Vertical Distribution Characteristics of PM2.5 Observed by a Mobile Vehicle Lidar in Tianjin, China in 2016

Lyu, Liqun; Dong, Yunsheng; Liu, Cheng; Liu, Wenqing; Xie, Zhouqing; Yan, Xiang; Zhang, Yi; Chen, Zhenyi; Fan, G. J.; Zhang, Leibo; Liu, Yang; Shi, Yongpeng; Shu, Xiaowen

doi:10.1007/s13351-018-7068-z

Cited by 20 publications

(11 citation statements)

References 17 publications

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“…The situation is even more complex in spring season, as mineral dust transports frequently occur, adding to the local anthropogenic aerosols, which makes this region an interesting study area. One previous study using mobile lidar measurements has been conducted in Tianjin, in different seasons in 2016 [7]. Nevertheless, this is the first time mobile observations with such rich instrumentation and methodology, combining remote sensing and in situ measurements, are conducted in Tianjin and in the Binhai New Area.…”

Section: Case Study: 17 May 2017mentioning

confidence: 99%

Mobile Observations by Lidar, Sun Photometer and in Situ in North China Plain

et al. 2020

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Section: Case Study: 17 May 2017mentioning

confidence: 99%

Mobile Observations by Lidar, Sun Photometer and in Situ in North China Plain

et al. 2020

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“…Additionally, it can be used as a mobile lidar system to conduct supplementary observation in areas with no lidar assembled. In the past few years, several vehiclebased observation experiments have been carried out (Lv et al, 2017;Lyu et al, 2018), but research aimed at multi-lidar observation and the effect of the vertical structure of meteorological factors to the distribution of particles had been a largely underexplored domain, especially in the GBA. The former research revealed that pollution of particulate matter frequently occurs in the western part of inland regions of GBA (Fang et al, 2019), affecting downstream cities under the northerly wind field.…”

Section: Introductionmentioning

confidence: 99%

Measurement report: Vehicle-based and In Situ Multi-lidar Observational Study on the Effect of Meteorological Elements on the Three-dimensional Distribution of Particles in the Western Guangdong–Hong Kong–Macao Greater Bay Area

Xie

et al. 2021

Preprint

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Abstract. The distribution of meteorological elements has always been an important factor in determining the horizontal and vertical distribution of particles in the atmosphere. To study the effect of meteorological elements on the three-dimensional distribution structure of particles, mobile vehicle lidar observations, and in situ observations were presented in the western Guangdong–Hong Kong–Macao Greater Bay Area of China during September and October of 2019 and 2020. Vertical aerosol extinction coefficient, depolarization ratio, wind and temperature profiles were measured by using a micro pulse lidar, a Raman scattering lidar, and a Doppler wind profile lidar installed on a mobile monitoring vehicle. The mechanism of how wind and temperature in the boundary layer affects the horizontal and vertical distribution of particles was analyzed. The result showed that particles were mostly distributed in downstream areas on days with moderate wind speed in the boundary layer, while they presented homogeneously on days with weaker wind. There are three typical types of vertical distribution of particles in the western Guangdong–Hong Kong–Macao Greater Bay Area (GBA): surface single layer, elevated single layer, and double layer. Analysis of wind profiles and Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) backward trajectory revealed different sources of particles for the three types. Particles concentrated near the temperature inversion and multiple inversions could cause more than one peak in the extinction coefficient profile. There are two mechanisms that affected the distribution of particulate matter in the upper and lower boundary layers. Based on observational study, a general model of meteorological elements affecting the vertical distribution of urban particulate matter was made.

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“…This way can observe a continuous variation of PM2.5 profiles near the surface, while the accuracy of the simulation results need to be improved through field observations. The last method is using lidar or ceilometer to measure the aerosol extinction coefficient (EC) profile, and then retrieve the PM2.5 profile based on the EC profile (Lv et al, 2017;Lyu et al, 2018). Owing to their continuous and large-scale (by changing inclination and https://doi.org/10.5194/acp-2021-726 Preprint.…”

mentioning

confidence: 99%

“…A series studies have been conducted to estimate the PM2.5 concentration profile from aerosol EC profile measured by lidar system (Tao et al, 2016;Lyu et al, 2018;Liu et al, 2019;Panahifar et al, 2020). Tao et al (2016) obtained the vertical distribution of PM2.5 mass concentration based on the EC observed by charge-coupled device side-scatter lidar and surface PM2.5 concentrations.…”

mentioning

confidence: 99%

“…Tao et al (2016) obtained the vertical distribution of PM2.5 mass concentration based on the EC observed by charge-coupled device side-scatter lidar and surface PM2.5 concentrations. Lyu et al (2018) used the EC profile measured by a mobile lidar to retrieved the PM2.5 concentration profile in different seasons at Tianjin. Liu et al, (2019) studied the vertical distribution and TF of PM2.5 based on lidar and Doppler wind radar observations.…”

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confidence: 99%

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Estimation of the vertical distribution of particle matter (PM2.5) concentration and its transport flux from lidar measurements based on machine learning algorithms

Zhu

et al. 2021

Preprint

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Abstract. The vertical distribution of aerosol extinction coefficient (EC) measured by lidar system 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 inducing 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, including 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 show 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 Location Time (LT) in 0.2–0.8 km. The diurnal variations of the TF shows a clear conveyor belt at approximately 12:00–18:00 LT in 0.5–0.8 km. These results indicated that air pollutants transport over Wuhan mainly occurs at approximately 12:00–18:00 LT in 0.5–0.8 km. The TF near the ground usually have 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 of atmospheric profile and provide us sufficient confidence to apply lidar in the study of air quality monitoring.

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Vertical Distribution Characteristics of PM2.5 Observed by a Mobile Vehicle Lidar in Tianjin, China in 2016

Cited by 20 publications

References 17 publications

Mobile Observations by Lidar, Sun Photometer and in Situ in North China Plain

Mobile Observations by Lidar, Sun Photometer and in Situ in North China Plain

Measurement report: Vehicle-based and In Situ Multi-lidar Observational Study on the Effect of Meteorological Elements on the Three-dimensional Distribution of Particles in the Western Guangdong–Hong Kong–Macao Greater Bay Area

Estimation of the vertical distribution of particle matter (PM2.5) concentration and its transport flux from lidar measurements based on machine learning algorithms

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