Evaluation of key factors influencing urban ozone pollution in the Pearl River Delta and its atmospheric implications

Huang, Xiao-Feng; Xia, Shi-Yong; Luo, Yiqi; Han, Heng-Xiao; He, Lingyan

doi:10.1016/j.atmosenv.2023.119807

Cited by 8 publications

(3 citation statements)

References 48 publications

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“…Among these meteorological variables, temperature, UV radiation, and relative humidity are the three most important meteorological factors for PM 2.5 and O 3 pollution in the NCP, the YRD, and the PRD, with their relative importance values ranging from 1.84% to 3.99%. These results are consistent with previous studies [46][47][48][49][50][51][52][53], and high temperatures, strong UV radiation, and suitable relative humidity provide an appropriate environment to promote ozone photochemistry and the formation of ozone pollution. Similar to O 3 pollution, appropriate temperature and solar radiation, as well as high relative humidity also provide a suitable environment for the generation and concentration of PM 2.5 pollution, which is also consistent with the results of previous studies [47,[54][55][56][57].…”

Section: Relative Importance Of Each Explanatory Variablesupporting

confidence: 93%

Quantitative Estimation of the Impacts of Precursor Emissions on Surface O3 and PM2.5 Collaborative Pollution in Three Typical Regions of China via Multi-Task Learning

Liu,

Ma,

Liu

et al. 2024

Sustainability

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The coordinated control of PM2.5 and O3 pollution has become a critical factor restricting the improvement of air quality in China. In this work, precursors and related influencing factors were utilized to establish PM2.5 and O3 estimation models in the North China Plain (NCP), the Yangzi River Delta (YRD), and the Pearl River Delta (PRD) using a multi-task-learning (MTL) model. The prediction accuracy of these three MTL models was high, with R2 values ranging from 0.69 to 0.83. Subsequently, these MTL models were used to quantitatively reveal the relative importance of each factor to PM2.5 and O3 collaborative pollution simultaneously. Precursors and meteorological factors were the two most critical influencing factors for PM2.5 and O3 pollution in three regions, with their relative importance values larger than 29.99% and 15.89%, respectively. Furthermore, these models were used to reveal the response of PM2.5 and O3 to each precursor in each region. In the NCP and the YRD, the two most important precursors of PM2.5 pollution are SO2 and HCHO, while the two most critical factors for O3 pollution are HCHO and NO2. Therefore, SO2 and VOC emissions reduction is the most important measure for PM2.5 pollution, while VOC and NO2 emission reduction is the most critical measure for O3 pollution in these two regions. In terms of the PRD, SO2 and NO2 are the most important precursors of PM2.5 pollution, while the most important precursors for O3 pollution are HCHO and SOX, respectively. Thus, NO2, SO2, and VOC emission reduction is the most critical measure for PM2.5 pollution, while VOC and NO2 emission reduction is the most critical measure for O3 pollution in the PRD. Overall, this study provides clues and references for the control of PM2.5 and O3 collaborative pollution in the NCP, the YRD, and the PRD.

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Section: Relative Importance Of Each Explanatory Variablesupporting

confidence: 93%

Quantitative Estimation of the Impacts of Precursor Emissions on Surface O3 and PM2.5 Collaborative Pollution in Three Typical Regions of China via Multi-Task Learning

Liu,

Ma,

Liu

et al. 2024

Sustainability

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“…During the resurgence of COVID-19 in March 2022 in Shenzhen, reducing daytime O x was more effective in reducing secondary organic aerosols than reducing precursor emissions (Tang et al 2023). In the fall, PM 2.5 promoted MDA8 O 3 formation by scattering light, and thus influencing the photolysis frequency of NO 2 and O 1 D (Lin et al 2023). During the winter, Tang et al (2022) showed that the decline in both PM 2.5 and O 3 during COVID-19 lockdown was directly driven by the decline in O x , rather than the large precursor emission reductions in Shenzhen.…”

Section: Bidirectional Causal Relationships Between Pm 25 and O 3 311...mentioning

confidence: 99%

Detecting causal relationships between fine particles and ozone based on observations in four typical cities of China

Qi,

Yin,

et al. 2024

Environ. Res. Lett.

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As concentration of fine particles (PM2.5) is declining, ozone (O3) concentration is increasing in China in recent years. To collaboratively control PM2.5 and O3, it is critical to understand the relationship between the two and identify major controlling factors. We use convergent cross mapping method to detect the causal relationship between daily PM2.5 and maximum daily 8h average O3 concentrations in Beijing, Taizhou, Shenzhen and Chengdu in China in the four seasons in 2015–2022. In addition, we also examined causal effects of atmospheric oxidation capacity, precursors, and meteorological elements on PM2.5 and O3 in the four cities. In summer, PM2.5 and O3 are strongly positively correlated and show bidirectional causal relationships. The causal effects of atmospheric oxidation capacity on PM2.5 was larger than those of nitrogen dioxide (NO2) in Beijing and Shenzhen, while in Chengdu the latter was larger than the former. In winter, PM2.5 and O3 show bidirectional causal relationship, but the two are significantly negatively correlated in Beijing and positively correlated in Shenzhen. In Beijing NO2 was positively related to PM2.5 and negatively related to O3 and the causal effects of NO2 on PM2.5 and O3 exceed those from other factors, indicating that NO2 is possibly the driving force of relationship of PM2.5 and O3 in Beijing. In contrast, atmospheric oxidation capacityshows the largest causal effects on PM2.5 and O3 in Shenzhen. Thus, we suggest different control measures in different regions and seasons.

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“…In these complex systems, there are numerous system factors, which are interrelated and work together on the operating state or output result of the system. However, there is no doubt that among all these factors, there are often a few that play a dominant role, which we call the key influencing factors [5,6]. In complex systems constrained by multiple factors, it is very important to identify the key influencing factors for mastering the evolution and development law of the system and for obtaining scientific decision-making suggestions or schemes [7].…”

Section: Introductionmentioning

confidence: 99%

Key Influencing Factors Identification in Complex Systems Based on Heuristic Causal Inference

Wu,

Lu,

et al. 2023

Applied Sciences

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In complex systems constrained by multiple factors, it is very important to identify the key influencing factors for mastering the evolution and development law of a system and for obtaining scientific decision-making suggestions or schemes. At present, the method based on experimental simulation is limited by the difficulty of system model construction; DEMATEL (Factual Decision Trial and Evaluation Laboratory) is inevitably influenced by subjective factors. In view of this, we propose a novel model based on heuristic causal inference. By combining the network analysis in complex network science, the model defines the global/local causal pathway and the causal pathway’s length in the causal network and takes the causal pathway contribution degree as an indicator to measure the approximate causal effects. The model includes steps such as causal network learning, causal pathway contribution degree calculation, and key influencing factor identification. The model uses the Fast Causal Inference (FCI) algorithm with prior knowledge to learn the global causal network of the complex system and uses the heuristic causal inference to calculate the causal pathway contribution degree. The heuristic method draws on the idea of complex network topology analysis and measures the influence degree between variables by the number and distance of causal pathways. The key influencing factors are finally identified according to the causal pathway contribution degree. Based on the SECOM dataset, we carried out simulation experiments and demonstrated the feasibility and effectiveness of the proposed method.

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Evaluation of key factors influencing urban ozone pollution in the Pearl River Delta and its atmospheric implications

Cited by 8 publications

References 48 publications

Quantitative Estimation of the Impacts of Precursor Emissions on Surface O3 and PM2.5 Collaborative Pollution in Three Typical Regions of China via Multi-Task Learning

Quantitative Estimation of the Impacts of Precursor Emissions on Surface O3 and PM2.5 Collaborative Pollution in Three Typical Regions of China via Multi-Task Learning

Detecting causal relationships between fine particles and ozone based on observations in four typical cities of China

Key Influencing Factors Identification in Complex Systems Based on Heuristic Causal Inference

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