A 10-year Analysis on the Reduction of Particulate Matter at the Green Buffer of the Sihwa Industrial Complex

Round 1

Reviewer 1 Report

 

This is an interesting manuscript with a large dataset however there are times that interpretations are difficult due to a lack of reporting of standard deviations. At times it is unclear how much data is contributing to the monthly averages and clarifying this would improve the interpretations that can be made.

Line 81: how close was this monitor to the area?

Lines 138-19: Does this mean that a single day per month was sampled? If so, considering this as a monthly concentration is a bit misleading since 1 out of 30/31 days was sampled. How does this compare to the collection in section 2.2?

Results

Fig 3a: making the connecting lines the same color as the dots would help

It is very difficult to determine how many data points exist per month in Figure 3 – is it one data point per month and if so how many days is this the average of? Why are there not error bars in fig3a?

Table 1: was an adjusted p-value based on the number of comparisons made created?

Figure 4: typo in the caption of “bad” days. I also think a better term could be used for this than bad

Author Response

Reviewer #1

This is an interesting manuscript with a large dataset however there are times that interpretations are difficult due to a lack of reporting of standard deviations. At times it is unclear how much data is contributing to the monthly averages and clarifying this would improve the interpretations that can be made.

• Thank you for your valuable comments. We made on overall revision following the reviewer’s comments as follows.

1. Line 81: how close was this monitor to the area?

• NAERS-IC and NAERS-RA is located in IC and RA, not near IC and RA. Admitting the reviewer’s points, we changed this part to better expression. The revision is reflected in line 84-86 on page 3.
• “We investigated the monthly PM10 at NAERS located in the IC and RA (NAERS-IC: 37°20′N, 126°43′E; NAERS-RA: 37°20′N, 126°44′E), respectively (n=30 or 31 per month).”

2. Line 138-19: Does this mean that a single day per month was sampled? If so, considering this as a monthly concentration is a bit misleading since 1 out of 30/31 days was sampled. How does this compare to the collection in section 2.2?

• We also knew that PM concentration during 1 day cannot represent the monthly PM concentration. So, we tried to reflect monthly weather characteristics as much as possible in measuring PM. We identified clear days with monthly weather characteristics in advance and measured PM concentration. Previous studies also showed monthly PM concentration by measuring PM for a short time (Gao, Tian et al., 2020; Yoo et al., 2020). Admitting the reviewer’s points, we explained this part more clearly to make understand. The revision is reflected in line 157-162 on page 4.

3-1. Fig 3a: making the connecting lines the same color as the dots would help.

• Admitting the reviewer’s points, we changed the connecting lines’ colors like same color as the dots. The revision is reflected in Figure 3a.

3-2. Fig 3a: It is very difficult to determine how many data points exist per month in Figure 3. Is it one data point per month and if so how many days is the average of? Why are there not error bars in fig 3a?.

• We investigated the monthly PM data sets at NAERS-IC and NAERS-RA (n=30 or 31 per month). Based on average monthly PM data by year (n=12 per year), we analyzed the annual PM concentration and the trends of PM concentration line slope for each point. Admitting the reviewer’s points, we added some information about how much data we analyzed in this study and put error bars in Figure 3a. The revision is reflected in line 84-93 on page 3 and Figure 3a.
• “We investigated the monthly PM10 at NAERS located in the IC and RA (NAERS-IC: 37°20′N, 126°43′E; NAERS-RA: 37°20′N, 126°44′E), respectively (n=30 or 31 per month). Since the NAERS began measuring PM2.5 in only 2015, this study used the PM10 data. To identify PM reduction from the GB, we compared the NAERS data to the monthly PM10 of the west coast area (Gwangmyeong, Ansan, Pyeongtaek, Bucheon, Osan, Hwaseong, Si-heung; WCA) where the government’s regulation for large PM emission sources was strongly enforced (n=30 or 31 per month). Based on monthly PM data by year (n=12 per year), we analyzed the annual PM10 concentration and the trends of PM concentration line slope (∆Concentration/ ∆Time) over time for yearly data.”

4. Table 1: was an adjusted p-value based on the number of comparisons made created?

• An adjusted p-value was based on the number of comparisons.

5. Figure 4: typo in the caption of “bad” days. I also think a better term could be used for this than bad.

• Admitting the reviewer’s points, we changed “bad” to “high PM pollution”.
• The revision is reflected in Figure 4.

Author Response File: Author Response.docx

Reviewer 2 Report

It is well known that  trees and greenspaces  are beneficial to impact on air quality. But how much could  trees in the street or a nearby green belt  improve urban air quality on a local scale? The authors set out to examine this very question  by studying the relationships between ambient  PM concentrations  in the industrial zone and residential area, which were separated with green buffer. The authors conducted a 10-year analysis of the degree of concentration reduction between these areas, i.e. from the start of the green buffer  implementation (2006) to 2017. They also analyzed the influence of other factors on these relationships (some meteorological and pollutants emissions). Although the dataset that the authors collected looks very interesting and promising, the manuscript  has a number of methodical ambiguities that require clarification before the manuscript would be suitable for publication. Accordingly, the methodological section is not completely clear.  There are also ambiguities in some of the results and discussions. Below, there are main  questions in order to make the manuscript clearer and more concise.

 

The value of the work would increase if the authors included  concentration fields from the start-up period and after the work was completed, and verified the calculations with measurements. To what extent will the change of the roughness coefficient improve the quality in the residential  part? This is, of course, a suggestion.

 

Comments:

1. In the Study site part, please describe the stages of creating the green buffer more clearly, i.e. start of works in ......, first planting in ......., end of implementation (plant height) and now (plant height).
2. In Fig. 1 please mark the north and the direction of prevailing winds, or better a wind rose for the test period. 1 “ The process of installing green buffer (GB) in 1995; (b) The process of in-75 stalling green buffer (GB) in 2016........”process”’ – In my opinion the word is inappropriate.
3. Why do the authors speak of a regional scale when they analyze changes on a local scale? The distance between the measurement points is approx. 2 km.
4. Formula (1) - Unclear? There should be a symbol” / “
5. 106-107 “We used the amount of annual air pollutant emissions 106 in Siheung from National Air Pollutants Emission Service based on NAERS measuring. Nitrogen oxides (NOx) and sulfur oxides (SOx) emissions were selected as they are main air pollutants generated in Sihwa IC. – please specify which sources and from which area were taken into account . Why was PM emissions not included?
6. 112 – “We hypothesized that the values from this station could represent the weather factors for the measuring points. – on what basis, please specify.
7. 116 – “NDVI was calculated the median value of satellite images every 2-month from 2006 to 2017 at 13  locations in the GB zone”. – please describe how. Please add one example.
8. 119 – “We grouped the annual PM reduction rate, air pollutants emissions, weather factors and NDVI by during and after the implementation of GB.  –  but the emission data were available every two years.
9. 127 – “As the OP-RA is located at northern part of the GB zone, we considered data from this observation point to be proper to represent the PM changes through the  GB.  – on what basis, please specify.
10. 142- “ We calculated the monthly concentration and PM reduction (Equation 1) for the measure ... measurements were carried out during 1 day (24h) a month, so why the monthly data .
11. 163 - “The annual deviation of PM reduction rate was larger before the implementation  of the GB zone, but it gradually decreased after the GB was installed, showing a stable  value. – Why? Please note that during this period the variability of the concentrations was also high at all points. In general, the downward trend was on all points, please elaborate on that.
12. 3 – see above, why monthly? The data does not agree, the reduction rate in August is 0 for both PM10 and PM2.5 and 0 in December and January for PM2.5.
13. 225 - But during and after the implementation of GB, the PM values at RA were higher than that .... – rather lower?
14. 230 –“ PM concentration gradient”---inappropriate word
15. 192 – should be “ air pollutants emission”
16. Part 3.2. - it is difficult to unequivocally assess whether it was the green buffer effect or the increased distance from the emitters. Please note that the concentration variations were convergent. Please use statistical methods to evaluate them.
17. 259-264 - the explanation is unconvincing. It is difficult to understand how SO2 emissions can affect the degree of PM reduction, likely due to the green buffer.
18. 255-256 and l. 269-270 - explanations are contradictory. As for the influence of wind, it increases turbulence in the atmosphere, especially in built-up areas, a stronger correlation could have been due to the existence of a green buffer.
19. The conclusions should be more specific. To what extent have the research objectives been achieved, what are the weaknesses and recommendations for future research.

 

 

 

 

 

 

 

 

 

Author Response

Reviewer #2

It is well known that trees and greenspaces are beneficial to impact on air quality. But how much could trees in the street or a nearby green belt improve urban air quality on a local scale? The authors set out to examine this very question by studying the relationships between ambient PM concentrations in the industrial zone and residential area, which were separated with green buffer. The authors conducted a 10-year analysis of the degree of concentration reduction between these areas, i.e. from the start of the green buffer implementation (2006) to 2017. They also analyzed the influence of other factors on these relationships (some meteorological and pollutants emissions). Although the dataset that the authors collected looks very interesting and promising, the manuscript has a number of methodical ambiguities that require clarification before the manuscript would be suitable for publication. Accordingly, the methodological section is not completely clear. There are also ambiguities in some of the results and discussions. Below, there are main questions in order to make the manuscript clearer and more concise.

• Thank you for your valuable comments. We made on overall revision following the reviewer’s comments as follows.

The value of the work would increase if the authors included concentration fields from the start-up period and after the work was completed, and verified the calculations with measurements. To what extent will the change of the roughness coefficient improve the quality in the residential part? This is, of course, a suggestion.

• We need some factors such as the friction velocity, average wind velocity at reference height, etc. for calculating roughness coefficient (Sun et al., 2014, Zhang et al., 2017, Han et al., 2020). But in this study, we focused on the PM reduction effect of GB on the local scale and at a human detection level. Thus, we did not measure those factors. Instead, we calculated NDVI which can show the changes in vegetation vitality after the implementation of GB. In future study, we will calculate the roughness coefficient to increase the value of this study. Thank you for your good suggestion.
• Sun, F.B., Yin, Z., Lun, X.X., 2014. Deposition velocity of PM2.5 in the winter and spring above deciduous and coniferous forests in Beijing, China. PLos One 9 (5), e97723.

Zhang, X.D., Du, J., Huang, T., Zhang, L., Zhao, Y., Ma, J., 2017. Atmospheric removal of PM2.5 by man-made three Northern regions shelter forest in Northern China estimated using satellite retrieved PM2.5 concentration. Sci. Total Environ. 593-594, 713-721.

Han, D., Shen, H., Duan, W., Chen, L., 2020. A review on particulate matter removal capacity by urban forests at different scales. Urban For. Urban Green 48, 126565.

1. In the study site part, please describe the stages of creating the green buffer more clearly, i.e. start of works in ….., first planting in ….., end of implementation (plant height) and now (plant height).

• Admitting the reviewer’s points, we added some information about the stages of creating the green buffer. The revision is reflected in line 69-73 on page 2.
• “This green area was created by reclaiming the mudflat area in 2000. But the trees were small and planted at low densities which did not reduce air pollution. Thus, the GB zone was supplemented with additional trees from 2006 to 2012 (Figure 1). End of GB implementation, tree height changed from 5~6 m to 8~12 m. Green land capacity (2.02 m³/m²) has also increased by 108 % than 2006 (0.97 m³/m²).”

2-1. In Fig.1: pleases mark the north and the direction of prevailing winds, or better a wind rose for the test period.

• Admitting the reviewer’s points, we marked the north and added the wind rose for the test period.
• The revision is reflected in Figure 1.

2-2. In Fig.1: (a) The process of installing green buffer (GB) in 1995; (b) The process of installing green buffer (GB) in 2016…“process” - In my opinion the word is inappropriate.

• Admitting the reviewer’s points, we changed the Figure 1 explanation. The revision is reflected in Figure 1.
• “(a) Before the implementation of green buffer (GB) in 1995; (b) After the implementation of green buffer (GB) in 2016; (c) Windrose during study period; (d) Cross section of green buffer (GB).”

3. Why do the author speak of a regional scale when they analyze changes on a local scale? The distance between the measurement points is approximately 2km.

• Admitting the reviewer’s points, we changed “regional scale” to “local scale”.

4. Formula (1): Unclear? There should be a symbol “/”.

• Admitting the reviewer’s points, we rephrased the equation (1) more clearly to understand.
• “PM reduction rate (%) = × 100              (1)”
• The revision is reflected in line 97 on page 3.

5. 106-107: “We used the amount of annual air pollutant emissions in Siheung from National Air Pollutants Emission Service based on NAERS measuring. Nitrogen oxides (NOx) and sulfur oxides (SOx) emissions were selected as they are main air pollutants generated in Sihwa IC. – please specify which sources and from which area were taken into accounts. Why was PM emissions not included?

• As the reviewer said, we need to specify emission sources, which area were taken into accounts and the reason why we did not include PM emissions. NOx and SOx in Sihwa IC was generated from the operation of cars and fossil-fuel combustion from manufacturing industries. Also, these are precursor of PM and produce secondary PM by chemical reaction. Thus, we did not consider PM emission separately. Admitting the reviewer’s points, we added some detail and changed this part. The revision is reflected in line 113-118 on page 4.
• “Nitrogen oxides (NO_x) and sulfur oxides (SO_x) are mainly produced by Sihwa IC with cars, and metal and plastic manufacturing industries such as burning fossil-fuel. We selected NOx and SO_x emissions as they can represent the characteristics of air pollutants emissions in Sihwa IC. The amount of emitted NO_x and SO_x are not only precursor of PM but also can produce secondary PM by chemical reactions in the atmosphere [2,3]. Thus, PM emission was not considered separately in this study.”

6. 112 - “We hypothesized that the values from this station could represent the weather factors for the measuring points. – on what basis, please specify.

• The National Weather Service station measured a representative weather factors in Siheung city and is nearby study site. Thus, we think the values from this station could represent the measuring points’ weather factors. Admitting the reviewer’s points, we explained detail to better understand. The revision is reflected in line 119-123 on page 4.
• “Annual weather factors (temperature, wind speed, annual precipitation) were measured by the National Weather Service (37°23′N, 126°46′E). The weather station is representative of weather factors in Siheung City and nearby in study site. We thought this station could represent the weather factors for the measuring points”

7. 116 - “NDVI was calculated the median value of satellite images every 2-month from 2006 to 2017 at 13 locations in the GB zone”. – please describe how. Please add one example.

• Admitting the reviewer’s points, we described how calculate the median value of satellite images. The revision is reflected in line 126-129 on page 4.
• “NDVI was calculated the median value of satellite images every 2-month from 2006 to 2017 at 13 locations in the GB zone (ex. NDVI₀₁ = The median NDVI value between 2016.12 and 2017.02). The median value minimizes the effect of ideal value.”

8. 119 – “We grouped the annual PM reduction rate, air pollutants emissions, weather factors and NDVI by during and after the implementation of GB. - but the emission data were available every two years.

• As we identified the characteristics of PM reduction rate according to the implementation of GB, we focused on during (2006-2012) and after (2013-2018) GB implementation. Admitting the reviewer’s points, we provided the reason to choose this period. The revision is reflected in line 130-133 on page 4.
• added some reason why we grouped the annual PM reduction rate, air pollutants emissions, weather factors and NDVI by during and after the implementation of GB.
• “As we identified the characteristic of PM reduction rate according to GB implementation, we focused on period during and after GB implementation. We grouped the annual PM reduction rate, air pollutants emissions, weather factors and NDVI by during and after the implementation of GB.”

9. 27 – “As the OP-RA is located at northern part of the GB zone, we considered data from this observation point to be proper to represent the PM changes through the GB. – on what basis, please specify.

• The Sihwa IC has the characteristic of wind blowing in the northwest direction from the coast to the land. Thus, OP-RA which located at norther part of the GB zone was proper point to represent the PM changes through the GB. Admitting the reviewer’s points, we added the basis. The revision is reflected in line 140-142 on page 4.
• “As the Sihwa IC has the wind blowing in the northwest direction from the coast to land, the OP-RA which located at northern part of the GB zone was proper observation point to represent the PM changes through the GB.”

10. 142- “We calculated the monthly concentration and PM reduction (Equation 1) for the measure ... measurements were carried out during 1 day (24h) a month, so why the monthly data.

• We also knew that PM concentration during 1 day cannot represent the monthly PM concentration. So, we tried to reflect monthly weather characteristics as much as possible in measuring PM. We identified days with monthly weather characteristics in advance and measured PM concentration. Previous studies also showed monthly PM concentration by measuring PM for a short time (Gao, Tian et al., 2020; Yoo et al., 2020). Admitting the reviewer’s points, we explained this part more clearly to make understand. The revision is reflected in line 157-162 on page 4.
• “It is difficult to represent the monthly PM concentration because PM measurement was carried out during just 1 day. However, we identified days with monthly weather characteristic in advance and previous studies also showed monthly PM concentration by measuring PM for a short time [24,29]. So, we can speculate that our measuring data can show the feature of monthly PM.”

11. 163 - “The annual deviation of PM reduction rate was larger before the implementation of the GB zone, but it gradually decreased after the GB was installed, showing a stable value. – Why? Please note that during this period the variability of the concentrations was also high at all points. In general, the downward trend was on all points, please elaborate on that.

• In Figure 3b, the average annual deviation of PM reduction rate was ±17.6 before the implementation of GB. The variability of PM reduction rate decreased since 2010 (±2.8) and showed stable value after the implementation of GB (±5.7). Admitting the reviewer’s points, we rephrased this part more clearly to understand. The revision is reflected in line 182– 185on page 5.
• “The average annual deviation of PM reduction rate was ±17.6 before the implementation of GB. The variability of PM reduction rate decreased since 2010 (±2.8) and showed stable value after the implementation of GB (±5.7).”

12. 3 – see above, why monthly? The data does not agree, the reduction rate in August is 0 for both PM10 and PM2.5 and 0 in December and January for PM2.5.

• We think that you thought there was no difference in PM concentration in August, December and January in figure 5a which resulted in PM reduction rate 0. However, there is a difference between OP-IC and OP-RA in August, December and January although it is very small and barely noticeable. Admitting the reviewer’s points, we widen the figure 5 to easily be read.

13. 225 - But during and after the implementation of GB, the PM values at RA were higher than that ...– rather lower?

• Admitting the reviewer’s points, we revised “higher” to “lower”.
• The revision is reflected in line 246 on page 7.

14. 230 - “PM concentration gradient”--- inappropriate word

• Admitting the reviewer’s points, we changed “PM concentration gradient” to “the trend line slope of PM concentration.”
• The revision is reflected in line 250 on page 7.

15. 192 - should be “air pollutants emission”

• Admitting the reviewer’s points, we changed “air pollutants” to “air pollutants emission”.
• The revision is reflected in line 211-212 on page 6.

16. Part 3.2. - it is difficult to unequivocally assess whether it was the green buffer effect or the increased distance from the emitters. Please note that the concentration variations were convergent. Please use statistical methods to evaluate them.

• It’s a good comment. We also considered the spatial distance effect on PM reduction according to the distance from pollutant emissions area. In this study, PM10 and PM2.5 which have a long atmospheric lifetime decreased. So, we can speculate that the effect of GB on PM reduction was greater than that of distance. Admitting the reviewer’s points, we added this discussion in Part 4.2 part. We think it is the right position to understand discussion. The revision is reflected in line 311-315 on page 9.
• “However, we considered not only the effect of GB but also the distance effect on PM reduction according to the distance from air pollutants emitters. PM10 and PM2.5 which have a long atmospheric lifetime decreased [59], thus we can speculate that the influence of GB on PM reduction was greater than that of distance [24].”

17. 259-264 - the explanation is unconvincing. It is difficult to understand how SO2 emissions can affect the degree of PM reduction, likely due to the green buffer.

• SO_x is precursor of PM, and high temperature could cause a photochemical reaction which produce the secondary PM. So, high SO_x and temperature can produce large PM. However, tree can accumulate PM on leaves up to their PM-retaining capacity. Precipitation or strong wind is needed to begin a new cycle of PM accumulation and PM removal is periodic. When PM accumulation by tree is saturated, high PM can be limited which results in low PM reduction rate. Admitting the reviewer’s points, we rephased this part more clearly to understand. The revision is reflected in line 280-286 on page 8.
• “SO_x and temperature showed a negative correlation with PM reduction rate, which might be related to the process of the secondary PM formation. SO_x is precursor of PM [44,45], and high temperature cause a photochemical reaction between SO_x and other gaseous contaminants, producing secondary PM [46,47]. However, tree can accumulate PM on leaves up to their PM-retaining capacity. Precipitation and strong wind are needed to begin a new cycle of PM accumulation [48]. Thus, when PM accumulation by tree is saturated, high PM can be limited which results in low PM reduction rate.”

18. 255-256 and 269-270 - explanations are contradictory. As for the influence of wind, it increases turbulence in the atmosphere, especially in built-up areas, a stronger correlation could have been due to the existence of a green buffer.

• You’re right. High wind speed influences active air currents and rapid dispersion of PM in the atmosphere which helps with PM reduction thorough GB. However, high density of trees can reduce wind velocity and suppress the air current, resulting in PM congestion and low PM reduction effect of GB zone.

19. The conclusions should be more specific. To what extent have the research objectives been achieved, what are the weaknesses and recommendations for future research.

• Admitting the reviewer’s points, we rephrased conclusion part more specific. The revision is reflected in line 323–346 on page 9.
• “This study showed that the GB zone was effective in reducing PM generated in the IC. In a local scale, PM concentration and the number of high pollution days of PM were higher in the IC than RA after the implementation of the GB zone. The trend slope line of PM concentration in the RA rapidly decreased over time which can be attributed to the in-direct blocking effects of GB by PM absorption, adsorption and deposition through trees. The effect of the PM reduction gradually increased over time as the physiological stabilization of the trees took place. Also, PM reduction rate was positively and negatively related to wind speed and SOx, respectively. This means that both proper management of tree density and large PM emissions are needed to improve PM reduction. In human’s breathing height, except for the month of the PM was low, the PM concentration in the IC was also higher than RA. Our results provided that proper management of tree density and PM emission sources are required to maintain the PM reduction effect of GB zone. In addition, the ecosystem services and benefits for environmental taxation of GB zone could be evaluated through this customized method for local and human’s breathing height.

However, this study was conducted only in the one region (Sihwa) in Korea. Also, the study on the exposure state of humans was measured on a 24-hour basis, which was not comparable to the monthly PM characteristics. So, the generalization on the PM reduction caused by GB zone could be limited to apply all over the country. In future studies, it is necessary to measure the monthly PM by continuous PM measurement for human’s breathing height. The overall PM reduction characteristics of the GB zone will need to be determined by comparing the local and human level.”

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

Thank you for answering my questions in detail. I believe the quality of the study has improved significantly.