Nonlinear relationship between air pollution and precursor emissions in Qingdao, eastern China

doi:10.1007/s11783-025-1929-3

Front. Environ. Sci. Eng.

2025, Vol. 19

Issue (1) : 9 https://doi.org/10.1007/s11783-025-1929-3

Nonlinear relationship between air pollution and precursor emissions in Qingdao, eastern China

Na Zhao, Yuqiang Zhang(

), Likun Xue(

)

Environment Research Institute, Shandong University, Qingdao 266237, China

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Abstract

Exploring the nonlinear relationship between air pollution and precursor emissions in Qingdao, eastern China is crucial for improving air quality. We simulated 32 emission reduction scenarios based on different volatile organic compound (VOC) and nitrogen oxide (NO_x) emission reduction ratios using the Weather Research and Forecasting-Comprehensive Air Quality Model Extensions model. The emission reduction of VOCs was beneficial for reducing fine particulate matter (PM_2.5) concentration in January and ozone (O₃) concentration in June. However, NO_x must be reduced by at least 48% and 70% to decrease PM_2.5 and O₃ concentrations, respectively, when VOCs are not reduced. The responses of PM_2.5 and O₃ concentrations to emission reductions from different sources were also evaluated. The reduction in VOC emissions from different sources decreased the PM_2.5 concentration in January, and O₃ concertation in June, while NO_x reduction resulted in an increase. Controlling VOC emissions from industry has a positive effect on improving local PM_2.5 and O₃, while the emission reductions of NO_x from transportation and industry are not conducive to reducing PM_2.5 and O₃ concentrations. The synergistic emission reduction pathways for NO_x and VOCs during PM_2.5 and O₃ combined pollution were also analyzed. The VOC and NO_x emission reductions were beneficial for reducing the comprehensive Air Quality Index (sAQI) values. When the NO_x emission reduction was large, the sAQI improvement gradually exceeded that of VOCs. A collaborative optimization path should be adopted that focuses on controlling VOCs first, and further control of combined pollution should depend on the deep reduction of NO_x.

Keywords PM_2.5 O₃ Emission reduction Nonlinear relationship WRF-CAMx

Corresponding Author(s): Yuqiang Zhang,Likun Xue

Issue Date: 21 November 2024

Cite this article:

Na Zhao,Yuqiang Zhang,Likun Xue. Nonlinear relationship between air pollution and precursor emissions in Qingdao, eastern China[J]. Front. Environ. Sci. Eng., 2025, 19(1): 9.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-025-1929-3
https://academic.hep.com.cn/fese/EN/Y2025/V19/I1/9

Tab.1 Description of simulation scenarios

Fig.1 Comparison of air pollutant concentrations and meteorological parameters between observed and simulated data during January and June, 2019 in Qingdao, China. (a)–(f) represent PM_2.5, O₃, temperature, humidity, wind speed, and sea-level pressure, respectively. The blue and red circles represent January and June, respectively. R: correlative coefficient. NMB: normalized mean bias. NME: normalized mean error.

Fig.2 Relationship between (a) PM_2.5 concentrations in January 2019 and (b) O₃ concentrations in June 2019 with different NO_x and VOC emissions. The white dashed line refers to a smooth curve formed by connecting all concentration turning points.

Fig.3 Responses of (a) PM_2.5 concentrations in January 2019 and (b) O₃ concentrations in June 2019 to VOCs and NO_x emission reductions. Positive and negative values represent an increase and a decrease in concentrations, respectively.

Fig.4 Responses of (a–b) PM_2.5 and (c–d) O₃ concentrations to VOCs and NO_x emission reductions under different emission reduction scenarios during PM_2.5 and O₃ combined pollution. Note: The x-axis in Figs. 4(b) and (d) represents the total reduction ratio for VOCs and NO_x with a maximum ratio of 200%, such as the emission reduction ratio of VOCs and NO_x was 1:1 and the each with a 100% reduction.

Fig.5 Response of sAQI to VOCs and NO_x emission reductions under different emission reduction scenarios during PM_2.5 and O₃ combined pollution.

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