A tool (SPOLERC) to guide the evaluation of phosphorus leaching for agricultural soil by using the change point value in the Xingkai Lake Basin, China

Background The soil P leaching change point (CP) has been widely used to evaluate soil P leaching risk. However, an automation calculation method for soil P leaching CP value, and an effective risk grading method performed for classifying soil P leaching risk evaluation have not been developed. Results This study optimized the calculation process for soil P leaching CP value with two different fitting models. Subsequently, based on the Python programming language, a computation tool named Soil Phosphorus Leaching Risk Calculator (SPOLERC) was developed for soil P leaching risk assessment. SPOLERC not only embedded the calculation process of the soil P leaching CP value, but also introduced the single factor index (SFI) method to grade the soil P leaching risk level. The relationships between the soil Olsen-P and leachable P were fitted by using SPOLERC in paddy soils and arid agricultural soils in the Xingkai Lake Basin, and the results showed that there was a good linear fitting relationship between the soil Olsen-P and leachable P; and the CP values were 59.63 and 35.35 mg Olsen-P kg−1 for paddy soils and arid agricultural soils, respectively. Additionally, 32.7, 21.8, and 3.64% of arid agricultural soil samples were at low risk, medium risk, and high risk of P leaching, and 40.6% of paddy soil samples were at low risk. Conclusions SPOLERC can accurately fit the split-line model relationship between the soil Olsen-P and leachable P, and greatly improved the calculation efficiency for the soil P leaching CP value. Additionally, the obtained CP value can be used for soil P leaching risk assessment, which could help recognize key area of soil P leaching.

Solidification/Stabilization of Arsenic-Containing Tailings by Steel Slag-Based Binders with High Efficiency and Low Carbon Footprint

The disposal of nonferrous metal tailings poses a global economic and environmental problem. After employing a clinker-free steel slag-based binder (SSB) for the solidification/stabilization (S/S) of arsenic-containing tailings (AT), the effectiveness, leaching risk, and leaching mechanism of the SSB S/S treated AT (SST) were investigated via the Chinese leaching tests HJ/T299-2007 and HJ557-2010 and the leaching tests series of the multi-process Leaching Environmental Assessment Framework (LEAF). The test results were compared with those of ordinary Portland cement S/S treated AT (PST) and showed that the arsenic (As) curing rates for SST and PST samples were in the range of 96.80–98.89% and 99.52–99.2%, respectively, whereby the leached-As concentration was strongly dependent on the pH of the leachate. The LEAF test results showed that the liquid–solid partitioning limit of As leaching from AT, SST, and PST was controlled by solubility, and the highest concentrations of leached As were 7.56, 0.34, and 0.33 mg/L, respectively. The As leaching mechanism of monolithic SST was controlled by diffusion, and the mean observed diffusion coefficient of 9.35 × 10−15 cm2/s was higher than that of PST (1.55 × 10−16 cm2/s). The findings of this study could facilitate the utilization of SSB in S/S processes, replacing cement to reduce CO2 emissions.

Probabilistic modelling of inherent field-level pesticide pollution risk in a small drinking water catchment using spatial Bayesian Belief Networks

Pesticides are contaminants of priority concern that continue to present a significant risk to drinking water quality. While pollution mitigation in catchment systems is considered a cost-effective alternative to costly drinking water treatment, the effectiveness of pollution mitigation measures is uncertain and needs to be able to consider local biophysical, agronomic, and social aspects. We developed a probabilistic decision support tool (DST) based on spatial Bayesian Belief Networks (BBN) that simulates inherent pesticide leaching risk to ground- and surface water quality to inform field-level pesticide mitigation strategies in a small drinking water catchment (3.1 km2) with limited observational data. The DST accounts for the spatial heterogeneity in soil properties, topographic connectivity, and agronomic practices; temporal variability of climatic and hydrological processes as well as uncertainties related to pesticide properties and the effectiveness of management interventions. The rate of pesticide loss via overland flow and leaching to groundwater and the resulting risk of exceeding a regulatory threshold for drinking water was simulated for five active ingredients. Risk factors included climate and hydrology (temperature, rainfall, evapotranspiration, overland and subsurface flow), soil properties (texture, organic matter content, hydrological properties), topography (slope, distance to surface water/depth to groundwater), land cover and agronomic practices, pesticide properties and usage. The effectiveness of mitigation measures such as delayed timing of pesticide application; 10 %, 25 % and 50 % reduction in application rate; field buffers; and presence/absence of soil pan on risk reduction were evaluated. Sensitivity analysis identified the month of application, land use, presence of buffers, field slope and distance as the most important risk factors, alongside several additional influential variables. Pesticide pollution risk from surface water runoff showed clear spatial variability across the study catchment, while groundwater leaching risk was uniformly low, with the exception of prosulfocarb. Combined interventions of 50 % reduced pesticide application rate, management of plough pan, delayed application timing and field buffer installation notably reduced the probability of high-risk from overland runoff and groundwater leaching, with individual measures having a smaller impact. The graphical nature of the BBN facilitated interactive model development and evaluation with stakeholders to build model credibility, while the ability to integrate diverse data sources allowed a dynamic field-scale assessment of ‘critical source areas’ of pesticide pollution in time and space in a data scarce catchment, with explicit representation of uncertainties.

A Tool (SPOLERC) to Guide the Evaluation of Phosphorus Leaching for Agricultural Soil by Using Soil Phosphorus Leaching Change Point in the Lake Xingkai Basin, China

BackgroundAs the key factor of soil P leaching risk assessment, soil P leaching change point (CP) has been widely reported. However, there was no report have clearly described the calculation method of soil P leaching CP value and its automation calculation. Additionally, there was no effective risk grading method performed on the classification of soil P leaching evaluation.ResultsThis study has optimized the calculation process of soil P leaching CP value under two different models. Subsequently, based on the Python programming language, a computation tool named SPOLERC (Soil Phosphorus Leaching Risk Calculator) was developed for soil P leaching risk assessment. SPOLERC not only embedded the calculation process of soil P leaching CP value, but also introduced the single factor index (SFI) method to grade the soil P leaching risk level. Considering the relationships between soil Olsen-P and leachable P fitted by using SPOLERC in paddy land soils and arid agricultural land soils in the Lake Xingkai basin, results have shown that there is a good linear fitting relationship between soil Olsen-P and leachable P; and the CP values were 59.63 and 35.35 mg Olsen-P kg-1 in paddy land soils and arid agricultural land soils, respectively. Additionally, 32.7%, 21.8%, and 3.64% of arid agricultural soil samples are at low risk, medium risk, and high risk of P leaching, and 40.6% of paddy land soil samples are at low risk. ConclusionsThe SPOLERC can accurately fit the split-line model relationship between soil Olsen-P and leachable P, and greatly improve the calculating efficiency for soil P leaching CP value. Additionally, the obtained CP value can be used for soil P leaching risk assessment, which can provide support for the quantitative study of soil P leaching loss and the control technology of soil P leaching loss.