Characteristics of aerosol within the nocturnal residual layer and its effects on surface PM2.5 over China

Shi, Yifan; Liu, Boming; Chen, Shihua; Gong, Wei; Ma, Yingying; Zhou, Ming; Jin, Shikuan; Jin, Yinbao

doi:10.1016/j.atmosenv.2020.117841

Cited by 18 publications

(12 citation statements)

References 53 publications

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“…The MERRA-2 PBLH is diagnosed from a turbulence parameterization module based on the thermal eddy diffusion coefficient in the general atmospheric circulation model (Jordan et al, 2010). Compared to the PBLHs obtained by other observation techniques (e.g., retrievals from micropulse lidar, radiosondes, and CALIOP), several previous analyses have demonstrated that MERRA-2 is sufficiently robust to capture the daily, monthly, seasonal, and annual variations in regional PBLHs (Jordan et al, 2010;Su et al, 2017Su et al, , 2018.…”

Section: Extraction Of Pbl Aod From Column Aodmentioning

confidence: 99%

Three-dimensional climatology, trends, and meteorological drivers of global and regional tropospheric type-dependent aerosols: insights from 13 years (2007–2019) of CALIOP observations

et al. 2021

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Abstract. Globally gridded aerosol extinction data from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) during 2007–2019 are utilized to investigate the three-dimensional (3D) climatological distribution of tropospheric type-dependent aerosols and to identify the trends in column aerosol optical depth (AOD), partitioned within different altitude regimes, and their meteorological drivers. Using detection samples of layer aerosols, we also yield a 3D distribution of the frequency of occurrence (FoO) of aerosol subtypes classified by CALIOP. The results show that the aerosol extinction coefficient (AEC) shows contrasting vertical distribution patterns over land and ocean, with the former possessing significant geographical dependence, while the enhancement of AEC in the latter is mainly located below 1 km. The vertical structures of the type-dependent AECs, however, are strongly dependent on altitude. When the total AOD (TAOD) is partitioned into the planetary boundary layer (PBL) and the free troposphere (FT), results demonstrate that the PBL and FT contribute 62.08 % and 37.92 %, respectively, of the global tropospheric TAOD averaged over daytime and nighttime. Yet this CALIOP-based partitioning of the different aerosol subtypes in the PBL and FT varies significantly. Among all 12 typical regions of interest analyzed, more than 50 % of TAOD is located in the lower troposphere (0–2 km), while the contribution is less than 2 % above 6 km. In global average terms, we found the aerosol FoO averaged over all layers is 4.45 %, with the largest contribution from “clean marine” (1.79 %) and the smallest from “clean continental” (0.05 %). Overall, the FoO vertical structures of the aerosol layer exhibit a distribution pattern similar to that of AEC. The resulting trend analyses show that CALIOP accurately captures significant regional anomalies in TAOD, as observed in other satellite measurements and aerosol reanalysis. Our correlation analysis between meteorological factors and TAOD suggests the interannual variability of TAOD is related to the variability of precipitation (PPT), volumetric soil moisture (VSM), and wind speed (WS) in the particular regions. For instance, the positive TAOD trend over the equatorial central Pacific is mainly attributable to the increased PPT and decreased WS. In contrast, in dry convective regions dominated by dust and smoke, the interannual variability/trend in TAOD is largely modified by the VSM driven by the PPT. Additionally, we further found that these significant regional correlations are more robust within the PBL and significantly weakened or even reversed within the FT. This highlights the superiority of using the TAOD partitioned within the PBL as a proxy variable for the widely applied TAOD to explore the relationships between atmospheric pollution and meteorology.

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Section: Extraction Of Pbl Aod From Column Aodmentioning

confidence: 99%

Three-dimensional climatology, trends, and meteorological drivers of global and regional tropospheric type-dependent aerosols: insights from 13 years (2007–2019) of CALIOP observations

et al. 2021

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“…Moreover, under the influence of large-scale dynamic forcing and land surface processes, wind speed and direction will vary dramatically, both temporally and spatially, which poses a large challenge for models to simulate or forecast the variation in wind very well (Weissmann et al, 2007;Michelson and Bao, 2008;Constantinescu et al, 2009). Particularly, the winds in the atmospheric boundary layer are mostly turbulent and hard to be well reproduced by models without assimilation of wind observations (Belmonte and Stoffelen, 2019;Benjamin et al, 2004;Simonin et al, 2014;Liu et al, 2017;Stoffelen et al, 2019). Therefore, continuous global wind profile observations are of great significance for advancing our knowledge of atmospheric dynamics as well as for improving the accuracy of numerical weather prediction (Stoffelen et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Technical note: First comparison of wind observations from ESA's satellite mission Aeolus and ground-based radar wind profiler network of China

et al. 2021

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Abstract. Aeolus is the first satellite mission to directly observe wind profile information on a global scale. After implementing a set of bias corrections, the Aeolus data products went public on 12 May 2020. However, Aeolus wind products over China have thus far not been evaluated extensively by ground-based remote sensing measurements. In this study, the Mie-cloudy and Rayleigh-clear wind products from Aeolus measurements are validated against wind observations from the radar wind profiler (RWP) network in China. Based on the position of each RWP site relative to the closest Aeolus ground tracks, three matchup categories are proposed, and comparisons between Aeolus wind products and RWP wind observations are performed for each category separately. The performance of Mie-cloudy wind products does not change much between the three matchup categories. On the other hand, for Rayleigh-clear and RWP wind products, categories 1 and 2 are found to have much smaller differences compared with category 3. This could be due to the RWP site being sufficiently approximate to the Aeolus ground track for categories 1 and 2. In the vertical, the Aeolus wind products are similar to the RWP wind observations, except for the Rayleigh-clear winds in the height range of 0–1 km. The mean absolute normalized differences between the Mie-cloudy (Rayleigh-clear) and the RWP wind components are 3.06 (5.45), 2.79 (4.81), and 3.32 (5.72) m/s at all orbit times and ascending and descending Aeolus orbit times, respectively. This indicates that the wind products for ascending orbits are slightly superior to those for descending orbits, and the observation time has a minor effect on the comparison. From the perspective of spatial differences, the Aeolus Mie-cloudy winds are consistent with RWP winds in most of east China, except in coastal areas where the Aeolus Rayleigh-clear winds are more reliable. Overall, the correlation coefficient R between the Mie-cloudy (Rayleigh-clear) wind and RWP wind component observation is 0.94 (0.81), suggesting that Aeolus wind products are in good agreement with wind observations from the RWP network in China. The findings give us sufficient confidence in assimilating the newly released Aeolus wind products in operational weather forecasting in China.

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“…TF is an important parameter to measure the horizontal transmission of pollutants (Y. Shi et al, 2020). In this study, the TF is determined by the WS and the PM 2.5 concentrations in the area under analysis.…”

Section: Calculation Methods Of Transport Fluxmentioning

confidence: 99%

“…Aerosol is a suspension of fine solid particles or liquid droplets in air (Hinds, 1999;Chen et al, 2014;Fan et al, 2019;Huang et al, 2019). In recent decades, with the anthropogenic aerosol emissions increasing in China, the concentration of fine particle matter with a diameter of less than 2.5 µm (PM 2.5 ) in the atmosphere has increased significantly (Ding et al, 2016;Shi et al, 2020;Jin et al, 2021). Moreover, the high concentrations of PM 2.5 cause haze frequently and reduce atmospheric visibility, directly affecting the ecological environment and human health (Huang et al, 2014;He et al, 2020;Yin et al, 2021;Raaschou-Nielsen et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Estimation of the vertical distribution of particle matter (PM<sub>2.5</sub>) concentration and its transport flux from lidar measurements based on machine learning algorithms

Zhu

Liu

et al. 2021

Atmos. Chem. Phys.

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Abstract. The vertical distribution of aerosol extinction coefficient (EC) measured by lidar systems has been used to retrieve the profile of particle matter with a diameter <2.5 µm (PM2.5). However, the traditional linear model (LM) cannot consider the influence of multiple meteorological variables sufficiently and then induce the low inversion accuracy. Generally, the machine learning (ML) algorithms can input multiple features which may provide us with a new way to solve this constraint. In this study, the surface aerosol EC and meteorological data from January 2014 to December 2017 were used to explore the conversion of aerosol EC to PM2.5 concentrations. Four ML algorithms were used to train the PM2.5 prediction models: random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) and extreme gradient boosting decision tree (XGB). The mean absolute error (root mean square error) of LM, RF, KNN, SVM and XGB models were 11.66 (15.68), 5.35 (7.96), 7.95 (11.54), 6.96 (11.18) and 5.62 (8.27) µg/m3, respectively. This result shows that the RF model is the most suitable model for PM2.5 inversions from EC and meteorological data. Moreover, the sensitivity analysis of model input parameters was also conducted. All these results further indicated that it is necessary to consider the effect of meteorological variables when using EC to retrieve PM2.5 concentrations. Finally, the diurnal and seasonal variations of transport flux (TF) and PM2.5 profiles were analyzed based on the lidar data. The large PM2.5 concentration occurred at approximately 13:00–17:00 local time (LT) in 0.2–0.8 km. The diurnal variations of the TF show a clear conveyor belt at approximately 12:00–18:00 LT in 0.5–0.8 km. The results indicated that air pollutant transport over Wuhan mainly occurs at approximately 12:00–18:00 LT in 0.5–0.8 km. The TF near the ground usually has the highest value in winter (0.26 mg/m2 s), followed by the autumn and summer (0.2 and 0.19 mg/m2 s, respectively), and the lowest value in spring (0.14 mg/m2 s). These findings give us important information on the atmospheric profile and provide us sufficient confidence to apply lidar in the study of air quality monitoring.

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Characteristics of aerosol within the nocturnal residual layer and its effects on surface PM2.5 over China

Cited by 18 publications

References 53 publications

Three-dimensional climatology, trends, and meteorological drivers of global and regional tropospheric type-dependent aerosols: insights from 13 years (2007–2019) of CALIOP observations

Three-dimensional climatology, trends, and meteorological drivers of global and regional tropospheric type-dependent aerosols: insights from 13 years (2007–2019) of CALIOP observations

Technical note: First comparison of wind observations from ESA's satellite mission Aeolus and ground-based radar wind profiler network of China

Estimation of the vertical distribution of particle matter (PM<sub>2.5</sub>) concentration and its transport flux from lidar measurements based on machine learning algorithms

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Characteristics of aerosol within the nocturnal residual layer and its effects on surface PM2.5 over China

Cited by 18 publications

References 53 publications

Three-dimensional climatology, trends, and meteorological drivers of global and regional tropospheric type-dependent aerosols: insights from 13 years (2007–2019) of CALIOP observations

Three-dimensional climatology, trends, and meteorological drivers of global and regional tropospheric type-dependent aerosols: insights from 13 years (2007–2019) of CALIOP observations

Technical note: First comparison of wind observations from ESA's satellite mission Aeolus and ground-based radar wind profiler network of China

Estimation of the vertical distribution of particle matter (PM&lt;sub&gt;2.5&lt;/sub&gt;) concentration and its transport flux from lidar measurements based on machine learning algorithms

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Product

Resources

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Estimation of the vertical distribution of particle matter (PM<sub>2.5</sub>) concentration and its transport flux from lidar measurements based on machine learning algorithms