Dust aerosol properties and radiative forcing observed in spring during 2001–2014 over urban Beijing, China

Yu, Xiangyao; Lü, Rui; Kumar, K. Raghavendra; Ma, Jianzhong; Zhang, Qiuju; Jiang, Yilun; Na, Kang; Yang, Shuangyan; Wang, Jing; Li, Mei

doi:10.1007/s11356-016-6727-9

Cited by 60 publications

(43 citation statements)

References 46 publications

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“…Compared with other regions in China (Table 3), the seasonal variations of IADRE over YRB are consistent with previous research. For example, the 24 h-averaged ADRE values in Nanjing based on the RTM were −21.9 Wm −2 , −30.0 Wm −2 and −31.5 Wm −2 during autumn, summer and spring, respectively [15]; the model-retrieved ADREs in Beijing were in the range of −66 Wm −2 to −116 Wm −2 and −13 Wm −2 to −23 Wm −2 for dust days and non-dust days, respectively [16]. (Table 2) are all negative, there are still few positive IADRE values (Figure 12) over 1° × 1° grid cells, probably due to the comprehensive effect of aerosol optical properties and surface reflectivity.…”

Section: Frequency Distribution Of Temporal Variationmentioning

confidence: 96%

“…Besides, efforts have been also made for estimating the ADRE through satellite remote sensing or model-simulation (i.e., radiative transfer model (RTM)). For the model-simulated method, aerosol optical properties obtained from ground-based or remote-sensing observations have been used as input to the RTM to calculate ADRE at TOA and surface [11][12][13][14][15][16]. For the remote sensing method, one is to estimate ADRE by the linear relationship between AOD and concurrent upward SW flux at TOA obtained from satellite observations [17][18][19][20][21][22]; the other is to take Fclr as the minimum upward SW flux at TOA in clear sky during one month, but it is not suitable for the regions with high cloud coverage or aerosol loading such as the study region (YRB) [4].…”

Section: Introductionmentioning

confidence: 99%

“…It triggers numerous studies for analyzing the aerosol optical and radiative properties in different regions of China. For instance, Sundström et al [22] estimated ADRE at TOA using a linear regression of MODIS AOD and CERES SWF in Middle-East China; Xia et al [14] analyzed the spatiotemporal distributions of aerosol optical and radiative properties at TOA and surface through ground-based data of 21 sites in China; Kang et al [15] and Yu et al [16] also reported the aerosol optical properties and ADREs based on RTM for Nanjing and Beijing, respectively. In general, previous studies in China mainly focused on the regions with intensive population and frequent industrial activities (short-term analyses), for example, Pearl River Delta (PRD), Yangtze River Delta (YRD) and Beijing-Tianjin-Hebei region.…”

Section: Introductionmentioning

confidence: 99%

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Aerosol Optical Properties and Associated Direct Radiative Forcing over the Yangtze River Basin during 2001–2015

Wang

Lin

et al. 2017

Remote Sensing

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Abstract:The spatiotemporal variation of aerosol optical depth at 550 nm (AOD 550 ), Ångström exponent at 470-660 nm (AE ), water vapor content (WVC), and shortwave (SW) instantaneous aerosol direct radiative effects (IADRE) at the top-of-atmosphere (TOA) in clear skies obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Clouds and the Earth's Radiant Energy System (CERES) are quantitatively analyzed over the Yangtze River Basin (YRB) in China during [2001][2002][2003][2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014][2015]. The annual and seasonal frequency distributions of AE and AOD 550 reveal the dominance of fine aerosol particles over YRB. The regional average AOD 550 is 0.49 ± 0.31, with high value in spring (0.58 ± 0.35) and low value in winter (0.42 ± 0.29). The higher AOD 550 (≥0.6) is observed in midstream and downstream regions of YRB and Sichuan Basin due to local anthropogenic emissions and long-distance transport of dust particles, while lower AOD 550 (≤0.3) is in high mountains of upstream regions. The IADRE is estimated using a linear relationship between SW upward flux and coincident AOD 550 from CERES and MODIS at the satellite passing time. The regional average IADRE is −35.60 ± 6.71 Wm −2 , with high value (−40.71 ± 6.86 Wm −2 ) in summer and low value (−29.19 ± 7.04 Wm −2 ) in winter, suggesting a significant cooling effect at TOA. The IADRE at TOA is lower over Yangtze River Delta (YRD) (≤−30 Wm −2 ) and higher in midstream region of YRB, Sichuan Basin and the source area of YRB (≥−45 Wm −2 ). The correlation coefficient between the 15-year monthly IADRE and AOD 550 values is 0.63, which confirms the consistent spatiotemporal variation patterns over most of the YRB. However, a good agreement between IADRE and AOD is not observed in YRD and the source area of YRB, which is probably due to the combined effects of aerosol and surface properties.

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Section: Frequency Distribution Of Temporal Variationmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Aerosol Optical Properties and Associated Direct Radiative Forcing over the Yangtze River Basin during 2001–2015

Wang

Lin

et al. 2017

Remote Sensing

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“…Meanwhile, Eck et al [44] and Wang et al [45] evaluate the changes of aerosol optical properties in coarse/fine modes by discussing the aerosol types. Yu et al [46] presented the dominance of fine particles, which are more scattering particles, by studying the characteristics of aerosol optical properties in spring during 2001-2014 over urban Beijing. Wang et al [47] pointed out that nitrates and sulfates were the major aerosol types on haze days in Beijing, which is consistent with the conclusion draw by Li [28].…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Aerosol Types in Beijing and the Associations with Air Pollution from 2004 to 2015

Zhao

Wang

et al. 2017

Remote Sensing

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Abstract:With the fast development of the economy and expansion, a large number of people have concentrated in Beijing over the past few decades, leading to the result that Beijing has become home to one of the most complex mixtures of aerosol types in the world. The various aerosol types play different roles in the determination of global climate change, visibility, and human health. However, to the best of our knowledge, research has rarely analyzed the correlation between aerosol types and air quality index (AQI) in Beijing (urban and suburban) over a long-term series of observations. Therefore, in this study, we aim to identify and discuss the different aerosol types and AQI in Beijing from 2004 to 2015. The aerosol types are classified into six categories: dust, mixed, highly-absorbing, moderately-absorbing, slightly-absorbing, and scattering by a multiple clustering method with the fine mode fraction (FMF) and single scattering albedo (SSA) data of retrievals from the global Aerosol Robotic Network (AERONET) sun photometer sites. The AQI levels: are good (0-50); moderate (51-100); unhealthy for sensitive groups (101-150); unhealthy (151-200); very unhealthy (201-300); and hazardous (>300). The results show that a significant FMF variability occurred among different seasons in Beijing, with maximum values present in spring and minimum values in winter. The SSA values exhibit variation, with small fluctuations from season to season. In the case of BJ station, the scattering aerosols are more frequent in summer (39%) and less in winter (1%), while the coarse particles (dust) are more frequent in spring (18%) and less in autumn (6%). In contrast, the absorbing aerosols (especially slightly-absorbing) are more frequent in summer (35%) and winter (15%). However, the mixed aerosol types are more frequent in spring (38%) and less in summer (8%). There is a similar seasonal variation in XH. In the past 12 years, the slightly-absorbing aerosol type in Beijing has increased by approximately 14%, which is believed to be due to the rapid development of industrial cities. In addition, comparing the urban and suburban regions, the slightly-absorbing aerosol type is the dominant aerosol type in both areas. Furthermore, to identify the dominant aerosol types which lead to air pollution, a related analysis was carried out by analyzing different aerosol types and the relationship between aerosol types and AQI. The results indicate that the air pollution was strongly correlated to slightly-absorbing aerosols, in which the percentage of slightly-absorbing aerosols was about 49% during the hazardous days in 2013-2015, and the correlation between AQI and aerosol types is also strong (R 2 = 0.76 and 0.97, in Beijing and Xianghe).

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“…In the spring season the atmosphere is typically loaded with a mixture of urban and dust aerosols (Yu et al, 2016). The OMI pixels that were analysed are located on rows 23-29 across the orbit on the first case day (i.e.…”

Section: Beijing Area On 16 and 27 April 2008mentioning

confidence: 99%

Aerosol-type retrieval and uncertainty quantification from OMI data

Kauppi¹,

Kolmonen²,

Laine³

et al. 2017

Atmos. Meas. Tech.

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Abstract. We discuss uncertainty quantification for aerosoltype selection in satellite-based atmospheric aerosol retrieval. The retrieval procedure uses precalculated aerosol microphysical models stored in look-up tables (LUTs) and top-of-atmosphere (TOA) spectral reflectance measurements to solve the aerosol characteristics. The forward model approximations cause systematic differences between the modelled and observed reflectance. Acknowledging this model discrepancy as a source of uncertainty allows us to produce more realistic uncertainty estimates and assists the selection of the most appropriate LUTs for each individual retrieval. This paper focuses on the aerosol microphysical model selection and characterisation of uncertainty in the retrieved aerosol type and aerosol optical depth (AOD). The concept of model evidence is used as a tool for model comparison. The method is based on Bayesian inference approach, in which all uncertainties are described as a posterior probability distribution. When there is no single best-matching aerosol microphysical model, we use a statistical technique based on Bayesian model averaging to combine AOD posterior probability densities of the best-fitting models to obtain an averaged AOD estimate. We also determine the shared evidence of the best-matching models of a certain main aerosol type in order to quantify how plausible it is that it represents the underlying atmospheric aerosol conditions. The developed method is applied to Ozone Monitoring Instrument (OMI) measurements using a multiwavelength approach for retrieving the aerosol type and AOD estimate with uncertainty quantification for cloud-free over-land pixels. Several larger pixel set areas were studied in order to investigate the robustness of the developed method. We evaluated the retrieved AOD by comparison with ground-based measurements at example sites. We found that the uncertainty of AOD expressed by posterior probability distribution reflects the difficulty in model selection. The posterior probability distribution can provide a comprehensive characterisation of the uncertainty in this kind of problem for aerosoltype selection. As a result, the proposed method can account for the model error and also include the model selection uncertainty in the total uncertainty budget.

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Dust aerosol properties and radiative forcing observed in spring during 2001–2014 over urban Beijing, China

Cited by 60 publications

References 46 publications

Aerosol Optical Properties and Associated Direct Radiative Forcing over the Yangtze River Basin during 2001–2015

Aerosol Optical Properties and Associated Direct Radiative Forcing over the Yangtze River Basin during 2001–2015

Characteristics of Aerosol Types in Beijing and the Associations with Air Pollution from 2004 to 2015

Aerosol-type retrieval and uncertainty quantification from OMI data

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