High-resolution regional emission inventory contributes to the evaluation of policy effectiveness: A case study in Jiangsu province, China
- 1State Key Laboratory of Pollution Control and Resource Reuse and School of the Environment, Nanjing University, 163 Xianlin Rd., Nanjing, Jiangsu 210023, China
- 2Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, CICAEET, Nanjing, Jiangsu 210044, China
- 3Jiangsu Key Laboratory of Environmental Engineering, Jiangsu Provincial Academy of Environmental Sciences, Nanjing, Jiangsu 210036, China
- 1State Key Laboratory of Pollution Control and Resource Reuse and School of the Environment, Nanjing University, 163 Xianlin Rd., Nanjing, Jiangsu 210023, China
- 2Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, CICAEET, Nanjing, Jiangsu 210044, China
- 3Jiangsu Key Laboratory of Environmental Engineering, Jiangsu Provincial Academy of Environmental Sciences, Nanjing, Jiangsu 210036, China
Abstract. China has been conducting a series of actions on air quality improvement for the past decades, and air pollutant emissions have been changing swiftly across the country. Province is an important administrative unit for air quality management, thus reliable provincial-level emission inventory for multiple years is essential for detecting the varying sources of pollution and evaluating the effectiveness of emission controls. In this study, we selected Jiangsu, one of the most developed provinces in China, and developed the high-resolution emission inventory of nine species for 2015–2019, with improved methodologies for different emission sectors, best available facility-level information on individual sources, and real-world emission measurements. Resulting from implementation of strict emission control measures, the anthropogenic emissions were estimated to have declined 53 %, 20 %, 7 %, 2 %, 10 %, 21 %, 16 %, 6 % and 18 % for SO2, NOX, CO, NMVOCs, NH3, PM10, PM2.5, BC, and OC from 2015 to 2019, respectively. Larger abatement of SO2, NOX and PM2.5 emissions were detected for the more developed southern Jiangsu. Since 2016, the ratio of biogenic volatile organic compounds (BVOCs) to anthropogenic volatile organic compounds (AVOCs) exceeded 50 % in July, indicating the importance of biogenic sources on summer O3 formation. Our estimates in annual emissions of NOX, NMVOCs, and NH3 were generally smaller than the national emission inventory MEIC, but larger for primary particles. The discrepancies between studies resulted mainly from different methods of emission estimation (e.g., the procedure-based approach for AVOCs emissions from key industries used in this work) and inconsistent information of emission source operation (e.g., the penetrations and removal efficiencies of air pollution control devices). Regarding the different periods, more reduction of SO2 emissions was found between 2015 and 2017, but NOX, AVOCs and PM2.5 between 2017 and 2019. Among the selected 13 major measures, the ultra-low emission retrofit on power sector was the most important contributor to the reduced SO2 and NOX emissions (accounting for 38 % and 43 % of the emission abatement, respectively) for 2015–2017, but its effect became very limited afterwards as the retrofit had been commonly completed by 2017. Instead, extensive management of coal-fired boilers and upgradation and renovation of non-electrical industry were the most important measures for 2017–2019, accounted collectively for 61 %, 49 % and 57 % reduction of SO2, NOX and PM2.5, respectively. Controls on key industrial sectors maintained the most effective for AVOCs reduction for the two periods, while measures on other sources (transportation and solvent replacement) became increasingly important for more recent years. Our provincial emission inventory was demonstrated to be supportive for high-resolution air quality modeling for multiple years. Through scenario setting and modeling, worsened meteorological conditions were found from 2015 to 2019 for PM2.5 and O3 pollution alleviation. However, the efforts on emission controls were identified to largely overcome the negative influence of meteorological variation. The changed anthropogenic emissions were estimated to contribute 4.3 and 5.5 μg·m-3 of PM2.5 concentration reduction for 2015–2017 and 2017–2019, respectively. While elevated O3 by 4.9 μg·m-3 for 2015–2017, the changing emissions led to 3.1 μg·m-3 of reduction for 2017–2019, partly (not fully though) offsetting the meteorology-driven growth. The analysis justified the validity of local emission control efforts on air quality improvement, and provided scientific basis to formulate air pollution prevention and control policies for other developed regions in China and worldwide.
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Chen Gu et al.
Status: final response (author comments only)
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RC1: 'Comment on acp-2022-734', Anonymous Referee #1, 14 Dec 2022
This study focuses on the development of emission inventory in the Jiangsu Province, China, during 2015-2019 based on multiple new data sources. Based on the new inventory, the influence of different policies on the changes of pollutant emissions, meteorology and emissions on the changes in PM2.5 and O3 concentrations were evaluated. Relative conclusions are helpful for further regional-level air quality improvement.
Overall, the manuscript is well written and its structure is well organized. Some clarifications and corrections are needed for the paper to reach the publication standard. The detailed comments are as follows (“Ln” indicates the line number is n):
General comments:
- I highly recommend to add the comparisons of observational and CMAQ-modelling NO2 and SO2 concentrations. Good performance of these two species can prove the higher accuracy of the province-level emission inventory than MEIC, especially its annual changes discussed in Section 3.2.1 (L576). Besides, in the analyses of ozone underestimation, a direct evidence can be provided for NOx emission overestimation (as discussed in L790).
- If possible, it would be better to have more comparisons between all bottom-up emission inventories mentioned in this paper with official emission statistics of China/Jiangsu Province and other top-down emission inventories to make the conclusions more solid.
- In the CMAQ modelling, the temporal and vertical profiles of emissions are also very important for a good performance. In this study, is there any improvement on these profiles, since new data applied may also provide such information?
- Please point out the specific meanings of pollutant concentrations in the identification of meteorology and emission contributions. Are they mean pollutant concentrations in the monitoring stations? Or geographical mean values? Or population-weighted mean values? For both observational and modelling results, are the annual values both the average concentrations of four representative months of each year?
- For the annual changes of BVOCs emissions, are they counted as the contributions of emissions or meteorology?
- As for the writing, adverbs (e.g. significantly, largely, increasingly) are massively used in this paper. It is recommended to reduce the usage of unnecessary adverbs and be careful with the accuracy of some adverbs. For example, in L553, “biogenic sources gradually became more influential”, “gradually” does not agree well the changes of BVOCs/AVOCs ratio. The tenses in some places are not correct. One example is in L571 — “it was probably due to …” should be “it is probably due to…”, since it discusses a general fact, not something happened before. Also be careful with the usage of articles — specifically, “the” should be added or deleted in many places. I pointed out some grammatical mistakes and unclear expressions in the detailed comments, but more careful checks are suggested for the authors.
Detailed comments:
Abstract
- L29: “in China” should be added after “province is an important administrative unit for air quality management”, because this is not true for many other countries. The same is for L134 in the introduction section.
- L38: the full names of “NMVOC”, “BC” and “OC” should be used or explained.
Introduction
- L79: “the” should be added before “magnitude, spatial pattern, and …”
- L95: “increasingly” should be “increasing”
- L106: “largely weakened” is unclear. Do you mean along with the increasing diversity of emission sources, the relationships between proxies and emission distributions are weakened recently?
- L140: “relatively” should be deleted, since there is no clear comparison.
- L150: “it comprised” should be “it contributed to”
- L159: “become” can be deleted.
Method
- L191, L197: the number “fifty-five” and “forty-two” can be directly written as “55” and “42”
- L192: because Table S1 also contains information on third-level emission sectors, “(see details in Table S1 in the Supplement)” can be put in the end as “(details on the first three level sectors are listed in Table S1 in the Supplement)”
- L196: “guidelines for development of national emission inventories” => “the guidelines of national emission inventory development”
- L205-206: “provided” => “provides”; “thus was able to considerably reduce” => “thus considerably reduces”
- L257: “meteorological” => “meteorology”; “and” should be deleted
- L259-260: in “the relatively high temperature” and “the NH3 volatilization”, “the” is not needed; in “NH3 volatilization for urea fertilizer use”, “for” should be “from”
- L261: what is “metrology”?
- L265-274: it would be clearer to put the last sentence “in this work… for multiple years” in the beginning
- L295: “we split the source profiles for some categories into finer ones” is not clear. Do you mean to use more detailed profiles for some second-level sources, instead of the more general ones for the corresponding coarser level source profiles? Also, “for example…” is suggested to be a new sentence.
- L305: “for information of stationary sources” should be “for stationary sources” or “for the information of stationary sources”
- L306: “location, raw material…” => “their location, raw material”
- L308, L310: “database” or “data source” should be added after “the former” and “the latter” to avoid confusion
- L328: “the” should be added before “estimation and spatial…”
- L329-330: “with” => “by using”; for “the average emission factor by city and sector”, do you mean the average emission factor of each sector in each city?
- L331: How GDP is used to distribute the emissions?
- L332: “including” => “on”
- L340-347: when introducing the control measures, it is not needed to use uppercase for the first letters. In order to avoid misunderstanding, the numbers or letters can be used like 1), 2), …
- L349: “the” should be added before “implementation of”
- L354-355: “it was worth noting” => “it is worth noting”; “equal” is mostly used as adjective, thus “did” should be “is”
- L362: it should be pointed out that four months are selected to represent the four seasons
- L365: “the horizontal resolutions at” => “the horizontal resolutions of”
- L375-376: Is the data used for the assimilation in simulations or the evaluation of modelling performance?
- L388-389: “Sp” and “Op” should be used to keep consistency with Eq. 3-4. Does “p” indicate the number of years, or the number of available data pairs?
- L391-395: the discussion on modelling performance comparisons is not very clear. According to my understanding, the authors mean that the d03 modelling performance using MEIC is worse than d02 modelling performance using MEIC, thus a better d03 modelling performance using provincial emission inventory than using MEIC can be expected from the comparisons in this study. Is that correct? Also, only one study is used to support the assumption. Is that universal? Can you provide more reported results of modelling performance comparison in different domains, especially for the modelling in the YRD region?
- L399-414: it would be reader-friendly to use the tables, formulas and only some necessary explanations to state how the contributions of emission and meteorology are modeled and calculated
- L416: “included both from JS and nearby regions” => “is from both JS and nearby regions”
Results and Discussions
- L422: “anthropogenic emissions by sector and their changes” may better summarize the contents in this section
- L423: where can the information on AVOCs emissions be found? (I might be confused of NMVOCs and AVOCs emissions in this study?) The same is for the discussions in L434-436.
- L440: “grew” => “grew by”
- L443: “clearly decoupling” is not clear
- L447: “accounting” => “of which the contribution accounts”
- L471: “the” should be added before “implementation of”
- L488-489: Table S4 can be also introduced in this sentence. I also recommend to introduce the definition of southern, northern and central cities after the sentence like “In further discussions, we classify 13 cities in Jiangsu as the southern cities (xxx), central cities (xxx) and northern cities (xxx) (their distributions are shown in Figure S1)”, rather than “see the city definitions in Figure S1”.
- L493: “calculated at” => “calculated as”; “for southern, central and northern cities” => “for the southern, central and northern cities” (the same is for other places including L498)
- L496: “were” => “are” if that is the case also for now
- L498: “(Figure 4)” can be deleted or written as “(Figure 4e)”
- L506-512: the structure of this paragraph should be altered — it seems like introducing the conclusions first and then analyzing the data.
- L513: “the” should be added before “spatial distribution of”
- L516: “Figure 5a-c” should be changed into Figure S3 or Figure 4
- L516-521: The sentence is too complex (especially, the subject of “facing” is not “more efforts”). Please express it in a more readable way
- L520: “opposite” is not precise according to the figures —maybe “different” is enough. “the” should be added before “spatial variation of …”
- L526: “thus” is used in “there is a thus great need for substantial improvement of emission controls…”, but I cannot see what is the reason for the need of emission control improvements
- L536: “season” should be deleted
- L537: “existed” => “is” or “was”
- L539-541: the authors mentioned that industrial development might explain lower BVOCs emissions in the south, but what is the influence of meteorological factors? For example, higher precipitations near the Yangtze River in some seasons?
- L542: in Table 1, “%” is used for the unit of ratios, which may be misunderstood as the percentage of BVOCs in AVOCs. Thus, “×10-2” is recommended. The same is for other discussions like in L546 and L547
- L543: “the emission trends of both BVOCs and AVOCs” => “the trends of both BVOCs and AVOCs emissions”
- L544: the numbers need to be consistent with what is shown in the table. Thus, “11” should be “11.1”, and “16” should be “15.8”
- L563: for this section, the main content is the comparisons between different emission inventories. “Influence of different data and methods on emission estimates” is not so direct
- L573: I suggest to add one sentence in the end, which is like “Therefore, we mainly compared the interannual variability of emissions in the provincial inventory and MEIC”
- L578: “existed” => “are”
- L581-582: “was more optimistic in…” => “describes a more optimistic…”
- L593: I suggest to use “assessment of emission amounts” or similar items. And maybe the orders of two sections in 3.2 need to be changed — introducing emission amounts first, and then their interannual variability. The authors can consider about it.
- L608: “resulted” => “results”
- L624: when comparing numbers, “significantly” is normally used when there is statistically significant difference. Therefore, “much” might be more precise here
- L627: “an estimation 45% smaller” => “an estimation of 45% smaller”
- L636: “Simaya” => “Simayi”
- L647-648: could you please give more details on “better agreement with ground and satellite observation”?
- L656: what is “thu”?
- L674: the discussion in this section is a little hard to follow. Please add some hints for the readers — for example, here, it should be pointed out that the contributions of policy on SO2, NOx and PM2.5 emission changes are similar and firstly introduced
- L722: “the” should be added before “implementation of …”
- L739: “in a summary” => “in summary”
- L741: “have been driving” => “have driven”
- L758: “application of” => “applying”
- L766: “suggested” => “suggests”
- L772: “higher concentrations were found in summer and lower in winter” is not the case for PM2.5
- L777: what does “higher levels” mean? Higher concentrations or higher increase rates?
- L781-783: the authors mentioned that HO2 uptake might influence ozone changes. Besides, the effects of aerosol on radiation might be the other related to ozone changes. How these two effects are considered in CMAQ, since it is an offline model without the HO2 uptake mechanism? If the effects of aerosol on ozone changes are not involved in the modelling, is it possible that the increase rates of annual ozone concentrations will be further overestimated when the effects of aerosol are considered?
- L800-808: three sentences in this paragraph discuss different contents with weak connections — please revise it to make it flow. I suggest the discussion like: the annual changes of PM2.5 and O3 -> the contributions of two factors (meteorology and emissions) are identified and discussed in the following -> it should be noted due to non-linearity… the mean goal is to compare the relative contributions of two factors
- L817: “bigger” => “higher”
Supplement
- Figure S2: be careful with the format in the first column to make is more readable
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RC2: 'Comment on acp-2022-734', Anonymous Referee #2, 11 Jan 2023
This study demonstrates the development of high-resolution emission inventory and its application on evaluating the effectiveness of emission control actions, for a typical developed and polluted province in China. The manuscript presents a sound analysis on the role of the refined emission data at the regional scale on improving the air quality simulation and supporting the pollution control evaluation. In general, the article is well organized and clearly written, with sufficient description and discussions on the relevant data and results. I have some concerns which need to be further stressed or clarified before the article can be accepted for publication.
(1) Language should be improved. Some English expression is not correct.
(2) I suggest adding more comparison and discussion between this study and other emission inventories (if available) to make the difference clearer.
(3) Line 365-369. D3 also includes some areas of Shandong, Anhui, and Zhejiang, but the provincial inventory in this study was developed for Jiangsu. How were the emission data obtained in regions out of Jiangsu in D3? Please clarify.
(4) Line 774. Please reword this sentence. Define “unfavorable meteorological conditions”. Is the meteorology simulated by WRF inaccurate or the meteorology condition not conducive to pollution formation and accumulation?
(5) Figures 2 and 3. I would like to see more analysis on the interannual variation of PM2.5, BC and OC. For example, the reduction in OC seems bigger than BC, which could be of some significance on both air quality and climate. Could you indicate why this happened?
(6) Figure 8. The citation format of references needs to be corrected. For example, “An (2021)” should be “An et al. (2021).”
(7) Figure 9. It is indicated that Figure 9 summarizes the effect of individual measures on net emission reduction (Line 674), but the total emission reduction of all the measures seems to be greater than the net change in emissions shown in Figure S6. Please check the numbers and clarify the calculation.
(8) It is great that the authors made detailed evaluation with CMAQ modeling and provided the results in the Supplement. However, the discussion in the main text seems descriptive. Could you be more specific on the causes of relatively big difference between observation and simulation, and also suggest the possibility for future improvement on emission estimation?
Chen Gu et al.
Chen Gu et al.
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