Unit Commitment Incorporating Spatial Distribution Control of Air Pollutant Dispersion

Air pollution problems are attracting increasing attention, especially among developing countries with frequent haze events. Renewable energy sources such as wind power are expected to help relieve such environmental concerns. However, air pollution issues under such a changing energy structure receive inadequate attention. Mostly, constraints for total pollutant emissions are considered in unit commitment (UC) and economic dispatch (ED) problems. In this paper, we propose a UC model with wind power that considers the dispersion of air pollutants. The dispersion process is described by models involving meteorological conditions and the system’s geographical distribution, to estimate the spatial distribution of air pollutants, i.e. the concentration of ground-level air pollutants at monitored locations such as load centers. A penalty cost is introduced based on this estimation. Particulate matter 2.5 micrometers or less in diameter, the major air pollutant concerning most developing countries, is selected as the focus of this work. To properly estimate and sufficiently utilize the benefits of wind power for air pollutant dispersion control, robust optimization is applied to accommodate wind power uncertainty. Case studies justify this consideration of air pollutant dispersion, and demonstrate the effectiveness of the proposed model for improving load centers’ air pollution control and utilizing wind power benefits.postprin

A Comparison in the Accuracy of Mapping Nuclear Fallout Patterns using HPAC, HYSPLIT, DELFIC FPT and an AFIT FORTRAN95 Fallout Deposition Code

Four nuclear fallout mapping tools are studied to determine which tool predicts the most accurate fallout dose-rate contours with low computation time and resources. The four programs consist of the FORTRAN95 based Fallout Deposition Code (FDC), the Hazard Prediction and Assessment Capability\u27s (HPAC) Nuclear Weapon (NWPN), the Defense Land Fallout Interpretative Code (DELFIC) Fallout Planning Tool (FPT) and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) Model. The models were compared to the Defense Nuclear Agency\u27s (DNA) DNA 1251-1-EX, Compilation of Local Fallout Data from Test Detonations 1945-1962 Extracted from DASA 1251, using Warner and Platt\u27s Measure of Effectiveness (MOE) method. In order to accurately compare models the use of the FDC and low resolution weather data was validated. HYSPLIT trials were studied varying the vertical distribution, horizontal distribution, emission rate, emission time and number of equal activity particle groups. HPAC trials were run varying the use of terrain and the terrain resolution and the DELFIC FPT trials were ran varying the length of time the ground zero winds were incorporated. The best results of each of the four nuclear mapping tools were compared with the results culminating in the determination that the DELFIC FPT is the preferred nuclear mapping tool

Health effects of air pollution : innovative approaches for spatio-temporal evaluations

Air pollution is one of the major risk factors to human health, causing both short- and long-term effects and the global burden on mortality is estimated in more than 4 million deaths every year. Most of the evidence on the short-term effects is based on studies conducted in major cities, because data or estimates of air pollutants exposures in non-urban settings have been historically lacking. This is a limitation, because a large fraction of the population lives outside the cities, where the vulnerability profile is different from that of urban populations. In the last decade, several attempts were made to estimate daily concentrations of particulate matter (PM) with high spatial resolution over large geographical domains. However, applications in Italy and Sweden, and on other pollutants as nitrogen dioxide (NO2) and ozone (O3), are almost lacking, leaving a gap in the knowledge of their health effects outside cities. This thesis has been designed to fill this gap, by providing daily estimates of multiple air pollutants at the national level, and exploring the spatial heterogeneity in their health effects. Italy represented a testing ground for the development of innovative mixed-effects regression models which combined PM measurements with satellite data, land-use parameters and meteorological fields, and produced daily estimates of PM10 (PM with diameter smaller than 10 m) for each squared kilometer of the country, and each day in 2006-2012 (Study I). More recently, machine learning methodologies have been tested in the U.S., therefore, we have updated estimates of PM10 till 2015 and produced new estimates of PM2.5 (PM < 2.5 m), using a random forest (RF) algorithm (Study II). We replicated the same approach in Sweden, to which we added models for NO2 and O3, and a few spatiotemporal predictors aimed at capturing sources of air pollutants’ variations missed in the previous studies (Study III). We collected national data on hospital discharges for all Italian public and private hospitals during 2013-2015. We created municipality-specific time-series of daily counts of acute admissions for multiple cardiovascular (CVD) endpoints, which we related to daily mean PM10 and PM2.5 concentrations. We found evidence of adverse effects of PM on total CVD admissions and on specific outcomes such as heart failure and atrial fibrillation. Also, we estimated highest effects at the lowest PM concentrations, also in non-urban municipalities (Study IV). Similarly, we collected daily mortality counts at small area level in the Stockholm county, that we analyzed in relation to daily mean exposure to PM10, PM2.5, NO2 and O3. We found evidence of an association between daily O3 and non-accidental mortality in the year-round analysis, and significant associations with PM and O3 in the warm (April-September) period only. Effects were slightly higher in more densely inhabited areas, but we found associations also in non- urban areas outside the Stockholm city (Study V). In conclusion, we developed novel spatiotemporal models to estimate air pollutant concentrations at fine spatial and temporal resolution in Italy and Sweden. These allowed us to document adverse short-term effects on mortality and morbidity at very low concentrations and in areas (and among populations) previously neglected by epidemiological investigations

Monitoring and modelling of nitrogen dioxide in urban areas.

Imperial Users onl

A spatial econometric model for transboundary air pollution control treaties: an analysis of noncooperative international behavior

This dissertation develops a theoretical model that explains differences in emission reductions of transboundary air pollutants among nations based on national income, political freedom, the cost of emission reductions, emissions from other countries, the type of pollutant, and the pollutant\u27s dispersion characteristics. The model is based on the theory of the private provision of impure public goods. This theoretical model is then used to derive a reduced form demand equation for emission reductions that can be econometrically estimated using spatial autoregressive techniques for time-series cross-section data;The econometric model is applied to 25 European nations and covers the period from 1980 to 1990. These nations were signatories to the 1985 Helsinki Protocol, which mandated reductions in sulfur dioxide (SO2), and the 1988 Sofia Protocol, which limited emissions of nitrogen oxides (NO x). The signing of these treaties indicated a recognition of the problems caused by acid rain and ozone pollution, yet the two treaties had very different requirements regarding emission reductions. By taking into account the differences between nations and the different characteristics of the pollutants, my model allows a closer examination of the reasons for the differences in treaty requirements and treaty adherence;The results indicate that nations follow a Nash-subscription model in choosing their emission reductions. In other words, nations tend to free ride on the emission reductions of other nations. The spatial autoregressive model performs convincingly for SO2, but the model for NO x is less satisfying. While nations continue to exhibit Nash behavior, other variables fail to be significant or have the wrong sign. However, these results may be explained as resulting from the characteristic nature of NO x as compared with SO2. Since it originates from a larger number of sources, it is harder to control than SO2 emissions;A better understanding of the factors that influence a nation\u27s decision to reduce its emissions may provide a foundation for the negotiation of future transboundary pollution control treaties. New treaties could require some nations to make greater (or smaller) cuts in emissions, but by taking into account differences among the nations, larger total reductions and greater compliance might result

Spatial modelling of air pollution for open smart cities

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsHalf of the world’s population already lives in cities, and by 2050 two-thirds of the world’s population are expected to further move into urban areas. This urban growth leads to various environmental, social and economic challenges in cities, hampering the Quality of Life (QoL). Although recent trends in technologies equip us with various tools and techniques that can help in improving quality of life, air pollution remains the ‘biggest environmental health risk’ for decades, impacting individuals’ quality of life and well-being according to World Health Organisation (WHO). Many efforts have been made to measure air quality, but the sparse arrangement of monitoring stations and the lack of data currently make it challenging to develop systems that can capture within-city air pollution variations. To solve this, flexible methods that allow air quality monitoring using easily accessible data sources at the city level are desirable. The present thesis seeks to widen the current knowledge concerning detailed air quality monitoring by developing approaches that can help in tackling existing gaps in the literature. The thesis presents five contributions which address the issues mentioned above. The first contribution is the choice of a statistical method which can help in utilising existing open data and overcoming challenges imposed by the bigness of data for detailed air pollution monitoring. The second contribution concerns the development of optimisation method which helps in identifying optimal locations for robust air pollution modelling in cities. The third contribution of the thesis is also an optimisation method which helps in initiating systematic volunteered geographic information (VGI) campaigns for detailed air pollution monitoring by addressing sparsity and scarcity challenges of air pollution data in cities. The fourth contribution is a study proposing the involvement of housing companies as a stakeholder in the participatory framework for air pollution data collection, which helps in overcoming certain gaps existing in VGI-based approaches. Finally, the fifth contribution is an open-hardware system that aids in collecting vehicular traffic data using WiFi signal strength. The developed hardware can help in overcoming traffic data scarcity in cities, which limits detailed air pollution monitoring. All the contributions are illustrated through case studies in Muenster and Stuttgart. Overall, the thesis demonstrates the applicability of the developed approaches for enabling air pollution monitoring at the city-scale under the broader framework of the open smart city and for urban health research

Science, Advocacy, Policy, Planning: Tools for Advancing Transportation Equity

The theme of this portfolio is how different tools and approaches can be used for advancing transportation equity. Broadly defined, transportation equity is about fairness in transportation. There are a number of ways this fairness can be assessed. The most common way to assess transportation equity is by looking at the fairness of outcomes, distributed geographically, socially, or even by mode of transportation. Equity can also be defined by the fairness of processes. The first half of the portfolio illustrates some of the problems with the current transportation system and how it is unhealthy (Piece 1) and unjust (Piece 2). The second half of the portfolio focuses on one of the potential solutions: encouraging and promoting increased levels of multimodal transportation (Piece 3); and then analyzing how community planners and leaders best work to achieve this in an equitable way (Piece 4). The first piece of this portfolio is a literature review of how scientists measure near-road air pollution exposure from mobile sources, which provides a better understanding of just one important environmental health impact our transportation systems. There are key social and geographic equity implications from those studies, which planners and activists can use as evidence in arguments for solutions. The second piece is an environmental justice analysis of a road expansion project in Missoula, Montana. This piece seeks to better understand the procedural, distributive, and social impacts from the project and brings in themes of advocacy and best practices in an effort to argue for sensible alternatives. The third piece is a reflection essay from my internship with the Missoula Metropolitan Planning Organization, where I wrote a white paper as part of the 2016 update to the Long Range Transportation Plan. This white paper discusses multimodal solutions to vehicle-oriented transportation development and provides a policy-based approach to increasing levels of multimodal transportation rates in Missoula. The last piece is a case study of a multimodal transportation project in Miami, Florida. This piece analyzes transportation planners’ approaches to procedural equity and discusses successes and areas for improvement.https://scholarworks.umt.edu/grad_portfolios/1009/thumbnail.jp