scholarly journals Influence of fossil-fuel power plant emissions on the surface fine particulate matter in the Seoul Capital Area, South Korea

2016 ◽  
Vol 66 (9) ◽  
pp. 863-873 ◽  
Author(s):  
Byeong-Uk Kim ◽  
Okgil Kim ◽  
Hyun Cheol Kim ◽  
Soontae Kim
Keyword(s):  
Particulate Matter ◽  
Power Plant ◽  
South Korea ◽  
Fossil Fuel ◽  
Fine Particulate Matter ◽  
Power Plant Emissions ◽  
Fine Particulate ◽  
Fossil Fuel Power Plant ◽  
Plant Emissions

scholarly journals Estimating Human Health Impacts and Costs Due to Iranian Fossil Fuel Power Plant Emissions through the Impact Pathway Approach

Energies ◽  
2017 ◽  
Vol 10 (12) ◽  
pp. 2136 ◽  
Author(s):  
Mojtaba Jorli ◽  
Steven Van Passel ◽  
Hossein Sadeghi ◽  
Alireza Nasseri ◽  
Lotfali Agheli
Keyword(s):  
Power Plant ◽  
Human Health ◽  
Fossil Fuel ◽  
Health Impacts ◽  
Power Plant Emissions ◽  
Human Health Impacts ◽  
Fossil Fuel Power Plant ◽  
Impact Pathway ◽  
Plant Emissions ◽  
The Impact

DIURNAL VARIATION OF PARTICULATE MATTER IN THE PROXIMITY OF ROVINARI FOSSIL-FUEL POWER PLANT

2011 ◽  
Vol 10 (1) ◽  
pp. 99-105 ◽  
Author(s):  
Catalin Nisulescu ◽  
Delia Calinoiu ◽  
Adrian Timofte ◽  
Andreea Boscornea ◽  
Camelia Talianu
Keyword(s):  
Particulate Matter ◽  
Power Plant ◽  
Diurnal Variation ◽  
Fossil Fuel ◽  
Fossil Fuel Power Plant

scholarly journals Towards space based verification of CO<sub>2</sub> emissions from strong localized sources: fossil fuel power plant emissions as seen by a CarbonSat constellation

2011 ◽  
Vol 4 (4) ◽  
pp. 5147-5182
Author(s):  
V. A. Velazco ◽  
M. Buchwitz ◽  
H. Bovensmann ◽  
M. Reuter ◽  
O. Schneising ◽  
...  
Keyword(s):  
Power Plant ◽  
Systematic Error ◽  
Co2 Emissions ◽  
Power Plants ◽  
Fossil Fuel ◽  
Systematic Errors ◽  
Random Errors ◽  
Power Plant Emissions ◽  
Global Coverage ◽  
Plant Emissions

Abstract. Carbon dioxide (CO2) is the most important man-made greenhouse gas (GHG) that cause global warming. With electricity generation through fossil-fuel power plants now as the economic sector with the largest source of CO2, power plant emissions monitoring has become more important than ever in the fight against global warming. In a previous study done by Bovensmann et al. (2010), random and systematic errors of power plant CO2 emissions have been quantified using a single overpass from a proposed CarbonSat instrument. In this study, we quantify errors of power plant annual emission estimates from a hypothetical CarbonSat and constellations of several CarbonSats while taking into account that power plant CO2 emissions are time-dependent. Our focus is on estimating systematic errors arising from the sparse temporal sampling as well as random errors that are primarily dependent on wind speeds. We used hourly emissions data from the US Environmental Protection Agency (EPA) combined with assimilated and re-analyzed meteorological fields from the National Centers of Environmental Prediction (NCEP). CarbonSat orbits were simulated as a sun-synchronous low-earth orbiting satellite (LEO) with an 828-km orbit height, local time ascending node (LTAN) of 13:30 (01:30 p.m.) and achieves global coverage after 5 days. We show, that despite the variability of the power plant emissions and the limited satellite overpasses, one CarbonSat can verify reported US annual CO2 emissions from large power plants (≥5 Mt CO2 yr−1) with a systematic error of less than ~4.9 % for 50 % of all the power plants. For 90 % of all the power plants, the systematic error was less than ~12.4 %. We additionally investigated two different satellite configurations using a combination of 5 CarbonSats. One achieves global coverage everyday but only samples the targets at fixed local times. The other configuration samples the targets five times at two-hour intervals approximately every 6th day but only achieves global coverage after 5 days. From the statistical analyses, we found, as expected, that the random errors improve by approximately a factor of two if 5 satellites are used. On the other hand, more satellites do not result in a large reduction of the systematic error. The systematic error is somewhat smaller for the CarbonSat constellation configuration achieving global coverage everyday. Finally, we recommend the CarbonSat constellation configuration that achieves daily global coverage.

Studies on the Combined Removal of the Fine Particulate Matter and Mercury from Coal-Fired Flue Gas

2013 ◽  
Vol 726-731 ◽  
pp. 1935-1939
Author(s):  
Jian Yi Lv ◽  
Li Yuan Cao ◽  
Ya Tao
Keyword(s):  
Particulate Matter ◽  
Power Plant ◽  
Flue Gas ◽  
Thermal Power Plant ◽  
Fine Particulate Matter ◽  
Thermal Power ◽  
Mercury Content ◽  
Mass Balance Method ◽  
Removal Process ◽  
Fine Particulate

This paper elaborates the formation, distribution, migration and transformation of mercury and particulate matter, also some of the existing removal process methods. Using the mass balance method calculates the mercury content of coal-fired product. According to the result, we can targeted design a strong practicality, feasibility joint removal process to fine particulate matter and mercury of the thermal power plant.

scholarly journals Continuous mapping of fine particulate matter (PM<sub>2.5</sub>) air quality in East Asia at daily 6×6 km<sup>2</sup> resolution by application of a random forest algorithm to 2011–2019 GOCI geostationary satellite data

2021 ◽  
Author(s):  
Drew C. Pendergrass ◽  
Daniel J. Jacob ◽  
Shixian Zhai ◽  
Jhoon Kim ◽  
Ja-Ho Koo ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Random Forest ◽  
South Korea ◽  
East Asia ◽  
Fine Particulate Matter ◽  
Eastern China ◽  
Ambient Air ◽  
Population Exposure ◽  
Fine Particulate

Abstract. We use 2011–2019 aerosol optical depth (AOD) observations from the Geostationary Ocean Color Imager (GOCI) instrument over East Asia to infer 24-h daily surface fine particulate matter (PM2.5) concentrations at continuous 6x6 km2 resolution over eastern China, South Korea, and Japan. This is done with a random forest (RF) algorithm applied to the gap-filled GOCI AODs and other data and trained with PM2.5 observations from the three national networks. The predicted 24-h PM2.5 concentrations for sites entirely withheld from training in a ten-fold crossvalidation procedure correlate highly with network observations (R2 = 0.89) with single-value precision of 26–32 % depending on country. Prediction of annual mean values has R2 = 0.96 and single-value precision of 12 %. The RF algorithm is only moderately successful for diagnosing local exceedances of the National Ambient Air Quality Standard (NAAQS) because these exceedances are typically within the single-value precisions of the RF, and also because of RF smoothing of extreme PM2.5 concentrations. The area-weighted and population-weighted trends of RF PM2.5 concentrations for eastern China, South Korea, and Japan show steady 2015–2019 declines consistent with surface networks, but the surface networks in eastern China and South Korea underestimate population exposure. Further examination of RF PM2.5 fields for South Korea identifies hotspots where surface network sites were initially lacking and shows 2015–2019 PM2.5 decreases across the country except for flat concentrations in the Seoul metropolitan area. Inspection of monthly PM2.5 time series in Beijing, Seoul, and Tokyo shows that the RF algorithm successfully captures observed seasonal variations of PM2.5 even though AOD and PM2.5 often have opposite seasonalities. Application of the RF algorithm to urban pollution episodes in Seoul and Beijing demonstrates high skill in reproducing the observed day-to-day variations in air quality as well as spatial patterns on the 6 km scale. Comparison to a CMAQ simulation for the Korean peninsula demonstrates the value of the continuous RF PM2.5 fields for testing air quality models, including over North Korea where they offer a unique resource.

scholarly journals Relating geostationary satellite measurements of aerosol optical depth (AOD) over East Asia to fine particulate matter (PM<sub>2.5</sub>): insights from the KORUS-AQ aircraft campaign and GEOS-Chem model simulations

2021 ◽  
Vol 21 (22) ◽  
pp. 16775-16791
Author(s):  
Shixian Zhai ◽  
Daniel J. Jacob ◽  
Jared F. Brewer ◽  
Ke Li ◽  
Jonathan M. Moch ◽  
...  
Keyword(s):  
Particulate Matter ◽  
South Korea ◽  
East Asia ◽  
Aerosol Optical Depth ◽  
Fine Particulate Matter ◽  
Organic Aerosols ◽  
Size Distributions ◽  
Satellite Measurements ◽  
Fine Particulate ◽  
Residential Coal

Abstract. Geostationary satellite measurements of aerosol optical depth (AOD) over East Asia from the Geostationary Ocean Color Imager (GOCI) and Advanced Himawari Imager (AHI) instruments can augment surface monitoring of fine particulate matter (PM2.5) air quality, but this requires better understanding of the AOD–PM2.5 relationship. Here we use the GEOS-Chem chemical transport model to analyze the critical variables determining the AOD–PM2.5 relationship over East Asia by simulation of observations from satellite, aircraft, and ground-based datasets. This includes the detailed vertical aerosol profiling over South Korea from the KORUS-AQ aircraft campaign (May–June 2016) with concurrent ground-based PM2.5 composition, PM10, and AERONET AOD measurements. The KORUS-AQ data show that 550 nm AOD is mainly contributed by sulfate–nitrate–ammonium (SNA) and organic aerosols in the planetary boundary layer (PBL), despite large dust concentrations in the free troposphere, reflecting the optically effective size and high hygroscopicity of the PBL aerosols. We updated SNA and organic aerosol size distributions in GEOS-Chem to represent aerosol optical properties over East Asia by using in situ measurements of particle size distributions from KORUS-AQ. We find that SNA and organic aerosols over East Asia have larger size (number median radius of 0.11 µm with geometric standard deviation of 1.4) and 20 % larger mass extinction efficiency as compared to aerosols over North America (default setting in GEOS-Chem). Although GEOS-Chem is successful in reproducing the KORUS-AQ vertical profiles of aerosol mass, its ability to link AOD to PM2.5 is limited by under-accounting of coarse PM and by a large overestimate of nighttime PM2.5 nitrate. The GOCI–AHI AOD data over East Asia in different seasons show agreement with AERONET AODs and a spatial distribution consistent with surface PM2.5 network data. The AOD observations over North China show a summer maximum and winter minimum, opposite in phase to surface PM2.5. This is due to low PBL depths compounded by high residential coal emissions in winter and high relative humidity (RH) in summer. Seasonality of AOD and PM2.5 over South Korea is much weaker, reflecting weaker variation in PBL depth and lack of residential coal emissions.

scholarly journals Global and regional drivers of power plant CO2 emissions over the last three decades

2022 ◽  
Author(s):  
Xinying Qin ◽  
Dan Tong ◽  
Fei Liu ◽  
Ruili Wu ◽  
Bo Zheng ◽  
...  
Keyword(s):  
United States ◽  
Power Plant ◽  
Co2 Emissions ◽  
Power Plants ◽  
Fossil Fuel ◽  
Driving Forces ◽  
The United States ◽  
The Past ◽  
Power Plant Emissions ◽  
Plant Emissions

The past three decades have witnessed the dramatic expansion of global biomass- and fossil fuel-fired power plants, but the tremendously diverse power infrastructure shapes different spatial and temporal CO2 emission characteristics. Here, by combining Global Power plant Emissions Database (GPED v1.1) constructed in this study and the previously developed China coal-fired power Plant Emissions Database (CPED), we analyzed global and regional changes in generating capacities, age structure, and CO2 emissions by fuel type and unit size, and further identified the major driving forces of these global and regional structure and emission trends over the past 30 years. Accompanying the growth of fossil fuel- and biomass-burning installed capacity from 1,774 GW in 1990 to 4,139 GW in 2019 (a 133.3% increase), global CO2 emissions from the power sector relatively increased from 7.5 Gt to 13.9 Gt (an 85.3% increase) during the same period. However, diverse developments and transformations of regional power units in fuel types and structure characterized various regional trends of CO2 emissions. For example, in the United States and Europe, CO2 emissions from power plants peaked before 2005, driven by the utilization of advanced electricity technologies and the switches from coal to gas fuel at the early stage. It is estimated the share of identified low-efficiency coal power capacity decreased to 4.3% in the United States and 0.6% in Europe with respectively 2.1% and 13.2% thermal efficiency improvements from 1990-2019. In contrast, CO2 emissions in China, India, and the rest of world are still steadily increasing because the growing demand for electricity is mainly met by developing carbon-intensive but less effective coal power capacity. The index decomposition analysis (IDA) to identify the multi-stage driving forces on the trends of CO2 emissions further suggests different global and regional characteristics. Globally, the growth of demand mainly drives the increase of CO2 emissions for all stages (i.e. 1990-2000, 2000-2010 and 2010-2019). Regional results support the critical roles of thermal efficiency improvement (accounting for 20% of the decrease in CO2 emissions) and fossil fuel mix (61%) in preventing CO2 emission increases in the developed regions (e.g., the United States and Europe). The decrease of fossil fuel share gradually demonstrates its importance in carrying the positive effects on curbing emissions in the most of regions, including the developing economics (i.e. China and India) after 2010 (accounting for 46% of the decrease in CO2 emissions). Our results highlight the contributions of different driving forces to emissions have significantly changed over the past 30 years, and this comprehensive analysis indicates that the structure optimization and transformations of power plants is paramount importance to curb or further reduce CO2 emissions from the power sector in the future.

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