Quantifying the Source Attribution of PM₁₀ Measured Downwind of the Oceano Dunes State Vehicular Recreation Area

Round 1

Reviewer 1 Report

The study revealed that mineral dust was the most significant contributor to the PM10, followed by sea salt and the unidentified category. The simultaneous increase in mineral dust and unidentified components with increasing levels of PM10 arriving from the direction of the ODSVRA suggests that the unidentified components are unmeasured oxides of minerals and carbonate, increasing the attribution of mineral dust for a mean exceedance day to 63.5%. The findings of this study are particularly important as they can help inform decision-making regarding the management of the ODSVRA and reduce PM10 concentrations, which can have significant health impacts on the surrounding communities. The manuscript is well-written and easily comprehensible. However, to further strengthen their research, the authors could consider plotting additional figures, such as averaging hourly PM concentration by wind direction or ocean wave height. Furthermore, it may be worthwhile to investigate which variable has the most significant influence on PM concentration, perhaps by using a PCA analysis as demonstrated in https://journals.ametsoc.org/view/journals/atot/32/9/jtech-d-15-0085_1.xml?tab_body=abstract-display

Author Response

Comments.

The study revealed that mineral dust was the most significant contributor to the PM10, followed by sea salt and the unidentified category. The simultaneous increase in mineral dust and unidentified components with increasing levels of PM10 arriving from the direction of the ODSVRA suggests that the unidentified components are unmeasured oxides of minerals and carbonate, increasing the attribution of mineral dust for a mean exceedance day to 63.5%. The findings of this study are particularly important as they can help inform decision-making regarding the management of the ODSVRA and reduce PM10 concentrations, which can have significant health impacts on the surrounding communities. The manuscript is well-written and easily comprehensible. However, to further strengthen their research, the authors could consider plotting additional figures, such as averaging hourly PM concentration by wind direction or ocean wave height. Furthermore, it may be worthwhile to investigate which variable has the most significant influence on PM concentration, perhaps by using a PCA analysis as demonstrated in https://journals.ametsoc.org/view/journals/atot/32/9/jtech-d-15-0085_1.xml?tab_body=abstract-display

Response:

Because of the already high number of figures in the paper, we made an effort to be concise with the number of figures added for the purposes of data analysis and have included the most compelling figures of the many that we produced. The use of averages of PM10 and wind speed are generally avoided, because the values are easily skewed and averaging obfuscates the various causes of PM10.  For example, many time periods are observed when light winds bring clean air onshore from the ocean, while high winds from the same direction bring large amounts of dust. Light and heavy offshore winds can also bring regional pollution to the CDF sensor. Time series of PM10 are generally significantly non-Gaussian, so the mean should not to be used to demonstrate causality. That said, the mean is used to determine a violation per the state standard, which is why the bar graphs of Figure 14 investigate the fraction of mass within 24 hour periods of mean PM10. This is analogous to what the reviewer is asking for but uses totals, which are more robust. We do not have measurements of wave height at this site. While a principal component analysis is a useful tool, it is not needed here due to the small number of variables and the relatively small sample size (47 days). Moreover, the directional filtering performed here avoids potentially misleading results from a PCA, since it may detect strong negative or low-concentration correlations (potentially meaningful relationships that are not related to our scientific goals).

Reviewer 2 Report

This interesting paper presents an exhaustive study of the sources attributed to PM10 mass-concentrations generated by the Oceano Dunes State Vehicular Recreation Area (ODSVRA) and transported  by winds. The analysis is very well conducted, providing useful conclusions concerning the contribution of the ODSCRA during pollutions peaks. The only criticism should be the style, with long sentences and sometimes with repetitions. Perhaps the authors should simplify some sentences to make the paper more fluent, but this is not mandatory.

Minor comments:

Line 95 : Do the authors speak of mass concentration or in number concentration?

Line 239: Replace “PM¹⁰” by “PM10”.

IN Figures 10 and 11; the authors speak of (*) in the legend put have plotted “x” instead

Figure 16: Dark green points are difficult to be distinguished from black points.

Author Response

Comments

This interesting paper presents an exhaustive study of the sources attributed to PM10 mass-concentrations generated by the Oceano Dunes State Vehicular Recreation Area (ODSVRA) and transported by winds. The analysis is very well conducted, providing useful conclusions concerning the contribution of the ODSVRA during pollutions peaks. The only criticism should be the style, with long sentences and sometimes with repetitions. Perhaps the authors should simplify some sentences to make the paper more fluent, but this is not mandatory.

Response

We have edited the paper in places to increase the clarity and to correct some misnumbered references but have not carried out extensive revisions to change the style of writing as reviewer #1 noted that the text was well-written and easily comprehensible.

Minor comments and Responses:

Line 95 : Do the authors speak of mass concentration or in number concentration?

Added “mass concentration” (line 95)

Line 239: Replace “PM¹⁰” by “PM₁₀”.

Corrected (line 239).

In Figures 10 and 11; the authors speak of (*) in the legend put have plotted “x” instead.

Symbol in text now matches symbol on Figures.

Figure 16: Dark green points are difficult to be distinguished from black points.

Dark green symbol changed to white.