Trends and Variability of Ozone Pollution over the Mountain-Basin Areas in Sichuan Province during 2013–2020: Synoptic Impacts and Formation Regimes

Parrish²,

et al. 2022

Atmos. Chem. Phys.

Abstract. In the past decade, ozone (O3) pollution has become a severe environmental problem in China's major cities. Here, based on available observational records, we investigated the long-term trend of O3 pollution in China during 2014–2020. The O3 concentrations were slightly higher in urban areas than in non-urban areas. During these 7 years, the highest O3 concentrations primarily occurred during summer in northern China, and during autumn or spring in southern China. Although O3 precursors, including nitrogen oxides (NOx) and carbon monoxide (CO), continuously decreased, O3 concentrations generally increased throughout the 7 years with a slower increasing rate after 2017. The long-term trend of O3 concentrations differed across seasons, especially from 2019 to 2020, when O3 concentrations decreased in summer and increased in winter. To analyse the causes of this observed trend, a photochemical box model was used to investigate the change in the O3 sensitivity regime in two representative cities – Beijing and Shanghai. Our model simulations suggest that the summertime O3 sensitivity regime in urban areas of China has changed from a VOC-limited regime to a transition regime during 2014–2020. By 2020, the urban photochemistry was in a transition regime in summer but in a VOC-limited regime in winter. This study helps to understand the distinct trends of O3 in China and provides insights into efficient future O3 control strategies in different regions and seasons.

Section: Introductionmentioning

confidence: 99%

Long-term trend of ozone pollution in China during 2014–2020: distinct seasonal and spatial characteristics and ozone sensitivity

Parrish²,

et al. 2022

Atmos. Chem. Phys.

“…There were 4 cities with O3 concentrations less than 70 μg m -3 , including Guangyuan (69.26 μg m -3 ), Bazhong (67.03 μg m -3 ), A'ba (66.43 μg m -3 ) and Ganzi (69.22 μg m -3 ). The spatial distribution characteristics of O3 concentrations might be affected by complex terrain, special climate conditions and the distribution of precursors in Sichuan Province (Yang et al, 2020;Chen et al, 2021). In addition, the average values of O3 concentrations in Chengdu and Meishan were much higher than those in other cities in Sichuan Province, possibly due to the high traffic density and local emissions.…”

Section: Results and Analysismentioning

confidence: 99%

“…Due to intense emissions, complex terrain, and special meteorological conditions, Sichuan Province in southwestern China suffers from severe ozone pollution in recent years (Yang et al, 2020;Chen et al, 2021). In this study, our study region covers the entire Sichuan Province (97~109°E and 26~35°N), including 21 prefecture-level cites.…”

Section: Study Area and Datamentioning

confidence: 99%

“…O3 has gradually become a primary pollutant in the atmosphere (Wang et al, 2019a;Shu et al, 2019;Xu et al, 2021;Li et al, 2021). As one of the most important detection method to provide both surface ozone and vertical ozone structure, ozonesonde has been successfully developed by Chinese scholars and used to conduct long-term observation at local site (Zhang et al, 2014(Zhang et al, , 2021.…”

Section: Accepted Manuscript 1 Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Machine-Learning-Based Classification Method for Meteorological Conditions of Ozone Pollution

Cao

Zhao²,

et al. 2023

Aerosol Air Qual. Res.

Ozone pollution is harmful to human health and ecosystem, which occurs in ecosystems and has occurred frequently in China in recent years, especially during the warm seasons.Meteorological conditions are among the important factors affecting the occurrence of ozone pollution. In this study, a classification method for meteorological conditions of ozone pollution levels based on a back propagation (BP) neural network was proposed to reflect the impact of meteorological conditions on the occurrence of ozone pollution. Ozone pollution was divided into three levels according to surface hourly ozone (O3) concentrations and thus into three groups of meteorological conditions. The input physical parameters for the BP neural network were determined by evaluating the relationship between surface O3 concentrations and meteorological parameters and precursors, including relative humidity, temperature, mixing layer height, precipitation, and nitrogen dioxide (NO2) concentrations. The study area focused on 21 cities in Sichuan Province in southwestern China, which was divided into 12 BP classifiers according to the urban geographical location and sample number of each city, and a single BP classifier was trained for 21 cities. The classification results of the trained BP classifiers were verified by comparison to the observations. With 12 individual BP classifiers, the classification accuracy of all 21 cities was more than 60%, of which 18 cities were more than 70%, and 9 cities were more than 80%. With the single BP classifier, the classification accuracy of 20 cities was more than 60%, of which 18 cities were more than 70%, and 14 cities were more than 80%. Overall, the classification performance of the trained single model was better than trained 12 individual models. The classification method can comprehensively reflect the impact of meteorological conditions on the occurrence of ozone pollution.

“…O 3 formation sensitivity is nonlinearly dependent on precursor concentrations and is usually categorized into three photochemical regimes: NOx limited, VOC limited, and VOC/NOx transitional. − Over the past few decades, various methods have been applied to evaluate the O 3 formation sensitivity, and these methods can be classified into emission-based methods (such as the Weather Research and Forecasting and Community Multiscale Air Quality (WRF–CMAQ) model) ,,− and observation-based methods (including the observation-based box model OBM, − satellite remote sensing observation, , and photochemical indicator , ) based on various data sources . Previous research on the sensitivity of O 3 –NOx–VOCs revealed that urban O 3 formation is generally in VOC-limited ,− regime.…”

Section: Introductionmentioning

confidence: 99%

Evolution of Ozone Formation Sensitivity during a Persistent Regional Ozone Episode in Northeastern China and Its Implication for a Control Strategy

Zhang,

Gao,

Zhu

et al. 2023

Environ. Sci. Technol.

In recent years, the magnitude and frequency of regional ozone (O 3 ) episodes have increased in China. We combined ground-based measurements, observation-based model (OBM), and the Weather Research and Forecasting and Community Multiscale Air Quality (WRF−CMAQ) model to analyze a typical persistent O 3 episode that occurred across 88 cities in northeastern China during June 19−30, 2021. The meteorological conditions, particularly the wind convergence centers, played crucial roles in the evolution of O 3 pollution. Daily analysis of the O 3 formation sensitivity showed that O 3 formation was in the volatile organic compound (VOC)-limited or transitional regime at the onset of the pollution episode in 92% of the cities. Conversely, it tended to be or eventually became a NOxlimited regime as the episode progressed in the most polluted cities. Based on the emission-reduction scenario simulations, mitigation of the regional O 3 pollution was found to be most effective through a phased control strategy, namely, reduction of a high ratio of VOCs to NOx at the onset of the pollution and lower ratio during evolution of the O 3 episode. This study presents a new possibility for regional O 3 pollution abatement in China based on a reasonable combination of OBM and the WRF−CMAQ model.