Temporal characteristics of atmospheric CO2 in urban Nanjing, China

Huang, Xianyu; Wang, Tijian; Talbot, R. W.; Xie, Min; Mao, Huiting; Shu, Li; Zhuang, Bingliang; Yang, Xiu‐Qun; Fu, Congbin; Zhu, Jialei; Huang, Xing; Xu, Ronghui

doi:10.1016/j.atmosres.2014.09.007

Cited by 33 publications

(12 citation statements)

References 39 publications

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“…The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) Model (Version 4) was developed by the National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory (ARL). It is one of the most extensively used atmospheric transport and dispersion models for the study of air parcel trajectories (Draxler and Rolph, 2013;Rolph et al, 2017;Stein et al, 2015), and it has been widely applied in simulations of the complex transport, diffusion, chemical transformation, and deposition processes of atmospheric pollutants (Mcgowan and Clark, 2008;Huang et al, 2015;Xie et al, 2016b).…”

Section: Hysplit Modelmentioning

confidence: 99%

Regional severe particle pollution and its association with synoptic weather patterns in the Yangtze River Delta region, China

Shu¹,

Xie²,

Gao³

et al. 2017

Preprint

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Abstract. Regional air pollution is significantly associated with the dominant weather systems. In this study, the relationship between the particle pollution over the Yangtze River Delta (YRD) region and the weather patterns is investigated. Firstly, the pollution characteristics of particles (PM2.5 and PM10) in YRD are studied by using the in situ monitoring data in 16 cities from December 2013 to November 2014. The results show that the annual average concentrations in the cities of Jiangsu Province all exceed the national air quality standard. The pollution level is higher in the inland areas. Highest values can be found in Nanjing, with the concentrations of PM2.5 and PM10 being 79&#8201;&#956;g&#8201;&#183;&#8201;m&#8722;3 and 130&#8201;&#8201;&#956;g&#8201;&#183;&#8201;m&#8722;3, respectively. The PM2.5/PM10 ratios are usually high in YRD, indicating that PM2.5 is the overwhelmingly dominant particle pollutant. The wintertime peak of particle concentrations is tightly linked to the increased emissions in the heating season and the poor meteorological condition. Secondly, based on NCEP reanalysis data, synoptic weather classification is conducted to reveal that the weather patterns are easy to cause heavy pollution in YRD. Five typical synoptic patterns are objectively identified, including the East Asian trough rear pattern, the depression&#160;inverted&#160;trough pattern, the transversal trough pattern, the high-pressure controlled pattern, and the northeast cold vortex pattern. Finally, synthetic analysis of meteorological fields and backward trajectory calculation are used to further clarify how these patterns impact particle concentrations. It is clarified that YRD is largely influenced by polluted air masses from the northern and the southern inland areas when it is at the rear of the East Asian major trough. In this case, the strong northwest wind hinders the vertical outward transport of pollutants. Thus, the East Asian trough rear pattern is quite favorable for the accumulation of pollutants in YRD, and respectively contributes 70.4&#8201;% and 78.3&#8201;% to the occurrence of large-scale regional PM2.5 and PM10 pollution episodes. While under the weather systems for other patterns, the clean marine air masses may play great roles in the mitigation of particle pollution in YRD. The correlation between weather patterns and particle pollution can provide valuable views in the decision-making on pollution control and mitigation strategies.

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Section: Hysplit Modelmentioning

confidence: 99%

Regional severe particle pollution and its association with synoptic weather patterns in the Yangtze River Delta region, China

Shu¹,

Xie²,

Gao³

et al. 2017

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Some scholars have done meaningful related research in this regard [25,29,33]. However, the study periods or spatial scales are limited [34,35]. In addition, most scholars focus their research on accuracy/uncertainty analysis [5,36,37], and some studies compared the difference of CO 2 concentration in the same area from different satellites [31,37] or investigated the effect of a single carbon source/sink on the concentration of CO 2 ; for example, the studies of CO 2 emissions from anthropogenic activities [32,38], the impact of vegetable absorption [14,39] and the estimated CO 2 emissions from forest fires [10,40].…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Variations of Atmospheric CO2 Concentration in China and Its Influencing Factors

Shi

Zang

et al. 2020

Atmosphere

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Over the past few decades, concentrations of carbon dioxide (CO2), a key greenhouse gas, have risen at a global rate of approximately 2 ppm/a. China is the largest CO2 emitter and is the principle contributor to the increase in global CO2 levels. Based on a satellite-retrieved atmospheric carbon dioxide column average dry air mixing ratio (XCO2) dataset, derived from the greenhouse gas observation satellite (GOSAT), this paper evaluates the spatial and temporal variations of XCO2 characteristics in China during 2009–2016. Moreover, the factors influencing changes in XCO2 were investigated. Results showed XCO2 concentrations in China increased at an average rate of 2.28 ppm/a, with significant annual seasonal variations of 6.78 ppm. The rate of change of XCO2 was greater in south China compared to other regions across China, with clear differences in seasonality. Seasonal variations in XCO2 concentrations across China were generally controlled by vegetation dynamics, characterized by the Normalized Difference Vegetation Index (NDVI). However, driving factors exhibited spatial variations. In particular, a distinct belt (northeast–southwest) with a significant negative correlation (r < −0.75) between XCO2 and NDVI was observed. Furthermore, in north China, human emissions were identified as the dominant influencing factor of total XCO2 variations (r > 0.65), with forest fires taking first place in southwest China (r > 0.47). Our results in this study can provide us with a potential way to better understand the spatiotemporal changes of CO2 concentration in China with NDVI, human activity and biomass burning, and could have an enlightening effect on slowing the growth of CO2 concentration in China.

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Section: Hysplit Modelmentioning

confidence: 99%

Regional severe particle pollution and its association with synoptic weather patterns in the Yangtze River Delta region, China

et al. 2017

Self Cite

View full text Add to dashboard Cite

Abstract. Regional air pollution is significantly associated with dominant weather systems. In this study, the relationship between the particle pollution over the Yangtze River Delta (YRD) region and weather patterns is investigated. First, the pollution characteristics of particles in the YRD are studied using in situ monitoring data (PM 2.5 and PM 10 ) in 16 cities and Terra/MODIS AOD (aerosol optical depth) products collected from December 2013 to November 2014. The results show that the regional mean value of AOD is high in the YRD, with an annual mean value of 0.71 ± 0.57. The annual mean particle concentrations in the cities of Jiangsu Province all exceed the national air quality standard. The pollution level is higher in inland areas, and the highest concentrations of PM 2.5 and PM 10 are 79 and 130 µg m −3 , respectively, in Nanjing. The PM 2.5 : PM 10 ratios are typically high, thus indicating that PM 2.5 is the overwhelmingly dominant particle pollutant in the YRD. The wintertime peak of particle concentrations is tightly linked to the increased emissions during the heating season as well as adverse meteorological conditions. Second, based on NCEP (National Center for Environmental Prediction) reanalysis data, synoptic weather classification is conducted and five typical synoptic patterns are objectively identified. Finally, the synthetic analysis of meteorological fields and backward trajectories are applied to further clarify how these patterns impact particle concentrations. It is demonstrated that air pollution is more or less influenced by high-pressure systems. The relative position of the YRD to the anti-cyclonic circulation exerts significant effects on the air quality of the YRD. The YRD is largely influenced by polluted air masses from the northern and the southern inland areas when it is located at the rear of the East Asian major trough. The significant downward motion of air masses results in stable weather conditions, thereby hindering the diffusion of air pollutants. Thus, this pattern is quite favorable for the accumulation of pollutants in the YRD, resulting in higher regional mean PM 10 (116.5 ± 66.9 µg m −3 ), PM 2.5 (75.9 ± 49.9 µg m −3 ), and AOD (0.74) values. Moreover, this pattern is also responsible for the occurrence of most large-scale regional PM 2.5 (70.4 %) and PM 10 (78.3 %) pollution episodes. High wind speed and clean marine air masses may also play important roles in the mitigation of pollution in the YRD. Especially when the clean marine air masses account for a large proportion of all trajectories (i.e., when the YRD is affected by the cyclonic system or oceanic circulation), the air in the YRD has a lesser chance of being polluted. The observed correlation between weather patterns and particle pollution can provide valuable insight into making decisions about pollution control and mitigation strategies.

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Temporal characteristics of atmospheric CO2 in urban Nanjing, China

Cited by 33 publications

References 39 publications

Regional severe particle pollution and its association with synoptic weather patterns in the Yangtze River Delta region, China

Regional severe particle pollution and its association with synoptic weather patterns in the Yangtze River Delta region, China

Spatial and Temporal Variations of Atmospheric CO2 Concentration in China and Its Influencing Factors

Regional severe particle pollution and its association with synoptic weather patterns in the Yangtze River Delta region, China

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