The global nitrous oxide budget revisited

We present an update of the global budget of atmospheric nitrous oxide (N2O) that accounts for recent revisions in estimates of global emissions. Most importantly, new estimates of N2O emissions from agriculture and from oceans and a surface sink of N2O have been included. Our estimates confirm that current food production is the largest anthropogenic source of N2O. However, its relative share in total anthropogenic emissions (about 60%) is smaller than in earlier studies (almost 80%). We estimate past trends in global emissions of N2O and use these as input to a simple atmospheric box model to calculate trends in atmospheric N2O concentrations for the period 1500-2006. We show that our revised estimates for global emissions of N2O are consistent with observed trends in atmospheric concentrations

Neglecting sinks for N2O at the earth's surface: does it matter?

The uptake of nitrous oxide (N2O) occurs at the surface of the earth both in terrestrial and aquatic systems. This uptake is usually neglected in N2O budget studies. In this article, we discuss the likeliness of N2O uptake in different systems. These systems include soils as well as groundwater systems, riparian zones and surface waters. We address the possible consequences of ignoring surface sinks for N2O in global budgets as well as in national emission inventories. Our estimated surface sink is relatively small compared to the estimates of the present-day global emissions. Neglecting a possible surface sink of N2O may, therefore, not have major consequences for atmospheric budget studies on the global scale. However, locally, N2O uptake may be considerable. For countries with large areas prone to N2O uptake, neglecting this sink in national budgets may have important consequence

Two N-visualisation tools: game versus reality

This article describes two different visualisation instruments that can be used for awareness raising and educational purposes: Nitrogenius and the N-visualisation tool. Both instruments aim to convey the complex interactions that occur in the nitrogen (N) cycle and the need for integrated measures when aiming to reduce N-related environmental problems such as eutrophication, acidification or global warming. Nitrogenius was developed in the year 2000. This four-player computer “game” focuses on the Dutch N problems caused by N2O, NH3 and NOx emissions as well as nitrate in surface and ground water. Underlying the glossy user interface is a set of comprehensive models and a database with potential measures that were considered to be feasible at the time. Since 2000, the model has been used for educational purposes annually at Wageningen University. About 150 MSc students played the game, with the aim to solve the N-related problems in the Netherlands. This article analyses these games, and presents the surprising correlation for the period 2000-2007 with the actual environmental trends in the Netherlands. The second tool is an N- visualisation tool that was developed in 2007. This tool provides both a historic overview of the nitrogen issue and demonstrates the effect of seven potential measures on the world wide N cycle. The effects of increased biomass use and intensification of agriculture are examples of included measures. The net effect on global warming, food availability and biodiversity are output parameters of this instrument. The calculations and assumptions underlying this tool are easily accessible through an open source spreadsheet. This tool was used in 2008 and 2009 at Wageningen University for educational purposes. The pros and cons of both games for awareness raising and educational purposes will be discusse

Cardiovascular Disease Prediction from Electrocardiogram by Using Machine Learning

Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. This study presents a case–control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R–R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, k-nearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population