A web-based pesticide risk assessment tool for drinking water protection zones in Sweden.

To protect human health, wildlife and the aquatic environment, "safe uses" of pesticides are determined at the EU level while product authorization and terms of use are established at the national level. In Sweden, extra precaution is taken to protect drinking water, and permits are therefore required for pesticide use within abstraction zones. This paper presents MACRO-DB, a tool for assessing pesticide contamination risks of groundwater and surface water, used by authorities to support their decision-making for issuing such permits. MACRO-DB is a meta-model based on 583,200 simulations of the physically-based MACRO model used for assessing pesticide leaching risks at EU and national level. MACRO-DB is simple to use and runs on widely available input data. In a qualitative comparative assessment for two counties in Sweden, MACRO-DB outputs were in general agreement with groundwater monitoring data and matched or were more protective than the national risk assessment procedure for groundwater.

Persisting symptoms common but inability to work rare: a one-year follow-up study of Finnish hospitalised COVID-19 patients.

BACKGROUND: Difficulties in recovery persisting for months have been reported in patients with severe COVID-19. Our aim was to investigate respiratory and overall recovery one year after hospital discharge. METHODS: Finnish patients hospitalised due to COVID-19 during the first wave of the pandemic were recruited to a survey of symptoms, quality of life (RAND-36), work status, and health care use one year after hospital discharge. Patients with lung function test and chest x-ray results available from 3-6 months after hospital discharge underwent spirometry and a chest x-ray at one year. RESULTS: Ninety-six patients responded to the one-year survey, 32 underwent spirometry and 32 a chest x-ray. Of those working full-time before COVID-19, median duration of sick leave was 40 days and 10% had not returned to work at one year. Health-care service use related to COVID-19 after discharge was reported by 79%, 50% using primary care, 34% occupational health care and 32% specialist care, respectively. Tiredness, fatigue, and physical difficulties increased in follow-up (p = 0.022-0.033). Quality of life did not change. Chest x-ray abnormalities decreased in follow-up, with an abnormal chest x-ray in 58% at 3-6 months and 25% at one year. A restrictive spirometry pattern was more common at one year (16 vs. 34%, p = 0.014). CONCLUSIONS: Prolonged symptoms are common, some patients have decreased lung function, and a small minority of patients still have not returned to work one year after severe COVID-19. This calls for further research into the underlying causes and risk factors for prolonged recovery.

Successful treatment of persisting SARS-CoV-2 infection in an immunocompromised patient with repeated nirmatrelvir/ritonavir courses: a case report.

BACKGROUND: In immunocompromised patients, persistent SARS-CoV-2 viral shedding and relapsing COVID-19 pneumonia have been described. Currently, little is known about the management of persisting COVID-19, and immunocompromised patients are recommended to be treated using antivirals and immunomodulatory therapies at similar doses and durations as the general population. Previous case reports have described treatment with repeated and prolonged courses of remdesivir and some evidence is emerging in the use of nirmatrelvir/ritonavir combination (NMV/r). METHODS: We describe a patient with recent chemotherapy including rituximab for follicular lymphoma with persisting SARS-CoV-2 infection. Polymerase chain reaction tests (PCR), cycle threshold values and blood SARS-CoV-2 antigen levels were evaluated. RESULTS: The patient presented with persisting SARS-CoV-2 with relapsing COVID-19 pneumonia. The patient was treated successfully with repeated courses of NMV/r without any observed adverse effects. After the third, prolonged course, the patient remained afebrile and PCR negative, and no relapses have been observed four months after the third NMV/r course. CONCLUSIONS: Nirmatrelvir-ritonavir could offer a more accessible alternative to remdesivir. Further research and guidelines for persisting SARS-CoV-2 infection in immunocompromised patients are urgently needed.

Lung function and exercise capacity 6 months after hospital discharge for critical COVID-19.

BACKGROUND: The significant morbidity caused by COVID-19 necessitates further understanding of long-term recovery. Our aim was to evaluate long-term lung function, exercise capacity, and radiological findings in patients after critical COVID-19. METHODS: Patients who received treatment in ICU for COVID-19 between March 2020 and January 2021 underwent pulmonary function tests, a 6MWD and CXR 6 months after hospital discharge. RESULTS: A restrictive ventilatory defect was found in 35% (23/65) and an impaired diffusing capacity in 52% (32/62) at 6 months. The 6-minute walk distance was reduced in 33% (18/55), and 7% (4/55) of the patients had reduced exercise capacity. Chest X-ray was abnormal in 78% (52/67) at 6 months after hospital discharge. CONCLUSION: A significant number of patients had persisting lung function impairment and radiological abnormalities at 6 months after critical COVID-19. Reduced exercise capacity was rare.

Women report more symptoms and impaired quality of life: a survey of Finnish COVID-19 survivors.

BACKGROUND: The long-term sequelae after COVID-19 are not yet fully known. Our aim was to evaluate subjective symptoms and quality of life in Finnish hospitalized COVID-19 patients at six months follow-up. METHODS: Hospitalised adult patients with laboratory-confirmed SARS-CoV-2 infection from March to June 2020 were recruited. We conducted a survey on demographics and comorbidities, ten specific symptoms, and a RAND-36 quality of life questionnaire six months after hospital discharge. We collected clinical data manually from medical records. RESULTS: 101 patients (54 male) out of 246 invited completed the survey. Their median age was 60 years, and the mean hospital length of stay was 15 d. Most patients (90%) experienced symptoms, the most common of which were tiredness (88%), fatigue (79%), sleeping problems (76%), and dyspnoea (70%). In regard to gender, women showed a shorter time of hospitalization (p = .048) and lower peak flow of supplementary oxygen (p = .043). Women reported more frequently dyspnoea, fatigue, tiredness, sleeping problems, and mood problems (p = .008-.033), and a lower quality of life in seven of eight dimensions (p < .001-.015). Five explanatory variables for the reduced quality of life were identified in multivariate analysis: age, female sex, BMI, sleep apnoea, and duration of mechanical ventilation. Of the patients who worked full-time before COVID-19, 11% had not returned to work. CONCLUSIONS: Most patients experienced symptoms six months after hospital discharge. Women reported more symptoms and a lower quality of life than men. These findings highlight the differences in recovery between men and women and call for active rehabilitation of COVID-19 patients.

Studies on CYP3A activity during the menstrual cycle as measured by urinary 6ß-hydroxycortisol/cortisol.

The 6ß-OH-cortisol/cortisol ratio (6ß-OHC/C) in urine is an endogenous marker of drug-metabolizing enzyme cytochrome P450 3A (CYP3A). The primary aim of this single center, prospective, non-interventional cohort study, was to investigate the variability of 6ß-OHC/C during the menstrual cycle. In addition, possible associations between the CYP3A activity and sex hormones, gut microbiota metabolite trimethylamine-N-Oxide (TMAO) and microRNA-27b, respectively, were investigated. Serum and urinary samples from healthy, regularly menstruating women followed for two menstrual cycles were analyzed. Twenty-six complete menstrual cycles including follicular, ovulatory, and luteal phase were defined based on hormone analyses in serum. 6ß-OHC/C were analyzed in urine and sex hormones, TMAO and miRNA-27b were analyzed in serum at the same time points. 6ß-OHC/C did not vary between the follicular, ovulatory, or luteal phases. There was a difference in the relative miRNA-27b expression between the follicular and ovulatory phase (p = .03). A significant association was found between 6ß-OHC/C and progesterone during the follicular (p = .005) and ovulatory (p = .01) phases (n = 26 for each phase). In addition, a significant association was found between the ratio and TMAO during the ovulatory (p = .02) and luteal (p = .002) phases. 6ß-OHC/C and gut microbiota TMAO were significantly associated (p = .003) when evaluating all values, for all phases (n = 78). Interestingly, the finding of an association between 6ß-OHC/C in urine and levels of TMAO in serum suggest that gut microbiota may affect CYP3A activity.

Small airway function in Finnish COVID-19 survivors.

Follow-up studies of COVID-19 patients have found lung function impairment up to six months after initial infection, but small airway function has not previously been studied. Patients (n = 20) hospitalised for a severe SARS-CoV-2 infection underwent spirometry, impulse oscillometry, and multiple measurements of alveolar nitric oxide three to six months after acute infection. None of the patients had small airway obstruction, nor increased nitric oxide concentration in the alveolar level. None of the patients had a reduced FEV1/FVC or significant bronchodilator responses in IOS or spirometry. In conclusion, we found no evidence of inflammation or dysfunction in the small airways.

Real-life clinical sensitivity of SARS-CoV-2 RT-PCR test in symptomatic patients.

BACKGROUND: Understanding the false negative rates of SARS-CoV-2 RT-PCR testing is pivotal for the management of the COVID-19 pandemic and it has implications for patient management. Our aim was to determine the real-life clinical sensitivity of SARS-CoV-2 RT-PCR. METHODS: This population-based retrospective study was conducted in March-April 2020 in the Helsinki Capital Region, Finland. Adults who were clinically suspected of SARS-CoV-2 infection and underwent SARS-CoV-2 RT-PCR testing, with sufficient data in their medical records for grading of clinical suspicion were eligible. In addition to examining the first RT-PCR test of repeat-tested individuals, we also used high clinical suspicion for COVID-19 as the reference standard for calculating the sensitivity of SARS-CoV-2 RT-PCR. RESULTS: All 1,194 inpatients (mean [SD] age, 63.2 [18.3] years; 45.2% women) admitted to COVID-19 cohort wards during the study period were included. The outpatient cohort of 1,814 individuals (mean [SD] age, 45.4 [17.2] years; 69.1% women) was sampled from epidemiological line lists by systematic quasi-random sampling. The sensitivity (95% CI) for laboratory confirmed cases (repeat-tested patients) was 85.7% (81.5-89.1%) inpatients; 95.5% (92.2-97.5%) outpatients, 89.9% (88.2-92.1%) all. When also patients that were graded as high suspicion but never tested positive were included in the denominator, the sensitivity (95% CI) was: 67.5% (62.9-71.9%) inpatients; 34.9% (31.4-38.5%) outpatients; 47.3% (44.4-50.3%) all. CONCLUSIONS: The clinical sensitivity of SARS-CoV-2 RT-PCR testing was only moderate at best. The relatively high false negative rates of SARS-CoV-2 RT-PCR testing need to be accounted for in clinical decision making, epidemiological interpretations, and when using RT-PCR as a reference for other tests.

High Cell Density Perfusion Culture has a Maintained Exoproteome and Metabolome.

The optimization of bioprocesses for biopharmaceutical manufacturing by Chinese hamster ovary (CHO) cells can be a challenging endeavor and, today, heavily relies on empirical methods treating the bioreactor process and the cells as black boxes. Multi-omics approaches have the potential to reveal otherwise unknown characteristics of these systems and identify culture parameters to more rationally optimize the cultivation process. Here, the authors have applied both metabolomic and proteomic profiling to a perfusion process, using CHO cells for antibody production, to explore how cell biology and reactor environment change as the cell density reaches ≥200 × 106 cells mL-1 . The extracellular metabolic composition obtained in perfusion mode shows a markedly more stable profile in comparison to fed-batch, despite a far larger range of viable cell densities in perfusion. This stable profile is confirmed in the extracellular proteosome. Furthermore, the proteomics data shows an increase of structural proteins as cell density increases, which could be due to a higher shear stress and explain the decrease in cell diameter at very high cell densities. Both proteomic and metabolic results shows signs of oxidative stress and changes in glutathione metabolism at very high cell densities. The authors suggest the methodology presented herein to be a powerful tool for optimizing processes of recombinant protein production.

Tuning Metabolome Coverage in Reversed Phase LC-MS Metabolomics of MeOH Extracted Samples Using the Reconstitution Solvent Composition.

Considering the physicochemical diversity of the metabolome, untargeted metabolomics will inevitably discriminate against certain compound classes. Efforts are nevertheless made to maximize the metabolome coverage. Contrary to the main steps of a typical liquid chromatography-mass spectrometry (LC-MS) metabolomics workflow, such as metabolite extraction, the sample reconstitution step has not been optimized for maximal metabolome coverage. This sample concentration step typically occurs after metabolite extraction, when dried samples are reconstituted in a solvent for injection on column. The aim of this study was to evaluate the impact of the sample reconstitution solvent composition on metabolome coverage in untargeted LC-MS metabolomics. Lysogeny Broth medium samples reconstituted in MeOH/H2O ratios ranging from 0 to 100% MeOH and analyzed with untargeted reversed phase LC-MS showed that the highest number of metabolite features (n = 1500) was detected in samples reconstituted in 100% H2O. As compared to a commonly used reconstitution solvent mixture of 50/50 MeOH/H2O, our results indicate that the small fraction of compounds increasing in peak area response by the addition of MeOH to H2O, 5%, is outweighed by the fraction of compounds with decreased response, 57%. We evaluated our results on human serum samples from lymphoma patients and healthy control subjects. Reconstitution in 100% H2O resulted in a higher number of significant metabolites discriminating between these two groups than both 50% and 100% MeOH. These findings show that the sample reconstitution step has a clear impact on the metabolome coverage of MeOH extracted biological samples, highlighting the importance of the reconstitution solvent composition for untargeted discovery metabolomics.

Discrimination of pancreatic cancer and pancreatitis by LC-MS metabolomics.

INTRODUCTION: Pancreatic ductal adenocarcinoma (PDAC) is the fifth most common cause of cancer-related death in Europe with a 5-year survival rate of <5%. Chronic pancreatitis (CP) is a risk factor for PDAC development, but in the majority of cases malignancy is discovered too late for curative treatment. There is at present no reliable diagnostic marker for PDAC available. OBJECTIVES: The aim of the study was to identify single blood-based metabolites or a panel of metabolites discriminating PDAC and CP using liquid chromatography-mass spectrometry (LC-MS). METHODS: A discovery cohort comprising PDAC (n = 44) and CP (n = 23) samples was analyzed by LC-MS followed by univariate (Student's t test) and multivariate (orthogonal partial least squares-discriminant analysis (OPLS-DA)) statistics. Discriminative metabolite features were subject to raw data examination and identification to ensure high feature quality. Their discriminatory power was then confirmed in an independent validation cohort including PDAC (n = 20) and CP (n = 31) samples. RESULTS: Glycocholic acid, N-palmitoyl glutamic acid and hexanoylcarnitine were identified as single markers discriminating PDAC and CP by univariate analysis. OPLS-DA resulted in a panel of five metabolites including the aforementioned three metabolites as well as phenylacetylglutamine (PAGN) and chenodeoxyglycocholate. CONCLUSION: Using LC-MS-based metabolomics we identified three single metabolites and a five-metabolite panel discriminating PDAC and CP in two independent cohorts. Although further study is needed in larger cohorts, the metabolites identified are potentially of use in PDAC diagnostics.

Overlap in serum metabolic profiles between non-related diseases: Implications for LC-MS metabolomics biomarker discovery.

Untargeted metabolic profiling has generated large activity in the field of clinical biomarker discovery. Yet, no clinically approved metabolite biomarkers have emerged with failure in validation phases often being a reason. To investigate why, we have applied untargeted metabolic profiling in a retrospective cohort of serum samples representing non-related diseases. Age and gender matched samples from patients diagnosed with pneumonia, congestive heart failure, lymphoma and healthy controls were subject to comprehensive metabolic profiling using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). The metabolic profile of each diagnosis was compared to the healthy control group and significant metabolites were filtered out using t-test with FDR correction. Metabolites found to be significant between each disease and healthy controls were compared and analyzed for overlap. Results show that despite differences in etiology and clinical disease presentation, the fraction of metabolites with an overlap between two or more diseases was 61%. A majority of these metabolites can be associated with immune responses thus representing non-disease specific events. We show that metabolic serum profiles from patients representing non-related diseases display very similar metabolic differences when compared to healthy controls. Many of the metabolites discovered as disease specific in this study have further been associated with other diseases in the literature. Based on our findings we suggest non-related disease controls in metabolomics biomarker discovery studies to increase the chances of a successful validation and future clinical applications.

Iron Deficiency Induces a Partial Inhibition of the Photosynthetic Electron Transport and a High Sensitivity to Light in the Diatom Phaeodactylum tricornutum.

Iron limitation is the major factor controlling phytoplankton growth in vast regions of the contemporary oceans. In this study, a combination of thermoluminescence (TL), chlorophyll fluorescence, and P700 absorbance measurements have been used to elucidate the effects of iron deficiency in the photosynthetic electron transport of the marine diatom P. tricornutum. TL was used to determine the effects of iron deficiency on photosystem II (PSII) activity. Excitation of iron-replete P. tricornutum cells with single turn-over flashes induced the appearance of TL glow curves with two components with different peaks of temperature and contributions to the total signal intensity: the B band (23°C, 63%), and the AG band (40°C, 37%). Iron limitation did not significantly alter these bands, but induced a decrease of the total TL signal. Far red excitation did not increase the amount of the AG band in iron-limited cells, as observed for iron-replete cells. The effect of iron deficiency on the photosystem I (PSI) activity was also examined by measuring the changes in P700 redox state during illumination. The electron donation to PSI was substantially reduced in iron-deficient cells. This could be related with the important decline on cytochrome c 6 content observed in these cells. Iron deficiency also induced a marked increase in light sensitivity in P. tricornutum cells. A drastic increase in the level of peroxidation of chloroplast lipids was detected in iron-deficient cells even when grown under standard conditions at low light intensity. Illumination with a light intensity of 300 µE m(-2) s(-1) during different time periods caused a dramatic disappearance in TL signal in cells grown under low iron concentration, this treatment not affecting to the signal in iron-replete cells. The results of this work suggest that iron deficiency induces partial blocking of the electron transfer between PSII and PSI, due to a lower concentration of the electron donor cytochrome c 6. This decreased electron transfer may induce the over-reduction of the plastoquinone pool and consequently the appearance of acceptor side photoinhibition in PSII even at low light intensities. The functionality of chlororespiratory electron transfer pathway under iron restricted conditions is also discussed.

Increased lanosterol turnover: a metabolic burden for daunorubicin-resistant leukemia cells.

The cholesterol metabolism is essential for cancer cell proliferation. We found the expression of genes involved in the cholesterol biosynthesis pathway up-regulated in the daunorubicin-resistant leukemia cell line CEM/R2, which is a daughter cell line to the leukemia cell line CCRF-CEM (CEM). Cellular (2)H2O labelling, mass spectrometry, and isotopomer analysis revealed an increase in lanosterol synthesis which was not accompanied by an increase in cholesterol flux or pool size in CEM/R2 cells. Exogenous addition of lanosterol had a negative effect on CEM/R2 and a positive effect on sensitive CEM cell viability. Treatment of CEM and CEM/R2 cells with cholesterol biosynthesis inhibitors acting on the enzymes squalene epoxidase and lanosterol synthase, both also involved in the 24,25-epoxycholesterol shunt pathway, revealed a connection of this pathway to lanosterol turnover. Our data highlight that an increased lanosterol flux poses a metabolic weakness of resistant cells that potentially could be therapeutically exploited.

Rewired metabolism in drug-resistant leukemia cells: a metabolic switch hallmarked by reduced dependence on exogenous glutamine.

Cancer cells that escape induction therapy are a major cause of relapse. Understanding metabolic alterations associated with drug resistance opens up unexplored opportunities for the development of new therapeutic strategies. Here, we applied a broad spectrum of technologies including RNA sequencing, global untargeted metabolomics, and stable isotope labeling mass spectrometry to identify metabolic changes in P-glycoprotein overexpressing T-cell acute lymphoblastic leukemia (ALL) cells, which escaped a therapeutically relevant daunorubicin treatment. We show that compared with sensitive ALL cells, resistant leukemia cells possess a fundamentally rewired central metabolism characterized by reduced dependence on glutamine despite a lack of expression of glutamate-ammonia ligase (GLUL), a higher demand for glucose and an altered rate of fatty acid ß-oxidation, accompanied by a decreased pantothenic acid uptake capacity. We experimentally validate our findings by selectively targeting components of this metabolic switch, using approved drugs and starvation approaches followed by cell viability analyses in both the ALL cells and in an acute myeloid leukemia (AML) sensitive/resistant cell line pair. We demonstrate how comparative metabolomics and RNA expression profiling of drug-sensitive and -resistant cells expose targetable metabolic changes and potential resistance markers. Our results show that drug resistance is associated with significant metabolic costs in cancer cells, which could be exploited using new therapeutic strategies.

Discrimination of conventional and organic white cabbage from a long-term field trial study using untargeted LC-MS-based metabolomics.

The influence of organic and conventional farming practices on the content of single nutrients in plants is disputed in the scientific literature. Here, large-scale untargeted LC-MS-based metabolomics was used to compare the composition of white cabbage from organic and conventional agriculture, measuring 1,600 compounds. Cabbage was sampled in 2 years from one conventional and two organic farming systems in a rigidly controlled long-term field trial in Denmark. Using Orthogonal Projection to Latent Structures-Discriminant Analysis (OPLS-DA), we found that the production system leaves a significant (p = 0.013) imprint in the white cabbage metabolome that is retained between production years. We externally validated this finding by predicting the production system of samples from one year using a classification model built on samples from the other year, with a correct classification in 83 % of cases. Thus, it was concluded that the investigated conventional and organic management practices have a systematic impact on the metabolome of white cabbage. This emphasizes the potential of untargeted metabolomics for authenticity testing of organic plant products.

Overoxidation of chloroplast 2-Cys peroxiredoxins: balancing toxic and signaling activities of hydrogen peroxide.

Photosynthesis, the primary source of biomass and oxygen into the biosphere, involves the transport of electrons in the presence of oxygen and, therefore, chloroplasts constitute an important source of reactive oxygen species, including hydrogen peroxide. If accumulated at high level, hydrogen peroxide may exert a toxic effect; however, it is as well an important second messenger. In order to balance the toxic and signaling activities of hydrogen peroxide its level has to be tightly controlled. To this end, chloroplasts are equipped with different antioxidant systems such as 2-Cys peroxiredoxins (2-Cys Prxs), thiol-based peroxidases able to reduce hydrogen and organic peroxides. At high peroxide concentrations the peroxidase function of 2-Cys Prxs may become inactivated through a process of overoxidation. This inactivation has been proposed to explain the signaling function of hydrogen peroxide in eukaryotes, whereas in prokaryotes, the 2-Cys Prxs of which were considered to be insensitive to overoxidation, the signaling activity of hydrogen peroxide is less relevant. Here we discuss the current knowledge about the mechanisms controlling 2-Cys Prx overoxidation in chloroplasts, organelles with an important signaling function in plants. Given the prokaryotic origin of chloroplasts, we discuss the occurrence of 2-Cys Prx overoxidation in cyanobacteria with the aim of identifying similarities between chloroplasts and their ancestors regarding their response to hydrogen peroxide.

Influence of input uncertainty on prediction of within-field pesticide leaching risks.

Previous research has suggested that pesticide losses at the field scale can be dominated by a small proportion of the field area. The objective of this study was to investigate whether site-specific applications (i.e. avoiding high-risk areas) at the field scale can contribute to a reduction of pesticide leaching despite uncertainty in the underlying model-based leaching risk map. Using a meta-model of the dual-permeability model MACRO, the annual average pesticide leaching concentrations were estimated for 162 sample sites on a 47 ha field. The procedure was repeated for different scenarios describing different patterns of spatial variation of degradation half-lives and the partition coefficient to soil organic carbon. To account for interpolation uncertainty, maps of predicted pesticide leaching risk were produced by the method of sequential Gaussian simulation. The results of the case study show that larger reductions of predicted leaching were achieved by site-specific application than by that of a comparable uniform dose reduction. Hence, site-specific-applications may be a feasible method to reduce pesticide leaching at the field-scale providing that the model approach gives reasonable estimates of the spatial pattern of pesticide leaching.

Meta-modeling of the pesticide fate model MACRO for groundwater exposure assessments using artificial neural networks.

Several simple index methods that use easily accessible data have been developed and included in decision-support systems to estimate pesticide leaching across larger areas. However, these methods often lack important process descriptions (e.g. macropore flow), which brings into question their reliability. Descriptions of macropore flow have been included in simulation models, but these are too complex and demanding for spatial applications. To resolve this dilemma, a neural network simulation meta-model of the dual-permeability macropore flow model MACRO was created for pesticide groundwater exposure assessment. The model was parameterized using pedotransfer functions that require as input the clay and sand content of the topsoil and subsoil, and the topsoil organic carbon content. The meta-model also requires the topsoil pesticide half-life and the soil organic carbon sorption coefficient as input. A fully connected feed-forward multilayer perceptron classification network with two hidden layers, linked to fully connected feed-forward multilayer perceptron neural networks with one hidden layer, trained on sub-sets of the target variable, was shown to be a suitable meta-model for the intended purpose. A Fourier amplitude sensitivity test showed that the model output (the 80th percentile average yearly pesticide concentration at 1 m depth for a 20 year simulation period) was sensitive to all input parameters. The two input parameters related to pesticide characteristics (i.e. soil organic carbon sorption coefficient and topsoil pesticide half-life) were the most influential, but texture in the topsoil was also quite important since it was assumed to control the mass exchange coefficient that regulates the strength of macropore flow. This is in contrast to models based on the advection-dispersion equation where soil texture is relatively unimportant. The use of the meta-model is exemplified with a case-study where the spatial variability of pesticide leaching is mapped for a small field. It was shown that the area of the field that contributes most to leaching depends on the properties of the compound in question. It is concluded that the simulation meta-model of MACRO should prove useful for mapping relative pesticide leaching risks at large scales.

A study of K variability and its effect on solute transport in subsurface-flow sand filters by measurement and modelling.

Hydraulics of subsurface flow filters (SSF) was studied by measurement of soil hydraulic conductivity (K) variation and performing tracer tests in two SSF filters consisting of 1-4 mm Ca rich sand (shell sand). Soil samples were carefully taken at several locations in Filter I. A tracer experiment was conducted in the undisturbed Filter II using KI. The measured K variability in Filer I was used to analyze the variations in tracer breakthrough. The spatially distribution of K was obtained by fitting a variogram to observed data and interpolation using Kriging. The tracer residence probability density function (PDF) was determined by modelling the tracer movement with a 3-D groundwater model. The observed and simulated tracer arrival was compared for cases with constant K, constant K and dispersion (D), and for spatially variable K and dispersion. The results show that groundwater models were well suited to simulate solute movement in the SSF system studied. An almost perfect fit to observed tracer PDF was obtained when variable K and dispersion was included in the model. This indicates that information on K variability and dispersion is important for studying solute movement in SSF constructed wetlands.