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

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.

Health impacts from power plant emissions in Mexico

2005 ◽  
Vol 39 (7) ◽  
pp. 1199-1209 ◽  
Author(s):  
M LOPEZ ◽  
M ZUK ◽  
V GARIBAY ◽  
G TZINTZUN ◽  
R INIESTRA ◽  
...  
Keyword(s):  
Power Plant ◽  
Health Impacts ◽  
Power Plant Emissions ◽  
Plant 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.

scholarly journals Air quality resolution for health impact assessment: influence of regional characteristics

2014 ◽  
Vol 14 (2) ◽  
pp. 969-978 ◽  
Author(s):  
T. M. Thompson ◽  
R. K. Saari ◽  
N. E. Selin
Keyword(s):  
Air Quality ◽  
Human Health ◽  
Rural Areas ◽  
Urban Areas ◽  
Health Benefits ◽  
Health Benefit ◽  
Health Impacts ◽  
Regional Characteristics ◽  
Human Health Impacts ◽  
The Impact

Abstract. We evaluate how regional characteristics of population and background pollution might impact the selection of optimal air quality model resolution when calculating the human health impacts of changes to air quality. Using an approach consistent with air quality policy evaluation, we use a regional chemical transport model (CAMx) and a health benefit mapping program (BenMAP) to calculate the human health impacts associated with changes in ozone and fine particulate matter resulting from an emission reduction scenario. We evaluate this same scenario at 36, 12 and 4 km resolution for nine regions in the eastern US representing varied characteristics. We find that the human health benefits associated with changes in ozone concentrations are sensitive to resolution. This finding is especially strong in urban areas where we estimate that benefits calculated using coarse resolution results are on average two times greater than benefits calculated using finer scale results. In three urban areas we analyzed, results calculated using 36 km resolution modeling fell outside the uncertainty range of results calculated using finer scale modeling. In rural areas the influence of resolution is less pronounced with only an 8% increase in the estimated health impacts when using 36 km resolution over finer scales. In contrast, health benefits associated with changes in PM2.5 concentrations were not sensitive to resolution and did not follow a pattern based on any regional characteristics evaluated. The largest difference between the health impacts estimated using 36 km modeling results and either 12 or 4 km results was at most ±10% in any region. Several regions showed increases in estimated benefits as resolution increased (opposite the impact seen with ozone modeling), while some regions showed decreases in estimated benefits as resolution increased. In both cases, the dominant contribution was from secondary PM. Additionally, we found that the health impacts calculated using several individual concentration–response functions varied by a larger amount than the impacts calculated using results modeled at different resolutions. Given that changes in PM2.5 dominate the human health impacts, and given the uncertainty associated with human health response to changes in air pollution, we conclude that, when estimating the human health benefits associated with decreases in ozone and PM2.5 together, the benefits calculated at 36 km resolution agree, within errors, with the benefits calculated using fine (12 km or finer) resolution modeling when using the current methodology for assessing policy decisions.

scholarly journals Pixel Size and Revisit Rate Requirements for Monitoring Power Plant CO2 Emissions from Space

2019 ◽  
Vol 11 (13) ◽  
pp. 1608 ◽  
Author(s):  
Tim Hill ◽  
Ray Nassar
Keyword(s):  
Power Plant ◽  
Co2 Emissions ◽  
Power Plants ◽  
Pixel Size ◽  
Multiple Images ◽  
Power Plant Emissions ◽  
Emission Monitoring ◽  
Plant Emissions ◽  
The Individual ◽  
The Impact

The observational requirements for space-based quantification of anthropogenic CO 2 emissions are of interest to space agencies and related organizations that may contribute to a possible satellite constellation to support emission monitoring in the future. We assess two key observing characteristics for space-based monitoring of CO2 emissions: pixel size and revisit rate, and we introduce a new method utilizing multiple images simultaneously to significantly improve emission estimates. The impact of pixel size ranging from 2–10 km for space-based imaging spectrometers is investigated using plume model simulations, accounting for biases in the observations. Performance of rectangular pixels is compared to square pixels of equal area. The findings confirm the advantage of the smallest pixels in this range and the advantage of square pixels over rectangular pixels. A method of averaging multiple images is introduced and demonstrated to be able to estimate emissions from small sources when the individual images are unable to distinguish the plume. Due to variability in power plant emissions, results from a single overpass cannot be directly extrapolated to annual emissions, the most desired timescale for regulatory purposes. We investigate the number of overpasses required to quantify annual emissions with a given accuracy, based on the mean variability from the 50 highest emitting US power plants. Although the results of this work alone are not sufficient to define the full architecture of a future CO 2 monitoring constellation, when considered along with other studies, they may assist in informing the design of a space-based system to support anthropogenic CO 2 emission monitoring.

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