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Lükewille, Anke; Bertok, I.; Amann, Markus; Cofała, J.; Gyárfáš, F.; Johansson, Matti; Klimont, Zbigniew; Pacyna, Elisabeth G.; Pacyna, Józef M.

doi:10.1023/a:1013807101561

Cited by 11 publications

(6 citation statements)

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“…Such an approach has been an integral part of the GAINS model development for both particulate matter (e.g. Klimont et al, 2002b;Lükewille et al, 2001) and other pollutants (e.g. Amann et al, 2011;Cofala and Syri, 1998;Klimont et al, 2002a); the details for PM are documented in Klimont et al (2002b) and the current structure can be reviewed in the online application of the GAINS model.…”

Section: Model Technology Resolutionmentioning

confidence: 99%

Global anthropogenic emissions of particulate matter including black carbon

et al. 2017

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Abstract. This paper presents a comprehensive assessment of historical global anthropogenic particulate matter (PM) emissions including the consistent and harmonized calculation of mass-based size distribution (PM 1 , PM 2.5 , PM 10 ), as well as primary carbonaceous aerosols including black carbon (BC) and organic carbon (OC). The estimates were developed with the integrated assessment model GAINS, where source-and region-specific technology characteristics are explicitly included. This assessment includes a number of previously unaccounted or often misallocated emission sources, i.e. kerosene lamps, gas flaring, diesel generators, refuse burning; some of them were reported in the past for selected regions or in the context of a particular pollutant or sector but not included as part of a total estimate. Spatially, emissions were calculated for 172 source regions (as well as international shipping), presented for 25 global regions, and allocated to 0.5 • × 0.5 • longitude-latitude grids. No independent estimates of emissions from forest fires and savannah burning are provided and neither windblown dust nor unpaved roads emissions are included.We estimate that global emissions of PM have not changed significantly between 1990 and 2010, showing a strong decoupling from the global increase in energy consumption and, consequently, CO 2 emissions, but there are significantly different regional trends, with a particularly strong increase in East Asia and Africa and a strong decline in Europe, North America, and the Pacific region. This in turn resulted in important changes in the spatial pattern of PM burden, e.g. European, North American, and Pacific contributions to global emissions dropped from nearly 30 % in 1990 to well below 15 % in 2010, while Asia's contribution grew from just over 50 % to nearly two-thirds of the global total in 2010. For all PM species considered, Asian sources represented over 60 % of the global anthropogenic total, and residential combustion was the most important sector, contributing about 60 % for BC and OC, 45 % for PM 2.5 , and less than 40 % for PM 10 , where large combustion sources and industrial processes are equally important. Global anthropogenic emissions of BC were estimated at about 6.6 and 7.2 Tg in 2000 and 2010, respectively, and represent about 15 % of PM 2.5 but for some sources reach nearly 50 %, i.e. for the transport sector. Our global BC numbers are higher than previously published owing primarily to the inclusion of new sources.This PM estimate fills the gap in emission data and emission source characterization required in air quality and climate modelling studies and health impact assessments at a regional and global level, as it includes both carbonaceous and non-carbonaceous constituents of primary particulate matter emissions. The developed emission dataset has been used in several regional and global atmospheric transport and climate model simulations within the ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants) project and beyo...

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Section: Model Technology Resolutionmentioning

confidence: 99%

Global anthropogenic emissions of particulate matter including black carbon

et al. 2017

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“…Lükewille et al 5 present a framework to estimate the potential to control PM 2.5 emissions in Europe. They discuss a module added to the Regional Air Pollution Information and Simulation (RAINS) model developed by the International Institute for Applied Systems Analysis (IIASA).…”

Section: Antecedentsmentioning

confidence: 99%

“…In addition to EPA criteria pollutants and their precursors, many hazardous air pollutants can be found in varying amounts when combusting coal; power plant emissions include As, Cd, Hg, K, Na, and many other elements. 5 These air pollutants have not been quantified for utilities to the extent that criteria pollutants have been, so it is difficult to accurately estimate the relationship between them and each unit of energy produced. They were not included in this paper, but it is important to keep in mind the fact that other potentially toxic pollutants are present in power plant emissions although they are not generally quantified or reported in this paper.…”

Section: Comparing Gin Emissions To Power Plantmentioning

confidence: 99%

“…Power plant emissions also contribute to O 3 (a criteria pollutant sometimes called "smog" in the press), acid rain, and eutrophication of lakes and streams. 5 In addition, coal-fired power plants emit other hazardous/ toxic air pollutants; they release 48 t of Hg vapor per year, or 41% of the total U.S. inventory. 6 Ambient air quality monitoring must be done with Federal Reference Method (FRM) samplers described in the Code of Federal Regulations (CFR) 40, Part 53.…”

Section: Introductionmentioning

confidence: 99%

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The Environmental Cost of Reducing Agricultural Fine Particulate Matter Emissions

Funk

2010

Journal of the Air & Waste Management Association

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The U.S. Environmental Protection Agency (EPA) revised the National Ambient Air Quality Standards (NAAQS) in 2006, reducing acceptable fine particulate matter (PM 2.5 ) levels; state environmental protection agencies in states with nonattainment areas are required to draft State Implementation Plans (SIPs) detailing measures to reduce regional PM 2.5 levels by reducing PM 2.5 and PM 2.5 precursor emissions. These plans need to account for increases in emissions caused by operating control technologies. Potential PM 2.5 emissions reductions realized by adding a second set of dust cyclones were estimated for the cotton ginning industry. Increases in energy consumption were calculated based on dust cyclone air pressure drop. Additional energy required was translated into increased emissions using published emission factors and state emissions inventories. Reductions in gin emissions were compared with increases in emissions at the power plant. Because of the electrical energy required, reducing one unit of agricultural PM 2.5 emissions at a cotton gin results in emitting 0.11-2.67 units of direct PM 2.5 , 1.39 -69

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“…5 Emissions of mineral particulate matter adversely impact on environmental quality in mining regions, 6 transport regions, 7 and even on a global scale. 9 Industrialization patterns changed due to stringent air quality standards with many heavily polluting industries moving from developed countries. and natural sources (forest fires, soil re-suspension, etc.)…”

Section: Introductionmentioning

confidence: 99%

Evaluation of optimization methods for solving the receptor model for chemical mass balance

Anu

Rangabhashiyam

Antony

et al. 2015

JSCS

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The chemical mass balance (CMB 8.2) model has been extensively used in order to determine source contribution for particulate matters (size diameters less than 10 and 2.5 µm) in air quality analysis. A comparison of the source contribution estimated from three CMB models was realized through optimization techniques, such as 'fmincon' (CMB-fmincon) and genetic algorithm (CMB-GA) using MATLAB. The proposed approach was validated using a San Joaquin Valley Air Quality Study (SJVAQS) California Fresno and Bakersfield PM 10 and PM 2.5 followed with Oregon PM 10 data. The source contribution estimated from CMB-GA was better in source interpretation in comparison with CMB 8.2 and CMB-fmincon. The performance accuracies of three CMB approaches were validated using R 2 , reduced χ 2 and percentage mass tests. The R 2 (0.90, 0.67 and 0.81, 0.83), χ 2 (0.36, 0.66 and 0.65, 0.43) and percentage mass (67.36, 55.03 and 94.24 %, 74.85 %) of CMB-GA showed high correlation for PM 10 , PM 2.5 , Fresno and Bakersfield data, respectively. To make a complete decision, the proposed methodology was bench marked with Portland, Oregon PM 10 data with the best fit with R 2 (0.99), χ 2 (1.6) and percentage mass (94.4 %) from CMB-GA. Therefore, the study revealed that CMB with genetic algorithm optimization method exhibiting better stability in determining the source contributions.

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Cited by 11 publications

References 0 publications

Global anthropogenic emissions of particulate matter including black carbon

Global anthropogenic emissions of particulate matter including black carbon

The Environmental Cost of Reducing Agricultural Fine Particulate Matter Emissions

Evaluation of optimization methods for solving the receptor model for chemical mass balance

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