Assessing the Impact of Lockdown on Atmospheric Ozone Pollution Amid the First Half of 2020 in Shenyang, China

Wang, Liyuan; Ju, Wang; Fang, Chunsheng

doi:10.3390/ijerph17239004

Cited by 11 publications

(8 citation statements)

References 56 publications

(50 reference statements)

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“…At the same time, the ozone content increased despite reduced precursor gas concentrations. Ozone increase by 69.7% was noted in Shenyang (China) [ 8 ], by 42% in Great Britain [ 9 ], and by 14 to 27% in European cities [ 10 ]. This effect is quite justified considering the distortion of the balance in the NO–O 2 –NO 2 triad [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Surface Ozone Concentration in Russia in the Second Half of 2020

et al. 2021

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We present information on ozone concentration in the surface air layer in the second half of 2020. Data were obtained at 13 stations located in different regions of Russia. We estimated the excess over hygienic standards of the Russian Federation, both in the second half of the year and throughout 2020. It is shown that the daily average maximum permissible concentration of ozone is regularly exceeded at all stations. There are cases of exceeding the one-time maximum permissible concentration.

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Section: Introductionmentioning

confidence: 99%

Surface Ozone Concentration in Russia in the Second Half of 2020

et al. 2021

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“…Strong northerly winds in winter and spring rapidly import polluted air masses from the Beijing–Tianjin–Hebei region upstream into Liaoning Province. Rapids transport and continuous water vapor accumulation due to the difference in pressure between land and incoming sea in summer favors the transport and stagnation of pollutants over Liaoning Province [ 44 ].…”

Section: Resultsmentioning

confidence: 99%

Spatial Characteristics and Regional Transmission Analysis of PM2.5 Pollution in Northeast China, 2016–2020

Fang

Wang

et al. 2021

IJERPH

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Northeast China is an essential industrial development base in China and the regional air quality is severely affected by PM2.5 pollution. In this paper, spatial autocorrelation, trajectory clustering, hotspot analysis, PSCF and CWT analysis are used to explore the spatial pollution characteristics of PM2.5 and determine the atmospheric regional transmission pattern for 40 cities in Northeast China from 2016 to 2020. Analysis of PM2.5 concentration characteristics in the northeast indicates that the annual average value and total exceedance days of PM2.5 concentration in Northeast China showed a U-shaped change, with the lowest annual average PM2.5 concentration (31 μg/m3) in 2018, decreasing by 12.1% year-on-year, and the hourly PM2.5 concentration exploding during the epidemic lockdown period in 2020. A stable PM2.5 pollution band emerges spatially from the southwest to Northeast China. Spatially, the PM2.5 in Northeast China has a high degree of autocorrelation and a south-hot–north-cool characteristic, with all hotspots concentrated in the most polluted Liaoning province, which exhibits the H–H cluster pattern and hotspot per year. Analysis of the air mass trajectories, potential source contributions and concentration weight trajectories in Northeast China indicates that more than 74% of the air mass trajectories were transmitted to each other between the three heavily polluted cities, with the highest mean value of PM2.5 pollution trajectories reaching 222.4 μg/m3, and the contribution of daily average PM2.5 concentrations exceeding 60 μg/m3 within Northeast China. Pollution of PM2.5 throughout the Northeast is mainly influenced by short-range intra-regional transport, with long-range transport between regions also being an essential factor; organized integration is the only fundamental solution to air pollution.

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“…The dominant wind direction in Changchun and Harbin is southwest followed by northwest, and the dominant wind direction in Shenyang is southward followed by northeast. The results show that one of the influential factors causing pollution in the northeast region may be the transfer of pollutants from other surrounding areas to the downwind region due to atmospheric flow, while higher wind speeds on clean days may also serve to disperse pollutants, which may also reduce pollutant concentrations in the northeast region [20].…”

Section: Relationship Between Meteorological Factors and Pm25 And O3mentioning

confidence: 99%

“…To facilitate the analysis of pollutant migration paths, we used the stepwise cluster analysis (SCA) algorithm to cluster the backward trajectories with some optimization [19,20]. The clustering analysis process is shown by Equations ( 1)-(3): In the above equation, i is the number of trajectories; j is the number of passing points; t is the movement time of airflow; d j is the distance between the jth point of two trajectories; X is the number of trajectories in the cluster; D is the distance between trajectories, so D ij indicates the distance from the jth passing point in the ith trajectory to the corresponding point on the average trajectory; SPVAR is the spatial variation of each group of trajectories; and TSV is the total spatial variation.…”

Section: Backward Trajectory Clustering Analysismentioning

confidence: 99%

Regional Air Pollutant Characteristics and Health Risk Assessment of Large Cities in Northeast China

Fang

Gao

et al. 2021

Atmosphere

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This study systematically investigated the pollution characteristics of atmospheric O3 and PM2.5, regional transport, and their health risks in three provincial capitals in northeast China during 2016–2020. The results show that O3 concentrations showed a trend of high summer and low winter, while PM2.5 concentrations showed a trend of high winter and low summer during these five years. The results of the correlation analysis indicate that external sources contribute more O3, while PM2.5 is more from local sources. The backward trajectory clustering analysis results showed that Changchun had the highest share of northwest trajectory with a five-year average value of 67.89%, and the city with the highest percentage of southwest trajectory was Shenyang with a five-year average value of 23.95%. The backward trajectory clustering analysis results showed that the share of the northwest trajectory decreased and the share of the southwest trajectory increased for all three cities in 2020 compared to 2016. The results of the potential source contribution function (PSCF) and concentration weighting trajectory (CWT) analysis showed that the main potential source areas and high concentration contribution areas for PM2.5 in the northeast were concentrated in Mongolia, Inner Mongolia, Shandong Province, and the northeast, and for O3 were mainly located in Shandong, Anhui, and Jiangsu Provinces, and the Yellow Sea and Bohai Sea. The non-carcinogenic risk of PM2.5 in Harbin was high with a HQ of 2.04, while the other cities were at acceptable levels (HQ < 0.69) and the non-carcinogenic risk of O3 was acceptable in all three cities (HQ < 0.22). However, PM2.5 had a high carcinogenic risk (4 × 10−4 < CR < 0.44) and further treatment is needed to reduce the risk.

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Assessing the Impact of Lockdown on Atmospheric Ozone Pollution Amid the First Half of 2020 in Shenyang, China

Cited by 11 publications

References 56 publications

Surface Ozone Concentration in Russia in the Second Half of 2020

Surface Ozone Concentration in Russia in the Second Half of 2020

Spatial Characteristics and Regional Transmission Analysis of PM2.5 Pollution in Northeast China, 2016–2020

Regional Air Pollutant Characteristics and Health Risk Assessment of Large Cities in Northeast China

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