Emission scenario model for regional air pollution

Air pollution emissions are produced in a wide variety of sources. They often result in detrimental impacts on both environments and human populations. To assess the emissions and impacts of air pollution, mathematical models have been developed. This study presents results from the application of an air pollution emission model, the Finnish Regional Emission Scenario (FRES) model, that covers the emissions of sulfur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3), non-methane volatile organic compounds (NMVOCs) and primary particulate matter (TSP, PM10, PM2.5 and PM1) in high 1 ´ 1 km2 spatial resolution over the area of Finland. The aims of the study were to identify key emission sources in Finland at present and in the future, to assess the effects of climate policies on air pollution, and to estimate emission reduction potentials and costs. Uncertainties in emission estimates were analyzed. Finally, emission model characteristics for use in different air pollution impact applications were discussed.The main emission sources in Finland are large industrial and energy production plants for SO2 (64% of 76 Gg a-1 total in the year 2000). Traffic vehicles are the main contributors for NOx (58% of 206 Gg a-1), NMVOCs (54% of 152 Gg a-1) and primary PM2.5 (26% of 31 Gg a-1) emissions. Agriculture is the key source for NH3 (97% of 33 Gg a-1). Other important sources are domestic wood combustion for primary PM2.5 (25%) and NMVOCs (12%), and fugitive dust emissions from traffic and other activities for primary PM10 (30% of 46 Gg a-1).In the future, the emissions of traffic vehicle exhaust will decrease considerably, by 76% (NMVOCs), 74% (primary PM2.5) and 60% (NOx), from 2000 to 2020, because of tightening emission legislations. Rather smaller decrease is anticipated in the emissions of large combustion plants, depending on future primary energy choices. Sources that are not subject to tight emission standards, e.g. domestic combustion and traffic-induced fugitive dust (i.e. non-exhaust), pose a risk for increasing emissions.The majority of measures to abate climate change, e.g. energy saving and non-combustion based energy production, lead to co-benefits as reduced air pollution emissions, especially of SO2 (20% to 28% reduction). However, promotion of domestic wood combustion poses a risk for increase in PM2.5 and NMVOCs emissions. Further emission reductions with feasible control costs are possible mainly for PM2.5 in small energy production plants and domestic combustion sources. Highest emission uncertainties were estimated for primary PM emission factors of domestic wood combustion, traffic non-exhaust sources and small energy production plants.The most important characteristics of emission models are correct location information of flue gas stacks of large plants for the assessment of acidification, and description of small polluters with high spatial resolution when assessing impacts on populations. Especially primary PM2.5 emissions originate to a considerable degree from small low-altitude sources in urban areas, and therefore it is important to be able to assess the impacts that take place near the emission sources. Detailed descriptions of large plants and 1 ´ 1 km2 spatial resolution for small emission sources applied in the FRES model enable its use in the assessment of various national environmental impacts and their reduction possibilities.The main contribution of this work was the development of a unique modeling framework to assess emission scenarios of multiple air pollutants in high sectoral and spatial resolution in Finland. The developed FRES model provides support for Finnish air pollution polices and a tool to assess the co-benefits and trade-offs of climate change strategies on air pollution

The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6

Model experiment description paperProjections of future climate change play a fundamental role in improving understanding of the climate system as well as characterizing societal risks and response options. The Scenario Model Intercomparison Project (ScenarioMIP) is the primary activity within Phase 6 of the Coupled Model Intercomparison Project (CMIP6) that will provide multi-model climate projections based on alternative scenarios of future emissions and land use changes produced with integrated assessment models. In this paper, we describe ScenarioMIP's objectives, experimental design, and its relation to other activities within CMIP6. The ScenarioMIP design is one component of a larger scenario process that aims to facilitate a wide range of integrated studies across the climate science, integrated assessment modeling, and impacts, adaptation, and vulnerability communities, and will form an important part of the evidence base in the forthcoming Intergovernmental Panel on Climate Change (IPCC) assessments. At the same time, it will provide the basis for investigating a number of targeted science and policy questions that are especially relevant to scenario-based analysis, including the role of specific forcings such as land use and aerosols, the effect of a peak and decline in forcing, the consequences of scenarios that limit warming to below 2 °C, the relative contributions to uncertainty from scenarios, climate models, and internal variability, and long-term climate system outcomes beyond the 21st century. To serve this wide range of scientific communities and address these questions, a design has been identified consisting of eight alternative 21st century scenarios plus one large initial condition ensemble and a set of long-term extensions, divided into two tiers defined by relative priority. Some of these scenarios will also provide a basis for variants planned to be run in other CMIP6-Endorsed MIPs to investigate questions related to specific forcings. Harmonized, spatially explicit emissions and land use scenarios generated with integrated assessment models will be provided to participating climate modeling groups by late 2016, with the climate model simulations run within the 2017-2018 time frame, and output from the climate model projections made available and analyses performed over the 2018-2020 period.CRESCENDO project members (V. Eyring, P. Friedlingstein, E. Kriegler, R. Knutti, J. Lowe, K. Riahi, D. van Vuuren) acknowledge funding received from the Horizon 2020 European Union’s Framework Programme for Research and Innovation under grant agreement no. 641816. C. Tebaldi, G. A. Meehl and B. M. Sanderson acknowledge the support of the Regional and Global Climate Modeling Program (RGCM) of the U.S. Department of Energy’s, Office of Science (BER), Cooperative Agreement DE-FC02-97ER6240

Virtual Environments for Training: From Individual Learning to Collaboration with Humanoids

The next generation of virtual environments for training is oriented towards collaborative aspects. Therefore, we have decided to enhance our platform for virtual training environments, adding collaboration opportunities and integrating humanoids. In this paper we put forward a model of humanoid that suits both virtual humans and representations of real users, according to collaborative training activities. We suggest adaptations to the scenario model of our platform making it possible to write collaborative procedures. We introduce a mechanism of action selection made up of a global repartition and an individual choice. These models are currently being integrated and validated in GVT, a virtual training tool for maintenance of military equipments, developed in collaboration with the French company NEXTER-Group

Approximation algorithms for stochastic and risk-averse optimization

We present improved approximation algorithms in stochastic optimization. We prove that the multi-stage stochastic versions of covering integer programs (such as set cover and vertex cover) admit essentially the same approximation algorithms as their standard (non-stochastic) counterparts; this improves upon work of Swamy \& Shmoys which shows an approximability that depends multiplicatively on the number of stages. We also present approximation algorithms for facility location and some of its variants in the $2$-stage recourse model, improving on previous approximation guarantees. We give a $2.2975$-approximation algorithm in the standard polynomial-scenario model and an algorithm with an expected per-scenario $2.4957$-approximation guarantee, which is applicable to the more general black-box distribution model.Comment: Extension of a SODA'07 paper. To appear in SIAM J. Discrete Mat

Modelling bark beetle disturbances in a large scale forest scenario model to assess climate change impacts and evaluate adaptive management strategies

To study potential consequences of climate-induced changes in the biotic disturbance regime at regional to national scale we integrated a model of Ips typographus (L. Scol. Col.) damages into the large-scale forest scenario model EFISCEN. A two-stage multivariate statistical meta-model was used to upscale stand level damages by bark beetles as simulated in the hybrid forest patch model PICUS v1.41. Comparing EFISCEN simulations including the new bark beetle disturbance module against a 15-year damage time series for Austria showed good agreement at province level (R² between 0.496 and 0.802). A scenario analysis of climate change impacts on bark beetle-induced damages in Austria¿s Norway spruce [Picea abies (L.) Karst.] forests resulted in a strong increase in damages (from 1.33 Mm³ a¿1, period 1990¿2004, to 4.46 Mm³ a¿1, period 2095¿2099). Studying two adaptive management strategies (species change) revealed a considerable time-lag between the start of adaptation measures and a decrease in simulated damages by bark beetle