Zbigniew Nahorski

Greenhouse gas inventories

Benefits of dealing with uncertainty in greenhouse gas inventories: introduction.- Statistical dependence in input data of national greenhouse gas inventories: effects on the overall inventory uncertainty.- Uncertainty analysis for estimation of landfill emissions and data sensitivity for the input variation.- Toward Bayesian uncertainty quantification for forestry models used in the United Kingdom Greenhouse Gas Inventory for land use, land use change, and forestry.- Atmospheric inversions for estimating CO2 fluxes: methods and perspectives.- European CO2 fluxes from atmospheric inversions using regional and global transport models.- Remotely sensed soil moisture integration in an ecosystem carbon flux model. The spatial implication.- Can the uncertainty of full carbon accounting of forest ecosystems be made acceptable to policymakers?.- Terrestrial full carbon account for Russia: revised uncertainty estimates and their role in a bottom-up/top-down accounting exercise.- Comparison of preparatory signal analysis techniques for consideration in the (post-)Kyoto policy process.- Verification of compliance with GHG emission targets: annex B countries.- Spatial GHG inventory at the regional level: accounting for uncertainty.- Quantitative quality assessment of the greenhouse gas inventory for agriculture in Europe.- A statistical model for spatial inventory data: a case study of N2O emissions in municipalities of southern Norway.- Carbon emission trading and carbon taxes under uncertainties.- CO2 emission trading model with trading prices.- Compliance and emission trading rules for asymmetric emission uncertainty estimates.- The impact of uncertain emission trading markets on interactive resource planning processes and international emission trading experiments.

Accounting for Climate Change

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The discrete-time maximum principle : a survey and some new results

In this paper we present a review of the development of the discrete maximum principle. In the presentation, the emphasis is on a geometrical interpretation. The crucial assumptions in the theory developed are pointed out, and the attempts to overcome the limitation in the resulting theorems ore exposed. Following the review, we present a new approach to optimization of the multi-stage optimization problems called the ’ upper boundary approach ’. The classical methods of solving this problem are shown to fit smoothly into the new approach. Moreover, using this approach a number of new results have been developed, among these a new generalized version of the discrete maximum principle. The new version does not require the assumption of directional convexity.

Emission Data Uncertainty in Urban Air Quality Modeling—Case Study

Air pollution models are often used to support decisions in air quality management. Due to the complexity of the forecasting system and difficulty in acquiring precise enough input data, an environmental prognosis of air quality with an analytical model of the air pollution dispersion is burdened with a substantial share of uncertainty, especially as regards urban areas. To ignore the uncertainty in the modeling would lead to incorrect policy decisions, with further negative environmental and health consequences. This paper presents a case study which shows how emission uncertainty of air pollutants generated by the industry, traffic, and the municipal sector relates to concentrations measured at receptor points. The computational experiment was implemented in the Warsaw metropolitan area, Poland. The main source of this adverse environmental impact is the transportation system, including the transit traffic. The Monte Carlo technique was used for assessing the key uncertainty factors. Several types of pollution species that are characteristic for the urban atmospheric environment (e.g., PM10, PM2.5, NOx, SO2, Pb) were included in the analysis. The results show significant spatial variability of the modeled uncertainty. The reason of this variability is discussed in detail. It depends not only on the category of the emission source but also on the contributing emission sources and their quantity.

Intake Fraction Variability Between Air Pollution Emission Sources Inside an Urban Area

The cost-effective mitigation of adverse health effects caused by air pollution requires information on the contribution of different emission sources to exposure. In urban areas the exposure potential of different sources may vary significantly depending on emission height, population density, and other factors. In this study, we quantified this intraurban variability by predicting intake fraction (iF) for 3,066 emission sources in Warsaw, Poland. iF describes the fraction of the pollutant that is inhaled by people in the study area. We considered the following seven pollutants: particulate matter (PM), nitrogen oxides (NOx), sulfur dioxide (SO2), benzo[a] pyrene (BaP), nickel (Ni), cadmium (Cd), and lead (Pb). Emissions for these pollutants were grouped into four emission source categories (Mobile, Area, High Point, and Other Point sources). The dispersion of the pollutants was predicted with the CALPUFF dispersion model using the year 2005 emission rate data and meteorological records. The resulting annual average concentrations were combined with population data to predict the contribution of each individual source to population exposure. The iFs for different pollutant-source category combinations varied between 51 per million (PM from Mobile sources) and 0.013 per million (sulfate PM from High Point sources). The intraurban iF variability for Mobile sources primary PM emission was from 4 per million to 100 per million with the emission-weighted iF of 44 per million. These results propose that exposure due to intraurban air pollution emissions could be decreased more effectively by specifically targeting sources with high exposure potency rather than all sources.

Intelligent Computations in an Agent-Based Prosumer-Type Electric Microgrid Control System

The growing number of small prosumers, progress in construction of the renewable energy sources and opening of the energy markets lead to development of the concept of a microgrid. To increase efficiency of electricity consumption, production, and trading, energy managing systems (EMS) are being developed. In this paper we present a project of a complex EMS that will combine load scheduling, power balancing and smart trading methods to optimize electric energy costs of running a simulated research and education center. We present a concept of a distributed Agent-based Power Balancing System that controls the power flow in a microgrid by decentralized and distributed decision making. The program optimizes the operating (exploitation) cost of the devices in the microgrid by internally balancing, as much as possible, the produced and consumed power and by trading the remaining energy excesses or deficits on the external market. Agents associated with the devices cooperate using communication protocols. Their aim is to use the energy from renewable energy sources whenever it is only available, and at the same time limit use of the energy from the sources that are more expensive and less environment friendly.

A review of mathematical models in economic environmental problems

The paper presents a review of mathematical models used in economic analysis of environmental problems. This area of research combines macroeconomic models of growth, as dependent on capital, labour, resources, etc., with environmental models describing such phenomena like natural resources exhaustion or pollution accumulation and degradation. In simpler cases the models can be treated analytically and the utility function can be optimized using, e.g., such tools as the maximum principle. In more complicated cases calculation of the optimal environmental policies requires a computer solution.

Dealing with Uncertainty in GHG Inventories: How to Go About It?

The assessment of greenhouse gases emitted to and removed from the atmosphere is high on both political and scientific agendas. Under the United Nations Framework Convention on Climate Change, Parties to the Convention publish annual or periodic national inventories of greenhouse gas emissions and removals. Policymakers use these inventories to develop strategies and policies for emission reductions and to track the progress of these policies. However, greenhouse gas inventories (whether at the global, national, corporate, or other level) contain uncertainty for a variety of reasons, and these uncertainties have important scientific and policy implications. For scientific, political, and economic reasons it is important to deal with the uncertainty of emissions estimates proactively. Proper treatment of uncertainty affects everything from our understanding of the physical system to the economics of mitigation strategies and the politics of mitigation agreements. A comprehensive and consistent understanding of, and a framework for dealing with, the uncertainty of emissions estimates should have a large impact on the functioning and effectiveness of the Kyoto Protocol and its successor. This chapter attempts to pull together relevant fragments of knowledge, allowing us to get a better picture of how to go about dealing with the uncertainty in greenhouse gas inventories.

Air quality modeling for Warsaw agglomeration

Abstract The paper investigates the air quality in the urban area of Warsaw, Poland. Calculations are carried out using the emissions and meteorological data from the year 2012. The modeling tool is the regional CALMET/CALPUFF system, which is used to link the emission sources with the distributions of the annual mean concentrations. Several types of polluting species that characterize the urban atmospheric environment, like PM10, PM2.5, NOx, SO2, Pb, B(a)P, are included in the analysis. The goal of the analysis is to identify the most polluted districts and polluting compounds there, to check where the concentration limits of particular pollutants are exceeded. Then, emission sources (or emission categories) which are mainly responsible for violation of air quality standards and increase the adverse health effects, are identified. The modeling results show how the major emission sources – the energy sector, industry, traffic and the municipal sector – relate to the concentrations calculated in receptor points, including the contribution of the transboundary inflow. The results allow to identify districts where the concentration limits are exceeded and action plans are needed. A quantitative source apportionment shows the emission sources which are mainly responsible for the violation of air quality standards. It is shown that the road transport and the municipal sector are the emission classes which substantially affect air quality in Warsaw. Also transboundary inflow contributes highly to concentrations of some pollutants. The results presented can be of use in analyzing emission reduction policies for the city, as a part of an integrated modeling system.

Simulation of an uncertain emission market for greenhouse gases using agent-based methods

The paper presents the problem of a simulation of the greenhouse gases emission permits market where only low accuracy emission amounts are known. An organization of the market with uncertain emissions is proposed and trading rules for individual market participants are discussed. Simulation of the market is based on a multi-agent system. Negotiation of purchase/sale prices between the parties are introduced, where the trading parties adopt one of two options: (i) bilateral negotiations, and (ii) sealed bid reverse auctions. Results of simulation runs show trajectories of transaction prices, as well as probability distributions of learning agents’ bidding prices.