In the study of the inversion of soil multi-species heavy metal element concentrations using hyperspectral techniques, the selection of feature bands is very important. However, interactions among soil elements can lead to redundancy and instability of spectral features. In this study, heavy metal elements (Pb, Zn, Mn, and As) in entisols around a mining area in Harbin, Heilongjiang Province, China, were studied. To optimise the combination of spectral indices and their weights, radar plots of characteristic-band Pearson coefficients (RCBP) were used to screen three-band spectral index combinations of Pb, Zn, Mn, and As elements, while the Catboost algorithm was used to invert the concentrations of each element. The correlations of Fe with the four heavy metals were analysed from both concentration and characteristic band perspectives, while the effect of spectral inversion was further evaluated via spatial analysis. It was found that the regression model for the inversion of the Zn elemental concentration based on the optimised spectral index combinations had the best fit, with R2 = 0.8786 for the test set, followed by Mn (R2 = 0.8576), As (R2 = 0.7916), and Pb (R2 = 0.6022). As far as the characteristic bands are concerned, the best correlations of Fe with the Pb, Zn, Mn and As elements were 0.837, 0.711, 0.542 and 0.303, respectively. The spatial distribution and correlation of the spectral inversion concentrations of the As and Mn elements with the measured concentrations were consistent, and there were some differences in the results for Zn and Pb. Therefore, hyperspectral techniques and analysis of Fe elements have potential applications in the inversion of entisols heavy metal concentrations and can improve the quality monitoring efficiency of these soils.

Estimating soil properties is important for maximizing the production of crops in sustainable agriculture. The hyperspectral data next input depends upon the previous one, and the current techniques do not take advantage of this sequential nature of hyperspectral signatures. The variants of RNN can learn the short-term and long-term dependencies between data. This paper proposes a deep learning hybrid framework for quantifying the soil minerals like Clay, CEC, pH of H2O, Nitrogen, Organic Carbon, Sand of European Union from the LUCAS library. The hyperspectral signatures contain the data in the range of 400-2500 nm captured from the FOSS spectroscope in the laboratory. As hyperspectral data is high dimensional, Principal Component Analysis and Locality Preserving Projections are utilized to form the hybrid features, which have low dimensions containing the local and global information of the original dataset. These hybrid features are passed on to Long Short Term Memory Networks, a deep learning framework for building an effective prediction model. The effectiveness of the prepared models is demonstrated by comparing it to existing state-of-the-art techniques.

Research on the environment in recent soils is important to understand geochemical processes in coastal landscapes and the rate of pedogenesis. In this article, we present original data on Gleyic Solonchaks (Loamic) and vegetation described at the eastern part of the Terek-Kuma lowland (Northern Dagestan, Russia). At the key site of 45 × 30 m released from water 293±13 years calBP, we described vegetation at 345 plots of 2 × 2 m (4 m2) and soil properties in 58 auger holes and 2 pedons, the latter characterizing a typical microhigh with Tamarix and a microlow with saltworts. The flora of the sites amounts to 32 species (predominantly, halophytes) belonging to 11 families. Shrubs represented by tamarixes are the dominant. Under their crowns, dense herb and grass microcommunities with a predominance of tall Puccinellia gigantea occur. Sparse stunted halophytic plants (Petrosimonia, Frankenia, Puccinellia) occupy open habitats between shrubs. In soil water extracts from auger holes (696 samples in total), we measured electrical conductivity (EC) and pH. In 49 soil samples from pedons, we described particle size distribution, total concentration of macro elements (Al, Ca, Fe, K, as well as Mg, Mn, P, Ti, and Si) and trace elements (As, Co, Cr, Cu, Ni, Pb, Sr, and Zn), EC, pH, basicity (HCO3 - and CO3 2-) as well as the content of cations (Ca2+, Mg2+, Na+, and K +) and anions (SO4 2- and Cl-) in soil water extracts. Gleyic Solonchaks (Loamic) with bulk density of 1.35±0.12 g/cm3 (mean and standard deviation) contain SiO2 69±8%> Al2O3 11.8 ± 3.5 and CaO 7.5 ± 2.5%, Fe2O3 3.6 ± 1.4%, K2O 2.0 ± 0.3 and MgO 1.9 ± 0.4%> TiO2 0.62±0.25%> P2O5 0.14±0.06% and MnO 713±268 mg/kg> Sr 481±262 mg/kg > Cr 79±9 mg/kg > V 76±36, Zn 68±31, Cu 62±10, and Ni 50±17 mg/kg, Co 32±6 mg/kg> Pb 11±6 mg/kg> As 5.6 ± 1.4 mg/kg. The particle-size distribution is (WRB system,%): clay 13±5, fine silt 34±12, coarse silt 30±18, as well as very fine sand 11±10, fine sand 7.3 ± 10.5, medium sand 3.5 ± 5.8, coarse sand 0.9 ± 3.2, and very coarse sand 0.08±0.31 (n = 38). Soil water extract has EC 9.4 ± 4.1 dSm/m (soils: water ratio of 1:2.5, n = 713), contains Na+ 15.9 ± 7.0 > Ca2+7.3 ± 5.0 and Mg2+ 7.3 ± 3.1 > K + 0.30±0.20 cmol(eq)/kg, as well as Cl- 15.7 ± 7.3 and SO4 2- 14.6 ± 7.9 > HCO3 - 0.55±0.15 > CO3 2 -< 0.01 cmol(eq)/kg, and has pH 7.9 ± 0.3 (soils: water ratio of 1:5, n = 21). In soil paste, pH is 8.3 ± 0.2 (n = 461). Data obtained can be used for more confident identification of pollution sources and pollutants\' migration routes and more effective conservation and remediation of human-affected soils at the Caspian Sea coast.

The contribution of land uses in sequestering soil carbon (C) is a key question of research in tropical regions, where C emissions due to land use changes, extreme rainfall events and high temperatures can aggravate the global issue of climate change. In the present study, different pools of soil organic C were assessed, from shifting cultivation lands (jhum), rubber, tea garden and coffee plantations of Tura district in Meghalaya, which is located in Northeastern region (NER) of India. To achieve this goal, different parameters such as bulk density (BD), total organic carbon (TOC) and C pools, viz., very labile (CVL), labile (CL), less labile (CLL) and non-labile (CNL) were analyzed. The results indicated significant correlations among TOC and C pools in most land uses, except for CNL pool. Tea gardens showed the highest value of TOC stock (62.75 ± 1.47 t C ha-1) while the lowest was found in the jhum lands (33.34 ± 5.04 t C ha-1). Similarly, both the active (12.03 mg g-1) and passive (4.60 mg g-1) C pools were highest under tea gardens. CVL was found to be positively correlated with TOC and active carbon pool (ACP), while CLL was also positively correlated with passive carbon pool (PCP). This study indicates the potential of tea gardens as a promising C sequestering land use, which can be promoted in jhum lands to mitigate the negative impacts of climate change.

A study on the sorption kinetics of Cd from soil solution to soils was conducted to assess the persistence of Cd in soil solution as it is related to the leaching, bioavailability, and potential toxicity of Cd. The kinetics of Cd sorption on two non-contaminated alkaline soils from Canning (22° 18\' 48.02″ N and 88° 39\' 29.0″ E) and Lakshmikantapur (22° 06\' 16.61″ N and 88° 19\' 08.66″ E) of South 24 Parganas, West Bengal, India, were studied using conventional batch experiment. The variable soil suspension parameters were pH (4.00, 6.00, 8.18, and 9.00), temperatures (308, 318, and 328 K) and Cd concentrations (5-100 mg L(-1)). The average rate coefficient (kavg) and half-life (t1/2) values indicate that the persistence of Cd in soil solution is influenced by both temperature and soil suspension pH. The concentration of Cd in soil solution decreases with increase of temperature; therefore, Cd sorption on the soil-solution interface is an endothermic one. Higher pH decreases the t 1/2 of Cd in soil solution, indicating that higher pH (alkaline) is not a serious concern in Cd toxicity than lower pH (acidic). Based on the energy of activation (Ea) values, Cd sorption in acidic pH (14.76±0.29 to 64.45±4.50 kJ mol(-1)) is a surface control phenomenon and in alkaline pH (9.33±0.09 to 44.60±2.01 kJ mol(-1)) is a diffusion control phenomenon The enthalpy of activation (ΔH∓) values were found to be between 7.28 and 61.73 kJ mol(-1). Additionally, higher positive energy of activation (ΔG∓) values (46.82±2.01 to 94.47±2.36 kJ mol(-1)) suggested that there is an energy barrier for product formation.