the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Apr 2024
Identifying the control cities of O3 Pollution using Complex networks
Abstract. In recent years, ozone (O3) pollution has been rapidly spreading, restricting further improvement of air quality in China. Investigating the interaction of O3 concentration and identifying their driven cities are important for the prevention and control of O3 pollution in China. However, the interaction between O3 pollution between cities and their driven cities has not yet been revealed. In this study, we fill this gap based on the integration of complex network methods, the Louvain community partitioning algorithm and the maximum matching network control theory. O3 network model exhibits a structured cluster framework, such as Northeast, North China, Sichuan and Chongqing, and Southeast coastal areas. And the driver nodes are mainly concentrated in the central region, while the non-driver nodes are mainly located in the coastal periphery. We also found that the proportion of driven nodes exhibits a positive relation with the threshold. In addition, the coincidence degree of the driven node is related to the choose of threshold. A closer threshold value corresponds to a higher coincidence ratio of the driven nodes. The correlation of driven nodes predicting non-driven nodes is stronger than non-driven nodes predicting driven nodes, suggesting that driven nodes have more influence in the O3 network than non-driven nodes. The results provide scientific guidance for national O3 pollution prevention and regional synergy formatting. Furthermore, the introduced network-based approaches offer a mythological framework for the study of air pollution in key cities and clusters.
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RC1: 'Comment on esd-2024-4', Anonymous Referee #1, 08 Aug 2024
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The manuscript presents an analysis of the time series of O3 concentrations from different Chinese cities using concepts and tools from network theory. Correlation networks are constructed in which nodes are the cities and links between two of them are set if Pearson correlation between O3 fluctuations in these two cities exceeds a given threshold. The obtained networks are analysed with methods of structural network controllability, community detection, and long short-term memory (LSTM) predictability. Â
In my opinion the quality of the paper is well below the standards expected for publication in Earth System Dynamics, and then I cannot recommend publication. The reasons for my negative assessment are two: lack of any discussion of the implications of the mathematical results, and lack of clarity in the explanation of the methodology and presentation of the results. In addition, in most cases there is no clear justification for the use of the particular methodologies employed. In the following I provide a non-exhaustive list of weak points, substantiating my general criticisms.
- The authors correctly identify key places where the results of their mathematical exercises need understanding and explanation. Examples are ‘Future research should focus more on the explainable practical role of these network structures’ (line 208). ‘Future work should therefore focus on understanding the practical significance and use of driven nodes in O3 networks’ (line 245). ‘Future research should focus more on understanding the practical implications of seasonal changes in O3 network nodes over time’ (line 271). ‘Future research should pay more attention to the significance of these nonlinear relationships in practical O3 application scenarios’ (line 304). ‘Future research should focus more on exploring the practical implications of these results’ (line 349). ‘Future research should pay more attention to the mechanism behind the above phenomenon’ (line 372). ‘Future research should focus on the underlying mechanisms leading to these phenomena’ (line 398) ... But no single attempt to interpret these or other results is included in this paper. Are the O3 fluctuations dominated by industrial processes, urban emissions or by climatic/meteorological effects? What is the meaning of the communities found? Just geographical proximity or some type of common behaviour? In which way can the network be controlled? Acting on industrial or urban sources of precursors of O3? Are the nodes identified as ‘driver’ nodes sources of pollution or sites sensible to climatic fluctuations? I recommend the authors to concentrate in one or a few of these relevant questions, and try to answer them using their networks results. Until this is done, the present paper remains a mathematical exercise of application of network tools to some time series, and then not particularly relevant to be published in Earth System Dynamics.
- The description of the network controllability is particularly confusing. The authors talk about nodes that are ‘driver’, ‘driven’, ‘non-driven’, ‘drive’, ‘drivens’, or ‘driving’, in a way that does not seem to be always consistent. As the paper advances the nomenclature seems to stabilize in ‘driven nodes’ and ‘non-driven nodes’. This is particularly inconvenient, since in the paper by Liu et al. 2011 (to which the authors refer for the methodology). The nomenclature used is of ‘driver nodes’ (=’unmatched’) and ‘matched nodes’. It is not clear from the manuscript, but it seems to me that what the authors call here ‘driven nodes’ are in fact the ‘driver nodes’ of Liu et al. Since the authors do not analyse any interpretation or implication of their identification of ‘driven nodes’, there is no way to check if my interpretation of the nomenclature is correct, although it helps to understand figures 7-9 (panels b and d). Â
- Continuing with the issue of controllability, note that the methodology in Liu et al. 2011 refers to ‘structural controllability’ and is based on the use of structural (physical) links between the different nodes. Here, links are of statistical nature, so that a statistical link can be originated from many different sets of structural connections. The authors do not discuss at all why the methodology used (maximum matching algorithm, etc. from Liu et al.) can be used here. Note the difference, for example, with the network constructed by Tian et al. 2014: there the links represent transport by winds, so that they can be considered ‘structural’ and not just statistical as in the present manuscript.
- The authors give no argument to justify for the use of the Louvain community detection method instead of the many others available (and some of them free of the resolution limit which affects the Louvain method). Since the resulting communities are not interpreted, it is difficult to assess if the method has achieved a good partitioning of the system.
- The role of the LSTM forecasting (which is not mentioned in the abstract) is difficult to assess: the authors do not give any hint on why the forecasting is done from/to driven to/from non-driven nodes. Is not more natural to do the forecasting exercise between all pair of nodes? And the final result, presented in Fig. 13, gives some kind of correlation as a function of distance. In which sense is this different or better than just plotting the correlation between all pair of cities as a function of its distance? As with the rest of approaches, there is no explanation to justify what is being done.
- In general, the description of the different procedures is very confusing. For example, in line 92 it is said that tau is positive, whereas in the next line tau takes positive and negative values. At the end it seems (but I am not completely sure by reading the description) that really two networks are constructed, one for tau>0 and another for tau<0. There is no reason given to associate positive values of tau to positive correlations, and negative tau to negative correlations. In equation (6) the quantities Sigma_xx, k_x, or m are not defined. Perhaps ‘i’ is ‘a’ and ‘m’ is ‘n’ …?
- Finally, there are many typos in the paper (e.g. line 22: ‘the choose of threshold’; line 25: ‘mythological’; line 37: ‘are one of the main precursor …’; line 75: ‘The The’, …). And in general the paper has not been re-read carefully: for example the sentence in lines 104-106 is clearly copied from an unrelated paper: ‘ Interventionary studies involving animals or humans, and other studies that require ethical approval, must list the authority that provided approval and the corresponding ethical approval code.’
In summary, the paper is not of enough quality to deserve publication in Earth System Dynamics. Â
Citation: https://doi.org/10.5194/esd-2024-4-RC1
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