Transport and distribution of residual nitrogen in ion-adsorption rare earth tailings.

A large amount of nitrogen remains in ion-absorption rare earth tailings with in-situ leaching technology, and it continually ends up in groundwater sources. However, the distribution and transport of ammonium nitrogen (NH4+-N) and nitrate nitrogen (NO3--N) across tailings with both depth and hill slopes is still unknown. In this study, the amount of NH4+-N and nitrate nitrogen (NO3--N) was determined in tailings, and a soil column leaching experiment, served to assess the transport and distribution following mine closure. Firstly, a high concentration of NH4+-N in the leachate at the initial leaching stage was detected, up to 2000 mg L-1, and the concentration of NH4+-N clearly diminished as time passed. Meanwhile, the NH4+-N contents remained relatively high in soil. Secondly, both the content of NH4+-N and NO3--N varied greatly according to vertical distribution after leaching lasting several years. The amounts of NH4+-N and NO3--N in surface soil were much smaller than those in deep soil, with 3-4 orders of magnitude variation with depth. Thirdly, when disturbed by NH4+-N, the pH not only diminished but also changed irregularly as depth increased. Fourthly, although the amount of NO3--N was smaller than that of NH4+-N, both their distribution trend was similar with depth. In fact, NH4+-N and NO3--N were significantly correlated but this declined from the knap to the piedmont. Based on these results, it is suggested that mining activity could cause nitrogen to be dominated by NH4+-N and acidification in a tailing even if leaching occurs over several years. NO3--N derived from NH4+-N transports easily and it becomes the main nitrogen pollutant with the potential to be a long-lasting threat to the environment around a mine.

Current Status and Health Risk Assessment of Heavy Metals Contamination in Tea across China.

Tea is a non-alcoholic beverage popular among Chinese people. However, due to the application of chemical and organic fertilizers in the tea planting process, the environment pollutionaround the tea plantation, and the instruments used in the processing, heavy metal elements will accumulate in the tea, which brings health risks for tea consumers. This study summarized heavy metal concentrations from 227 published papers and investigated the current contamination status of tea and tea plantation soils, and, finally, the risk of heavy metal exposure to tea consumers in China is assessed, in terms of both non-carcinogenic and carcinogenic risk. The average contamination of six heavy metals in tea-arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), and lead (Pb)-were 0.21, 0.14, 1.17, 14.6, 0.04, and 1.09 mg/kg, respectively. The areas with high concentrations of heavy metals in tea were concentrated primarily in southwest China, some areas in eastern China, and Shaanxi Province in northwest China. The non-carcinogenic risks of heavy metals in tea are all within safe limits. The national average HI value was 0.04, with the highest HI value of 0.18 in Tibet, which has the largest tea consumption in China. However, the carcinogenic risks of Cd in Shaanxi Province, Anhui Province, and southwest China exceed the acceptable range, and due attention should be given to these areas.

Characteristics of Soil Erodibility in the Yinna Mountainous Area, Eastern Guangdong Province, China.

Soil erodibility research is of theoretical and practical significance to the prediction and prevention of regional soil erosion. At present, the study on soil erodibility in the lateritic red soil area of eastern Guangdong province is relatively lacking. Taking the forest land soil of the Yinna mountainous area as the research object, the physical and chemical properties (organic matter mass fraction, texture, moisture, bulk density, pH, aggregate content) of soil samples at different altitudes were measured with field survey sampling and indoor analysis. Soil erodibility K values were simulated with different models (the EPIC model, the Torri model, and the Shirazi model) and the regional applicability of the K simulation models was discussed. The influence of soil properties on soil erodibility was analyzed. The results showed that: (1) K values in the Yinna mountainous area are between 0.0250 and 0.0331 t·hm2·h/MJ·mm·hm2, and the K value in the subsoil layer (20-40 cm) is higher than that of the topsoil layer (0-20 cm). These values decreased significantly with the increase of altitude. The soil in the study area belongs to low-medium to medium erodible soil types. (2) The three models have certain applicability in the Yinna mountainous area, but the simulation results still lack validation. (3) Soil particle size composition is the most important factor affecting the K value in the study area. As far as the topsoil is concerned, K values increase with the increase of clay and silt content and decrease with the increase of sand content and aggregate stability. Soil erodibility has no significant correlation with pH and bulk density and has no clear relationship with the content of soil organic carbon and soil moisture. The research results can provide basic data for regional soil and water conservation and the construction of K value databases of different soil types in China.

Deep learning-based remote sensing estimation of water transparency in shallow lakes by combining Landsat 8 and Sentinel 2 images.

Water transparency is a key indicator of water quality as it reflects the turbidity and eutrophication in lakes and reservoirs. To carry out remote sensing monitoring of water transparency rapidly and intelligently, deep learning technology was used to construct a new retrieval model, namely, point-centered regression convolutional neural network (PSRCNN) suitable for Sentinel 2 and Landsat 8 images. The impact of input feature variables on the accuracy of the inversion model was examined, and the performance of an optimized PSRCNN model was also assessed. This model was applied to remote sensing images of three shallow lakes in the eastern China plain acquired in summer. The PSRCNN model, constructed using five identical bands from Landsat 8 and Sentinel 2 images and 20 band combinations as the input variables, the input window size of 5 × 5 pixels, proves a good predictive ability, with a verification accuracy of R2 = 0.85, the root mean squared error (RMSE) = 13.0 cm, and the relative predictive deviation (RPD) = 2.58. After the sensitive spectral analysis of water transparency, the band combinations that had correlation coefficients higher than 0.6 were selected as the new input feature variables to construct an optimized PSRCNN model (PSRCNNopt) for water transparency. The PSRCNNopt model has an excellent predictive ability, with a verification accuracy of R2 = 0.89, RMSE = 11.48 cm, and RPD =3.0. It outperforms the commonly retrieval models (band ratios, random forest, support vector machine, etc.), with higher accuracy and robustness. Spatial variations in water transparency of three lakes from the retrieval results by PSRCNNopt model are consistent with the field observations.

[Hydro-chemical Characteristics and Ion Origin Analysis of Surface Groundwater at the Shengjin Lake and Yangtze River Interface].

The interface between Shengjin Lake and Yangtze River was selected as the study area. The water chemical composition and hydrogen and oxygen isotope values of different types of water bodies were tested, and their seasonal variation characteristics were analyzed to explore the sources of chemical ions in the surface and groundwater. Finally, the contribution of mixed water sources to chemical ions in groundwater was estimated. The results show the following ① The concentration of chemical ions in the surface and groundwater of the study area is higher than that of atmospheric precipitation, and the physical and chemical parameters exhibit seasonal variation. ② Surface water mainly belongs to the Ca-HCO3 type, but the proportion of the Ca-SO4 type in summer is significantly lower than that in other seasons; Groundwater primarily belongs to Ca-HCO3 and Ca-SO4 types, with proportions of 46% and 27%, respectively, and the seasonal difference is not significant. ③ Ca2+ and Mg2+ in the surface and groundwater mainly come from the dissolution of carbonate rocks, carbonic acid and sulfate ions participate in the dissolving process of carbonate minerals. Na+and Cl- are partly derived from atmospheric precipitation and partly sourced from agricultural fertilization and manure sewage. ④ Mixed water sources is also an important source of chemical ions in groundwater, with the average contribution 28% to Cl-, showing a seasonal trend.

Assessment of Zeolite, Biochar, and Their Combination for Stabilization of Multimetal-Contaminated Soil.

In this study, the natural zeolite and rice husk biochar were mixed as a combination amendment for metal immobilization in a Cd, Pb, As, and W co-contaminated soil. A 90 day incubation study was conducted to investigate the effects of amendments on toxic metal in soil. Zeolite, biochar, and their combination application increased the soil pH and cation exchange capacity. A combination of amendments decreased the bioavailability of Cd, Pb, As, and W. Besides, the potential drawback of biochar application on As and W release was overcome by the combination agent. Zeolite, biochar, and combination treatment decreased total bioavailability toxicity from 335.5 to 182.9, 250.5, and 143.4, respectively, which means that combination was an optimum amendment for soil remediation. The results of the Community Bureau of Reference sequential extraction and scanning electron microscopy-energy-dispersive spectrometry images confirmed the Cd and Pb adsorption onto biochar. However, As and W immobilization was dominantly controlled by zeolite. It appears that the combination of amendments is an efficient amendment to remediate Cd, Pb, As, and W co-contamination in soil, although the combination of amendments has a lower stabilization rate for W than for zeolite.

Distribution and ecological risks of heavy metals in river sediments and overlying water in typical mining areas of China.

The distribution and potential ecological risks of eight heavy metal elements including Cr, Cu, Zn, As, Cd, Pb, Hg, and W in the overlying water and sediments of the Taojiang River were investigated. The concentrations of eight heavy metals were measured by inductively coupled plasma mass spectrometer (ICP-MS), and the distribution coefficients were exploited to estimate the partition coefficient between overlying water and sediment phases, which were subsequently used to establish the potential ecological risk of heavy metals in sediments. The results revealed that the contents of Pb (33.47 µg·L-1), Cd (153.03 µg·L-1) and Hg (1.12 µg·L-1) in the water samples exceeded threshold values as proposed by the limits of the class III environmental quality standard. On the other hand, Cr, Cu, Zn, As, and Pb within sediments were below threshold limits.