Evaluating the Governing Factors of Variability in Nocturnal Boundary Layer Height Based on Elastic Lidar in Wuhan

Retrieval of the planetary boundary layer height from lidar measurements by a deep-learning method based on the wavelet covariance transform

Understanding and characterization of the planetary boundary layer (PBL) are of great importance in terms of air pollution management, weather forecasting, modelling of climate change, etc. Although many lidar-based approaches have been proposed for the retrieval of the PBL height (PBLH) in case studies, development of a robust lidar-based algorithm without human intervention is still of great challenging. In this work, we have demonstrated a novel deep-learning method based on the wavelet covariance transform (WCT) for the PBLH evaluation from atmospheric lidar measurements. Lidar profiles are evaluated according to the WCT with a series of dilation values from 200 m to 505 m to generate 2-dimensional wavelet images. A large number of wavelet images and the corresponding PBLH-labelled images are created as the training set for a convolutional neural network (CNN), which is implemented based on a modified VGG16 (VGG - Visual Geometry Group) convolutional neural network. Wavelet images obtained from lidar profiles have also been prepared as the test set to investigate the performance of the CNN. The PBLH is finally retrieved by evaluating the predicted PBLH-labelled image and the wavelet coefficients. Comparison studies with radiosonde data and the Micro-Pulse-Lidar Network (MPLNET) PBLH product have successfully validated the promising performance of the deep-learning method for the PBLH retrieval in practical atmospheric sensing.

Bagged Tree Model to Retrieve Planetary Boundary Layer Heights by Integrating Lidar Backscatter Profiles and Meteorological Parameters

The planetary boundary layer (PBL) is the part of the troposphere in which the soil’s influence is noticeable. It plays an important role in the fields of air pollution, meteorology, weather forecasting, and climate. Continuous observation of lidar makes obtaining the day–night PBL height (PBLH) with a high temporal resolution possible. A high-precision PBLH retrieval method is the key to achieving this goal. In this study, we propose a new method based on a bagged tree model to retrieve the PBLH from micro-lidar backscatter profiles. With the radiosonde measurements taken as the true reference, lidar features (the ten maximum slopes identified by the maximum gradient method) and four meteorological parameters (atmospheric pressure, temperature, relative humidity, and wind speed) serve as characteristic variables. The PBLH retrieval model is evaluated using a 10-fold cross-validation (CV) method and then compared with the four traditional methods (i.e., maximum gradient, maximum standard deviation, wavelet covariance, and the ideal profile method). The correlation coefficient (R) between the retrieved PBLHs and the radiosonde measurements is 0.89, which is much bigger than the R (0.2–0.48) from the four traditional methods. Moreover, the root mean square error and mean absolute error for the retrieved PBLH are 0.3 km and 0.2 km, respectively, which are lower than those of the four traditional methods (0.5~0.6 km for RMSE and 0.4–0.5 for MAE). Cases with different conditions show that this new method is almost undisturbed by cloud and suspended/thick aerosol layers. It can also be used to retrieve shallow PBL in cases in which using traditional methods would be difficult. Long-term analysis of averaged PBLHs retrieved by the proposed model from 2013 to 2016 shows that the hourly PBLH rises at sunrise and sets at sunset, and that the monthly PBLH in summer is higher than that in winter. The results suggest that the proposed method is better than the four traditional methods and available for use in conditions such as existing cloud layers and multiple-layers.

Variations in Nocturnal Residual Layer Height and Its Effects on Surface PM2.5 over Wuhan, China

Large amounts of aerosols remain in the residual layer (RL) after sunset, which may be the source of the next day’s pollutants. However, the characteristics of the nocturnal residual layer height (RLH) and its effect on urban environment pollution are unknown. In this study, the characteristics of the RLH and its effect on fine particles with diameters <2.5 μm (PM2.5) were investigated using lidar data from January 2017 to December 2019. The results show that the RLH is highest in summer (1.55 ± 0.55 km), followed by spring (1.40 ± 0.58 km) and autumn (1.26 ± 0.47 km), and is lowest in winter (1.11 ± 0.44 km). The effect of surface meteorological factors on the RLH were also studied. The correlation coefficients (R) between the RLH and the temperature, relative humidity, wind speed, and pressure were 0.38, −0.18, 0.15, and −0.36, respectively. The results indicate that the surface meteorological parameters exhibit a slight correlation with the RLH, but the high relative humidity was accompanied by a low RLH and high PM2.5 concentrations. Finally, the influence of the RLH on PM2.5 was discussed under different aerosol-loading periods. The aerosol optical depth (AOD) was employed to represent the total amount of pollutants. The results show that the RLH has an effect on PM2.5 when the AOD is small but has almost no effect on PM2.5 when the AOD is high. In addition, the R between the nighttime mean RLH and the following daytime PM2.5 at low AOD is −0.49, suggesting that the RLH may affect the following daytime surface PM2.5. The results of this study have a guiding significance for understanding the interaction between aerosols and the boundary layer.

Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism

Air pollution over China has attracted wide interest from public and academic community. PM_2.5 is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM_2.5 concentrations are essential to understand the variability of PM_2.5 and seek methods to control PM_2.5. Since 2013, the measurement of PM_2.5 has been widely made at 1436 stations across the country and more than 300 papers focusing on PM_2.5-meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM_2.5 concentrations_. We start with an introduction of general meteorological conditions and PM_2.5 concentrations across China, and then seasonal and spatial variations of meteorological influences on PM_2.5 concentrations. Next, major methods used to quantify meteorological influences on PM_2.5 concentrations are checked and compared. We find that causality analysis methods are more suitable for extracting the influence of individual meteorological factors whilst statistical models are good at quantifying the overall effect of multiple meteorological factors on PM_2.5 concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM_2.5 concentrations by considering anthropogenic emissions and the transport and evolution of pollutants. We then comprehensively examine the mechanisms how major meteorological factors may impact the PM_2.5 concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM_2.5. The feedback effects of PM_2.5 concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM_2.5 pollution are made finally.

Boundary Layer Height as Estimated from Radar Wind Profilers in Four Cities in China: Relative Contributions from Aerosols and Surface Features

The turbulent mixing and dispersion of air pollutants is strongly dependent on the vertical structure of the wind, which constitutes one of the major challenges affecting the determination of boundary layer height (BLH). Here, an adaptive method is proposed to estimate BLH from measurements of radar wind profilers (RWPs) in Beijing (BJ), Nanjing (NJ), Chongqing (CQ), and Wulumuqi (WQ), China, during the summer of 2019. Validation against simultaneous BLH estimates from radiosondes (RSs) yielded a correlation coefficient of 0.66, indicating that the method can be used to derive BLH from RWPs. Diurnal variations of BLH and the ventilation coefficient (VC) at four sites were then examined. A distinct diurnal cycle of BLH was observed over all four cities; BLH gradually increased from sunset, reached a maximum in the afternoon, and then dropped sharply after sunset. The maximum hourly average BLH (1.426 ± 0.46 km) occurred in WQ, consistent with the maximum hourly mean VC larger than 5000 m2/s observed there. By comparison, the diurnal variation of VC was not strong, with values ranging between 2000 and 3000 m2/s, likely owing to the high-humidity environment. Furthermore, surface sensible heat flux, latent heat flux, and dry mass of particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5) concentrations were found to somehow affect the vertical structure of wind and thermodynamic features, leading to a difference between RS and RWP BLH estimates. This indicates that the atmospheric environment can affect BLH estimates using RWP data. The BLH results from RWPs were better in some specific cases. These findings show great potential of RWP measurements in air quality research, and will provide key data references for policy-making toward emission reductions.

Roles of Relative Humidity in Aerosol Pollution Aggravation over Central China during Wintertime

Aerosol pollution elicits considerable public concern due to the adverse influence on air quality, climate change, and human health. Outside of emissions, haze formation is closely related to meteorological conditions, especially relative humidity (RH). Partly due to insufficient investigations on the aerosol hygroscopicity, the accuracy of pollution prediction in Central China is limited. In this study, taking Wuhan as a sample city, we investigated the response of aerosol pollution to RH during wintertime based on in-situ measurements. The results show that, aerosol pollution in Wuhan is dominated by PM_2.5 (aerodynamic particle size not larger than 2.5 μm) on wet days (RH ≥ 60%), with the averaged mass fraction of 0.62 for PM₁₀. Based on the RH dependence of aerosol light scattering (f (RH)), aerosol hygroscopicity was evaluated and shows the high dependence on the particle size distribution and chemical compositions. f (RH = 80%) in Wuhan was 2.18 (±0.73), which is comparable to that measured in the Pearl River Delta and Yangtze River Delta regions for urban aerosols, and generally greater than values in Beijing. Ammonium (NH₄⁺), sulfate (SO₄²⁻), and nitrate (NO₃⁻) were enhanced by approximately 2.5-, 2-, and 1.5-fold respectively under wet conditions, and the ammonia-rich conditions in wintertime efficiently promoted the formation of SO₄²⁻ and NO₃⁻, especially at high RH. These secondary ions play an important role in aggravating the pollution level and aerosol light scattering. This study has important implications for understanding the roles of RH in aerosol pollution aggravation over Central China, and the fitted equation between f (RH) and RH may be helpful for pollution forecasting in this region.

Detection of the planetary boundary layer height by employing the Scheimpflug lidar technique and the covariance wavelet transform method

A portable Scheimpflug lidar system has been employed for atmospheric boundary layer studies. Atmospheric backscattering signals were continuously recorded from 21 August to 28 August 2018. The covariance wavelet transform (CWT) method was utilized to identify the maximum gradient of recorded lidar curves as the planetary boundary layer (PBL) height. As the directly retrieved PBL height could be underestimated or overestimated due to the presence of residual layers and thin clouds, localized atmospheric turbulence, aerosol stratification, etc., a CWT-based quality-control algorithm has also been developed to improve the reliability of the PBL height retrieval. The temporal distribution of the final PBL height has shown a clear diurnal variation as the ambient temperature changed due to the increasing and decreasing of surface heating during a one-week continuous measurement campaign. The promising results have shown great potential in employing the Scheimpflug lidar technique and the CWT-based method in the determination of the PBL height.

Two-stage model for estimating the spatiotemporal distribution of hourly PM1.0 concentrations over central and east China

Widespread and severe PM_1.0 (particulate matter ≤1.0 μm) pollution in China has a significant negative influence on human health. However, knowledge of the regional spatiotemporal distribution of PM_1.0 has been hindered by sparsely distributed PM_1.0 concentration data. In this work, a two-stage model (linear mixed effect-bagged tree model) was proposed for estimating hourly PM_1.0 pollution levels from July 2015 to June 2017 over central and east China by using Himawari-8 aerosol products and coincident geographic data, meteorology, and site-based PM_1.0 concentrations from ground monitoring network. The cross-validation for the developed model displayed R² and mean absolute error value of 0.80 and 9.3 μg/m³, respectively. Validation demonstrated that the model accurately estimated hourly PM_1.0 concentrations with high R² of 0.63-0.85 and low bias of 8.7-10.1 μg/m³. The estimated PM_1.0 concentrations on daily scale showed peaks with PM_1.0 of 36.9 ± 8.4 μg/m³ at rush hours during daytime. Seasonal distribution displayed that summer was cleanest with an average PM_1.0 of 20.9 ± 6.8 μg/m³ and winter was the most polluted season with an average PM_1.0 of 45.6 ± 16.8 μg/m³. These results indicated that the proposed satellite-based model can estimate reliable spatial distribution of PM_1.0 concentrations over a large-scale region.