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We synthesized N2O emissions over North America using 17 bottom‐up (BU) estimates from 1980–2016 and five top‐down (TD) estimates from 1998 to 2016. The BU‐based total emission shows a slight increase owing to U.S. agriculture, while no consistent trend is shown in TD estimates. During 2007–2016, North American N2O emissions are estimated at 1.7 (1.0–3.0) Tg N yr−1 (BU) and 1.3 (0.9–1.5) Tg N yr−1 (TD). Anthropogenic emissions were twice as large as natural fluxes from soil and water. Direct agricultural and industrial activities accounted for 68% of total anthropogenic emissions, 71% of which was contributed by the U.S. Our estimates of U.S. agricultural emissions are comparable to the EPA greenhouse gas (GHG) inventory, which includes estimates from IPCC tier 1 (emission factor) and tier 3 (process‐based modeling) approaches. Conversely, our estimated agricultural emissions for Canada and Mexico are twice as large as the respective national GHG inventories. Plain Language Summary: Nitrous oxide (N2O) is the third most important greenhouse gase (GHG) after CO2 and CH4 causing global warming. Among world regions, North America (defined herein as U.S., Canada, and Mexico) is the second largest source of N2O emissions globally, and previous source estimates for this region vary widely. This study aims to provide a comprehensive N2O assessment over North America including all available estimates based on a number of approaches. We report total emissions, and emissions from four anthropogenic source sectors, over the past four decades. Agriculture and industry are two major N2O sources in North America. Our results show a minor increase in the total N2O emission due to agricultural trends in the U.S. Our bottom‐up estimate of U.S. agricultural N2O emissions are close to those in the EPA national GHG inventory that includes both empirical and model results. The high consistency suggests the need to take process‐based modeling results into account for future national GHG inventories. Key Points: North American N2O emissions during 2007–2016 are estimated at 0.9–3.0 Tg N yr−1 using a combination of bottom‐up and top‐down approachesNorth American anthropogenic N2O emissions grew by ∼0.2 Tg N during 1980–2016; U.S. agriculture was the largest cause of that growthOur modeled N2O fluxes reflect an IPCC tier 3 approach, and can improve greenhouse gase inventories that largely use tier 1 and tier 2 approaches [ABSTRACT FROM AUTHOR]

Displaying Nitrogen (N) indicators on a global grid poses unique opportunities to quantify environmental impacts from N application in different world regions under a variety of conditions. Such calculations require the use of maps showing the geo‐spatial distribution of crop production. Although there are several crop maps in the scientific literature to choose from, the consequences of this choice for the calculation of N indicators still need to be evaluated. In this study we analyze the differences in results for N Use Efficiency (NUE) and N surplus calculated on the global scale using two different crop maps (SPAM and M3). For our calculations we used publicly available statistical and literature data combined with each crop map and carefully traced the origins of the differences in the results. Our results showed that the regions most affected by discrepancies caused by differences in crop maps (yields and physical area) are Central Asia and the Russian Federation, Australia and Oceania, and North Africa. However, we also found that the inclusion or exclusion of grass crops influences the results, as does the aggregation of crops to categories. Considering all these differences, we note that M3 seems to provide the more plausible results for the calculation of N indicators. Our analysis not only highlights the importance of determining the critical parameters for N indicator calculation, but also allows key parameters connected with N use and overuse to be identified on the global scale. Key Points: Parameters like N surplus and NUE values are highly sensitive to the choice of crop mapAssumptions on grass crops also strongly influence N surplus and NUELeast bias in N indicators has been noted when using the M3 crop map [ABSTRACT FROM AUTHOR]

Carbon markets, like other commodity markets, are volatile. They react to stochastic 'disequilibrium' spot prices, which may be affected by inadequate policies, speculations and bubbles. The market-based emission trading, therefore, does not necessarily minimize abatement costs and achieve emission reduction goals. We introduce a basic stochastic model integrating emissions reduction, monitoring and trading costs allowing us to analyze the robustness of emission and uncertainty reduction policies under environmental safety constraints asymmetric information and other multiple anthropogenic and natural uncertainties. Explicit treatment of uncertainties provides incentives for reducing them before trading. We illustrate functioning of the robust market with numerical results involving such countries as the US, Australia, Canada, Japan, EU27, Russia, Ukraine. In particular, we analyze if the knowledge about uncertainties may affect portfolios of technological and trade policies or structure of the market and how uncertainty characteristics may affect market prices and change the market structure. [ABSTRACT FROM AUTHOR]

Growing population, rapid urbanization, rising incomes, and changing consumption preferences stimulate intensification of livestock production and excessive fertilization of crops in China. We present an innovative approach that sheds light on options to prevent negative environmental consequences of food production. Trends indicate that agricultural production expansion will take place in 'profitable' locations around densely populated areas, where there are generally insufficient natural resources to recycle production wastes. This will likely lead to increased environmental impacts and risks to human health, with the largest impacts in close proximity to population hotspots. We identify trends in Chinese agricultural production and devise and compare feasible mitigation scenarios. We present a spatial allocation procedure that facilitates management of agricultural production expansion, accounting for environmental and health constraints. This procedure, based on behavioral principles, uses a spatial risk preference structure induced by local conditions, including environment, production, and demand, with important research and policy implications. [ABSTRACT FROM AUTHOR]

The relationship, on a global basis, between the amount of N fixed by chemical, biological or atmospheric processes entering the terrestrial biosphere, and the total emission of nitrous oxide (N2O), has been re-examined, using known global atmospheric removal rates and concentration growth of N2O as a proxy for overall emissions. For both the pre-industrial period and in recent times, after taking into account the large-scale changes in synthetic N fertiliser production, we find an overall conversion factor of 3-5% from newly fixed N to N2O-N. We assume the same factor to be valid for biofuel production systems. It is covered only in part by the default conversion factor for "direct" emissions from agricultural crop lands (1%) estimated by IPCC (2006), and the default factors for the "indirect" emissions (following volatilization/deposition and leaching/runoff of N: 0.35-0.45%) cited therein. However, as we show in the paper, when additional emissions included in the IPCC methodology, e.g. those from livestock production, are included, the total may not be inconsistent with that given by our "topdown" method. When the extra N2O emission from biofuel production is calculated in "CO2-equivalent" global warming terms, and compared with the quasi-cooling effect of "saving" emissions of fossil fuel derived CO2, the outcome is that the production of commonly used biofuels, such as biodiesel from rapeseed and bioethanol from corn (maize), depending on N fertilizer uptake efficiency by the plants, can contribute as much or more to global warming by N2O emissions than cooling by fossil fuel savings. Crops with less N demand, such as grasses and woody coppice species, have more favourable climate impacts. This analysis only considers the conversion of biomass to biofuel. It does not take into account the use of fossil fuel on the farms and for fertilizer and pesticide production, but it also neglects the production of useful co-products. Both factors partially compensate each other. This needs to be analyzed in a full life cycle assessment. [ABSTRACT FROM AUTHOR]

The relationship, on a global basis, between the amount of N fixed by chemical, biological or atmospheric processes entering the terrestrial biosphere, and the total emission of nitrous oxide (N2O), has been re-examined, using known global atmospheric removal rates and concentration growth of N2O as a proxy for overall emissions. The relationship, in both the pre-industrial period and in recent times, after taking into account the large-scale changes in synthetic N fertiliser production and deforestation, is consistent, showing an overall conversion factor of 3-5%. This factor is covered only in part by the ∼1% of "direct" emissions from agricultural crop lands estimated by IPCC (2006), or the "indirect" emissions cited therein. This means that the extra N2O entering the atmosphere as a result of using N to produce crops for biofuels will also be correspondingly greater than that estimated just on the basis of IPCC (2006). When the extra N2O emission from biofuel production is calculated in "CO2-equivalent" global warming terms, and compared with the quasi-cooling effect of "saving" emissions of fossil fuel derived CO2, the outcome is that the production of commonly used biofuels, such as biodiesel from rapeseed and bioethanol from corn (maize), can contribute as much or more to global warming by N2O emissions than cooling by fossil fuel savings. Crops with less N demand, such as grasses and woody coppice species have more favourable climate impacts. This analysis only considers the conversion of biomass to biofuel. It does not take into account the use of fossil fuel on the farms and for fertilizer and pesticide production, but it also neglects the production of useful co-products. Both factors partially compensate each other. This needs to be analyzed in a full life cycle assessment. [ABSTRACT FROM AUTHOR]

We investigated the Austrian national greenhouse gas emission inventory to review the reliability and usability of such inventories. The overall uncertainty of the inventory (95% confidence interval) is just over 10% of total emissions, with nitrous oxide (N2O) from soils clearly providing the largest impact. Trend uncertainty – the difference between 2 years – is only about five percentage points, as important sources like soil N2O are not expected to show different behavior between the years and thus exhibit a high covariance. The result is very typical for industrialized countries – subjective decisions by individuals during uncertainty assessment are responsible for most of the discrepancies among countries. Thus, uncertainty assessment cannot help to evaluate whether emission targets have been met. Instead, a more rigid emission accounting system that allows little individual flexibility is proposed to provide harmonized evaluation uninfluenced by the respective targets. Such an accounting system may increase uncertainty in terms of greenhouse gas fluxes to the atmosphere. More importantly, however, it will decrease uncertainty in intercountry comparisons and thus allow for fair burden sharing. Setting of post-Kyoto emission targets will require the independent evaluation of achievements. This can partly be achieved by the validation of emission inventories and thorough uncertainty assessment. [ABSTRACT FROM AUTHOR]

This paper presents a three–stage approach to data mining which puts special emphasis on the visualization and interpretability of the results. In the first stage, the input data are represented by a self–organizing map in order to allow visualization and to reduce the amount of data while removing noise, outliers and missing values. Then this preprocessed information is used to identify and display fuzzy clusters of similarity. Finally, descriptions close to natural language are computed for these clusters in order to provide the analyst with qualitative information. This is accomplished by generating fuzzy rules using an inductive learning method. The proposed approach is applied to three case studies, including image data and real–world data sets. The results illustrate the robustness, intuitiveness and wide applicability of the method. [ABSTRACT FROM AUTHOR]

An annular denuder system for simultaneous determination of gaseous and particulate pollutants in ambient air was developed. Inorganic acids (HCl, HNO) were collected in a NaF-coated denuder, while organic acids (HCOOH, CHCOOH) were trapped in one coated with KOH. NH was sampled with a HPO-coated denuder tube. Particulate HSO was evaporated at elevated temperature (145°-155°C) and deposited on a NaF-coating together with HCl and HNO originating from thermal decomposition of NHCl and NHNO. NH resulting from deammoniation of (NH)SO as well as NH remaining from NHCl and NHNO were collected in a HPO-coated denuder. The practical collection capacity of the tubes ranged from 0.35 to 1.0 μmol calculated from an experimentally determined sorption efficiency of at least 90%. The precision, expressed as relative standard deviation, of sampling and analytical procedures was determined from duplicate measurements in ambient air. The reproducibility varied from 9% to 14% for the gaseous components, while that of the particulate compounds ranged from 12% to 23%. Aqueous extracts of the denuder coatings were analysed for ionic components by ion chromatography using conductivity detection. The minimum detectable concentration in air was found to be 1.5 to 14 nmol/m for the different compounds calculated for 1.6 m sample volume, based on 3-h measurements at a flow of 9 l/min. [ABSTRACT FROM AUTHOR]