High denitrification potential but low nitrous oxide emission in a constructed wetland treating nitrate-polluted agricultural run-off.

Constructed wetlands (CW) can efficiently remove nitrogen from polluted agricultural run-off, however, a potential caveat is nitrous oxide (N2O), a harmful greenhouse gas and stratospheric ozone depleter. During five sampling campaigns, we measured N2O fluxes from a 0.53 ha off-stream CW treating nitrate-rich water from the intensively fertilized watershed in Rampillon, France, using automated chambers with a quantum cascade laser system, and manual chambers. Sediment samples were analysed for potential N2 flux using the HeO2 incubation method. Both inlet nitrate (NO3-) concentrations and N2O emission varied significantly between the seasons. In the Autumn and Winter inlet concentrations were about 11 mg NO3--N L-1, and < 6.5 mg NO3--N L-1 in the Spring and Summer. N2O emission was highest in the Autumn (mean ± standard error: 9.7 ± 0.2 µg N m-2 h-1) and lowest in the Summer (wet period: 0.2 ± 0.3 µg N m-2 h-1). The CW was a very weak source of N2O emitting 0.32 kg N2O-N ha-1 yr-1 and removing around 938 kg NO3--N ha-1 yr-1, the ratio of N2O-N emitted to NO3--N removed was 0.033%. The automated and manual chambers gave similar results. From the potential N2O formation in the sediment, only 9% was emitted to the atmosphere, the average N2 N 2O ratio was high: 89:1 for N2-Npotential: N2O-Npotential and 1353:1 for N2-Npotential: N2O-Nemitted. These results indicate complete denitrification. The focused principal component analysis showed strong positive correlation between the gaseous N2O fluxes and the following environmental factors: NO3--N concentrations in inlet water, streamflow, and nitrate reduction rate. Water temperature, TOC and DOC in the water and hydraulic residence time showed negative correlations with N2O emissions. Shallow off-stream CWs such as Rampillon may have good nitrate removal capacity with low N2O emissions.

Shrubby cinquefoil (Dasiphora fruticosa (L.) Rydb.) mapping in Northwestern Estonia based upon site similarities.

BACKGROUND: Different methods have been used to map species and habitat distributions. In this paper, similarity-based reasoning-a methodological approach that has received less attention-was applied to estimate the distribution and coverage of Dasiphora fruticosa for the region in the Baltic states where grows the most abundant population of this species. METHODS: Field observations, after thinning to at least 50 m interval, included 1480 coverage estimations in the species presence locations and 8317 absence locations. Species coverage for the 750 km2 of directly unobserved area was calculated using machine learning in the similarity-based prediction system Constud. Separate predictive sets of site features (e.g. land cover, soil type) and exemplar weights were calibrated for spatial partitions of the study area (probable presence region, unclear region, proved absence region). A modified version of the Gower's distance metric, as used in Constud, is described. RESULTS: The resulting maps depicted the predicted coverage, the certainty of decision when predicting presence or absence, and the mean similarity to the exemplar locations used while predicting. Coverage prediction errors were smaller in the unclear partition-where the species was mostly absent-than in the probable presence partition, where coverage ranged from 0 to 90%. CONCLUSIONS: We call for methodological comparisons using the same data set.

Geographical aspects of enterobiasis in Estonia.

This paper examines the geographical pattern of the prevalence of enterobiasis among children in Estonian counties, and methods for risk modelling. The methodological questions were as follows: Case-based predictions were more liable to result in over fitting in the case of small training samples than the classification tree models. The spatial autocorrelation of the prevalence was significant up to a distance of 20 km. The main reason for the differences in the predictions might simply be the regional differences in the prevalence.

Effectiveness of repeated examination to diagnose enterobiasis in nursery school groups.

The aim of this study was to estimate the benefit from repeated examinations in the diagnosis of enterobiasis in nursery school groups, and to test the effectiveness of individual-based risk predictions using different methods. A total of 604 children were examined using double, and 96 using triple, anal swab examinations. The questionnaires for parents, structured observations, and interviews with supervisors were used to identify factors of possible infection risk. In order to model the risk of enterobiasis at individual level, a similarity-based machine learning and prediction software Constud was compared with data mining methods in the Statistica 8 Data Miner software package. Prevalence according to a single examination was 22.5%; the increase as a result of double examinations was 8.2%. Single swabs resulted in an estimated prevalence of 20.1% among children examined 3 times; double swabs increased this by 10.1%, and triple swabs by 7.3%. Random forest classification, boosting classification trees, and Constud correctly predicted about 2/3 of the results of the second examination. Constud estimated a mean prevalence of 31.5% in groups. Constud was able to yield the highest overall fit of individual-based predictions while boosting classification tree and random forest models were more effective in recognizing Enterobius positive persons. As a rule, the actual prevalence of enterobiasis is higher than indicated by a single examination. We suggest using either the values of the mean increase in prevalence after double examinations compared to single examinations or group estimations deduced from individual-level modelled risk predictions.

Case-based estimation of the risk of enterobiasis.

OBJECTIVE: To introduce an original case-based machine learning (ML) and prediction system Constud and its application on tabular data for estimation of the risk of enterobiasis among nursery school children in Estonia. METHODS AND MATERIALS: The system consists of a software application and a knowledge base of observation data, parameters, and results. The data were obtained from anal swabs for the diagnosis of enterobiasis, from questionnaires for children's parents, observations in nursery schools and interviews with supervisors of the groups. The total number of studied children was 1905. Ten parallel ML processes were conducted to find the best set of weights for features and cases. RESULTS: The best goodness-of-fit according to the true skill statistic (TSS) was 0.381. Approximately equal fit can be reached using different sets of features. Cross-validation TSS of logit-regression and classification tree models was <0.24. In addition to the higher prediction fit, Constud is not sensitive to missing values of explanatory variables. The overall prevalence of enterobiasis was 22.8%; the mean of risk estimations was 47.8%. The overestimation of the prevalence in risk calculations can be interpreted as an inefficacy of the single swab analysis, or may be due to the relative constancy of the risk compared to the lability of infection and the applied objective function. CONCLUSIONS: In addition to the higher prediction fit, Constud is not sensitive to missing values of explanatory variables. The main risk factors of enterobiasis among nursery school children were the child's age, communication partners, habits, and cleanness of rooms in the nursery school. Mixed age groups at nursery schools also enhance the risk.