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.

Antibiotic resistance genes (ARGs) as a novel type of environmental pollutant pose a health risk to humans. Oxazolidinones are one of the most important antibiotics for the treatment of Gram-positive bacterial infections in humans. Although oxazolidinones are not utilized in the livestock industry, florfenicol is commonly used on farms to treat bacterial infections, which may contribute to the spread of the cfr, optrA, and poxtA genes on farms. Using metagenomics sequencing, we looked into the antibiotic resistome context of florfenicol and oxazolidinone in 10 large-scale commercial farms in China. We identified 490 different resistance genes and 1,515 bacterial genera in the fecal samples obtained from 10 farms. Florfenicol-resistant Kurthia, Escherichia, and Proteus were widely present in these samples. The situation of florfenicol and oxazolidone resistance in pig farms is even more severe. The total number of genes and the abundance of drug resistance genes were higher in pigs than in chickens, including optrA and poxtA. All the samples we collected had a high abundance of fexA and floR. Through nanopore metagenomic analysis of the genetic environment, we found that plasmids, integrative and conjugative element (ICE), and transposons (Tn7-like and Tn558) may play an important role in the spread of floR, cfr, and optrA. Our findings suggest that florfenicol and oxazolidinone resistance genes have diverse genetic environments and are at risk of co-transmission with, for example, tetracycline and aminoglycoside resistance genes. The spread of florfenicol- and oxazolidinone–resistant bacteria on animal farms should be continuously monitored.

Based on the U.S. Weather Research and Forecasting (WRF) numerical model, this study has developed the Northwest Mesoscale Numerical Prediction Service and Experimental System (NW-MNPS). Surface and sounding data assimilation has been introduced for this system. Effects of model vertical layers and land-use data replacement have been assessed. A year-long forecast validation and analysis have been performed. The following results have been obtained: (1) Data assimilation can improve the performance of regional numerical forecasting. (2) Compared to simulations with 40 vertical layers, simulations with 55 vertical layers are more accurate. The average absolute error and root-mean-square error of the 48 h surface element forecast decrease. The analysis of threat score (TS) and equitable threat score (ETS) shows that there are higher TS and ETS values for various precipitation intense levels, in particular for heavy rainfall when comparing a 55-vertical-layer test with a 40-vertical-layer test. (3) Updating the database to include vegetation coverage can more accurately reflect actual surface conditions. The updated land-use data reduce prediction errors in all domains of the NW-MNPS.