the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
02 Feb 2021
02 Feb 2021
Unexpected enhancement of ozone exposure and health risks during National Day in China
- 1Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China
- 2State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China
- 3School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- 4Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, China
- 5Department of Environmental Science and Engineering, Fudan University, Shanghai, China
- 6Institute of Eco-Chongming (IEC), Shanghai, China
- 1Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China
- 2State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China
- 3School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- 4Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, China
- 5Department of Environmental Science and Engineering, Fudan University, Shanghai, China
- 6Institute of Eco-Chongming (IEC), Shanghai, China
Abstract. China is confronting increasing ozone (O3) pollution that worsens air quality and public health. Extremely O3 pollution occurs more frequently under special events and unfavorable meteorological conditions. Here we observed significantly elevated maximum daily 8-h average (MDA8) O3 (up to 98 ppb) during the Chinese National Day Holidays (CNDH) in 2018 throughout China, with a prominent rise by up to 120 % compared to the previous week. The air quality model shows that increased precursor emissions and regional transport are major contributors to the elevation. In the Pearl River Delta region, the regional transport contributed up to 30 ppb O3 during the CNDH. Simultaneously, aggravated health risk occurs due to high O3, inducing 33 % additional deaths throughout China. Moreover, in tourist cities such as Sanya, daily mortality even increases by up to 303 %. This is the first comprehensive study to investigate O3 pollution during CNDH at national level, aiming to arouse more focuses on the O3 holiday impact from the public.
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Peng Wang et al.
Status: final response (author comments only)
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RC1: 'Comment on acp-2020-1302', Anonymous Referee #1, 01 Mar 2021
General:
The manuscript presents a topical research, i.e. to understand the elevated O3 issue in China due to the holiday impact. This study reported that the drastically rising MDA8 O3 were observed during the CNDH with the increasing rate up to 120% even in some pristine regions, which also induced 33% additional deaths through China. It was shown that
increased precursor emissions and regional transport were corresponding to the O3 elevation. This is the first comprehensive study to investigate O3 pollution during CNDH at national level and could provide useful suggestion for the policy makers. The manuscript is easy to follow and fit to the scope of ACP very well. I have some minor comments below for the authors to address.
Minor comments:
Line 90~91: Could the author explain more for the IPR and PA tools in the CMAQ model?
Line 135~136: as readers may not be familiar with West China, please add a reference to show that West China has less anthropogenic impacts.
Line 147~148: it should be mentioned that MDA8 O3 in Shanghai during the CNDH slightly decreased compared with that before CNDH.
Line 188: could the author explain more about meteorology impacts such as the variation of the temperature on the O3 during the CNDH?
Line 195: Could the author discuss how will the coefficients from the AMAP be applied in the emission inventory?
Line 228: please label the key cities in the PRD in the Figure 4
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RC2: 'Comment on acp-2020-1302', Anonymous Referee #2, 08 Mar 2021
This manuscript investigates the causes of high O3 episode during Chinese National Day Holiday using CMAQ modeling. The high O3 concentration is found to be caused by enhanced anthropogenic emissions and regional transport. Further, the health risks of these high O3 episode are estimated based on the response function of premature mortality of O3 exposure. The scope of this manuscript is of interest and fits the Atmospheric Chemistry and Physics journal. However, the illustration of the manuscript makes it a bit difficult to review fairly. The readability can be easily improved by elaborating several key terminologies and large fonts in figures. For example, two terms “O3_VOC” and “O3_NOx” are discussed throughout the manuscript to diagnose the O3 chemistry, but they are not clearly defined in the manuscript. The font of figure 3 is too small. The color scheme in figure 4b is impossible to read. I believe this study is publishable, but requires substantial revisions.
Comments:
- The importance of regional transport. Look at Figure 2a CNDH, the O3 concentration is up to 100 ppb in south China sea around Hainan and it is higher than the mainland China. Is this real? If so, what’s the impact of such high O3 concentration on southern China? Is this the major cause of the high O3 episode during CNDH? Figure S5 suggests the prevailing wind direction is from mainland to ocean during CNDH in CMAQ. Is this consistent with local measurements? Line 217 indicates the south wind is prevailing. I am confused.
- Line 24. This “303%” overstates the health risk, because the absolute difference is small (0.4 vs 1.6 in Figure 5).
- The following terminologies/calculations should be elaborated: O3_VOC, O3_NOx, O3 production rate, and exceeding rate (Figure 1c).
- To corroborate the estimated health risks, the estimated daily mortality (non-accidental causes) should be compared to real mortality data, if possible.
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RC3: 'Comment on acp-2020-1302', Anonymous Referee #3, 18 Mar 2021
The paper investigates the causes of increase in surface ozone concentration in China during the Chinese National Day Holidays (CNDH) in 2018. The authors used CMAQ model to simulation O3 production during three periods of pre, during, and after (CNDH). The result shows that the increased O3 values during CNDH are due to increase in precursor emissions and also regional transport. The impact of enhanced O3 during CNDH on public health and mortality rate in major cities in China.
The paper is well-written and fits to scope of ACP. However, it needs some clarifications on the changes in the anthropogenic emissions in the three periods. If no changes were made to the anthropogenic emission inventory to reflected the changes in emissions due to the national holiday how are you attributing the changes in O3 to this event? The relative contribution of biogenic vs. anthropogenic emissions needs to be discussed further in the paper. It may play an important role in the variation in O3 concentrations and it is totally dismissed. Please see the comment sections for the details.
General comments:
- What does regional transport mean in the scale of your study? All the paper on regional transport in China that are cited in the introduction discuss one region in China and the impact of transport from a region to another hence “regional transport”. Specifically, I am referring to P3, L61 where you stated the rapid increase of O3 throughout China is attributed partly to regional transport. What does this mean if the transport is between subregions in your domain?
- Section 2.1: Please note in the main text that October emission is industry and residential sectors are higher than September emissions. The monthly variation in emission inventory (between September and October) can play a role in variations in O3 concentrations and it is not discussed in the paper.
- Section 2.1: I am not familiar with MEIC inventory, does it have a diurnal or monthly variation? Please provide more information.
- Section 2.1: This is my main questions to the authors: Is the anthropogenic emission different during CNHD? If no then how are you attributing changes in emission as one the reasons for enhanced O3. If yes then please provide more information about the changes.
- Section 2.1: PRE-CNDH and CNDH periods have 6 days and AFT-CNDH is 23days. Is there a specific reason for this? This makes your statistical comparisons (for example in fig 1) not fair because you are including more days in one of the periods compared to others.
- Figure 1. Can you add model values to plots a and b to show if model captured the variation in the MDA8 O3?
- Figure 1. Can you show on one of the maps where each the regions in plot (a) are? Why AFT-CNDH in east China is so much lower than PRE-CNDH?
- P6 – section 3.2: Having a discussion on changes in O3 production regime in valuable. However, I suggest starting this section by discussing the differences between emissions. This way you can better distinguish between uncertainties in emissions and in the uncertainties in simulation of O3 production process.
Having a figure that shows the differences between NOx and VOC emissions (in different periods within your simulation) as one the main figures will be very helpful.
- P8 – Discussion on changes in transportation emission.
I think making these changes in transportation sector emission and running another simulation can reflect the actual changes that occur in the emissions during the national holiday. Without considering these changes the conclusion seems weak and incomplete to me. I’m not suggesting to add a real time vehicle emission inventory. You can simply increase the emission from transportation sector by factor of 2.2 during the national holiday and study the impact on O3 values.
Specific comments:
P4 – L83. It is not clear to me if or how much the anthropogenic emission has changed on CNDH days. Also having September and October months in the simulation, probably biogenic emission changes as well. Please be more specific about changes in emissions during the simulation period.
P4 – L100-107. I suggest briefly explain which benchmarks you used for meteorology and O3 performance in this paragraph.
P6 – L158. In south China…
Are you referring to model or obs values? Please clarify.
P6 – L 159. In contrast…
What is the reason for this?
P6-L160. High O3_NOx…
What is a high O3_NOx and O3_VOC level? Also in Fig 2 b and c can you use same range for O3_NOx and O3_VOC? And perhaps a better color bar? The values of O3_NOx in south China during CNDH are not readable.
P8 – L183 – Fig S4:
This is the part that confuses me the most. Is the increase of NOx (and AVOC) emission in Oct due to the national holiday or it is for the whole month? If it’s for the whole month how are you attributing it to the national holiday (only from Oct 1-7)?
The differences between e and f (if it shows biogenic VOC emissions) is a natural occurring event and not related to changes in anthropogenic activities. How much of the changes in ozone can be attributed to this? I would like to see BVOC emission maps for PRE-CNDH as well given the highest temperature occurred in PRE-CNDH. Can this justify lower O3 values in AFT-CNDH that we see in Fig 1?
Can you provide difference plots for AVOC and BVOS plots? Also, why different time frames are considered for BVOC plots?
P9 – L 221: Fig S11 and Fig S12. this is not correct.
Peng Wang et al.
Peng Wang et al.
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