the statements on this.
L 99 ~ : As the authors mentioned, sulfuric acid is more critically important in historical heritages. Please more carefully and clearly explain why the air pollutants presented in this study is important should added in the INTRODUCTION.
L128: remove “Aerosol optical depth – not discussed on the present study”. This is not necessary here.
L 165: Table 1 - The sampling period should be more clearly clarified. As shown in Fig. 6, all instruments had not properly operated during the study period.
L169: What’s the uncertainty of PM concentration measured with GRIMM EDM164? Especially the quantitative uncertainty of EDM164 for such a high PM concentration level should be discussed, because PM concentration in here is estimated from light scattering measurements.
Figures 3 & 6 : There is large differences between observations and model simulations. First, more specific explanation and discussion on why the model results were not well agreed with the observations must be addressed. Why the WRF-STEM simulation cannot well simulate the precipitation events and why there is big difference in RH, WD and WS. Why BC is too underestimated compare to the aethalometer data? This should be made for all parameters. This is very important to convincing the results given in Section 3.3 and Section 3.4, as the authors mentioned in L261-262.
Figure 3: WD should NOT be plotted with solid line, because, for example, WD at 355 and 5 degree is almost the same direction. So make a plot with dots.
L242-243: It is hard to agree to this argument. As shown in Fig. 3, there is large difference between modeled and observed wind speed.
L258-259: This reviewer also cannot agree to this conclusive sentence. Apparently, the observed RH is two times higher than the modeled one. There is no evidence that RH by model is how well captured the regional variation. Is there a reference data to back this up?
L265- Figure 5: How about the winds at 850 hPa or 700 hPa pressure level?
L266-267: Here, what “calm winds” means? This discussion in Figure 6 is conflict the winds discussed in Figures 3 and 4. Please clarify.
Figure 6: As commented above, more explanations are needed why there is a large discrepancy between modeled and observed values. Without clarifying this, the results given in the next sections (sections 3.3.2. and 3.4) are not truly reliable.
L384-385: This reviewer understand the PBL height observation was not available during the measurement period. However, the modelled PBL height has also large uncertainty and not believe it. The authors cited several previous works, but need to add some information on the PBL height, not general seasonal characteristics.
L407-408: The authors mentioned ‘Global Monthly Fire Location Products’ were used. However, daily data were used in Figure 9. Please clarify this.
L416: Clearly present how much higher? This is very vague sentence.
L421: Quality of Figure 10 is very bad. It’s hard to read.
Figure 10: How the authors get the modelled biomass CO concentration? Generally the modelled biomass CO concentration is not capture the observed CO concentration. What is the major reason that the author gusse? This reviewer cannot agree to the sentence given in Li435-436.
Figure 10 (I) and (J): Instead of wind roses, regional-scale wind patterns will be more helpful to understand the transport in the interested region.
L441-450 and Figure 11: The two ozone peaks were possible contributed by local pollution, induced by NO2, but also by the transport from the fire plume. However, satellite NO2 data shown in Figure 11 is not direct evidence of the effects of fires on high ozone concentration. Clarify this.
L455 and Figure 12: It should be provided how the authors calculated the contributions from different conturies? Can you provide PSCF results to back this up? In addition, have the authors estimated other pollutants (i.e., PM2.5 and PM10) for their contribution like CO?
Figure 14: The authors only showed wavelength dependency of normalized light-absorption coefficients for two time periods. First question is why the authors do not show all times? The authors can present with time (x-axis), wavelength (y-axis) and normalized light-absorption coefficient with different color. This figure will be more helpful to understand the difference of light-absorption coefficient in around 380 nm wavelength. Second, the difference at 380 nm in current Figure 14 is statistically significant? And how many data points were used? Last question for Figure 12 is that data during the fire periods discussed section 3.2 were included or excluded here?