Jet Aircraft Emissions and Air Quality in the Vicinity of The Los Angeles International Airport

1971 ◽  
Author(s):  
Eloy R. Lozano ◽  
Ralph E. George
2013 ◽  
Vol 13 (18) ◽  
pp. 9285-9302 ◽  
Author(s):  
J. Rissman ◽  
S. Arunachalam ◽  
M. Woody ◽  
J. J. West ◽  
T. BenDor ◽  
...  

Abstract. This study examined the impacts of aircraft emissions during the landing and takeoff cycle on PM2.5 concentrations during the months of June and July 2002 at the Hartsfield–Jackson Atlanta International Airport. Primary and secondary pollutants were modeled using the Advanced Modeling System for Transport, Emissions, Reactions, and Deposition of Atmospheric Matter (AMSTERDAM). AMSTERDAM is a modified version of the Community Multiscale Air Quality (CMAQ) model that incorporates a plume-in-grid process to simulate emissions sources of interest at a finer scale than can be achieved using CMAQ's model grid. Three fundamental issues were investigated: the effects of aircraft on PM2.5 concentrations throughout northern Georgia, the differences resulting from use of AMSTERDAM's plume-in-grid process rather than a traditional CMAQ simulation, and the concentrations observed in aircraft plumes at subgrid scales. Comparison of model results with an air quality monitor located in the vicinity of the airport found that normalized mean bias ranges from −77.5% to 6.2% and normalized mean error ranges from 40.4% to 77.5%, varying by species. Aircraft influence average PM2.5 concentrations by up to 0.232 μg m−3 near the airport and by 0.001–0.007 μg m−3 throughout the Atlanta metro area. The plume-in-grid process increases concentrations of secondary PM pollutants by 0.005–0.020 μg m−3 (compared to the traditional grid-based treatment) but reduces the concentration of non-reactive primary PM pollutants by up to 0.010 μg m−3, with changes concentrated near the airport. Examination of subgrid-scale results indicates that median aircraft contribution to grid cells is higher than median puff concentration in the airport's grid cell and outside of a 20 km × 20 km square area centered on the airport, while in a 12 km × 12 km square ring centered on the airport, puffs have median concentrations over an order of magnitude higher than aircraft contribution to the grid cells. Maximum puff impacts are seen within the 12 km × 12 km ring, not in the airport's own grid cell, while maximum grid cell impacts occur within the airport's grid cell. Twenty-one (21)% of all aircraft-related puffs from the Atlanta airport have at least 0.1 μg m−3 PM2.5 concentrations. Near the airport, median daily puff concentrations vary between 0.017 and 0.134 μg m−3 (0.05 and 0.35 μg m−3 at ground level), while maximum daily puff concentrations vary between 6.1 and 42.1 μg m−3 (7.5 and 42.1 μg m−3 at ground level) during the 2-month period. In contrast, median daily aircraft contribution to grid concentrations varies between 0.015 and 0.091 μg m−3 (0.09 and 0.40 μg m−3 at ground level), while the maximum varies between 0.75 and 2.55 μg m−3 (0.75 and 2.0 μg m−3 at ground level). Future researchers may consider using a plume-in-grid process, such as the one used here, to understand the impacts of aircraft emissions at other airports, for proposed future airports, for airport expansion projects under various future scenarios, and for other national-scale studies specifically when the maximum impacts at fine scales are of interest.


2011 ◽  
Vol 45 (36) ◽  
pp. 6526-6533 ◽  
Author(s):  
Yifang Zhu ◽  
Elinor Fanning ◽  
Rong Chun Yu ◽  
Qunfang Zhang ◽  
John R. Froines

2013 ◽  
Vol 13 (1) ◽  
pp. 1089-1132 ◽  
Author(s):  
J. Rissman ◽  
S. Arunachalam ◽  
M. Woody ◽  
J. J. West ◽  
T. BenDor ◽  
...  

Abstract. This study examined the impacts of aircraft emissions during the landing and takeoff cycle on PM2.5 concentrations during the months of June 2002 and July 2002 at the Hartsfield-Jackson Atlanta International Airport. Primary and secondary pollutants were modeled using the Advanced Modeling System for Transport, Emissions, Reactions, and Deposition of Atmospheric Matter (AMSTERDAM). AMSTERDAM is a modified version of the Community Multiscale Air Quality (CMAQ) model that incorporates a plume-in-grid process to simulate emissions sources of interest at a finer scale than can be achieved using CMAQ's model grid. Three fundamental issues were investigated: the effects of aircraft on PM2.5 concentrations throughout northern Georgia, the differences resulting from use of AMSTERDAM's plume-in-grid process rather than a traditional CMAQ simulation, and the concentrations observed in aircraft plumes at sub-grid scales. Comparison of model results with an air quality monitor located in the vicinity of the airport found that normalized mean bias ranges from −77.5% to 6.2% and normalized mean error ranges from 40.4% to 77.5%, varying by species. Aircraft influence average PM2.5 concentrations by up to 0.232 μg m−3 near the airport and by 0.001–0.007 μg m−3 throughout the Atlanta metro area. The plume-in-grid process increases concentrations of secondary PM pollutants by 0.005–0.020 μg m−3 (compared to the traditional grid-based treatment) but reduces the concentration of non-reactive primary PM pollutants by up to 0.010 μg m−3, with changes concentrated near the airport. Examination of sub-grid scale results indicates that puffs within 20 km of the airport often have average PM2.5 concentrations one order of magnitude higher than aircraft contribution to the grid cells containing those puffs, and within 1–4 km of emitters, puffs may have PM2.5 concentrations 3 orders of magnitude greater than the aircraft contribution to their grid cells. 21% of all aircraft-related puffs from the Atlanta airport have at least 0.1 μg m−3 PM2.5 concentrations. Median daily puff concentrations vary between 0.017 and 0.134 μg m−3, while maximum daily puff concentrations vary between 6.1 and 42.1 μg m−3 during the 2-month period. In contrast, median daily grid concentrations vary between 0.015 and 0.091 μg m−3, while maximum daily grid concentrations vary between 0.751 and 2.55 μg m−3. Future researchers may consider using AMSTERDAM to understand the impacts of aircraft emissions at other airports, for proposed future airports, for airport expansion projects under various future scenarios, and for other national-scale studies specifically when the maximum impacts at fine scales are of interest.


2018 ◽  
Vol 72 ◽  
pp. 198-207 ◽  
Author(s):  
Xiaowen Yang ◽  
Shuiyuan Cheng ◽  
Jianlei Lang ◽  
Ran Xu ◽  
Zhe Lv

2017 ◽  
Vol 151 ◽  
pp. 82-93 ◽  
Author(s):  
Farimah Shirmohammadi ◽  
Mohammad H. Sowlat ◽  
Sina Hasheminassab ◽  
Arian Saffari ◽  
George Ban-Weiss ◽  
...  

2020 ◽  
Vol 13 (1) ◽  
pp. 5
Author(s):  
William Straka ◽  
Shobha Kondragunta ◽  
Zigang Wei ◽  
Hai Zhang ◽  
Steven D. Miller ◽  
...  

The COVID-19 pandemic has infected almost 73 million people and is responsible for over 1.63 million fatalities worldwide since early December 2019, when it was first reported in Wuhan, China. In the early stages of the pandemic, social distancing measures, such as lockdown restrictions, were applied in a non-uniform way across the world to reduce the spread of the virus. While such restrictions contributed to flattening the curve in places like Italy, Germany, and South Korea, it plunged the economy in the United States to a level of recession not seen since WWII, while also improving air quality due to the reduced mobility. Using daily Earth observation data (Day/Night Band (DNB) from the National Oceanic and Atmospheric Administration Suomi-NPP and NO2 measurements from the TROPOspheric Monitoring Instrument TROPOMI) along with monthly averaged cell phone derived mobility data, we examined the economic and environmental impacts of lockdowns in Los Angeles, California; Chicago, Illinois; Washington DC from February to April 2020—encompassing the most profound shutdown measures taken in the U.S. The preliminary analysis revealed that the reduction in mobility involved two major observable impacts: (i) improved air quality (a reduction in NO2 and PM2.5 concentration), but (ii) reduced economic activity (a decrease in energy consumption as measured by the radiance from the DNB data) that impacted on gross domestic product, poverty levels, and the unemployment rate. With the continuing rise of COVID-19 cases and declining economic conditions, such knowledge can be combined with unemployment and demographic data to develop policies and strategies for the safe reopening of the economy while preserving our environment and protecting vulnerable populations susceptible to COVID-19 infection.


2008 ◽  
Vol 7 (2) ◽  
pp. 1-3 ◽  
Author(s):  
Manish Jain ◽  
James Pita ◽  
Milind Tambe ◽  
Fernando Ordóñez ◽  
Praveen Paruchuri ◽  
...  

2010 ◽  
Vol 3 (4) ◽  
pp. 2291-2314
Author(s):  
G. Sarwar ◽  
K. W. Appel ◽  
A. G. Carlton ◽  
R. Mathur ◽  
K. Schere ◽  
...  

Abstract. A new condensed toluene mechanism is incorporated into the Community Multiscale Air Quality Modeling system. Model simulations are performed using the CB05 chemical mechanism containing the existing (base) and the new toluene mechanism for the western and eastern US for a summer month. With current estimates of tropospheric emission burden, the new toluene mechanism increases monthly mean daily maximum 8-h ozone by 1.0–3.0 ppbv in Los Angeles, Portland, Seattle, Chicago, Cleveland, northeastern US, and Detroit compared to that with the base toluene chemistry. It reduces model mean bias for ozone at elevated observed ozone mixing ratios. While the new mechanism increases predicted ozone, it does not enhance ozone production efficiency. Sensitivity study suggests that it can further enhance ozone if elevated toluene emissions are present. While changes in total fine particulate mass are small, predictions of in-cloud SOA increase substantially.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ilkay Orhan

Purpose The purpose of this study is to present the pollutant gas produced by hydrocarbons (HC), carbon monoxide (CO) and nitrogen oxides (NOx) and the quantity of fuel burned from commercial aircraft at Ordu-Giresun International Airport, Turkey during the landing and take-off (LTO) cycles in 2017. Design/methodology/approach The flight data recorded by the General Directorate of State Airports Authority and the aircraft engine emission data from International Civil Aviation Organization (ICAO) Engine Exhaust Emission Databank were used for calculation. The aircraft and engine types used by the airlines for flight at Ordu-Giresun International Airport were determined. To evaluate the effect of taxi time on emission amounts, analysis and evaluations were made by taking different taxi times into consideration. Findings As a result of the emission analysis, the amount of fuel consumed by the aircraft were calculated as 6,551.52 t/y, and the emission amounts for CO, HC and NOx were estimated as 66.81, 4.20 and 79.97 t/y, respectively. Practical implications This study is aimed to reveal the effect and contribution of taxi time on the emitted emission at the airport during the LTO phase of the aircraft. Originality/value This study helps aviation authorities explain the importance of developing procedures that ensure the delivery of aircraft to flights in minimum time by raising awareness of the impact of taxi time on emitted emissions, and contributes to the determination of an aircraft emission inventory at Ordu-Giresun International Airport.


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