Refractory black carbon (rBC) is a primary aerosol species, produced through incomplete combustion, that absorbs sunlight and contributes to positive radiative forcing. The overall climate effect of rBC depends on its spatial distribution and atmospheric lifetime, both of which are impacted by the efficiency with which rBC is transported or removed by convective systems. These processes are poorly constrained by observations. It is especially interesting to investigate rBC transport efficiency through the Asian Summer Monsoon (ASM) since this meteorological pattern delivers vast quantities of boundary layer air from Asia, where rBC emissions are high to the upper troposphere/lower stratosphere (UT/LS) where the lifetime of rBC is expected to be long. Here, we present in situ observations of rBC made during the Asian Summer Monsoon Chemistry and Climate Impact Project of summer, 2022. We use observed relationships between rBC and CO in ASM outflow to show that rBC is removed nearly completely (>98%) from uplifted air and that rBC concentrations in ASM outflow are statistically indistinguishable from the UT/LS background. We compare observed rBC and CO concentrations to those expected based on two chemical transport models and find that the models reproduce CO to within a factor of 2 at all altitudes whereas rBC is overpredicted by a factor of 20-100 at altitudes associated with ASM outflow. We find that the rBC particles in recently convected air have thinner coatings than those found in the UTLS background, suggesting transport of a small number of rBC particles that are negligible for concentration.

Featured Application Python application that uses data science and machine learning to estimate the main properties of acid tars. Its main advantage is that determinations for acid tar properties are no longer necessary, thus saving time and money. However, good machine learning estimations are highly dependent on the number and quality of the training data, meaning that the larger and more consistent the training database, the better the estimations.Abstract Hazardous petroleum wastes are an inevitable source of environmental pollution. Leachates from these wastes could contaminate soil and potable water sources and affect human health. The management of acid tars, as a byproduct of refining and petrochemical processes, represented one of the major hazardous waste problems in Romania. Acid tars are hazardous and toxic waste and have the potential to cause pollution and environmental damage. The need for the identification, study, characterization, and subsequently either the treatment, valorization, or elimination of acid tars is determined by the fact that they also have high concentrations of hydrocarbons and heavy metals, toxic for the storage site and its neighboring residential area. When soil contamination with acid tars occurs, sustainable remediation techniques are needed to restore soil quality to a healthy production state. Therefore, it is necessary to ensure a rapid but robust characterization of the degree of contamination with hydrocarbons and heavy metals in acid tars so that appropriate techniques can then be used for treatment/remediation. The first stage in treating these acid tars is to determine its properties. This article presents a software program that uses machine learning to estimate selected properties of acid tars (pH, Total Petroleum Hydrocarbons-TPH, and heavy metals). The program uses the Automatic Machine Learning technique to determine the Machine Learning algorithm that has the lowest estimation error for the given dataset, with respect to the Mean Average Error and Root Mean Squared Error. The chosen algorithm is used further for properties estimation, using the R2 correlation coefficient as a performance criterion. The dataset used for training has 82 experimental points with continuous, unique values containing the coordinates and depth of acid tar samples and their properties. Based on an exhaustive search performed by the authors, a similar study that considers machine learning applications was not found in the literature. Further research is required because the method presented therein can be improved because it is dataset dependent, as is the case with every ML problem.

Soil surface roughness (SSR) is an important factor affecting soil erosion and soil nutrient transport. Human tillage leads to increased instability in SSR, and the characteristics of SSR caused by different tillage practices await further study. This research utilizes terrestrial laser scanning (TLS) to measure the SSR of six farmland plots (25 m x 25 m) and analyzes the characteristics of SSR under different tillage practices (plowing, harrowing, ridging, crusting, etc.). The study results show: 1) Different agricultural tillage practices lead to significant differences in SSR. The plowed and harrowed plot corresponds to the maximum (2.49 cm) and minimum (1.5 cm) root mean square height (RMSH), respectively. Correlation length (CL) is more affected by different tillage practices than RMSH. The difference in CL between the ridged and harrowed plot is 2.6 times. 2) Ridging and crusting caused significant directional variation in SSR. The SSR anisotropy of the harrowed plot can be disregarded. 3) Under the condition of measuring soil profile in 12 directions and randomly sampling 70 times in each direction, the profile length must be at least 3 m to ensure that the measurement error of SSR is better than 5% compared to the true value. TLS can measure two-dimensional SSR. Therefore, it is only necessary to ensure that the measurement range is at least 3 m x 3 m. The study results provide a reference for the high-precision measurement of SSR (RMSH and CL) under different agricultural tillage practices.

The eastern Tien Shan hosts substantial mid-latitude glaciers, but in situ glacier mass balance records are extremely sparse. Haxilegen Glacier No. 51 (eastern Tien Shan, China) is one of the very few well-measured glaciers, and comprehensive glaciological measurements were implemented from 1999 to 2011 and re-established in 2017. Mass balance of Haxilegen Glacier No. 51 (1999-2015) has recently been reported, but the mass balance record has not extended to the period before 1999. Here, we used a 1:50,000-scale topographic map and long-range terrestrial laser scanning (TLS) data to calculate the area, volume, and mass changes for Haxilegen Glacier No. 51 from 1964 to 2018. Haxilegen Glacier No. 51 lost 0.34 km(2) (at a rate of 0.006 km(2) a(-1) or 0.42% a(-1)) of its area during the period 1964-2018. The glacier experienced clearly negative surface elevation changes and geodetic mass balance. Thinning occurred almost across the entire glacier surface, with a mean value of -0.43 +/- 0.12 m a(-1). The calculated average geodetic mass balance was -0.36 +/- 0.12 m w.e. a(-1). Without considering the error bounds of mass balance estimates, glacier mass loss over the past 50 years was in line with the observed and modeled mass balance (-0.37 +/- 0.22 m w.e. a(-1)) that was published for short time intervals since 1999 but was slightly less negative than glacier mass loss in the entire eastern Tien Shan. Our results indicate that Riegl VZ (R)-6000 TLS can be widely used for mass balance measurements of unmonitored individual glaciers.

High-precision measuring of glacier evolution remains a challenge as the available global and regional remote sensing techniques cannot satisfactorily capture the local-scale processes of most small- and medium-sized mountain glaciers. In this study, we use a high-precision local remote sensing technique, long-range terrestrial laser scanning (TLS), to measure the evolution of Urumqi Glacier No. 1 at an annual scale. We found that the dense point clouds derived from the TLS survey can be used to reconstruct glacier surface terrain, with certain details, such as depressions, debris-covered areas, and supra-glacial drainages can be distinguished. The glacier experienced pronounced thickness thinning and continuous retreat over the last four mass-balance years (2015 - 2019). The mean surface slope of Urumqi Glacier No. 1 gradually steepened, which may increase the removal of glacier mass. The glacier was deeply incised by two very prominent primary supra-glacial rivers, and those rivers presented a widening trend. Extensive networks of supra-glacial channels had a significant impact on accelerated glacier mass loss. High-precision measuring is of vital importance to understanding the annual evolution of this type of glacier.

Fragmented surfaces and harsh environments have always been the main obstacles hindering observation works of glaciers in central Tibetan Plateau (TP). The advent of Terrestrial Laser Scanner (TLS) technology offers a potential revolution in this context. While TLS has been effectively applied to smaller glaciers in the Alps and Tianshan, this study extends its use to the large and topographically complex Ganglongjiama (GLJM) glacier in the Tanggula Mountains. Over a 5-year period, TLS, with a precision of up to 0.012 m, has documented an accelerated melting trend, with the terminus retreating by 13.305 m and a total mass loss of 2.580 m water equivalent. The research also underscores the role of supraglacial channels and lakes in intensifying surface melting and glacier front instability. Despite challenges in data acquisition due to occlusions and logistical constraints at high altitudes, this first TLS survey of a TP glacier provides invaluable insights into glacier dynamics. Future research could integrate TLS with Unmanned Aerial Vehicle-Structure-from-Motion (UAV-SfM) data fusion to achieve more comprehensive coverage and improve the temporal resolution of observations for a detailed analysis of glacier features.