Distinct spatiotemporal variation patterns of surface ozone in China due to diverse influential factors

Минг, Ма; Yao, Guangchun; Guo, Jianping; Bai, Kaixu

doi:10.1016/j.jenvman.2021.112368

Cited by 43 publications

(26 citation statements)

References 53 publications

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“…[9][10][11][12][13][14] Thus, PM 2.5 and O 3 pollution are the two most important air pollutants restricting the improvement of air quality in China. 6,14 O 3 and the secondary components of PM 2.5 are both produced by atmospheric chemical reactions, and the precursors are essential factors of these two kinds of pollution. The O 3 precursors include nitrogen oxides (NO X ; NO þ NO 2 ) and volatile organic compounds (VOCs) and those of PM 2.5 include NO X (NO þ NO 2 ) and SO X (SO 2 ).…”

Section: Introductionmentioning

confidence: 99%

Resolving contributions of NO2 and SO2 to PM2.5 and O3 pollutions in the North China Plain via multi-task learning

Ma,

Liu,

Liu

et al. 2023

J. Appl. Rem. Sens.

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It is of great significance to explore the spatial-temporal variations and estimate the relative importance of the influencing factors of PM 2.5 and O 3 pollution. The study established nationwide surface O 3 , NO 2 , and SO 2 estimation models using the extreme gradient boosting model and the data fusion method. The cross-validation results indicated that the forecasted models performed well (R-values from 0.86 to 0.95). The results revealed that the pollution levels of O 3 , PM 2.5 , NO 2 , and SO 2 in the North China Plain (NCP) were the highest in China. Subsequently, a multi-task learning model was utilized to estimate the relative importance of influential factors on the PM 2.5 and O 3 pollution in the NCP. The sensitivity analysis results indicated that the O 3 pollution from 2010-2020 in the NCP was susceptible to meteorological factors such as ultraviolet radiation and temperature, as well as anthropogenic precursors such as NO X , and PM 2.5 pollution in the NCP was constrained by both meteorological factors (44.62%) and anthropogenic emissions (16.86%). The impact of NO 2 on PM 2.5 pollution was similar to its impact on O 3 pollution; therefore, the importance of NO 2 emission reduction to PM 2.5 pollution is as important as that of O 3 pollution, whereas the impact of SO 2 on PM 2.5 was much greater than its impact on O 3 pollution, so SO 2 emission reduction is more important for PM 2.5 .

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Section: Introductionmentioning

confidence: 99%

Resolving contributions of NO2 and SO2 to PM2.5 and O3 pollutions in the North China Plain via multi-task learning

Ma,

Liu,

Liu

et al. 2023

J. Appl. Rem. Sens.

Self Cite

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“…Zhao et al (2018) [ 54 ] pointed out that the PM 2.5 could directly transport from one city to another city, while Tibetan Plateau may be an important source region of high ozone in Sichuan Basin of southwest China. Ma et al (2021b) [ 55 ] illustrated that the increase in volatile organic compounds (VOCs) along with depletions in NO 2 and CO significantly boosted the ozone photochemical production in North China plain. Wang et al (2021) [ 56 ] showed that the annual summertime ozone over Central China was significantly correlated with the springtime thermal forcing, indicated by total atmospheric energy over Tibetan Plateau in an interdecadal timescale.…”

Section: Discussionmentioning

confidence: 99%

Multi-Year Variation of Ozone and Particulate Matter in Northeast China Based on the Tracking Air Pollution in China (TAP) Data

Zhao

Gui

et al. 2022

IJERPH

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With the rapid development of economy and urbanization acceleration, ozone (O3) pollution has become the main factor of urban air pollution in China after particulate matter. In this study, 90th percentile of maximum daily average (MDA) 8 h O3 (O3-8h-90per) and PM2.5 data from the Tracking Air Pollution in China (TAP) dataset were used to determine the mean annual, seasonal, monthly, and interannual distribution of O3-8h-90per and PM2.5 concentrations in Northeast China (NEC). The O3-8h-90per concentration was highest in Liaoning (>100 μg/m3), whereas the highest PM2.5 concentration was observed mainly in urban areas of central Liaoning and the Harbin–Changchun urban agglomeration (approximately 60 μg/m3). The O3-8h-90per concentrations were highest in spring and summer due to more intense solar radiation. On the contrary, the PM2.5 concentration increased considerably in winter influenced by anthropogenic activities. In May and June, the highest monthly mean O3-8h-90per concentrations were observed in central and western Liaoning, about 170–180 μg/m3, while the PM2.5 concentrations were the highest in January, February, and December, approximately 100 μg/m3. The annual mean O3-8h-90per concentration in NEC showed an increasing trend, while the PM2.5 concentration exhibited an annual decline. By 2020, the annual mean O3-8h-90per concentration in southern Liaoning had increased considerably, reaching 120–130 μg/m3. From the perspective of city levels, PM2.5 and O3-8h-90per also showed an opposite variation trend in the 35 cities of NEC. The reduced tropospheric NO2 column is consistent with the decreasing trend of the interannual PM2.5, while the increased surface temperature could be the main meteorological factor affecting the O3-8h-90per concentration in NEC. The results of this study enable a comprehensive understanding of the regional and climatological O3-8h-90per and PM2.5 distribution at distinct spatial and temporal scales in NEC.

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“…BLH affects the diffusion space of pollutants in a vertical direction [ 36 ]. Wind usually disperses concentrations from the emission sources [ 37 ]. Temperature has an effect on the process of precursor generation [ 38 ], and precipitation hints unstable atmospheric conditions [ 39 ].…”

Section: Methodsmentioning

confidence: 99%

New Deep Learning Model to Estimate Ozone Concentrations Found Worrying Exposure Level over Eastern China

Wang

Jiang

et al. 2022

IJERPH

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Ozone (O3), whose concentrations have been increasing in eastern China recently, plays a key role in human health, biodiversity, and climate change. Accurate information about the spatiotemporal distribution of O3 is crucial for human exposure studies. We developed a deep learning model based on a long short-term memory (LSTM) network to estimate the daily maximum 8 h average (MDA8) O3 across eastern China in 2020. The proposed model combines LSTM with an attentional mechanism and residual connection structure. The model employed total O3 column product from the Tropospheric Monitoring Instrument, meteorological data, and other covariates as inputs. Then, the estimates from our model were compared with real observations of the China air quality monitoring network. The results indicated that our model performed better than other traditional models, such as the random forest model and deep neural network. The sample-based cross-validation R2 and RMSE of our model were 0.94 and 10.64 μg m−3, respectively. Based on the O3 distribution over eastern China derived from the model, we found that people in this region suffered from excessive O3 exposure. Approximately 81% of the population in eastern China was exposed to MDA8 O3 > 100 μg m−3 for more than 150 days in 2020.

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Distinct spatiotemporal variation patterns of surface ozone in China due to diverse influential factors

Cited by 43 publications

References 53 publications

Resolving contributions of NO2 and SO2 to PM2.5 and O3 pollutions in the North China Plain via multi-task learning

Resolving contributions of NO2 and SO2 to PM2.5 and O3 pollutions in the North China Plain via multi-task learning

Multi-Year Variation of Ozone and Particulate Matter in Northeast China Based on the Tracking Air Pollution in China (TAP) Data

New Deep Learning Model to Estimate Ozone Concentrations Found Worrying Exposure Level over Eastern China

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