Soil Background Research Articles

An innovative soil mesocosm system for studying the effect of soil moisture and background NO on soil surface C and N trace gas fluxes

Nitric oxide (NO) is a key substance in atmospheric chemistry, influencing the formation and destruction of tropospheric ozone and the atmosphere's oxidizing capacity. It also affects the physiological functions of organisms. NO is produced, consumed, and emitted by soils, the effects of soil NO concentrations on microbial C and N cycling and associated trace gas fluxes remain largely unclear. This study describes a new automated 12-chamber soil mesocosm system that dynamically changes incoming airflow composition. It was used to investigate how varying NO concentrations affect soil microbial C and N cycling and associated trace gas fluxes under different moisture conditions (30% and 50% WFPS). Based on detection limits for NO, NO2, N2O, and CH4 fluxes of < 0.5 µg N or C m−2 h−1 and for CO2 fluxes of < 1.2 mg C m−2 h−1, we found that soil CO2, CH4, NO, NO2, and N2O were significantly affected by different soil moisture levels. After 17 days cumulative fluxes at 50% WFPS increased by 40, 400, and 500% for CO2, N2O, and CH4, respectively, when compared to 30% WFPS. However, cumulative fluxes for NO, and NO2, decreased by 70, and 40%, respectively, at 50% WFPS when compared to 30% WFPS. Different NO concentrations tended to decrease soil C and N fluxes by about 10–20%. However, with the observed variability among individual soil mesocosms and minor fluxes change. In conclusion, the developed system effectively investigates how and to what extent soil NO concentrations affect soil processes and potential plant–microbe interactions in the rhizosphere.

Predicting soil ecological criteria of 17 metal(loid)s in China based on quantitative ion character-activity relationship - Species sensitivity distribution (QICAR-SSD) coupled model

Soil pollution caused by metal(loid)s is increasingly serious and poses unexpected risks to terrestrial organisms. Establishing soil quality standards is essential for assessing ecological risks of metal(loid)s and protecting soil ecosystems. However, the limited availability of metal(loid) ecotoxicological data has hampered the development of soil quality standards due to financial and practical constraints on toxicity testing. This study collected 77 normalization equations and 58 cross-species extrapolation equations to calculate the normalized EC10 (the added concentration causing a 10 % inhibition effect) of metal(loid)s under a representative scenario. A set of quantitative ion character-activity relationship (QICAR) models were then constructed using normalized EC10 and nine critical ionic characters (AR, AR/AW, BP, MP, Z/r2, Z/r, Xm, σp, and |Log(KOH)|). Subsequently, these QICAR models were employed to predict ecotoxicological EC10 of 17 metal(loid)s to 12 soil species and coupled with species sensitivity distribution (SSD) to determine Predicted No Effect Concentration (PNEC). The results demonstrated the coupled QICAR-SSD model could effectively derive terrestrial PNEC for data-poor metal(loid)s, with errors between the predicted PNEC and reported soil standards (excluding soil background levels) from different countries mostly <0.3 orders of magnitude. Finally, soil ecological criteria (SEC) for 17 metal(loid)s were calculated using an added risk approach based on PNEC and national soil background concentration. Overall, the coupled model proposed here can provide a valuable supplement to the development of soil quality standards for numerous metal(loid)s in soil components.

Pollution Assessment and Source Apportionment of Heavy Metals in the Surrounding Soil of Typical Mining Areas in Tongling, Anhui Province

To study the level of heavy metal pollution and ecological risks in the soil around typical mining areas in Tongling, a total of 150 soil samples were collected from the study area. The content characteristics of 10 elements, namely, As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Fe, and Zn, in the soils were analyzed. Methods including enrichment factor, the geo-accumulation index, single-factor pollution index, Nemero comprehensive pollution index, and potential ecological risk index were used to evaluate the pollution status of heavy metals in the soil of the study area. The pollution sources of heavy metals in the soil were also analyzed using correlation analysis, cluster analysis, and principal component analysis. The results showed that except for Cr and Fe, the average contents of the other eight heavy metal elements were higher than the soil background values in the study area. Pb, Zn, As, Cu, and Cd had a high degree of variation and were significantly affected by external interference. The spatial distribution showed that both Cr and Ni showed a decreasing trend from the edge to the central region, whereas the other eight heavy metals showed a decreasing trend from the central region to the surrounding areas. The pollution level of Cd and Cu in the soil of the research area was relatively severe. The overall ecological risk was at a medium to low level. Cd and Hg were the main contributing factors. As, Cd, Cu, Fe, Mn, Pb, and Zn mainly came from agricultural, industrial, and transportation sources, whereas Cr and Ni were mainly from natural sources. However, the sources of Hg were relatively complex. The research results can provide a scientific basis for the prevention and control of soil heavy metal pollution in metal mining areas, as well as the remediation of mine pollution.

Heavy metal migration and lithium isotope fractionation under extreme weathering of basalt on tropical Hainan Island, China

Basalt weathering can cause heavy metal enrichment in soils, and lithium isotopes serve as an effective tracer for this weathering process. However, the relations among weathering intensity, heavy metal migration, and Li isotope fractionation remain unclear. We conducted systematic examinations of a basalt weathering profile on tropical Hainan Island in China. The characteristics of mineralogy, elemental and Li isotopic geochemistry indicate that the profile has undergone changes from initial to advanced and ultimately extreme weathering stages. The corresponding dominant minerals are primary minerals, clay minerals such as kaolin-group, and the Al–Fe–Ti (hydro)oxides. The variations of Fe-group heavy metals within the profile are very similarly to that of Fe, exhibiting a pattern of loss-gain-loss. Newly formed secondary minerals preferentially incorporate lighter 6Li, leading to a gradual increase in Li content and a decrease in δ7Li. Due to the high δ7Li value of rainwater input, the ongoing impact of secondary mineral adsorption is partially counteracted. This results in the δ7Li of laterite and topsoil being maintained within a narrow range (−3.2‰ ∼ −1.9‰), despite the broader variation observed across the entire profile (−3.2‰ ∼ +2.8‰). Comparing basalt weathering profiles in different climatic zones reveals a very complex Li isotope behavior and its controlling factors. Therefore, it is essential to carefully evaluate the influence of atmospheric input, secondary mineral adsorption, dissolution, and redeposition processes on Li isotopes. Throughout the entire profile, Cr, Ni, and Cu exhibit enrichment relative to soil background values, indicating that the weathering of tropical basalt may pose potential environmental risks for these elements.

Distribution characteristics, sources and risk assessment of heavy metals in the surface sediments from the largest tributary of the Lancang River in the Tibet Plateau, China.

Angqu, positioned in the eastern expanse of the Tibet Plateau, claims the title of the largest tributary to the Lancang River. In October and December of 2018, in the sediment of Angqu, a comprehensive investigation was conducted on nine heavy metals-arsenic (As), manganese (Mn), chromium (Cr), cadmium (Cd), lead (Pb), mercury (Hg), copper (Cu), zinc (Zn), and nickel (Ni). This investigation aimed to scrutinize the spatial and temporal distribution patterns of these metals, assess the pollution status and ecological risks associated with the sediments, and delve into the sources contributing to their presence. The research results indicate that the average concentrations of As, Hg, and Cd in Angqu sediments exceed the soil background values of Tibet, while the concentrations of other heavy metals are below the soil background values of Tibet. Notably, arsenic poses potential ecological risks. In Angqu sediments, the concentrations of Mn, Cu, Ni, and Pb are generally higher in the wet season, but the seasonal variations of heavy metals in Angqu sediments are not significant. The sediments in the Angqu Basin are predominantly affected by mercury Hg, Cd, and As, with varying degrees of pollution at different sampling points. In the main stream of Angqu (City section), Hg pollution has reached above a moderate level, whereas As pollution near the tributary is only slightly polluted. The analysis of heavy metal sources reveals that there are five primary contributors to heavy metals in surface sediments of Angqu: parent material, agricultural activities, groundwater, atmospheric deposition, and other unidentified sources. Mn, Cr, Pb, and Ni are mainly derived from soil parent material, accounting for more than 50%. About 60.82% of As comes primarily from groundwater. Zn and Cd are mainly sourced from agricultural activities, accounting for 41.25% and 34.33%, respectively. Additionally, 20.6% of Hg originates from atmospheric deposition.

Retrieval of snow and soil properties for forward radiative transfer modeling of airborne Ku-band SAR to estimate snow water equivalent: the Trail Valley Creek 2018/19 snow experiment

Abstract. Accurate snow information at high spatial and temporal resolution is needed to support climate services, water resource management, and environmental prediction services. However, snow remains the only element of the water cycle without a dedicated Earth observation mission. The snow scientific community has shown that Ku-band radar measurements provide quality snow information with its sensitivity to snow water equivalent and the wet/dry state of snow. With recent developments of tools like the snow micropenetrometer (SMP) to retrieve snow microstructure data in the field and radiative transfer models like the Snow Microwave Radiative Transfer (SMRT) model, it becomes possible to properly characterize the snow and how it translates into radar backscatter measurements. An experiment at Trail Valley Creek (TVC), Northwest Territories, Canada, was conducted during the winter of 2018/19 in order to characterize the impacts of varying snow geophysical properties on Ku-band radar backscatter at a 100 m scale. Airborne Ku-band data were acquired using the University of Massachusetts radar instrument. This study shows that it is possible to calibrate SMP data to retrieve statistical information on snow geophysical properties and properly characterize a representative snowpack at the experiment scale. The tundra snowpack measured during the campaign can be characterize by two layers corresponding to a rounded snow grain layer and a depth hoar layer. Using RADARSAT-2 and TerraSAR-X data, soil background roughness properties were retrieved (msssoil=0.010±0.002), and it was shown that a single value could be used for the entire domain. Microwave snow grain size polydispersity values of 0.74 and 1.11 for rounded and depth hoar snow grains, respectively, were retrieved. Using the geometrical optics surface backscatter model, the retrieved effective soil permittivity increased from C-band (εsoil=2.47) to X-band (εsoil=2.61) and to Ku-band (εsoil=2.77) for the TVC domain. Using the SMRT and the retrieved soil and snow parameterizations, an RMSE of 2.6 dB was obtained between the measured and simulated Ku-band backscatter values when using a global set of parameters for all measured sites. When using a distributed set of soil and snow parameters, the RMSE drops to 0.9 dB. This study thus shows that it is possible to link Ku-band radar backscatter measurements to snow conditions on the ground using a priori knowledge of the snow conditions to retrieve snow water equivalent (SWE) at the 100 m scale.

Enhancing cotton seedling recognition: a method for high-resolution UAV remote sensing images

ABSTRACT Timely and accurate acquisition of cotton seedling information is crucial for seedling replenishment, yield increase, and effective cotton field management. This study proposes a cotton seedling recognition model and weed removal method for high-resolution UAV (Unmanned Aerial Vehicle) remote sensing images. First, the image information of cotton seedlings is enhanced by calculating the remote sensing index and using threshold segmentation. Second, the recognition model is constructed based on the spatial morphological characteristics of cotton seedlings, with weed interference removed using the straight-line method. Finally, the growth condition of cotton seedlings in farmland is comprehensively evaluated by calculating the seedling emergence rate and coverage rate. The experimental results show that: (1) the combination of the GLI (Green Leaf Index) and Otsu threshold segmentation algorithm effectively distinguishes cotton seedlings from the soil background and accurately portrays cotton seedling contours, further enhancing image information; (2) the proposed recognition model achieves an average overall accuracy of 95.75%, making it a practical method for cotton seedling recognition; and (3) the overall growth condition of cotton seedlings in the study area is good, enabling farmers to apply appropriate seedling replenishment and fertilizer based on the comprehensive cotton evaluation map to further improve the final cotton yield. This study aims to provide new research ideas for crop seedling identification and serve as a reference for the practical application of UAVs in crop monitoring.

A Precise Segmentation Algorithm of Pumpkin Seedling Point Cloud Stem Based on CPHNet.

Accurate segmentation of the stem of pumpkin seedlings has a great influence on the modernization of pumpkin cultivation, and can provide detailed data support for the growth of pumpkin plants. We collected and constructed a pumpkin seedling point cloud dataset for the first time. Potting soil and wall background in point cloud data often interfere with the accuracy of partial cutting of pumpkin seedling stems. The stem shape of pumpkin seedlings varies due to other environmental factors during the growing stage. The stem of the pumpkin seedling is closely connected with the potting soil and leaves, and the boundary of the stem is easily blurred. These problems bring challenges to the accurate segmentation of pumpkin seedling point cloud stems. In this paper, an accurate segmentation algorithm for pumpkin seedling point cloud stems based on CPHNet is proposed. First, a channel residual attention multilayer perceptron (CRA-MLP) module is proposed, which suppresses background interference such as soil. Second, a position-enhanced self-attention (PESA) mechanism is proposed, enabling the model to adapt to diverse morphologies of pumpkin seedling point cloud data stems. Finally, a hybrid loss function of cross entropy loss and dice loss (HCE-Dice Loss) is proposed to address the issue of fuzzy stem boundaries. The experimental results show that CPHNet achieves a 90.4% average cross-to-merge ratio (mIoU), 93.1% average accuracy (mP), 95.6% average recall rate (mR), 94.4% F1 score (mF1) and 0.03 plants/second (speed) on the self-built dataset. Compared with other popular segmentation models, this model is more accurate and stable for cutting the stem part of the pumpkin seedling point cloud.

Increasing drought sensitivity of plant photosynthetic phenology and physiology

Understanding the impacts of drought on plant photosynthetic phenology and physiology is essential for accurately predicting annual gross primary productivity (GPP) and global carbon cycles. While droughts in different seasons can have different effects on plant growth and photosynthesis, a comprehensive understanding of how plant photosynthetic phenology and physiology respond to different seasons’ droughts and their temporal changes is lacking. Here, we comprehensively evaluated the drought sensitivities of plant photosynthetic phenology and physiology and their temporal trends in the past two decades, using the start/end of the photosynthetic seasons (SOS/EOS) and peak GPP (GPPmax) derived from FLUXNET ground observations since the 1990s and remote-sensing data (2001–2020) over the globe. Our results show that spring droughts leads to delayed SOS in arid ecosystems and advanced SOS in humid ecosystems. In contrast, droughts in summer and autumn generally advance EOS. In addition, spring droughts decrease GPPmax in arid regions but increase it in humid regions, whereas summer droughts lead to significant GPPmax reductions. More importantly, we observed significant increases in the drought sensitivity of SOS, EOS, and GPPmax over the last two decades, which is driven by long-term decreases in background soil moisture and increases in atmospheric water deficit under climate change. Furthermore, these increasing trends were projected to persist in the 21st century under Shared Socioeconomic Pathways 8.5 and 4.0. Overall, our findings underscore the critical role of drought timing in shaping ecosystem responses and highlight the growing vulnerability of ecosystem phenology and physiology to drought, which may restrict the increases in terrestrial carbon uptake under warming.

Soil Cadmium Prediction and Health Risk Assessment of an Oasis on the Eastern Edge of the Tarim Basin Based on Feature Optimization and Machine Learning

Soil heavy metal pollution poses a serious threat to food security, human health, and soil ecosystems. Based on 644 soil samples collected from a typical oasis located at the eastern margin of the Tarim Basin, a series of models, namely, multiple linear regression （LR）, neural network （BP）, random forest （RF）, support vector machine （SVM）, and radial basis function （RBF）, were built to predict the soil heavy metal content. The optimal prediction result was obtained and utilized to analyze the spatial distribution features of heavy metal contamination and relevant health risks. The outcomes demonstrated that： ① The average Cd content in the study area was 0.14 mg·kg-1, which was 1.17 times the soil background value of Xinjiang, making it the primary factor of soil heavy metal contamination in the area. Additionally, the carcinogenicity risk coefficients of Cd for both adults and children were less than 10-4, indicating that there were no significant long-term health risks for humans in the area. ② The estimation accuracies of the five inversion models were compared, and the validation set of the RF model had an R2 value of 0.763 7, which was the highest among the five models. Additionally, the RMSE, MAE, and MBE of the RF model were the smallest among the five models. Therefore, the predicted values of the RF model were most consistent with the measured values of the soil Cd content. The predicted map of soil Cd distribution derived from the RF model coincided best with the interpolation map. ③ The RF model outperformed the other four models in predicting health risks associated with the soil Cd element for both adults and children, resulting in better prediction results. Comparatively, the predicted values of the LR model in the validation set varied greatly, leading to unreliable results. It was demonstrated that the RF was the best model for predicting soil Cd content and evaluating health risks in the study area, considering its superior generalization capability and anti-overfitting ability.

Distribution Characteristics and Ecological Risk Assessment of Heavy Metals in Roadside Soil of Important National Highways on the Qinghai-Xizang Plateau

To clarify the impact of transportation on the sensitive and fragile ecosystems of the Qinghai Tibet Plateau and major ecological safety barrier functions, soil samples within 0-25 m on the roadside were collected from sections of national highways such as G214, G213, G345, G109, G316, and G317, and the contents of six heavy metals were analyzed. Then, the degree of heavy metal pollution and the risk of ecological hazards were evaluated using the single-factor pollution index method （Pi）, Nemero comprehensive index method （PN）, and potential ecological risk index method （RI）. The results showed that the heavy metal contents of As, Cd, Hg, Ni, Pb, and Zn in the soil of important transportation national roads on the Qinghai Tibet Plateau ranged from 5.65 to 176.00, 0.04 to 0.27, 0.01 to 0.14, 9.52 to 113.00, 9.16 to 54.50, and 24.70 to 109.00 mg·kg-1, respectively, showing high variability. In some sections of the soil, the values of the elements As, Cd, and Hg were higher than the local soil background values. The single-factor pollution index of heavy metals in roadside soil was Pi （As） &gt; Pi （Hg） &gt; Pi（Cd） &gt; Pi （Pb） &gt; Pi （Ni） &gt; Pi （Zn）. The Nemero comprehensive pollution index ranged from 0.41 to 9.20, with an average value of 1.53, indicating clean and mild pollution. Some areas showed a moderate or severe pollution. The average potential ecological risk index of the research section was 106.2, and the soil was generally in a state of no pollution and light pollution. Only two road sections had soil heavy metal enrichment reaching moderate and strong ecological hazards. The comprehensive potential risk of the G213a road section indicated moderate to severe ecological risk, mainly contributed by Hg, As, and Cd. The comprehensive pollution risk of the G317 road section indicated mild to moderate ecological risk, mainly contributed by Hg and Cd. The heavy metal content in the soil of the Qinghai Tibet Plateau road area was not significantly correlated with the roadside distance and soil depth but was significantly positively correlated with the annual average temperature （P &lt; 0.05）. In all, there was a trend of heavy metal input into the soil environment in areas with intense human activities and high traffic flow during road construction on the Qinghai Tibet Plateau.

Assessment and Source Analysis of Heavy Metal Pollution in Farmland Soil Around Tin Mine

Studying the status and source analysis of heavy metal pollution in farmland in typical mining and processing areas is an important prerequisite for promoting farmland soil ecological restoration and farmland protection in concentrated mining areas. In this study, the heavy metal content of farmland soil around a mining area in southwest China was detected, and the pollution status, distribution law, health risks, and sources of heavy metals were studied by using the land accumulation index method, comprehensive pollution index method, kriging interpolation method, health risk assessment method, and PMF receptor model on the sampling data. The results showed that the mean values of eight heavy metals in farmland soil except Ni exceeded the local soil background values, and the results of the ground accumulation index evaluation showed that Cd and Hg were extremely polluted； Pb and As showed medium pollution-heavy pollution； and Cr, Zn, Ni, and Cu were lightly polluted. In the health risk assessment, oral ingestion was the main exposure route posing a health risk to the human body； the main element that constituted non-carcinogenic health risks was As, and the carcinogenic risks were from As and Cd. PMF model analysis showed that the contribution rate of weathering natural sources of iron-bearing ore was 28.02%, and the main factors were Ca and Fe. The contribution rate of agricultural sources was 3.02%, and the main factors were Pb and As. The contribution rate of industrial and atmospheric deposition composite sources was 33.09%, and the main factor was Hg. The contribution rate of the parent material source was 17.27%, and the main factor was Ca. The contribution rate of mining activities such as mining and smelting was 18.60%, and the main factors were Zn and Cd.

Calculating Vegetation Index-Based Crop Coefficients for Alfalfa in the Mesilla Valley, New Mexico Using Harmonized Landsat Sentinel-2 (HLS) Data and Eddy Covariance Flux Tower Data

The goal of this study is to investigate the usefulness of the relatively new 30 m spatial and &lt;5.7-day temporal resolution Harmonized Landsat Sentinel-2 (HLS) dataset for calculating vegetation index-based crop coefficients (KcVI) for estimating field scale crop evapotranspiration (ETc). Increased spatial and temporal resolution ETc estimates are needed for improving irrigation scheduling, monitoring impacts of water conservation programs, and improving crop yield. The crop coefficient (Kc) method is widely used for estimating ETc. Remote sensing vegetation indices (VI) are highly correlated to Kc and allow the creation of a KcVI but the approach is limited by the availability of high temporal and spatial resolutions. We selected and calculated sixteen commonly used VIs using HLS data and regressed them against field-measured ET for alfalfa in the Mesilla Valley, New Mexico to create linear KcVI models. All models showed good agreement with Kc (r2 &gt; 0.67 and RMSE &lt; 0.15). ETc prediction resulted in an MAE ranging between 0.35- and 0.64-mm day−1, an MSE ranging between 0.20- and 0.75-mm day−1 and an MAPD ranging between 10.0 and 16.5%. The largest differences in predicted ETc occurred early in the growing season and during cutting periods when the spectral signal could be influenced by soil background or irrigation events. The results suggest that applying the KcVI approach to the HLS dataset can help fill in the data gap in remote sensing ET tools. Future work should focus on assessing additional crops and integration into other tools such as the emerging OpenET platform.

Soil physicochemical and microbial properties affect nitrogen cycling in technogenically transformed coal dump soils

Mining industrial sectors deteriorate local and regional environmental quality, contributing to global ecosystem contamination. The activities of microorganisms and enzymes involved in nitrogen cycling processes were investigated in coal dump soils that had undergone technogenic transformations and reclamation. Compared to the background uncontaminated soil, the heavy metal concentrations increased to high contamination levels (the contents of mobile forms of Zn, Ni, and Cu were 66.9; 35.6; 12.9 mg/kg, respectively), salinity increased to 0.1–2.41 %. The activities of nitrification and denitrification as well as the abundance of ammonifiers reduced due to high concentrations of mobile forms, the presence of coal particles, and the salinity of soil samples. The urease activity decreased due to reduced content of soluble organic matter in disturbed soils (2.3 times lower than the background soils). Mechanical reclamation leads to a 4-fold decrease in soil toxicity levels; however, it does not enhance nitrogen cycling activity. The activity of nitrification and urease decreased by >4 times as compared to the soil of the protected area. Microbial community composition of coal mine soils was similar to that of undisturbed soil. Nitrifiers in coal mine soil were represented by the genera Nitrososphaera, Candidatus Nitrosocosmicus, Nitrosospira, Nitrosomonas, Nitrosococcus, Nitrospira, Nitrobacter. The results indicated that the disruption of nitrification process led to low nitrate content in technogenically transformed coal dump soils.

Pollution characteristics of heavy metals, antibiotic and antibiotic resistance genes in the crested ibis and their habitat across different lifestyle and geography

Antibacterial resistance in wild animals has been increasingly reported worldwide, even though they are usually not directly exposed to clinically relevant antibiotics. Crested ibis, one of the rarest birds in the world, usually forages in paddy fields and prefer to nest and breed near villages that is greatly influenced by anthropogenic activities. We sampled the feces of crested ibises, as well as their habitat environment samples, to explore the pollution characteristics of heavy metals, antibiotics and antibiotic resistance genes (ARGs). Results showed that the pollution characteristics of heavy metals, antibiotic, ARGs and gut microbiota of crested ibis were more related by host lifestyle and habitats. Captive ibises had higher relative abundances of the total ARGs and tetracycline concentrations compared with feralization and wild ibises, while the heavy metal contents had shown the opposite result. The Characteristics of pollutants in the corresponding environmental samples also exhibited high similarity with the results of fecal samples. The relative abundances of Proteobacteria and Actinobacteria were significantly different between captive and wild individuals, while the abundance of majority bacterial genera was generally higher in wild populations. The concentrations of heavy metals in soil (Cd, Cu and Zn) and water (Cd, Cu, Zn and Cr) were both exceeded the background soil levels or surface water quality standards, suggesting multi-element contamination in the habitat. Ecological risk assessments of soils by Igeo and Er showed that the habitats of wild ibises were heavily and moderately contaminated by Cd, which would possibly pose a threat to the health of ibises. PLS-PM analysis indicated that microbial compositions and residual antibiotics had the most substantial impact on the dynamic changes in ARGs of ibis. Overall, this work provides a comprehensive understanding of the characteristics, risks of those contaminations, and their effects on the ARGs in the habitat of crested ibis.

Metal(loid) bioaccessibility and risk assessment of ashfall deposit from Popocatépetl volcano, Mexico

Ash emission from volcanic eruptions affects the environment, society, and human health. This study shows the total concentration and lung bioaccessible fraction of eight potential toxic metal(loid)s in five Popocatépetl ashfall samples. Mineralogical phases and particle size distribution of the ashfall were analyzed by X-ray diffraction (XRD) and Scanning Electron Microscope (SEM) techniques, respectively. The bioaccessibility test of Gamble solution (GS) and Artificial Lysosomal Fluid (ALF) were conducted to simulate extracellular (pH 7) and intracellular (pH 4.5) conditions, respectively. The studied metal(loid)s showed the following total concentration (mg kg−1): 1.98 (As), 0.17 (Cd), 134.09 (Cr), 8.66 (Cu), 697.33 (Mn), 55.35 (Ni), 8.77 (Pb), and 104.10 (Zn). Geochemical indices suggested that some metal(loid)s are slightly enriched compared to the local soil background concentrations. Several mineralogical phases were identified in the collected ashfall deposits, such as plagioclase, pyroxene, and Fe–Ti oxide, among others. According to the risk assessment results, the non-carcinogenic risk related to ashfall exposure returns an HQ > 1 for children. In contrast, the estimation of carcinogenic risk was found to be within the tolerable limit. Metal(loid)s showed low bioaccessibility (< 30%) in GS and ALF, with the highest values found in ALF solution for As (12.18%) and Cu (7.57%). Despite their metal-bioaccessibility, our findings also showed that dominant ash particle size ranged between fine (< 2.5 μm) and extremely fine (< 1 μm), considered highly inhalable fractions. The results obtained in this work indicate that volcanic ashes are bioinsoluble and biodurable, and exhibit low bioaccessibility when in contact with lung human fluids.

Cumulative characteristics and ecological risk source analysis of soil potentially toxic elements in the northern margin of the Tibetan Plateau.

To understand the soil toxic and hazardous elements content, pollution level, and ecological risk status in the northern margin of the Tibetan Plateau, we collected and analyzed 8273 sets of surface soil samples. Evaluations were conducted using the single-factor pollution index, geo-accumulation, pollution load, and potential ecological risk indices, and source identification correlation and principal component analysis. The results revealed that, compared with the background levels in China, the accumulation of soil arsenic, cadmium, nickel, and chromium was greater in the surface soil of the study area. Additionally, in comparison with Qinghai Province, more mercury accumulated in the surface soil of the study area and owing to the influence of anthropogenic activities. Benchmarking against soil environmental quality standards, the study area exhibited pollution control zones primarily dominated by arsenic and cadmium (3.9%). The spatial distribution revealed distinct zones: a ridge mountain type characterized by arsenic-cadmium-chromium-nickel, a Daban mountain type with solely cadmium presence, and a Longyangxia-Jianzha South type dominated by arsenic. Compared with the Qinghai Province soil background values, evaluations using the Pollution loading index, Geological Cumulative Index, and Potential Ecological Risk Index methods revealed varying degrees of potentially toxic element content exceedance. From an ecological risk perspective, the individual element with the highest potential ecological risk coefficients were mercury, followed by cadmium and arsenic; however, the region's overall ecological risk index was classified as low. Three distinct sources were identified: natural sources leading to high background levels of chromium, nickel, copper, zinc, and mercury; mixed natural and industrial/agricultural sources contributing to elevated cadmium levels; and human activity-related mercury enrichment. Based on the evaluation results, synergistic monitoring of soil and biota in naturally occurring risk zones is recommended to ensure the safety of agricultural and pastoral products. Additionally, ecological impact assessments and pollution source mitigation studies should be conducted in regions influenced by human activities to curb the further degradation of soil ecological quality.

Assessing the risk of subsoil compaction in croplands using the soil compaction stratification ratio as an indicator

AbstractAccurate prediction of subsoil compaction in cropland areas is important for food security and sustainable agricultural production development. This study evaluated the validity and advantages of the soil compaction stratification ratio (SR) as an indicator for assessing the risk of subsoil compaction and presented a theoretical framework for predicting and evaluating subsoil compaction risk. Geographic information system and remote sensing technologies were utilized in combination with a state‐of‐the‐art ensemble machine learning model to quantitatively predict the spatial distribution and uncertainty of subsoil compaction risk in croplands. The results showed that the subsoil bulk density (BD) was significantly greater than the topsoil BD in Shaanxi Province, China. Compared with that of the subsoil BD, the stratification ratio of the bulk density (SRBD) exhibited greater sensitivity to subsoil compaction, and it accurately reflected spatial variations in subsoil compactness. The SRBD exhibited obvious spatial heterogeneity, and the mean annual precipitation, elevation, and mean land surface temperature substantially influenced its spatial distribution. The critical thresholds of the SRBD for the low‐risk, medium‐risk, and high‐risk groups were 1.01, 1.21, and 1.32, respectively. Low subsoil compaction dominated in the mountainous and hilly areas, whereas the Guanzhong Plain and Hanzhong Basin exhibited medium levels of subsoil compaction. Some croplands in the central Guanzhong Plain experienced severe subsoil compaction issues. In conclusion, the soil compaction SR was found to be an effective indicator of subsoil compaction by effectively eliminating soil background differences. This approach has potential applications in cropland management, digital soil mapping, and early warning of soil compaction risk.

Dynamic evaluation of the ecological evolution and quality of arid and semi-arid deserts in the Aibugai River Basin based on an improved remote sensing ecological index

The ecological quality of arid and semi-arid regions (ASRs) is fragile, and the evaluation of dynamic changes in the multi-factor, long-time-series ecological quality of these regions can provide a scientific basis for sustainable regional development. Based on the remote sensing ecological index (RSEI) and its derivative indices dedicated to monitoring ecological quality in ASRs, this study proposes a new modified RSEI (nmRSEI) suitable for ASRs. We used the nmRSEI to evaluate the dynamic changes in the ecological quality and analyse the factors driving these changes in the Aibugai River Basin in the middle of the Inner Mongolian Plateau in the arid core of Asia from 1986 to 2022. The results led to the following conclusions: (1) the use of the nmRSEI helps solve the problems related to the original greenness index, i.e. normalised difference vegetation index, which was readily affected by the soil background in areas with low vegetation coverage; (2) the new dryness index can meet the evaluation requirements for the surface dryness degree in >98.65% of the study area; (3) the introduced salinity index showed a significant negative correlation with the nmRSEI; (4) the nmRSEI exhibits a gradual downward trend (Slope = −0.00326/10a); and (5) temperature was the main factor controlling the ecological quality during the research period. The nmRSEI provides a fast and effective new method for regularly monitoring ecological quality in ASRs. In addition, driving factor analysis can provide theoretical support for ecological protection in ASRs and the realisation of the United Nations 2030 Sustainable Development Goals.

Source-specific ecological risk analysis and critical source identification of heavy metal(loid)s in the soil of typical abandoned coal mining area

Long-term coal mining activities in abandoned coal mining areas have resulted in the migration of large quantities of heavy metals into the surrounding soil environment, posing a threat to the regional ecological environment. This study focuses on the surface soil collected from a typical abandoned coal mining area. Methods such as the pollution index (PI) and potential ecological risk index (RI) were used to comprehensively evaluate the pollution levels and ecological risks of soil heavy metals. Geostatistical analysis and the APCS-MLR model were used to quantify the sources of soil heavy metals, and Nemerow integrated ecological risk (NIRI) model was coupled to apportion the ecological risks from different pollution sources. The results indicate that the average concentrations of Cd, As, and Zn are 4.58, 2.44, and 1.67 times the soil background values, respectively, while the concentrations of other heavy metals are below the soil background values. The soil of study area is strongly polluted by heavy metals, with the pollution level and ecological risk of Cd being significantly higher than those of other heavy metals. The NIRI calculation results show that the overall comprehensive ecological risk level is considerable, with sample points classified as relatively considerable, moderate, and low at 60.53 %, 36.84 %, and 2.63 %, respectively. The sources of soil heavy metals can be categorized into four types: traffic activities, natural sources, coal gangue accumulation, and a combined source of coal mining and agricultural activities, with contribution rates of 35.3 %, 36.1 %, 19.5 %, and 9.1 %, respectively. The specific source ecological risk assessment results indicate that coal gangue accumulation contributes the most to ecological risk (36.4 %) and should be prioritized for pollution control, with Cd being the priority control element for ecological risk. The findings provide theoretical support for the refined management of soil heavy metal pollution in abandoned coal mining areas.