Estimating nitrogen flows of agricultural soils at a landscape level – A modelling study of the Upper Enns Valley, a long-term socio-ecological research region in Austria

This paper explores the fate of reactive nitrogen (Nr) on the landscape scale of present agricultural production practice on arable and grassland soils. We use the soil modelling tool LandscapeDNDC (landscape scale DeNitrification-DeComposition model) to quantify resulting flows of Nr distributed to the atmosphere, hydrosphere and the crops. Test area is a watershed in the Austrian Alps characterized by arable production in the low-lying areas and grassland in the mountains. The approach considers an overall budget of nitrogen, and determines the nitrogen use efficiency for individual crops and crop rotations, with average levels found at 85% for the arable area and 68-98% for the grassland areas. Modelled Nr flows are compared to the values resulting from the national emission factor (EF) method used for the Austrian emission inventory. For the arable part of the study region, the annual sum of released Nr emissions derived from LandscapeDNDC modelling is lower than the result of the EF method by about 13% (or 7 kg N ha-1). Model results are lower also for other Nr species, yet nitrate leaching rates as well as ammonia emissions contribute a major share. For grassland areas, nitrate leaching values estimated by LandscapeDNDC greatly depend on local specifics and substantially exceed EF estimates. All other modelled Nr species are lower than the EF results. The model set-up allows to characterize spatially explicit effects of mitigation measures. As an example, we identify nitrous oxide (N2O) hot spots in the study region, and we quantify the N2O emission saving potential if focusing reduction efforts to such hot spots. Reducing fertilization of hot spots by half could remove 14% of N2O emission for 5% less crop yield and a loss of grassland yield by <1% when extrapolated to the whole study area

GAINS ASIA: Scenarios for cost-effective control of air pollution and greenhouse gases in India

There is growing recognition that a comprehensive and combined analysis of air pollution and climate change could reveal important synergies of emission control measures. Insight into the multiple benefits of measures could make emission controls economically more viable, both in industrialized and developing countries. However, while scientific understanding on many individual aspects of air pollution and climate change has considerably increased in the last years, little attention has been paid to a holistic analysis of the interactions between both problems. The Greenhouse gas - Air pollution Interactions and Synergies (GAINS) model has been developed as a tool to identify emission control strategies that maximize synergies between the control of local air quality and the mitigation of greenhouse emissions. GAINS investigates how specific mitigation measures simultaneously influence different pollutants that threaten human health via the exposure of fine particles and ground-level ozone, damage natural vegetation and crops, contribute to climate change. In recent years the GAINS model has been implemented for India in collaboration between the International Institute for Applied Systems Analysis (IIASA) and The Energy and Resources Institute (TERI). This report presents a first analysis conducted with the GAINS model that highlights how strategies to control local air quality could be designed in such a way that co-benefits on greenhouse gas mitigation could be maximized

River histories : A thematic review

Peer reviewe

An analysis of factors that influence personal exposure to toluene and xylene in residents of Athens, Greece

BACKGROUND: Personal exposure to pollutants is influenced by various outdoor and indoor sources. The aim of this study was to evaluate the exposure of Athens citizens to toluene and xylene, excluding exposure from active smoking. METHODS: Passive air samplers were used to monitor volunteers, their homes and various urban sites for one year, resulting in 2400 measurements of toluene and xylene levels. Since both indoor and outdoor pollution contribute significantly to human exposure, volunteers were chosen from occupational groups who spend a lot of time in the streets (traffic policemen, bus drivers and postmen), and from groups who spend more time indoors (teachers and students). Data on individual and house characteristics were obtained using a questionnaire completed at the beginning of the study; a time-location-activity diary was also completed daily by the volunteers in each of the six monitoring campaigns. RESULTS: Average personal toluene exposure varied over the six monitoring campaigns from 53 to 80 μg/m(3). Urban and indoor concentrations ranged from 47 – 84 μg/m(3 )and 30 – 51 μg/m(3), respectively. Average personal xylene exposure varied between 56 and 85 μg/m(3 )while urban and indoor concentrations ranged from 53 – 88 μg/m(3 )and 27 – 48 μg/m(3), respectively. Urban pollution, indoor residential concentrations and personal exposures exhibited the same pattern of variation during the measurement periods. This variation among monitoring campaigns might largely be explained by differences in climate parameters, namely wind speed, humidity and amount of sunlight. CONCLUSION: In Athens, Greece, the time spent outdoors in the city center during work or leisure makes a major contribution to exposure to toluene and xylene among non-smoking citizens. Indoor pollution and means of transportation contribute significantly to individual exposure levels. Other indoor residential characteristics such as recent painting and mode of heating used might also contribute significantly to individual levels. Groups who may be subject to higher exposures (e.g. those who spent more time outdoors because of occupational activities) need to be surveyed and protected against possible adverse health effects

Computational approaches for modeling human intestinal absorption and permeability

Human intestinal absorption (HIA) is an important roadblock in the formulation of new drug substances. Computational models are needed for the rapid estimation of this property. The measurements are determined via in vivo experiments or in vitro permeability studies. We present several computational models that are able to predict the absorption of drugs by the human intestine and the permeability through human Caco-2 cells. The training and prediction sets were derived from literature sources and carefully examined to eliminate compounds that are actively transported. We compare our results to models derived by other methods and find that the statistical quality is similar. We believe that models derived from both sources of experimental data would provide greater consistency in predictions. The performance of several QSPR models that we investigated to predict outside the training set for either experimental property clearly indicates that caution should be exercised while applying any of the models for quantitative predictions. However, we are able to show that the qualitative predictions can be obtained with close to a 70% success rate

The consolidated European synthesis of CH₄ and N₂O emissions for the European Union and United Kingdom: 1990–2019

Knowledge of the spatial distribution of the fluxes of greenhouse gases (GHGs) and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its global stocktake. This study provides a consolidated synthesis of CH₄ and N₂O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27 + UK) and updates earlier syntheses (Petrescu et al., 2020, 2021). The work integrates updated emission inventory data, process-based model results, data-driven sector model results and inverse modeling estimates, and it extends the previous period of 1990–2017 to 2019. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported by parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. Uncertainties in NGHGIs, as reported to the UNFCCC by the EU and its member states, are also included in the synthesis. Variations in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates, e.g., anthropogenic and natural fluxes, which in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. For CH₄ emissions, over the updated 2015–2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.5 Tg CH₄ yrc (EDGARv6.0, last year 2018) and 18.4 Tg CH₄ yr⁻¹ (GAINS, last year 2015), close to the NGHGI estimates of 17.5±2.1 Tg CH₄ yr⁻¹. TD inversion estimates give higher emission estimates, as they also detect natural emissions. Over the same period, high-resolution regional TD inversions report a mean emission of 34 Tg CH₄ yr⁻¹. Coarser-resolution global-scale TD inversions result in emission estimates of 23 and 24 Tg CH₄ yr⁻¹ inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soil emissions from the JSBACH–HIMMELI model, natural rivers, lake and reservoir emissions, geological sources, and biomass burning together could account for the gap between NGHGI and inversions and account for 8 Tg CH₄ yr⁻¹. For N₂O emissions, over the 2015–2019 period, both BU products (EDGARv6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9 Tg N₂O yr⁻¹, close to the NGHGI data (0.8±55 % Tg N₂O yr⁻¹). Over the same period, the mean of TD global and regional inversions was 1.4 Tg N₂O yr⁻¹ (excluding TOMCAT, which reported no data). The TD and BU comparison method defined in this study can be operationalized for future annual updates for the calculation of CH₄ and N₂O budgets at the national and EU27 + UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-annual timescales, which is of great importance for CH₄ and N₂O, and may help identify sector contributions to divergence between prior and posterior estimates at the annual and/or inter-annual scale. Even if currently comparison between CH₄ and N₂O inversion estimates and NGHGIs is highly uncertain because of the large spread in the inversion results, TD inversions inferred from atmospheric observations represent the most independent data against which inventory totals can be compared. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emission inventories for CH₄, N₂O and other GHGs. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.7553800 (Petrescu et al., 2023)