Spatiotemporal patterns and quantitative analysis of influencing factors of PM2.5 and O3 pollution in the North China Plain

Ma, Mingliang; Liu, Mengnan; Song, Xueyan; Liu, Mengjiao; Fan, Wenping; Wang, Yuqiang; Xing, Huaqiao; Meng, Fei; Lv, Yongqiang

doi:10.1016/j.apr.2023.101950

Cited by 7 publications

(6 citation statements)

References 82 publications

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“…Since the Action Plan for Prevention and Control of Air Pollution, PM 2.5 has been the primary target of environmental regulations, and its emissions are decreasing very quickly [48]. The emission of NO x and VOCs, the precursors of O 3 , is still high [3]. It is very difficult to adjust the YRB's industrial structure in a short time [4].…”

Section: Spatial Auto-correlation Characteristicmentioning

confidence: 99%

“…Therefore, during the initial period of the policy, the correlation between PM 2.5 and O 3 concentrations was weak in the YRB. Meanwhile, the Chinese Ambient Air Quality Standard (GB3095-2012) was implemented in 2016, proposing further limits for the concentrations and further strengthening environmental regulations, and the trend of co-frequency changes between PM 2.5 concentration and O 3 concentration in neighboring regions began to appear [3]. Therefore, the correlation between the two pollutants' concentrations began to gradually increase significantly.…”

Section: Spatial Auto-correlation Characteristicmentioning

confidence: 99%

“…So as to investigate the spatial auto-correlation, we measured the univariate and bivariate Moran's I of the two pollutants' concentrations by Geoda1.22 (Figure 7). The univariate Moran's I of PM 2.5 concentration fluctuated in the range of 0.812-0.876 during 2013-2020, indicating that PM 2.5 concentration has a positive auto-correlation-that is, cities with similar PM 2. of O3, is still high [3]. It is very difficult to adjust the YRB's industrial structure in a short time [4].…”

Section: Spatial Auto-correlation Characteristicmentioning

confidence: 99%

“…With the implementation of a series of environmental regulations in China, PM 2.5 pollution has been significantly improved [1]. However, O 3 concentration has been increasing gradually [2], and the phenomenon of PM 2.5 -O 3 synergistic pollution has appeared increasingly prominent [3,4]. PM 2.5 -O 3 synergistic pollution is due to the fact that the two have the same precursors (NO x and VOCs) [5], which generate O 3 through photochemical reactions under light conditions and interact with PM 2.5 through transmission and diffusion, aerosol processes, and cloud processes, ultimately forming regional synergistic pollution [6].…”

Section: Introductionmentioning

confidence: 99%

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Synergistic Evolution of PM2.5 and O3 Concentrations: Evidence from Environmental Kuznets Curve Tests in the Yellow River Basin

Qi,

Miao,

Xie

et al. 2024

Sustainability

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Air pollution, especially the synergistic pollution of PM2.5 and O3, poses a severe threat to human life and production. The synergistic formation mechanism of PM2.5 and O3 pollution is relatively confirmed, while research on their spatiotemporal synergy is urgent. Based on remotely sensed interpretation data from 90 cities in the Yellow River Basin, we analyzed the synergistic evolution of PM2.5 and O3 concentrations during 2013–2020. Combined with the environmental Kuznets curve, we performed factor analysis using a panel regression model. The synergistic evolution pattern shows a gradual decrease in PM2.5 concentration and a gradual increase in O3 concentration. There is a strong spatial auto-correlation in the two pollutants’ concentrations. The relationship between economy and PM2.5 concentration shows an “N-shaped” curve, while that between O3 concentration and economic development presents an inverse “N-shaped” curve. The environmental Kuznets curve shows that the deterioration of O3 pollution takes place later than the mitigation of PM2.5 pollution. Various factors have obvious heterogeneous effects on PM2.5 and O3 concentrations. Meanwhile, the sensitivity effect of per capita GDP on PM2.5 concentration in the midstream region is stronger than that in the upstream region, while the sensitivity effect of per capita GDP on O3 concentration is strongest in the midstream region than that in upstream and downstream region.

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Section: Spatial Auto-correlation Characteristicmentioning

confidence: 99%

Section: Spatial Auto-correlation Characteristicmentioning

confidence: 99%

Section: Spatial Auto-correlation Characteristicmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Synergistic Evolution of PM2.5 and O3 Concentrations: Evidence from Environmental Kuznets Curve Tests in the Yellow River Basin

Qi,

Miao,

Xie

et al. 2024

Sustainability

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show abstract

“…Nowadays, air pollution has become the most important environmental problem in the world [1][2][3][4], and for China, PM 2.5 and O 3 are the two main air pollutants restricting the improvement of air quality in China [5,6]. PM 2.5 pollution harms human health and ecosystems and affects climate change [7,8].…”

Section: Introductionmentioning

confidence: 99%

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

Self Cite

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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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How does the impact of a two-stage air pollution control policy on air quality different? Evidence from 258 cities in China

Li,

He,

Liang

et al. 2024

Atmospheric Pollution Research

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Spatiotemporal patterns and quantitative analysis of influencing factors of PM2.5 and O3 pollution in the North China Plain

Cited by 7 publications

References 82 publications

Synergistic Evolution of PM2.5 and O3 Concentrations: Evidence from Environmental Kuznets Curve Tests in the Yellow River Basin

Synergistic Evolution of PM2.5 and O3 Concentrations: Evidence from Environmental Kuznets Curve Tests in the Yellow River Basin

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

How does the impact of a two-stage air pollution control policy on air quality different? Evidence from 258 cities in China

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