Temporal variability in trace metal solubility in a paddy soil not reflected in uptake by rice (Oryza sativa L.)

Alternating flooding and drainage conditions have a strong influence on redox chemistry and the solubility of trace metals in paddy soils. However, current knowledge of how the effects of water management on trace metal solubility are linked to trace metal uptake by rice plants over time is still limited. Here, a field-contaminated paddy soil was subjected to two flooding and drainage cycles in a pot experiment with two rice plant cultivars, exhibiting either high or low Cd accumulation characteristics. Flooding led to a strong vertical gradient in the redox potential (Eh). The pH and Mn, Fe, and dissolved organic carbon concentrations increased with decreasing Eh and vice versa. During flooding, trace metal solubility decreased markedly, probably due to sulfide mineral precipitation. Despite its low solubility, the Cd content in rice grains exceeded the food quality standards for both cultivars. Trace metal contents in different rice plant tissues (roots, stem, and leaves) increased at a constant rate during the first flooding and drainage cycle but decreased after reaching a maximum during the second cycle. As such, the high temporal variability in trace metal solubility was not reflected in trace metal uptake by rice plants over time. This might be due to the presence of aerobic conditions and a consequent higher trace metal solubility near the root surface, even during flooding. Trace metal solubility in the rhizosphere should be considered when linking water management to trace metal uptake by rice over time

Prediction models for diagnosis and prognosis of covid-19: : systematic review and critical appraisal

Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity. Funding: LW, BVC, LH, and MDV acknowledge specific funding for this work from Internal Funds KU Leuven, KOOR, and the COVID-19 Fund. LW is a postdoctoral fellow of Research Foundation-Flanders (FWO) and receives support from ZonMw (grant 10430012010001). BVC received support from FWO (grant G0B4716N) and Internal Funds KU Leuven (grant C24/15/037). TPAD acknowledges financial support from the Netherlands Organisation for Health Research and Development (grant 91617050). VMTdJ was supported by the European Union Horizon 2020 Research and Innovation Programme under ReCoDID grant agreement 825746. KGMM and JAAD acknowledge financial support from Cochrane Collaboration (SMF 2018). KIES is funded by the National Institute for Health Research (NIHR) School for Primary Care Research. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. GSC was supported by the NIHR Biomedical Research Centre, Oxford, and Cancer Research UK (programme grant C49297/A27294). JM was supported by the Cancer Research UK (programme grant C49297/A27294). PD was supported by the NIHR Biomedical Research Centre, Oxford. MOH is supported by the National Heart, Lung, and Blood Institute of the United States National Institutes of Health (grant R00 HL141678). ICCvDH and BCTvB received funding from Euregio Meuse-Rhine (grant Covid Data Platform (coDaP) interref EMR187). The funders played no role in study design, data collection, data analysis, data interpretation, or reporting.Peer reviewedPublisher PD

Assessment of the Impact of Climate Change and Land Management Change on Soil Organic Carbon Content, Leached Carbon Rates and Dissolved Organic Carbon Concentrations

Climate change is projected to significantly affect the concentrations and mobility of contaminants, such as metals and pathogens, in soil, groundwater and surface water. Climate- and land management-induced changes in soil organic carbon and dissolved organic carbon levels may promote the transport of toxic substances, such as copper and cadmium, and pathogenic microorganisms, ultimately affecting the exposure of humans and ecosystems to these contaminants. In this study, we adopted the Century model to simulate past (1900 - 2010), present, and future (2010 - 2100) SOC and DOC levels for a sandy and a loamy soil typical for Central and Western European conditions under three land use types (forest, grassland and arable land) and several future scenarios addressing climate change and land management change. The climate scenarios were based on the KNMI'06 G+ and W+ scenarios from the Royal Dutch Meteorological Institute. The simulated current SOC levels were compared to observed SOC values derived from various Dutch soil databases, taking into account the different soil depths the simulated and observed values refer to. The simulated SOC levels were generally in line with the observed values for the different kinds of soil and land use types. Climate change scenarios resulted in a decrease in both SOC and DOC for the grassland systems, whereas in the arable land (on sandy soil) and in the forest systems, SOC was found to increase and DOC to decrease. A sensitivity analysis of the individual effects of changes in temperature and precipitation showed that the effect of temperature predominates over the effect of precipitation. A reduction in the application rates of artificial fertilizers leads to a decrease in the SOC stocks and the leached carbon rates in the arable land systems, but has a negligible effect on SOC and DOC levels of the grassland systems. This study demonstrated the ability of the Century model to simulate climate change and agricultural management effects on SOC dynamics. The following step of this study will involve the translation of the soil organic matter pools as simulated with Century model, into pools of different metal binding capacity to be used for the metal partitioning and leaching modelling

Dynamic geochemical models to assess deposition impacts of metals for soils and surface waters

This chapter describes the use of geochemical models to assess the impacts of the deposition of metals on the concentrations of metals in soils and surface waters. We describe three dynamic models: SMART2-metals, SMARTml and CHUM-AM, each with their specific purpose and geographical scale of application. All three models include the most relevant metal fluxes and soil chemical processes, but with various level of detail related to their specific aim and scale. The ability of the models to simulate the long-term trends of metal fate was assessed by comparing model results and observations of either the present metal status, using hind cast simulations with historical deposition trends, or metal pools in chronosequences of afforested agricultural land of different stand age, or metal concentrations observed in a long-term monitoring study. The model simulations show the long times needed to approach equilibrium concentrations of metals due to changes in the atmospheric deposition of metals, sulphur and nitrogen. Dynamic models are therefore indispensable tools for the assessment of metal concentrations at changing levels of metal inputs to soil-water system

Influence of pH on the redox chemistry of metal (hydr)oxides and organic matter in paddy soils

The primary purpose of this study was to determine how flooding and draining cycles affect the redox chemistry of metal (hydr)oxides and organic matter in paddy soils and how the pH influences these processes. Our secondary purpose was to determine to what extent a geochemical thermodynamic equilibrium model can be used to predict the solubility of Mn and Fe during flooding and draining cycles in paddy soils

Solubility of trace metals in two contaminated paddy soils exposed to alternating flooding and drainage

Uptake of trace metals by crops is determined by the solubility of trace metals. In paddy soils, flooding and drainage influence redox chemistry and consequently trace metal solubility and thus uptake by rice plants. Current knowledge on how the dynamics in redox chemistry affect the solubility of trace metals in contaminated paddy soils is still limited. The objectives of our study were to investigate (i) the effects of flooding and drainage on trace metal solubility in paddy soils and (ii) to what extent a multi-surface modeling approach can predict trace metal solubility under changing redox conditions. We performed a column experiment with two contaminated paddy soils with similar soil properties but contrasting pH. During two successive flooding and drainage cycles, dynamics in Eh, pH and dissolved organic matter concentrations greatly affected trace metal solubility for both soils. Multi-surface model predictions indicate that under aerobic conditions, the higher pH of the alkaline soil leads to a stronger complexation of trace metals by reactive surfaces of the soil and, consequently, to lower dissolved concentrations than in the acidic soil. Under anaerobic conditions, predictions shows that sulfide precipitates control trace metal solubility in both soils, but still the higher pH of the alkaline soil leads to lower trace metal concentrations in soil solution at equilibrium. Furthermore, model calculations showed that stoichiometry and solubility of copper sulfide minerals can substantially affect solubility of other trace metals especially when trace element concentrations exceed soil sulfate concentrations. This stoichiometry and solubility should be considered when predicting the solubility of trace metals under anaerobic conditions. (C) 2015 Elsevier B.V. All rights reserved

Model-Based Analysis of the Long-Term Effects of Fertilization Management on Cropland Soil Acidification

Agricultural soil acidification in China is known to be caused by the over-application of nitrogen (N) fertilizers, but the long-term impacts of different fertilization practices on intensive cropland soil acidification are largely unknown. Here, we further developed the soil acidification model VSD+ for intensive agricultural systems and validated it against observed data from three long-term fertilization experiments in China. The model simulated well the changes in soil pH and base saturation over the last 20 years. The validated model was adopted to quantify the contribution of N and base cation (BC) fluxes to soil acidification. The net NO3 - leaching and NO4 +input accounted for 80% of the proton production under N application, whereas one-third of acid was produced by BC uptake when N was not applied. The simulated long-term (1990-2050) effects of different fertilizations on soil acidification showed that balanced N application combined with manure application avoids reduction of both soil pH and base saturation, while application of calcium nitrate and liming increases these two soil properties. Reducing NH4 + input and NO3 - leaching by optimizing N management and increasing BC inputs by manure application thus already seem to be effective approaches to mitigating soil acidification in intensive cropland systems.</p

Assessment of the Impact of Climate Change and Land Management Change on Soil Organic Carbon Content, Leached Carbon Rates and Dissolved Organic Carbon Concentrations

Climate change is projected to significantly affect the concentrations and mobility of contaminants, such as metals and pathogens, in soil, groundwater and surface water. Climate- and land management-induced changes in soil organic carbon and dissolved organic carbon levels may promote the transport of toxic substances, such as copper and cadmium, and pathogenic microorganisms, ultimately affecting the exposure of humans and ecosystems to these contaminants. In this study, we adopted the Century model to simulate past (1900 - 2010), present, and future (2010 - 2100) SOC and DOC levels for a sandy and a loamy soil typical for Central and Western European conditions under three land use types (forest, grassland and arable land) and several future scenarios addressing climate change and land management change. The climate scenarios were based on the KNMI'06 G+ and W+ scenarios from the Royal Dutch Meteorological Institute. The simulated current SOC levels were compared to observed SOC values derived from various Dutch soil databases, taking into account the different soil depths the simulated and observed values refer to. The simulated SOC levels were generally in line with the observed values for the different kinds of soil and land use types. Climate change scenarios resulted in a decrease in both SOC and DOC for the grassland systems, whereas in the arable land (on sandy soil) and in the forest systems, SOC was found to increase and DOC to decrease. A sensitivity analysis of the individual effects of changes in temperature and precipitation showed that the effect of temperature predominates over the effect of precipitation. A reduction in the application rates of artificial fertilizers leads to a decrease in the SOC stocks and the leached carbon rates in the arable land systems, but has a negligible effect on SOC and DOC levels of the grassland systems. This study demonstrated the ability of the Century model to simulate climate change and agricultural management effects on SOC dynamics. The following step of this study will involve the translation of the soil organic matter pools as simulated with Century model, into pools of different metal binding capacity to be used for the metal partitioning and leaching modelling

Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal

OBJECTIVE: To review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of becoming infected with covid-19 or being admitted to hospital with the disease. DESIGN: Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. DATA SOURCES: PubMed and Embase through Ovid, arXiv, medRxiv, and bioRxiv up to 5 May 2020. STUDY SELECTION: Studies that developed or validated a multivariable covid-19 related prediction model. DATA EXTRACTION: At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). RESULTS: 14 217 titles were screened, and 107 studies describing 145 prediction models were included. The review identified four models for identifying people at risk in the general population; 91 diagnostic models for detecting covid-19 (60 were based on medical imaging, nine to diagnose disease severity); and 50 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequently reported predictors of diagnosis and prognosis of covid-19 are age, body temperature, lymphocyte count, and lung imaging features. Flu-like symptoms and neutrophil count are frequently predictive in diagnostic models, while comorbidities, sex, C reactive protein, and creatinine are frequent prognostic factors. C index estimates ranged from 0.73 to 0.81 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.68 to 0.99 in prognostic models. All models were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and vague reporting. Most reports did not include any description of the study population or intended use of the models, and calibration of the model predictions was rarely assessed. CONCLUSION: Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that proposed models are poorly reported, at high risk of bias, and their reported performance is probably optimistic. Hence, we do not recommend any of these reported prediction models for use in current practice. Immediate sharing of well documented individual participant data from covid-19 studies and collaboration are urgently needed to develop more rigorous prediction models, and validate promising ones. The predictors identified in included models should be considered as candidate predictors for new models. Methodological guidance should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, studies should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. SYSTEMATIC REVIEW REGISTRATION: Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. READERS' NOTE: This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 2 of the original article published on 7 April 2020 (BMJ 2020;369:m1328), and previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp).status: publishe