Piotr Holnicki

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.

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.

Decision support for optimal emission reduction

In this paper, a problem related to the optimal allocation of financial means for reduction of air pollutants concentration in a given region is considered. The implementation is sulfur oxides oriented. The problem is formally stated as the cost‐constrained minimization of the environmental damage function by the optimal choice of desulfurization technologies, within the predefined set of controlled (power and heating) plants. Optimization methods have been implemented and tested on the real data for the selected region. The case study relates to the set of major power plants in the region of Silesia (Poland) and the basic desulfurization technologies, which are to be allocated.

Decision support method for cost-effective emission control on a regional scale

In the paper an implementation of a decision support algorithm for selection of emission abatement strategy on a regional scale is presented. The approach refers to optimal allocation of financial means for emission reduction in a given set of power and heating plants. The implementation considered is sulfur-oriented. The problem is formally stated as cost-constrained minimization of environmental damage function by the optimal choice of desulfurization technologies, within the set of the controlled plants. The receptor-oriented objective function utilizes air pollution forecast preprocessed by a regional scale dispersion model. An heuristic algorithm is implemented to solve the optimization problem. This is the improved and more general version of the method discussed earlier in [1]. Compared with that version, the cost constraints are considered in a more realistic form; two components of the total costs – investment and operational – are considered individually for each power plant and for the selected emission abatement technology. This requires a special construction of the optimization algorithm. Computational test results are presented for the set of the major power plants in the Silesia Region.

A Decision Support System for Air Quality Control Based on Soft Computing Methods

The problem considered is related to air quality protection on a regional scale. The approach refers to optimal allocation of financial means for emission reduction in a given set of power and heating plants. The implementation considered is sulfur-oriented. The problem is formally stated as cost-constrained minimization of environmental damage function by the optimal choice of desulfurization technologies, within the predefined set of the controlled plants. The receptor-oriented objective function utilizes air pollution forecast preprocessed by a regional scale dispersion model. An integer-type optimization problem is solved by two methods. The first method utilizes a heuristic algorithm designed for solving this specific problem, which directly finds discrete solution. Another approach is based on the classical gradient optimization algorithm and gives continuous, technologically not applicable solution. Then the continuous solution is transformed to the discrete form by enumeration of some discrete cases. Both algorithms has been implemented and tested on the real data for selected region. The case study relates to the set of major power plants in Silesia Region (Poland) and the basic desulfurization technologies, which are to be allocated. The test calculations allows us to evaluate accuracy of the heuristic method as well as applicability of both approaches for supporting decisions concerning optimal strategies of emission abatement on a regional scale.

Intra-urban variability of the intake fraction from multiple emission sources

Background Ambient air pollution and associated adverse health effects are among most acute environmental problems in many cities worldwide. The intake fraction (iF) approach can be applied for evaluating the health benefits of reducing emissions, especially when rapid decisions are needed. Intake fraction is a metric that represents emission-to-intake relationship and characterizes abatement of exposure potential attributed to specific emission sources. Aim In this study, the spatial variability of iF in Warsaw agglomeration, Poland, is discussed. Methods The iF analysis is based on the earlier air quality modeling results, that include the main pollutants characterizing an urban atmospheric environment (SO2, NOx, PM10, PM2.5, CO, C6H6, B(a)P, heavy metals). The annual mean concentrations were computed by the CALPUFF modeling system (spatial resolution 0.5 × 0.5 km2) on the basis of the emission and meteorological data from year 2012. The emission field comprised 24 high (power generation) and 3880 low (industry) point sources, 7285 mobile (transport) sources, and 6962 area (housing) sources. Results The aggregated iFs values are computed for each emission class and the related polluting compounds. Intra-urban variability maps of iFs are attributed to an emission sources by emission category and pollutant. Conclusions Intake fraction is shown as a decision support tool for implementing the cost-effective emission policy and reducing the health risk of air pollution.

KARO the Computer System for Simulation of the Impact of Energy Sector Expansion on Atmosphere Pollution

One of the most important problems of the polish economy is the transformation to the ecologically sustainable economy development, in particular in the energy production and consumption sector, which now is characterized by low efficiency and big sulphur dioxides emission. The paper desribes the computer system KARO whose main functions are: (i) to evaluate the consequences of the selected expansion scenarios for the energy demand and energy supply sectors, (ii) to verify the possibility of national economy to meet the assumed expansion scenario, (iii) to suggest the optimal strategy of new investments and technologies location that minimizes the negative impact of the economy expansion on the environment.