Interpolative mapping of mean precipitation in the Baltic countries by using landscape characteristics

Maps of the long-term mean precipitation involving local landscape variables were generated for the Baltic countries, and the effectiveness of seven modelling methods was compared. The precipitation data were recorded in 245 meteorological stations in 1966–2005, and 51 location-related explanatory variables were used. The similarity-based reasoning in the Constud software system outperformed other methods according to the validation fit, except for spring. The multivariate adaptive regression splines (MARS) was another effective method on average. The inclusion of landscape variables, compared to reverse distance-weighted interpolation, highlights the effect of uplands, larger water bodies and forested areas. The long-term mean amount of precipitation, calculated as the station average, probably underestimates the real value for Estonia and overestimates it for Lithuania due to the uneven distribution of observation stations

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

Acknowledgements The study was conducted within the framework of several scientific projects: “Efficacité des Zones Tampons” by OFB (French Office for Biodiversity, and technical group “Zones Tampons “), and HydroGES (financed by the Agency for the Environment and Mastery of Energy, ADEME). The travel was supported by two French–Estonian Parrot RTD projects “Ecological engineering for nutrient control in rural catchments” and “Process-based approach and enhanced technologies of treatment wetlands” (2014–2016). The PIREN-Seine programme and the Fédération Ile-de-France de Recherche pour l'Environnement (FIRE) are also acknowledged for their support. The authors also thank AQUI'Brie association for their support and stakeholders' involvement. This study was also supported by the Estonian Research Council (grants IUT2 16, PRG352 and MOBERC20) and by the EU through the European Regional Development Fund (Centres of Excellence ENVIRON and EcolChange, and MOBTP101 returning researcher grant by the Mobilitas Pluss programme).Peer reviewedPostprin

An online calculator for spatial data and its applications

An online calculator (http://digiarhiiv.ut.ee/kalkulaator/) for statistical analysis of spatial data is introduced. The calculator is applicable in a wide range of spatial research and for courses involving spatial data analysis. The present version of the calculator contains 35 web pages for statistical functions with several options and settings. The input data for most functions are pure Cartesian coordinates and variable values, which should be copied to the input cell on the page of a particular spatial operation. The source code for the computational part of all functions is freely available in C# programming language. Examples are given for thinning spatially dense observation points to a predefined minimum distance, for calculating spatial autocorrelations, for creating habitat suitability maps and for generalising movement data into spatio-temporal clusters

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

06_09-024(¿Ü±¹)

Abstract: 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

Variability and trends in daily minimum and maximum temperatures and in the diurnal temperature range in Lithuania, Latvia and Estonia in 1951–2010

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