The height of the atmospheric boundary layer (ABLH) or the mixing layer height (MLH) is a key parameter characterizing the planetary boundary layer, and the accurate estimation of that is critically important for boundary layer related studies, which include air quality forecasts and numerical weather prediction. Aerosol lidar is a powerful remote sensing instrument frequently used to retrieve the ABLH through detecting the vertical distributions of aerosol concentration. Presently available methods for ABLH determination from aerosol lidar are summarized in this review, including a lot of classical methodologies as well as some improved versions of them. Some new recently developed methods applying advanced techniques such as image edge detection, as well as some new methods based on multi-wavelength lidar systems, are also summarized. Although a lot of techniques have been proposed and have already given reasonable results in several studies, it is impossible to recommend a technique which is suitable in all atmospheric scenarios. More accurate instantaneous ABLH from robust techniques is required, which can be used to estimate or improve the boundary layer parameterization in the numerical model, or maybe possible to be assimilated into the weather and environment models to improve the simulation or forecast of weather and air quality in the future.

Accurate estimation of the atmospheric boundary layer height (ABLH) is critically important and it mainly relies on the detection of the vertical profiles of atmosphere variables (temperature, humidity,’ and horizontal wind speed) or aerosols. Aerosol Lidar is a powerful remote sensing instrument frequently used to retrieve ABLH through the detection of the vertical distribution of aerosol concentration. A challenge is that cloud, residual layer (RL), and local signal structure seriously interfere with the lidar measurement of ABLH. A new objective technique presenting as giving a top limiter altitude is introduced to reduce the interference of RL and cloud layer on ABLH determination. Cloud layers are identified by looking for the rapid increase and sharp attenuation of the signal combined with the relative increase in the signal. The cloud layers whether they overlay the ABL are classified or are decoupled from the ABL are classified by analyzing the continuity of the signal below the cloud base. For cloud layer capping of the ABL, the limiter is determined to be the altitude where a positive signal gradient first occurs above the cloud upper edge. For a cloud that is decoupled from the ABL, the cloud base is considered to be the altitude limiter. For RL in the morning, the altitude limiter is the greatest positive gradient altitude below the RL top. The ABLH will be determined below the top limiter altitude using Haar wavelet (HM) and the curve fitting method (CFM). Besides, the interference of local signal noise is eliminated through consideration of the temporal continuity. While comparing the lidar-determined ABLH by HM (or CFM) and nearby radiosonde measurements of the ABLH, a reasonable concordance is found with a correlation coefficient of 0.94 (or 0.96) and 0.79 (or 0.74), presenting a mean of the relative absolute differences with respect to radiosonde measurements of 10.5% (or 12.3%) and 22.3% (or 17.2%) for cloud-free and cloudy situations, respectively. The diurnal variations in the ABLH determined from HM and CFM on four selected cases show good agreement with a mean correlation coefficient higher than 0.99 and a mean absolute bias of 0.22 km. Also, the determined diurnal ABLH are consistent with surface turbulent kinetic energy (TKE) combined with the time-height distribution of the equivalent potential temperature.

Groundwater pollution in the Pingshuo mining area is strongly associated with mining activities, with heavy metals (HMs) representing predominant pollutants. To obtain accurate information about the pollution status and health risks of groundwater, 189 groups of samples were collected from four types of groundwater, during three periods of the year, and analyzed for HMs. The results showed that the concentration of HMs in groundwater was higher near the open pit, waste slag pile, riverfront area, and human settlements. Except for Ordovician groundwater, excessive HMs were found in all investigated groundwater of the mining area, as compared with the standard thresholds. Fe exceeded the threshold in 13–75% of the groundwater samples. Three sources of HMs were identified and quantified by Pearson’s correlation analysis and the PMF model, including coal mining activities (68.22%), industrial, agricultural, and residential chemicals residue and leakage (16.91%), and natural sources (14.87%). The Nemerow pollution index revealed that 7.58% and 100% of Quaternary groundwater and mine water samples were polluted. The health risk index for HMs in groundwater showed that the non-carcinogenic health risk ranged from 0.18 to 0.42 for adults, indicating an acceptable level. Additionally, high carcinogenic risks were identified in Quaternary groundwater (95.45%), coal series groundwater (91.67%), and Ordovician groundwater (26.67%). Both carcinogenic and non-carcinogenic risks were greater for children than adults, highlighting their increased vulnerability to HMs in groundwater. This study provides a scientific foundation for managing groundwater quality and ensuring drinking water safety in mining areas.

The present study investigates the influences of stratospheric quasi-biennial oscillation (QBO) and El Niño–Southern Oscillation (ENSO) on the intensity of stratospheric isentropic mixing based on ERA-Interim and MERRA-2 reanalysis products. It is found that isentropic mixing in the stratosphere is modulated by QBO and ENSO. An analysis of the QBO basis function in the multiple regression model reveals that isentropic mixing in the lower stratosphere is suppressed in the equatorial region in the WQBO phase, while the mixing enhances in the subtropical and mid-latitude regions. This result is not consistent with the Holton–Tan mechanism. However, isentropic mixing in the mid-latitudes becomes stronger in the middle stratosphere in the EQBO phase, which agrees well with the Holton–Tan effect. Composite analysis indicates that QBO-induced changes in the direction and speed of the stratospheric zonal wind can affect wave propagation and wave breaking. In the WQBO phase, zonal wind weakens, and a planetary wave is anomalously converging near 30°N, which leads to an increase in isentropic mixing; on the contrary, wind speed becomes large, and the upward propagation of planetary wave divergence, which lead to the isentropic mixing, becomes weak near 60°N. In the EQBO phase, the wind is relatively weak around 60°N, and the isentropic mixing is strong. Multiple regression analysis reveals the ENSO impact on the intensity of isentropic mixing, which shows weak mixing in the middle and high latitudes and strong mixing in the low latitudes of the lower stratosphere in the El Niño years. In the middle stratosphere, isentropic mixing enhances in the mid-latitude region due to intensified upward propagation of planetary waves but weakens in the polar region. Composite analysis reveals a clear relationship between the mixing strength zones of the El Niño and La Niña years with the position of the polar jet and changes in zonal wind speed.

The entrainment and detrainment rates are important quantities characterizing airmass exchange between clouds and the environment. One of the challenges in calculating the rates is the need to know the velocity vector of the cloud interface in relation to that of the cloud-free air; however, the interface is not well resolved in most cloud model simulations and so the precise value of the vector is not known. Here a new method is described to approximately calculate mass fluxes across the cloud surface in well-resolved simulations of cumulus convection. The method does away with the need to calculate a cloud interface velocity and instead uses gradients of a defined cloud scalar across the cloud interface. As a result, the entrainment and detrainment rates are expressed as an integration over a small region around the cloud interface. The integrand is composed of the total derivative of the cloud scalar. The new method is applied to large-eddy simulations (LES) of a shallow cumulus case and a deep convection case. Compared to a previous method, the approach described here gives 1.5-2 times smaller exchange rates and shows less noise. The smaller exchange rates are explained as the result of differences in how the two methods correct for the advective contribution to variations of cloud volume. Derived two-dimensional distributions of the exchange rates agree well for both methods. Spatial correlation coefficients are about 0.69-0.88 for entrainment and 0.55-0.78 for detrainment.

Isentropic mixing properties in the stratosphere modeled by the forward calculation of an inverse model (TM5‐4DVAR) are evaluated against Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and Microwave Limb Sounder (MLS) observations. The isentropic mixing processes are separated into large‐scale stirring described by the “equivalent length” and small‐scale diffusion described by the diffusivity. Compared to the measurements, we find that the modeled stirring is not strong enough and that the small‐scale diffusivity is too large. TM5‐4DVAR produces excessive mixing‐induced poleward flux for stratospheric CH₄. The flux convergence presents negative biases in the tropics and positive biases in the polar regions. The biases cannot be reduced by improving the horizontal resolution only. Modeled isentropic mixing depends on the horizontal as well as the vertical resolution of the model. An increase in vertical resolution reduces numerical diffusion of the model in the vertical. The decreased vertical diffusion leads to reduction in the modeled isentropic diffusivity. Biases in modeled total column‐averaged mixing ratios of CH₄ are significant for both models with a coarse vertical resolution of 4° × 6° × 25 (and 2° × 3° × 25, 1° × 1° × 25) and an improved one of 1° × 1° × 40. They are estimated to be 7–14 and 3–7 ppb in the winter extratropics under the assumption that isentropic mixing is dominant over vertical transport on a time scale of 3 days. Correspondingly, resulted biases in inverted CH₄ surface emissions are estimated to be 0.5–1 and 0.2–0.5 mg/m²/hr, respectively, in the extratropics.