key: cord-0972363-7fvd6swf
authors: Xu, Hao; Xiao, Kai; Pan, Jun; Fu, Qingyan; Wei, Xiaodong; Zhou, Junrui; Yu, Yamei; Hu, Xue; Ren, Huarui; Cheng, Jinping; Peng, Shitao; Hong, Ningning; Ye, Yin; Su, Ning; He, Zehui; Hu, Tao
title: Evidence of aircraft activity impact on local air quality: A study in the context of uncommon airport operation
date: 2022-03-07
journal: J Environ Sci (China)
DOI: 10.1016/j.jes.2022.02.039
sha: ac3612ecd36d47da7500eea8d6eb618c01d471a4
doc_id: 972363
cord_uid: 7fvd6swf

Wuhan Tianhe International Airport (WUH) was suspended to contain the spread of COVID-19, while Shanghai Hongqiao International Airport (SHA) saw a tremendous flight reduction. Closure of a major international airport is extremely rare and thus represents a unique opportunity to straightforwardly observe the impact of airport emissions on local air quality. In this study, a series of statistical tools were applied to analyze the variations in air pollutant levels in the vicinity of WUH and SHA. The results of bivariate polar plots show that airport SHA and WUH are a major source of nitrogen oxides. NO(x), NO(2) and NO diminished by 55.8%, 44.1%, 76.9%, and 40.4%, 33.3% and 59.4% during the COVID-19 lockdown compared to those in the same period of 2018 and 2019, under a reduction in aircraft activities by 58.6% and 61.4%. The concentration of NO(2), SO(2) and PM(2.5) decreased by 77.3%, 8.2%, 29.5%, right after the closure of airport WUH on 23 January 2020. The average concentrations of NO, NO(2) and NO(x) scatter plots at downwind of SHA after the lockdown were 78.0%, 47.9%, 57.4% and 62.3%, 34.8%, 41.8% lower than those during the same period in 2018 and 2019. However, a significant increase in O(3) levels by 50.0% and 25.9% at WUH and SHA was observed, respectively. These results evidently show decreased nitrogen oxides concentrations in the airport vicinity due to reduced aircraft activities, while amplified O(3) pollution due to a lower titration by NO under strong reduction in NO(x) emissions.

Aviation is an integral part of the global transportation sys-1 tem and contributes significantly to the world's economy ( Lee 2 et al., 2009 ; ICAO, 2019 ) ; however, while aviation enables eco- 3 nomic prosperity, it represents one of the most intensive en-4 ergy consumers and its emissions impose adverse impacts 5 on the climate, surface air quality and human health ( Morris 6 et al., 2003 ; Lee et al., 2010 ; Carslaw et al., 2006 ) . Aircraft emis-7 sions impose direct radiative effects on the climate system 8 through emissions of carbon dioxide (CO 2 ), soot, and water 9 vapor (H 2 O) , while nitrogen oxides (NO x ), 10 sulfur oxides (SO x ), carbon monoxide (CO), and hydrocarbons 11 (HC) emitted from aircraft operations have indirect radia-12 tive impacts through their interactions with complex gaseous 13 and aerosol processes affecting ozone, methane, and clouds 14 ( Holmes et al., 2011 ) , occurring at roughly 9 -11 km ( Morris 15 et al., 2003 ; Lee et al., 2010 ) . Pollutants emitted during landing 16 and take-off cycle (LTO) can lead to air quality deterioration 17 ( Carslaw et al., 2006 ; Barrett et al., 2010 ; Lee et al., 2013 ) . ous studies have shown that aircraft emissions can substan- 19 tially deteriorate surface air quality by increasing the concen-20 trations of NO x ( Carslaw et al., 2006 ) , CO ( Schürmann et al., 21 2007 ), PM 2.5 ( Unal et al., 2005 ; Hu et al., 2009 ) and the level of 22 hazardous airborne particle-bound polycyclic aromatic hydro-23 carbons (PB-PAH), vapor-phase PAH ( Childers et al., 2000 ) and 24 particle-bound lead (PBL) concentrations ( Fine, 2007 ) in the 25 vicinity of airports. Ozone (O 3 ) is a secondary pollutant, which 26 is generated through a series of photochemical reactions be-27 tween its precursors, nitrogen oxides (NO x = NO + NO 2 ) and 28 volatile organic compounds (VOCs) ( Liu et al., 2012 ) . The for-29 mation of O 3 is much depending on the O 3 sensitivity regime, 30 determined by the ratio of VOCs and NO x ( Lu et al., 2010 ) . 31 Studies also reported that aircraft activities contribute posi-32 tively to elevated O 3 level ( Yim et al., 2015 ; Ashok et al., 2013 ) . 33 Additionally, aircraft emissions are recognized to cause pre-34 mature mortality ( Barrett et al., 2010 ; Levy et al., 2012 ; Yim 35 et al., 2015 ) , and deleterious consequences for human health, 36 e.g., increased incidence of cardiovascular and pulmonary dis-37 eases, asthma, diabetes and cancers, have been linked to ele-38 vated NO x and PM concentrations ( Hertel et al., 2013 ; Shiraiwa 39 et al., 2017 ) . 40 A few studies have attempted to estimate the negative con- tors such as take-off weight and aircraft thrust setting ( Masiol 56 and Harrison, 2015 ). In the case of aircraft emission model-57 ing, Song et al. (2015) ( Woody et al., 2011 ) . Additionally, statistical models were also 69 used to quantify the aircraft and airport related emissions on 70 local air quality on the basis of measured air pollutant concen-71 trations. For instance, Carslaw et al. (2006) The closure of Wuhan Tianhe International Airport (WUH) 96 resulted in flight-ban at WUH (only an extremely small 97 amount of cargo and medical personnel flights) and suspen-98 sion of related airport activities (e.g., ground service equip-99 ment (GSE), auxiliary power unit (APU)). SHA also saw a signifi-100 cant reduction in aircraft activities. Decline in emissions from 101 aircraft activities and other airport related sources thus could 102 reasonably be expected at airport WUH and SHA. The closure 103 of a major international airport and a tremendous drop in 104 flights for a duration of over two months are extremely rare, 105 and as such represent a unique opportunity to investigate the 106 effect of airport emissions on the near-field air quality. 107 This study initiates a novel perspective on the impact of 108 COVID-19 related lockdown, from the perspective of reduced 109 aircraft activities on air quality. We applied a series of statisti-110 cal tools to analyze the air pollutant concentrations measured 111 at airport WUH and SHA before and during the airport clo-112 sure (WUH) and significant flight reduction (SHA). Both mea-113 suring sites are located in the close vicinity of the airports. The 114 main objectives of this study are: i) investigating the potential 115 sources of air pollutants; ii) identifying the difference between 116 long-term trends of air pollutant levels and those during the 117 Another key dataset used in this study is the information 168 on aircraft take-offs and landings at SHA and WUH. These ( Carslaw et al., 2006 ; Carslaw and Ropkins, 2012 ; Grange et al., 198 2016 ) . Polar coordinates essentially map the pollutant concen-199 trations with wind speed and direction as a continuous sur-200 face that are highly useful in providing directional informa-201 tion concerning the source type and characteristics as well as 202 the wind speed dependence of concentrations (Yu et al., 2004; 203 Carslaw et al., 2006 ; Jones et al., 2010 ) , e.g., dispersion charac-204 teristics, street canyons ( Tomlin et al., 2009 ; Carslaw and Rop-205 kins, 2012 ) and airport emission sources ( Carslaw et al., 2006 ; 206 Masiol and Harrison, 2015 ) Carslaw et al. (2006) . identified air-207 craft plumes on the basis of wind speed dependence upon NO x 208 concentrations.

209 Figures 2 and 3 show the polar plots of monitored species at 210 SHA and WUH, respectively. Concentration and meteorologi-211 cal records of SHA were filtered, and only data for hours of a 212 day mostly affected by airport activities, i.e. between 7:00 and 213 22:00, were used for the polar plot analysis. From Figure 2 , a 214 few interesting features of measured species can be perceived. 215 Firstly, high NO concentrations occurred under very low wind 216 speeds from almost all wind directions, but there is an evi-217 dent increment in NO concentrations as the wind came from 218 southeast (corresponding to the direction of airport), north-219 east and northwest. NO x concentrations are also found to be 220 highest under low wind speed, but particularly in wind direc-221 tion of southeast, northeast and northwest, while the highest 222 NO 2 concentrations were recorded in wind direction of south-223 east, northeast and northwest under wind speeds of smaller 224 than 5 m/s. This could be totally expected because the airport 225 is located to the southeast of the measuring station and only 226 400 m away. And there is also apparent indication of sources 227 to the northeast and northwest. As the wind speed enhanced 228 from any direction, the concentrations of NO 2 , NO and NO x 229 show a decrease, and the lowest concentrations are observed 230 under the highest wind speed in the wind direction of east. 231 Secondly, the concentrations of PM 2.5 , PM 10 , SO 2 and CO were 232 also highest under low wind speeds of less than 5 m/s, but 233 dominantly in the direction of northwest. It is worth noting 234 that in the direction of southeast, high concentrations of SO 2 235 in a range of wind speeds and high CO concentrations un-236 der high wind speeds (10 -15 m/s) are also depicted by the 237 polar plot coordinates. On the contrary, the lowest concentra-238 tions of O 3 were under very low wind speed, which is differ-239 ent from other pollutants, while the highest concentrations of 240 O 3 were in the direction of south to west under wind speeds 241 On the basis of the polar plot analysis above, it can be 265 concluded that both SHA and WUH can be identified as a 266 major contributor of nitrogen oxides. This could be entirely 267 expected, as many previous studies have revealed that air-268 craft operations lead to increase in local nitrogen oxides 269 concentrations Masiol and Harrison, 270 2015 ) . However, the O 3 concentrations show no distinct direc-271 tionality in its source locations. The reason can be that O 3 is 272 a secondary photochemical air pollutant. The formation of O 3 273 is subjected to solar radiation and its precursors, i.e., NO x and 274 VOCs ( Sillman, 1995 ) . The chemical mechanism of O 3 produc-275 tion and the relationship between O 3 and its precursors are 276 complicated ( Wang et al., 2017 ) and an important feature of 277 O 3 production is that the dependence of O 3 production on its 278 precursors is highly nonlinear (Zhang et al., 2008) . Figure S1, ( Jung et al., 2011 ; Yang et al., 2019) . However, during the lock-338 down period in 2020, the B/T ration is 0.93, which may suggest 339 that the air quality is affected by the long-range transport of 340 pollutants that has been photochemically degraded by the OH 341 radical rather than by fresh local emissions that are rich in 342 toluene ( Beyer et al., 2003 ) .

343 NO x , NO 2 and NO diminished by 55.8%, 44.1% and 76.9% 344 when comparing with those in the same period of 2018, and 345 40.4%, 33.3% and 59.4% in the same period of 2019. As con-346 cluded in the polar plot analysis, airport activities can be a 347 major contributor of NO x concentrations at SHA, the reduction 348 in nitrogen oxides concentrations was highly correlated to 349 contrast, the level of O 3 increased by 9.2% and 46.4% Figure 5 . 384 depicts the comparison between aircraft activity and the con-385 centration of measured air pollutants on a 5-minute basis be-386 fore and right after the closure of airport WUH. Aircraft activ-387 ity saw a dramatic fall by 84.3% and dropped to 0 at 23 o'clock 388 on 23 January 2020. The air pollutant concentrations on 22 389 January 2020 were evidently higher than those on 23 January 390 2020, NO 2 , SO 2 , CO, PM 2.5 and PM 10 decreased by 77.3%, 8.2%, 391 10.8%, 29.5% and 23.9%, respectively, while O 3 concentration 392 was higher on 23 January 2020 till around 15:00 than that on 393 22 January 2020, but lower afterwards. The unprecedented clo-394 sure of airport WUH has an evident impact on the concen-395 tration of air pollutants and this effect has dropped the NO 2 396 concentration even to near zero. As also has been observed 397 at Gatwick Airport and Heathrow Airport that local NO 2 has 398 fallen to zero or near zero during the eruption of Eyjafjalla-399 jökull in April 2010 ( Barratt and Fuller, 2010 ) . 400 An increase in O 3 concentrations at both SHA and WUH 401 was observed during the COVID-19 lockdown. Studies con-402 cerning the formation of O 3 show that the local O 3 formation 403 is VOCs-limited in Wuhan (Zeng et al., 2018) and Shanghai 404 ( Xing et al., 2017 ; Ran et al., 2009 ; Cai et al., 2010 ; Tan et al., 405 2019 ), and a VOCs to NO x ratio of no higher than 0.73 could be 406 conducive to the O 3 pollution mitigation in Wuhan (Zeng et al., 407 2018) Song et al. (2015) . demonstrates that area in the vicinity 408 of airports are VOC-limited. However, emission estimates for 409 various airports have shown that aircraft operations during 410 LTO emit significantly more NO x than HCs ( Unal et al., 2005 ; 411 Kesgin, 2006 ; Stettler et al., 2011 ; Xu et al., 2020a ) , which can 412 also be observed from the reduction rate of NO x and BTEX con-413 centrations during the COVID-19 lockdown as depicted above. 414 During the closure of WUH and travel restriction at SHA, more 415 NO x emissions than VOCs emissions were reduced, resulting 416 in higher VOCs-NO x ration, which then strengthen the O 3 417 generation. The fresh exhausted NO emissions consume O 3 418 locally ( Solberg et al., 2005 ; Molina et al., 2009 ) . Aircraft and 419 APU nitrogen oxides are predominantly emitted in the form 420 of NO ( Stettler et al., 2011 ) and the O 3 titration occurs partic-421 ularly in winter times under high NO x levels ( Sillman, 1999 ) , 422 thus a lower titration of O 3 by NO due to strong reduction 423 in local NO x emissions ( Sicard et al., 2020 ) may promote the 424 increase in local O 3 concentrations during airport closure. 425 Additionally, higher solar radiation due to lower PM 2.5 and 426 PM 10 concentrations ( Murphy et al., 2007 ; Wolff et al., 2013 ) ( Carslaw, 2019 ) . The scatter plot analysis provides a straight-436 forward demonstration of how two variables are related to one 437 another on the dependence of a third variable. Here we applied 438 the scatter plot function in "openair" to investigated the tem-439 poral variation of air pollutant concentrations before and dur-440 ing the COVID-19 lockdown depending on aircraft movements 441 at SHA and WUH Figure 6 . depicts the hourly concentration of 442 NO, NO 2 and NO x under various aircraft movements at SHA 443 for the period of 1 January to 31 March of 2018 and 2019, and 1 444 January to 10 March 2020, respectively. Dates and concentra-445 tion levels were handled on the x-axis and y-axis, respectively, 446 while the number of hourly flights was coded as a color scale 447 shown to the right. The concentration data of NO, NO 2 and 448 NO x at SHA were filtered and only the data during the hours 449 of intensive aircraft activity and downwind of the airport were 450 considered in the scatter plot depiction. As such, a straight-451 forward demonstration of the aircraft activity impact on the 452 concentration of air pollutants can be seen. 453 As shown in Figure 6 , a significant distinction between 454 the distribution of scatter plots for 2018 and 2019, and that 455 for 2020 is observed. The scatter plots demonstrated for 2020 456 can be evidently divided into two groups, one of the groups 457 are colored in blue, green and light yellow, indicating lower 458 aircraft movements, and obviously on the days during the 459 COVID-19 lockdown policy on the right side of the figures, 460 while plots before the lockdown policy are in red and dark-461 red, and positioned in upper zone of the figures, suggest-462 ing higher level of aircraft activity and concentrations. In 463 the contrast, a clear divide in the distribution of the scat-464 ter plots for 2018 and 2019 cannot be observed. The average 465 concentration of NO, NO 2 and NO x scatter plots during lock-466 down were 78. 0%, 47.9%, 57.4% and 62.3%, 34.8%, 41.8% lower 467 than those during the same period for 2018 and 2019, respec-468 tively. Studies have shown that aircraft and APU nitrogen ox-469 ides are predominantly emitted in the form of NO ( Stettler 470 et al., 2011 ; Xu et al., 2020a ) . This explains the largest drop 471 in NO concentrations. BTEX and other air pollutants also saw 472 a significant reduction in concentration levels as shown in 473 Figure S8 , e.g., PM 2.5 diminished by 21.5% and 48.4% com-474 pared with those for 2018 and 2019, respectively. However, 475 JID: JES [m7; March 5, 2022; 18:2 ] Restrictive measures in confining the COVID-19 pandemic 502 contributed significant decline in greenhouse gas (GHG) and 503 air pollutant emissions. Early estimates suggest that global 504 GHG will decrease by 6% in 2020 compared to that in 2019, 505 representing the largest fall since World War II ( Stoll and 506 Mehling, 2020 ), while air pollutant, such as NO 2 , saw a steep 507 fractional reduction by 93% at the peak of the COVID-19 out-508 break in Wuhan ( Le et al., 2020 ) . The decline in aircraft activity 509 at SHA and WUH is a microcosm of the decline in the global 510 civil aviation that a lot of regions experienced a huge decline 511 of more than 90% ( IEA, 2020 ). Correspondingly, aviation emis-512 sions saw a significant decrease, e.g., GHG emissions declined 513 by 74% during the early lockdown period ( Stoll and Mehling, 514 2020 ) and reduction in air pollutant levels (NO x , PM 2.5 , etc.) as 515 demonstrated in this study. Aviation plays a problematic role 516 for climate change, air quality and human health (Lee et al., 517 2020; Chen and Sun, 2018 ; ) , but the industry didn't react at the 518 proper level on reducing emissions ( Vaughan, 2020 ) , and the 519 probability of achieving all the environmental targets for avi-520 ation industry is extremely low ( Hassan et al., 2018 ) . At this 521 background, the COVID-19 pandemic may represent an oppor-522 tunity to critically reconsider global aviation development and 523 sustainability. Results in this study could be an implication of 524 cleaner airport operation. However, on the other hand, reduc-525 tion in air pollutant emissions may not ensure direct improve-526 ment of air quality. The emissions-meteorology interactions 527 

This study provides some indications of the impact of a spe-547 cific emission source -airport activities, upon local air quality, 548 from an unprecedented intervention by the COVID-19 epi-549 demic, which has resulted in closure of airport WUH and sig- and wind direction at SHA and WUH. Aircraft and airport re-559 lated emissions can be identified as major impact factor of ni-560 trogen oxides. From the long-term trend analysis, an evident 561 distinction between the concentration level on the long-term 562 basis and those during the COVID-19 outbreak is observed, 563 Jan 2020, when compared with those at the same hours on 22 574 Jan 2020. Additionally, the distribution of scatter plots for both 575 SHA and WUH depict an evident distinction before and after 576 the COVID-19 lockdown. It is shown that the concentration av-577 erages of NO, NO 2 and NO x scatter plots after lockdown policy 578 were 78.0%, 47.9%, 57.4% and 62.3%, 34.8%, 41.8% lower than 579 those during the same period for 2018 and 2019 at SHA, re-580 spectively. At WUH, the average concentration of NO 2 , PM 2.5 , 581 PM 10 , CO after the airport closure were 79.3%, 28.4%, 32.4% 582 and 21.5% lower than those before, and a Pearson analysis 583 shows that NO 2 , PM 2.5 , PM 10 and CO concentrations were sig-584 nificantly positively correlated with aircraft activity. However, 585 O 3 level increased by 11.1% and 25.9% when the average hourly 586 number of flights decreased by 66.4% and 66.8% at airport SHA, 587 respectively, while a significant increase in O 3 concentrations 588 by 50.0% under an unprecedented fall of aircraft movements 589 by 95.2% after the closure of airport WUH was observed. These 590 results depict that reduction in aircraft activities contributes 591 to mitigation of NO x pollution in the airport vicinity, but en-592 hanced level of O 3 concentrations, which may be attributed 593 JID: JES [m7; March 5, 2022; 18:2 ] to lower titration of O 3 by NO under strong reduction in NO x 594 emissions.

Q2 Huss et al., 2010 , Lee et al., 2021 , Amini et al., 2017 2014 , Targino et al., 2017 , Yang et al., 2018 

The authors declare that they have no known competing fi-598 nancial interests or personal relationships that could have ap-599 peared to influence the work reported in this paper. 

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