scholarly journals Status of Air Pollution Regulations Affecting Gas Turbines in 80 Nations

Author(s):  
R. J. Ketterer ◽  
N. R. Dibelius

This paper summarizes regulations from 80 countries covering air pollution emissions from gas turbines. The paper includes emission and ground level concentration standards for particulates, sulfur dioxide, oxides of nitrogen, visible emissions, and carbon monoxide.

1976 ◽  
Author(s):  
N. R. Dibelius

The measurement of air pollutants emitted to the atmosphere in exhaust gases from stationary gas turbines must be made in accordance with applicable government specifications in those cases where the measurements are being made to determine compliance with regulations. This paper reviews the methods for measuring opacity, sulfur dioxide, oxides of nitrogen, carbon monoxide, carbon dioxide, oxygen, hydrocarbons, and particulates. In addition, the paper references the Federal Register (volume, number, and page) in which the official specification appears. Other methods, including ASME, SAE, and ASTM, are listed where applicable.


Author(s):  
R. H. Johnson ◽  
Colin Wilkes

At this point in time, everyone is “for the environment” and this is true the world world over because the atmosphere is shared by peoples of all nations. Air pollution from hydrocarbon fuel combustion, both worldwide and local, is discussed by reviewing known measurements of contaminants. Application of gas turbines by industry is one way to provide power needs for attaining and maintaining an industrial society. Environmental performance of industrial gas turbines with respect to exhaust emissions and environmental impact is presented for oxides of nitrogen, hydrocarbons, carbon monoxide, particulate matter and visible smoke. Results of recent abatement efforts are also presented together with estimates of potential improvements to show the place of the industrial combustion turbine in a world with growing concern for environmental improvement.


Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 290
Author(s):  
Akvilė Feiferytė Skirienė ◽  
Žaneta Stasiškienė

The rapid spread of the coronavirus (COVID-19) pandemic affected the economy, trade, transport, health care, social services, and other sectors. To control the rapid dispersion of the virus, most countries imposed national lockdowns and social distancing policies. This led to reduced industrial, commercial, and human activities, followed by lower air pollution emissions, which caused air quality improvement. Air pollution monitoring data from the European Environment Agency (EEA) datasets were used to investigate how lockdown policies affected air quality changes in the period before and during the COVID-19 lockdown, comparing to the same periods in 2018 and 2019, along with an assessment of the Index of Production variation impact to air pollution changes during the pandemic in 2020. Analysis results show that industrial and mobility activities were lower in the period of the lockdown along with the reduced selected pollutant NO2, PM2.5, PM10 emissions by approximately 20–40% in 2020.


2016 ◽  
Author(s):  
Wan Jiao ◽  
Gayle Hagler ◽  
Ronald Williams ◽  
Robert Sharpe ◽  
Ryan Brown ◽  
...  

Abstract. Advances in air pollution sensor technology have enabled the development of small and low cost systems to measure outdoor air pollution. The deployment of a large number of sensors across a small geographic area would have potential benefits to supplement traditional monitoring networks with additional geographic and temporal measurement resolution, if the data quality were sufficient. To understand the capability of emerging air sensor technology, the Community Air Sensor Network (CAIRSENSE) project deployed low cost, continuous and commercially-available air pollution sensors at a regulatory air monitoring site and as a local sensor network over a surrounding ~ 2 km area in Southeastern U.S. Co-location of sensors measuring oxides of nitrogen, ozone, carbon monoxide, sulfur dioxide, and particles revealed highly variable performance, both in terms of comparison to a reference monitor as well as whether multiple identical sensors reproduced the same signal. Multiple ozone, nitrogen dioxide, and carbon monoxide sensors revealed low to very high correlation with a reference monitor, with Pearson sample correlation coefficient (r) ranging from 0.39 to 0.97, −0.25 to 0.76, −0.40 to 0.82, respectively. The only sulfur dioxide sensor tested revealed no correlation (r  0.5), step-wise multiple linear regression was performed to determine if ambient temperature, relative humidity (RH), or age of the sensor in sampling days could be used in a correction algorithm to improve the agreement. Maximum improvement in agreement with a reference, incorporating all factors, was observed for an NO2 sensor (multiple correlation coefficient R2adj-orig = 0.57, R2adj-final = 0.81); however, other sensors showed no apparent improvement in agreement. A four-node sensor network was successfully able to capture ozone (2 nodes) and PM (4 nodes) data for an 8 month period of time and show expected diurnal concentration patterns, as well as potential ozone titration due to near-by traffic emissions. Overall, this study demonstrates a straightforward methodology for establishing low-cost air quality sensor performance in a real-world setting and demonstrates the feasibility of deploying a local sensor network to measure ambient air quality trends.


2008 ◽  
Vol 47 (8) ◽  
pp. 2105-2114 ◽  
Author(s):  
Xiangde Xu ◽  
Lian Xie ◽  
Xinghong Cheng ◽  
Jianming Xu ◽  
Xiuji Zhou ◽  
...  

Abstract A major challenge for air quality forecasters is to reduce the uncertainty of air pollution emission inventory. Error in the emission data is a primary source of error in air quality forecasts, much like the effect of error in the initial conditions on the accuracy of weather forecasting. Data assimilation has been widely used to improve weather forecasting by correcting the initial conditions with weather observations. In a similar way, observed concentrations of air pollutants can be used to correct the errors in the emission data. In this study, a new method is developed for estimating air pollution emissions based on a Newtonian relaxation and nudging technique. Case studies for the period of 1–25 August 2006 in 47 cities in China indicate that the nudging technique resulted in improved estimations of sulfur dioxide (SO2) and nitrogen dioxide (NO2) emissions in the majority of these cities. Predictions of SO2 and NO2 concentrations in January, April, August, and October using the emission estimations derived from the nudging technique showed remarkable improvements over those based on the original emission data.


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