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Emissions from an International Airport Increase Particle Number Concentrations 4-fold at 10 km Downwind


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We measured the spatial pattern of particle number (PN) concentrations downwind from the Los Angeles International Airport (LAX) with an instrumented vehicle that enabled us to cover larger areas than allowed by traditional stationary measurements. LAX emissions adversely impacted air quality much farther than reported in previous airport studies. We measured at least a 2-fold increase in PN concentrations over unimpacted baseline PN concentrations during most hours of the day in an area of about 60 km(2) that extended to 16 km (10 miles) downwind and a 4- to 5-fold increase to 8-10 km (5-6 miles) downwind. Locations of maximum PN concentrations were aligned to eastern, downwind jet trajectories during prevailing westerly winds and to 8 km downwind concentrations exceeded 75 000 particles/cm(3), more than the average freeway PN concentration in Los Angeles. During infrequent northerly winds, the impact area remained large but shifted to south of the airport. The freeway length that would cause an impact equivalent to that measured in this study (i.e., PN concentration increases weighted by the area impacted) was estimated to be 280-790 km. The total freeway length in Los Angeles is 1500 km. These results suggest that airport emissions are a major source of PN in Los Angeles that are of the same general magnitude as the entire urban freeway network. They also indicate that the air quality impact areas of major airports may have been seriously underestimated.
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Emissions from an International Airport Increase Particle Number
Concentrations 4fold at 10 km Downwind
Neelakshi Hudda,
Tim Gould,
Kris Hartin,
Timothy V. Larson,
and Scott A. Fruin*
Keck School of Medicine, Department of Preventive Medicine, University of Southern California, Los Angeles, California 90089,
United States
Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195, United States
Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington 98195, United
SSupporting Information
ABSTRACT: We measured the spatial pattern of particle number (PN)
concentrations downwind from the Los Angeles International Airport
(LAX) with an instrumented vehicle that enabled us to cover larger areas
than allowed by traditional stationary measurements. LAX emissions
adversely impacted air quality much farther than reported in previous
airport studies. We measured at least a 2-fold increase in PN
concentrations over unimpacted baseline PN concentrations during
most hours of the day in an area of about 60 km2that extended to 16 km
(10 miles) downwind and a 4- to 5-fold increase to 810 km (56
miles) downwind. Locations of maximum PN concentrations were
aligned to eastern, downwind jet trajectories during prevailing westerly
winds and to 8 km downwind concentrations exceeded 75 000 particles/
cm3, more than the average freeway PN concentration in Los Angeles.
During infrequent northerly winds, the impact area remained large but shifted to south of the airport. The freeway length that
would cause an impact equivalent to that measured in this study (i.e., PN concentration increases weighted by the area impacted)
was estimated to be 280790 km. The total freeway length in Los Angeles is 1500 km. These results suggest that airport
emissions are a major source of PN in Los Angeles that are of the same general magnitude as the entire urban freeway network.
They also indicate that the air quality impact areas of major airports may have been seriously underestimated.
Previous studies that directly measured the impact of aviation
activity on air quality have mostly conducted measurements in
close proximity of airports. Few studies have reported
signicant air quality impacts extending beyond a
Carslaw et al. 2006
analyzed dierences in
pollutant concentrations by wind speed and direction along
with dierences in aircraft and ground trac activity at
Heathrow Airport in London. They found airport contributions
of up to 15% of total oxides of nitrogen (NOx) at a site 1.5 km
downwind of the nearest runway. At Hong Kong International
Airport, Yu et al. 2004
used nonparametric regression analysis
on pollutant concentrations by wind speed and direction. They
calculated that aircraft nearly doubled sulfur dioxide concen-
trations 3 km away and also increased concentrations of carbon
monoxide and respirable suspended particles under similar
wind speeds and directions. Fanning et al. 2007
particle numbers concentrations in the 10100 nm range and
found signicant increases above background at 1.9, 2.7, and 3.3
km downwind of the Los Angeles International Airport (LAX)
blast fence. Although measurements were stationary and not
concurrent, they also noted that takeos produced high
concentrations and downwind gradients within 600 m of the
blast fence. Dodson et al. 2009
found that aircraft activity at a
regional airport in Warwick, RI contributed 2428% of the
total black carbon (BC) measured at ve sites 0.163.7 km
from the airport.
Several other airport and aviation emissions studies focused
on quantifying the air quality impacts from jet takeos
measured air pollutant concentrations very close to runways. Of
particular relevance to this study, Hsu et al. 2013
linked ight
activity at LAX with 1 min average PN concentrations. Their
models suggested that aircraft produced a median PN
concentration of nearly 150 000 particles/cm3at the end of
the departure runway. PN concentrations decreased rapidly
with distance to 19 000 particles/cm3at a location 250 m
downwind and to 17 000 particles/cm3at a location 500 m
further downwind. The rapid drop-oin concentration,
however, may have reected an increasing oset from the
centerline of impacts with greater downwind measurement
distance. Similar magnitude PN concentrations and correlations
Received: January 22, 2014
Revised: May 12, 2014
Accepted: May 14, 2014
Published: May 29, 2014
© 2014 American Chemical Society 6628 |Environ. Sci. Technol. 2014, 48, 66286635
Terms of Use
with departures were reported by Westerdahl et al. 2008
Zhu et al. 2011
at sites located within 100200 m of the Hsu
et al. 2013
Our study was motivated by mobile monitoring platform
(MMP) based observations of large but gradual increases in PN
concentrations as we approached locations under LAX jet
landing trajectories on multiple transects up to 10 km
downwind of LAX. We hypothesized that emissions from
LAX activities were increasing PN concentrations over much
larger areas and longer downwind distances than previously
observed in studies that focused on near freeway and jet takeo
impacts to air quality. An extensive monitoring campaign
conrmed that LAX-related emissions increased PN concen-
trations downwind at least 2-fold to 16 km. This large,
previously undiscovered spatial extent of the air quality impacts
downwind of major airports may mean a signicant fraction of
urban dwellers living near airports likely receive most of their
outdoor PN exposure from airports rather than roadway trac.
Monitoring Area. LAX is the sixth busiest airport in the
world and third busiest in the United States. About 95% of
ights take oand land into the prevailing westerly/west-
southwesterly (W/WSW) onshore winds
(i.e., 263 degrees,
the direction of runway alignment
) using two sets of parallel
runways separated by about 1.5 km. In the busiest hours, 40
60 jets per hour arrive during hours 07001900 and depart
during hours 08002100. Reduced activity is typical for the
early morning and late evening hours. 2040 jets per hour
arrive during hours 0600 and 10000100 and depart during
hours 0700 and 22002300. During other hours typically fewer
than ve jets per hour arrive or depart.
The airport complex is about 4.5 km east to west (E-W) and
about 2.5 km north to south (NS) and is surrounded by
major roadways and freeways, as highlighted in Figure 1 (Figure
S.1 in Supporting Information (SI) shows a map of this area
with street name labels). The Federal Aviation Administration
noise contours of the modeled annual 65 dB A-weighted
equivalent (LAeq) noise threshold are shown
eastward along the predominant downwind direction and
reect the jet trajectories used for landing. They also extend
west of the airport over the Pacic Ocean (not shown).
Mobile Monitoring. Monitoring consisted of transects 4
16 km in length, nearly perpendicular (i.e., NS) to the
direction of the prevailing winds, at varying downwind
distances. Dierent monitoring routes were required to fully
capture the changes in impact locations due to shifts in wind
direction. A general downwind direction was chosen based on
meteorological predictions but transect lengths and locations
were determined during the monitoring run based on
observations of the rate of change of PN concentrations. For
each transect, monitoring was extended several hundred meters
beyond the location where baseline PN concentrations
appeared stable.
Measurements were conducted over 29 days with the
University of Southern California (USC) MMP, a gasoline-
powered hybrid vehicle. A second MMP, the University of
Washington (UW) MMP, a gasoline-powered minivan, joined
the monitoring on 3 days (June 22, 27 and July 1, 2013). Table
1 gives monitoring dates and times.
Most measurements were conducted during times of onshore
westerly winds, typically strongest during 11001600, but we
also conducted measurements during early morning and late
night hours when air trac was low and onshore winds were
reduced (August 13, 16, 23, 24 and 25, December 03, 09, 15
and 16, 2013). Monitoring focused on the area east of LAX
(i.e., the predominant downwind direction) but included
several runs along the boundary of the airport in the upwind
direction and south of the airport complex during occasions of
northerly winds in winter months.
Instrumentation. Concentration measurements included
PN, BC, NO, NO2, NOx,and particle surface UV-photo-
ionization potential (measured using Ecochem Photoelectric
Aerosol Sensor [PAS] that responds to elemental carbon and
particle-bound polycyclic aromatic hydrocarbons [PBPAH]).
Instrument details are provided in SI (Table S.1 and S.2).
Instruments were powered by two deep-cycle marine batteries
via DC-to-AC inverter. Our power arrangement allowed for 5 h
of run time if all instruments were running. For sampling runs
that were anticipated to exceed 5 h, several instruments were
shut down to extend battery life and the Condensation Particle
Counter (CPC) was run on the vehicles 12 V cell phone power
outlet. If other instruments were turned on later, the required
warm-up time was 25 min.
Instrument clock times were regularly synchronized to be
within 1 s of the global positioning system device time, which
also recorded speed and location. Measurements from
instruments with a delayed response time were advanced to
match the instantaneous instruments and the GPS time and
location recorded at 1 s intervals. For pollutant measurements
recorded at 10 s intervals, all locations within the recording
interval were assigned the pollutant value reported for that
Meteorological Data. Minute and hourly wind speed and
wind direction data were obtained from the Automated Surface
Observing Systems monitor at LAX airport (latitude 33.943
and longitude 118.407). Due to the 16 km distance between
eastern edge of the study area and the meteorological station
located at LAX, we could not assume that wind speed and
direction were identical to those measured at LAX, but wind
direction in this region of Los Angeles tends to be similar over
large areas during daytime.
The average wind direction at LAX is WSW (252°).
Daytime southwesterly sea breezes typically occur 16 h per day
in the summer (09000100 for JuneAugust), decreasing to 6
Figure 1. Los Angeles International Airport and 65 dB noise contours
indicating eastern jet trajectories.
Environmental Science & Technology Article |Environ. Sci. Technol. 2014, 48, 662866356629
h in the winter (12001800 in December). Only during the
winter months (NovemberFebruary, 00000900) are light
easterly o-shore winds common.
Wind speed and direction
during the monitoring periods are summarized in Table 1.
Wind roses based on 1 min data are shown in Figure S.2 and
S.3 of the SI.
Data Processing. MMP measurements included a localized
trac emissions signal representing microscale and middle scale
variations (10100 m and 100500 m, respectively) and an
underlying baselinepollutant concentration that varied
gradually over the neighborhood scale (500 m4 km).
Watson et al. 1997
derived these categories by considering
the spatial scales of impact of various types of air pollution
sources. We adopted a smoothing methodology to estimate
baseline PN concentrations that excluded the microscale and
middle scale impacts due to local sources, usually specic
Baseline PN concentrations were derived from our mobile
measurements by taking a rolling 30-s fth percentile value of
the 1-s concentration time series, and assigning that value to the
measured location. This removed the microscale and middle
scale impacts from trac sources such as specic vehicle
plumes. Baseline concentrations for a run were relatively
spatially uniform outside of the LAX impact areas, with
coecients of variation (CV) of less than 5%. In comparison,
the raw PN concentrations on roadways outside the LAX
impact areas had CVs on the order of 40%. On rare occasions,
the MMP was behind a high emitter for longer than 30 s. Such
events, only if veriable by video and eld notes, were censored.
However, less than 0.5% of data were censored in this manner,
generated from about a dozen instances of prolonged inuence
from high emitting vehicles. An illustration of both raw and
smoothed concentration time series is presented in the SI
(Figures S.4S.7). The gures in this text are based on
smoothed data.
Spatial Pattern and Extent of Elevated PN Concen-
trations. Downwind of LAX we observed gradual but large
increases in baseline PN concentrations occurring over transect
distances of multiple kilometers. PN concentrations were
elevated 4-fold or more above nearby unimpacted baseline
concentrations up to 10 km in the downwind direction from
Table 1. Sampling Days, Time Periods and Meteorological Conditions during Sampling
time sampling distance
from LAX (km) WD
WS (m/s)
ratio of impacted to unimpacted
baseline PN, 10 km downwind
4/6/2011 14:3016:45 812 WSW, W5.0 ±1.8 15 000 2.0
4/10/2011 15:0017:30 812 W6.9 ±1.2 10 000 4.5
5/24/2011 09:0011:00 812 Calm, W 1.0 ±2.5 10 000 3.0
5/27/2011 12:1514:45 812 WSW, W6.3 ±1.3 10 000 4.7
1/26/2012 17:2820:22 812 WSW, W2.9 ±2.1 20 000 6.0
9/29/2012 13:3017:30 08W6.1 ±1.1 10 000 3.7
9/30/2012 15:4518:30 08W6.1 ±0.4 5000 5.2
6/11/2013 14:1415:14 2.58.5 WSW, W 6.7 ±0.0 15 000 5.0
6/12/2013 13:3016:30 2.510.5 W4.0 ±0.4 15 000 4.0
6/22/2013 11:4718:50
08 WSW, W5.7 ±0.4 10 000 4.4
6/27/2013 11:4918:00
08 WSW, W5.3 ±0.7 10 000 4.0
7/01/2013 10:3018:30
08W, ESE 3.8 ±1.0 15 000 3.8
8/6,7/2013 23:5602:45 08 WSW, W, S 3.3 ±0.7 10 000 3.3
8/13/2013 06:3015:00 08 Calm, WSW, W, NNE, NE,
3.0 ±2.0 10 000 4.0
8/15/2013 08:3015:30 016 Calm, WSW,W2.5 ±2.1 20 000 3.8
8/16/2013 09:4520:50 016 SW, WSW,W, WNW 4.4 ±1.3 10 000 3.0
8/23,24/2013 12:0001:30 016 SSW, WSW,W4.4 ±2.2 20 000 4.0, 5.0
17:3001:00 016 Calm, SSW, SW, WSW,W,
ESE 3.1 ±2.1 15 000 6.0
11/1/2013 16:0019:50 012 SSE, W, WSW 3.7 ±0.7 10 000 3.8
12/3/2013 19:4500:20 012 WSW, W, WNW 8.8 ±1.4 5000 6.0
12/5/2013 13:0018:30 012 WSW, W, WNW 5.5 ±0.6 10 000 2.8
12/9/2013 16:0000:00 010 N, NNE 2.7 ±0.6 20 000 n/a
12/10/2013 15:3021:30 010 WNW,N, NW 3.1 ±1.1 20 000 5.0
12/14/2013 17:0020:30 010 W, Calm 2.1 ±0.5 20 000 data lost
12/15,16/2013 22:0002:00 010 N, NE, ESE 2.9 ±1.0 17 500 n/a
12/16/2013 10:0016:00 012 N, W 2.8 ±1.6 10 000 4.5
12/18/2013 17:3020:30 010 WSW, SSW, SSE 3.3 ±1.3 10 000 6.0
12/20/2013 16:3020:00 010 WSW, Calm, E 2.6 ±1.3 15 000 4.0
12/23/2013 15:1519:00 012 W, Calm, E 2.8 ±1.3 10 000 11.0
The runs for which maps are presented are formatted in bold.
Predominant wind direction is formatted as bold.
Urban background value
concentrations are reported to nearest 2500 particles/cm3and are the average baseline values in the unimpacted areas away from local trac sources
Concurrent MMP sampling times: June 22:13201720, June 27:13251510, July 1:12401640.
Monitoring route did not cover the full NS
extent of the impact on Western Av (10 km downwind) on these days, values have been reported for Crenshaw Blvd. (8 km downwind).
ow was recorded in morning hours (until 1000) and westerly later morning to afternoon
08/25/2013 was not counted as an additional monitoring
day because only 1 h of monitoring (00000100) was conducted on this date
Environmental Science & Technology Article |Environ. Sci. Technol. 2014, 48, 662866356630
LAX. Figure 2 shows an example of the spatial pattern of the
elevated PN concentrations.
The size of the impacted areas with high PN concentration
increases was remarkable. At 16 km downwind, a 2-fold
increase in PN concentration over baseline concentrations was
measured across 6.5 km. Assuming a trapezoidal shaped plume
with parallel edges of length 1.5 and 6.5 km, PN concentrations
were at least doubled over an area of 60 km2. Eight km
downwind, a 5-fold increase in PN concentrations over baseline
concentrations extended across 3 km and covered a total area of
24 km2. (Concentrations in this large area exceeded 71 000
particles/cm3, the average concentration on Los Angeles
) Within 3 km of the airport boundary, concen-
trations were elevated nearly 10-fold, exceeding 100 000
particles/cm3, with concentrations of 150 000 particles/cm3
occurring over a several km2area.
This pattern of elevated PN concentrations over large areas
east of LAX was consistently observed during periods when
there were both westerly winds and high air trac volumes,
typically all daylight hours and well into the night. Figure 3
Figure 2. Spatial pattern of PN concentration (colored by deciles) for
the afternoon and evening hours of August 23, 2013.
Figure 3. Spatial pattern of impact during dierent monitoring events. Wind direction during monitoring is shown in insets on bottom left. PN
concentrations are classied and colored by deciles.
Environmental Science & Technology Article |Environ. Sci. Technol. 2014, 48, 662866356631
shows the consistency of the patterns over eight monitoring
runs at various times of day, displayed in each row by similarity
of spatial scale.
In directions other than the downwind direction, no large
areas of elevated PN concentrations were observed. Figures
3(c)(e) include concentrations measured upwind of the LAX
boundary (these are indicated by faint yellow lines within the
noise contour); the concentrations recorded were typical of the
coastal baseline concentrations, less than 10 000 particles per
cm3(also see Figure S.8 in SI). Of possible other PN sources, a
large renery is located south of the airport but we did not
observe elevated PN or other pollutant concentrations directly
downwind of this source. In general, industrial point sources of
pollution in the Los Angeles Air Basin are very tightly regulated
by the South Coast Air Quality Management District.
We did not observe distinct day versus night dierences, as
might be expected based on the large change in meteorolog-
ically driven dilution between day and night for ground level
sources. It appeared that the distant impacts we observed
downwind of LAX required sucient wind speeds for the jet
climbing and landing emissions to reach the ground, as
observed in Yu et al., 2004
at LAX and Hong Kong
International Airports and Carslaw et al. 2006
at Heathrow
Airport. At LAX, this probably corresponded to the develop-
ment of the on-shore sea breezes that typically started 46h
after sunrise and lasted until 36 h after sunset.
We also did not see the impacts of individual jets at the
distances monitored, but the merging of individual jet impacts
is not unexpected at distances of multiple km. Considering the
frequency of landings and takeos (>90 per hour from 0900
), at an average wind speed of 4 m/s, for example, an
incoming parcel of air will travel only about 160 m before
another jet landing or takeooccurs. Under normal daytime air
turbulence and the enhanced turbulence produced by jets,
signicant mixing is expected over a 510 km distance (2040
min). The generally smooth increases and decreases observed
across the length of transects at such distances are additional
evidence that mixing of plumes occurs. Examples of these
smooth concentration increases for individual transects are
shown in Figures S.6 and S.7 in the SI.
The consistent and distinctive spatial pattern of elevated
concentrations was aligned to prevailing westerly winds and
landing jet trajectories, and roughly followed the shape of the
contours of noise from landing jets, indicating that landing jets
probably are an important contributor to the large downwind
spatial extent of elevated PN concentrations. As dened by the
International Civil Aviation Organization, typical engine thrust
during landing is 30%, as compared to 100% for takeoand
85% for the climbing phase.
Stettler et al. 2011
18% of total NOxemissions from landings, with 12% from
taxiing and holding, 18% from takeo, and 52% from the climb
and climb out phases, respectively. When the extra upwind
distance of the climb and climb out phases are taken into
account, the landing approach emissions likely produce a
signicant fraction of the increased PN concentrations observed
Inuence of Wind Direction on Location of Impact.
The downwind location of the impact changed with shifts in
the prevailing wind direction, although signicant shifts in wind
direction during the daytime are not typical of this area of Los
Figure 4(a) and (b) illustrate one such change in
impacted locations due to a shift in wind direction on a gusty
day with frontal weather that also resulted in cleaner upwind
baseline PN concentrations of less than 5000 particles/cm3.
The impacted locations were aligned along the NE direction
during 20002220 h when winds were from W to WSW (250
280°). The impact then moved southwards between 2220
0000 h as winds turned more W to WNW (280330°). During
this shift, the impact centerline moved by 5.5 km on transects
810 km east of LAX.
Monitoring was also conducted during N to NE prevailing
winds that tend to occur late at night in November and
December (21002300).
This N to NE wind direction
resulted in impacts that were centered south of the airport
(Figure 4(c)). The PN concentrations in this southerly impact
were roughly twice as high as on other days, in part because the
baseline PN concentrations reected urban air from northerly
winds instead of marine air from westerly winds.
Diurnal wind patterns change little by season in Los Angeles
Onshore westerly winds are common during midday
hours, even in winter. As a result, areas of elevated PN
Figure 4. Change in location of impact due to shift in wind direction. Wind direction during monitoring is shown in insets on bottom left. PN
concentrations are classied and colored by deciles.
Environmental Science & Technology Article |Environ. Sci. Technol. 2014, 48, 662866356632
concentrations downwind and east of LAX likely occur in all
seasons. Monitoring in dierent seasons demonstrated the
consistent year round presence of this impact. Examples of
similarly extensive impacts in non-summer months are shown
in the SI (Figures S.8 and S.9).
Other Pollutants. Over large areas downwind of LAX,
concentrations of pollutants other than PN were also elevated.
Figure 5(a)(c) show nearly indistinguishable spatial patterns
for PN, BC, and NO2concentration measured simultaneously
at distances of 9.512 km from LAX. This suggests a common
source for these pollutants, although the BC concentration
increases were not large when compared to PN and NOx, about
0.51μg/m3at 810 km downwind. While jet aircraft are not
known to produce large amounts of BC, two studies found
elevated BC from plane takeos at LAX. Zhu et al. 2011
measured an increase of about 1 μg/m3of BC due to plane
activity 140 m downwind of the runway. Westerdahl et al.
measured increases in BC concentration of several μg/
m3during takeoevents near the eastern LAX boundary, but
also observed elevated BC concentrations at all times. At a
smaller airport, Dodson et al. 2009
found median contribu-
tions of about 0.1 μg/m3, about one-quarter of total BC
measured at ve sites ranging in downwind distance from
0.33.7 km, and also observed departures producing about
twice the impact as arrivals. Therefore, it appears some jets at
LAX are capable of producing measurable increases in BC,
particularly at takeos.
Spatial patterns of simultaneously measured PN and PAS
response (PBPAH and EC) were also similar on transects
4.57.5 km from LAX (Figure 5(d)(e)). The NOXelevation
pattern was less regular (Figure 5(f)). This was likely due to
smaller LAX related contributions compared to baseline
concentrations, thus reducing the signal-to-noise ratio.
Figure 5. Spatial pattern of simultaneously measured pollutants during 14001530 on June 27, 2013. Concentrations are classied and colored by
deciles. Panels (a)(c) show data measured by the UW MMP and (d)(f) show data measured by the USC MMP.
Figure 6. Comparison of the spatial scale of freeway impacts compared to airport impacts for monitoring during nighttime on August 2324, 2013.
Environmental Science & Technology Article |Environ. Sci. Technol. 2014, 48, 662866356633
Overall, the top quartile concentrations (highly impacted) of
all pollutants were about three times higher than the lowest
quartile within 7.5 km from LAX and two times higher at 12 km
distance. In addition, concurrent sampling with the two mobile
platforms demonstrated high temporal (SI Figure S.10) and
spatial consistency (SI Figure S.11) for PN measurements.
Comparison of LAX and Freeway PN Impacts. PN
concentration increases from ground level line sources such as
freeways, under conditions of daytime crosswind dilution,
decrease exponentially with increasing downwind distance and
return to baseline concentrations within 200300 m.
two NS freeways (I-405 and I-110 that run perpendicular to
the prevailing winds) did not contribute appreciably to elevated
PN concentrations in areas where we observed large impacts
from LAX on PN concentrations. This is illustrated in Figure 6,
which contains two enlargements to show the increase in PN
number concentrations over approximately 250 m distance
downwind of I-405, a distance and an increase in PN
concentration that is not discernible at the scale of Figures 2
and 3. The panel in Figure 6(c) at 1:10 000 scale shows the PN
concentration increase of about 24 000/cm3. The maximum PN
concentration was not immediately downwind of the freeway
because at this location there is an elevated overpass and some
distance is needed for emissions to reach the ground.
To put into further perspective the extent of the elevated PN
concentrations observed downwind of LAX, we estimated the
freeway length necessary to produce an equivalent impact in
terms of PN concentration-weighted area of impact assuming
typical daytime dilution conditions for freeways.
For the days we captured the fullest downwind extent of the
impact under typical daytime wind conditions (August 15, 23,
and 24), we calculated an integrated PN impact above baseline
PN concentrations of 2.3, 1.6, and 1.1 ×106(particles/cm3)×
km2, respectively. See Table S.3(a)(c) of SI for calculations.
Impacted areas were calculated using ArcGIS spatial analysis
tools and were conservatively dened as areas where increased
PN concentration were at least double the baseline
concentrations measured north and south of the impact zone.
The resulting impact areas were 3065 km2. For comparison, a
less conservative criterion for dening the impact area such as a
50% or 33% increase over baseline PN concentrations increased
the impacted area by 40% and 80%, respectively.
To calculate PN impacts downwind of freeways, we
combined the exponential regression t of near-freeway
measurements made downwind of I-405 by Zhu et al.
with updated average daytime on-freeway PN
concentrations taken from Li et al. 2013
(71 000 particles/
cm3). PN concentrations were at least double the baseline PN
concentrations of 15 00020 000 particles/cm3for 90130 m
This resulted in a concentration-weighted impact
area of 29303930 (particles/cm3)×km2per km of freeway
Based on these concentration-weighted impact areas, 280
790 km of freeway are needed to produce the equivalent PN-
concentration-weighted impact area of LAX. (The less
conservative criteria resulted in ranges of freeway length of
3401000 km and 4301100 km for thresholds of 50% and
33%, respectively.) There are only about 1500 km of freeways
and highways in Los Angeles County.
Therefore, LAX should
be considered one of the most important sources of PN in Los
Angeles. For comparison, within the 60 km2area of elevated
PN concentrations downwind and east of LAX, the 1525 km
of freeways contributed less than 5% of the PN concentration
Recommendations for Other Studies. LAX is in a region
of Los Angeles with highly consistent wind direction. This
provided the several hours necessary for a single mobile
platform to monitor a sucient number of transects to cover
the large area impacted by LAX emissions. At airport locations
where the prevailing wind direction frequently shifts during the
day, multiple platforms would be necessary to quickly capture
the full spatial extent of emissions impacts to surrounding air
The emissions from LAX are likely not unique on a per-
activity basis. The large area of impact from LAX suggests that
air pollution studies involving PN, localized roadway impacts,
or other sources whose impacts are in the inuence zone of a
large airport should carefully consider wind conditions and
whether measurements are inuenced by airport emissions.
Source apportionment of specic airport sources or activities
was beyond the scope of our study but would be necessary to
evaluate the eectiveness of possible mitigation options.
Diering NO2to NOxratios at dierent levels of engine
might be used to distinguish the contributions of jet
landing, idling or takeoactivities. Takeoand idling emission
also dier in surface properties (i.e., the ratio of active surface
area to surface bound photoionizable species)
and particle
size distributions dier between aircraft and ground support
equipment emissions.
SSupporting Information
Map of monitoring area (Figure S.1), the instruments used
(Tables S.1S.2), wind roses (Figures S.2 and S.3), illustration
of data processing (Figures S.4S.7), additional maps
illustrating the spatial pattern (Figures S.8 and S.9), concurrent
sampling with two mobile measurement platforms (Figures
S.10 and S.11) and calculations for comparing freeway impact
(Table S.3 (a)(c)) are presented in the Supporting
Information. This material is available free of charge via the
Internet at
Corresponding Author
*Phone: 323-442-2870; fax: 323-442-3272; e-mail: fruin@usc.
Present Address
S.A.F.: Keck School of Medicine, Department of Preventive
Medicine, University of Southern California, 2001 North Soto
Street, Los Angeles, CA 90089-9013, United States.
The authors declare no competing nancial interest.
This work was funded by National Institute of Environmental
Health Sciences (NIEHS) Grant 1K25ES019224-01 and
5P30ES007048 to the University of Southern California and
by US EPA Grant RD-83479601-0. This publications contents
are solely the responsibility of the grantee and do not
necessarily represent the ocial views of NIEHS or US EPA.
Further, NIEHS and U.S. EPA do not endorse the purchase of
any commercial products or services mentioned in the
publication. We thank Andrea Hricko of USC for helpful
Environmental Science & Technology Article |Environ. Sci. Technol. 2014, 48, 662866356634
(1) Carslaw, D. C.; Beevers, S. D.; Ropkins, K.; Bell, M. C. Detecting
and quantifying aircraft and other on-airport contributions to ambient
nitrogen oxides in the vicinity of a large international airport. Atmos.
Environ. 2006,40 (28), 54245434.
(2) Yu, K. N.; Cheung, Y. P.; Cheung, T.; Henry, R. C. Identifying
the impact of large urban airports on local air quality by nonparametric
regression. Atmos. Environ. 2004,38 (27), 45014507.
(3) Fanning, E., Yu, R. C., Lu, R., Froines, J. Monitoring and Modeling
of Ultrane Particles and Black Carbon at the Los Angeles International
Airport; California Air Resources Board, 2007.
(4) Dodson, R. E.; Houseman, E. A.; Morin, B.; Levy, J. I. An analysis
of continuous black carbon concentrations in proximity to an airport
and major roadways. Atmos. Environ. 2009,43 (24), 37643773.
(5) Klapmeyer, M. E.; Marr, L. C. CO2,NO
xand particle emissions
from aircraft and support activities at a regional airport. Environ. Sci.
Technol. 2012,46, 10974110981.
(6) Stettler, M. E. J.; Eastham, S.; Barrett, S. R. H. Air quality and
public health impacts of UK airports. Part I: Emissions. Atmos. Environ.
2011,45, 54155424.
(7) Hsu, H. H.; Adamkiewicz, G.; Houseman, E. A.; Zarubiak, D.;
Spengler, J. D.; Levy, J. I. Contributions of aircraft arrivals and
departures to ultrafine particle counts near Los Angeles International
Airport. Sci. Total Environ. 2013,444, 347355.
(8) Westerdahl, D.; Fruin, S. A.; Fine, P. M.; Sioutas, C. The Los
Angeles International Airport as a source of ultrafine particles and
other pollutants to nearby communities. Atmos. Environ. 2008,42,
(9) Zhu, Y.; Fanning, E.; Yu, R. C.; Zhang, Q.; Froines, J. R. Aircraft
emissions and local air quality impacts from takeoff activities at a large
international airport. Atmos. Environ. 2011,45, 652633.
(10) California State Airport Noise Standards Quarterly Report, First
Quarter, Los Angeles World Airports, September 18, 2013. http:// (accessed December 03,
(11) Los Angeles County Regional Planning Department, Airport
Land Use Commission, DRP_Airport_Inuence_Areas. http://egis3. (ac-
cessed April 19, 2014).
(12) Fisk, C. J. Diurnal and Seasonal Wind Variability for Selected
Stations in Southern California Climate Regions, 20th Conference on
Climate Variability and Change; American Meteorological Society:
New Orleans, January 2024, 2008;
pdfpapers/135164.pdf (accessed November 11, 2013).
(13) Watson, J. G.; Chow, J. C.; DuBois, D. W.; Green, M. C.; Frank,
N. H.; Pitchford, M. L. Guidance for Network Design and Optimal Site
Exposure for PM2.5 and PM10, Report No. EPA-454/R-99-022; U.S.
Environmental Protection Agency, Research Triangle Park, NC. 1997.
(14) Li, L.; Wu, J.; Hudda, N.; Sioutas, C.; Fruin, S. A.; Delfino, R. J.
Modeling the concentrations of on-road air pollutants in southern
California. Environ. Sci. Technol. 2013,47 (16), 92919299.
(15) Graham, A.; Raper, D. W. Transport to ground of emissions in
aircraft wakes. Part I: Processes. Atmos. Environ. 2006,40, 587485.
(16) Graham, A.; Raper, D. W. Transport to ground of emissions in
aircraft wakes. Part II: Effect on NOxconcentrations in airport
approaches. Atmos. Environ. 2006,40, 582436.
(17) Karner, A.; Eisinger, A.; Niemeier, D. Near-roadway air quality:
Synthesizing the findings from real-world data. Environ. Sci. Technol.
2010,44, 53345344.
(18) Zhu, Y.; Hinds, W. C.; Kim, S.; Shen, S.; Sioutas, C.
Concentration and size distribution of ultrafine particles near a
major highway. J. Air Waste Manage. Assoc. 2002a,36 (27), 4323
(19) California Department of Transportation.
gov/dist07/aboutus/prole/d7p_print.html (accessed November 11,
(20) Herndon, S. C.; Shorter, J. H.; Zahniser, M. S.; Nelson, D. D. J.;
Jayne, J. T.; Brown, R. C.; Miake-Lye, R. C.; Waitz, I. A.; Silva, P.;
Lanni, T.; Demerjian, K. L.; Kolb, C. E. NO and NO2emissions ratios
measured from in use commercial aircraft during taxi and take-off.
Environ. Sci. Technol. 2004,38, 607884.
(21) Herndon, S. C.; Onasch, T. B.; Frank, B. P.; Marr, L. C.; Jayne,
J. T.; Canagaratna, M. R.; Grygas, J.; Lanni, T.; Anderson, B. E.;
Worsnop, D.; Miake-Lye, R. C. Particulate emissions from in-use
commercial aircraft. Aerosol Sci. Technol. 2005,39 (8), 799809,
DOI: 10.1080/02786820500247363.
Environmental Science & Technology Article |Environ. Sci. Technol. 2014, 48, 662866356635
... For instance, Hudda et al. (2020) 5 found increased ultrafine particulate (UFP) particle number concentrations (PNCs) in residential areas up to 4 km downwind of the Logan International Airport in Boston, Massachusetts. In an earlier study, Hudda et al. (2014) 6 observed enhanced particulate matter and NO 2 concentrations at distances up to 10 km away from the Los Angeles International Airport (LAX) in California. The health impacts of ozone and particulate matter associated with airport emissions include adverse respiratory and cardiovascular health effects. ...
... For instance, Hudda et al. (2020) 5 found increased ultrafine particulate (UFP) particle number concentrations (PNCs) in residential areas up to 4 km downwind of the Logan International Airport in Boston, Massachusetts. In an earlier study, Hudda et al. (2014) 6 observed enhanced particulate matter and NO 2 concentrations at distances up to 10 km away from the Los Angeles International Airport (LAX) in California. The health impacts of ozone and particulate matter associated with airport emissions include adverse respiratory and cardiovascular health effects. ...
Impacts of emissions from the Atlanta Hartsfield-Jackson Airport (ATL) on ozone (O3), ultrafine particulates (UFPs), and fine particulate matter (PM2.5) are evaluated using the Community Multiscale Air Quality (CMAQ) model and high-resolution satellite observations of NO2 vertical column densities (VCDs) from TROPOMI. Two airport inventories are compared: an inventory using emissions where landing and take-off (LTO) processes are allocated to the surface (default) and a modified (3D) inventory that has LTO and cruise emissions vertically and horizontally distributed, accounting for aircraft climb and descend rates. The 3D scenario showed reduced bias and error between CMAQ and TROPOMI VCDs compared to the default scenario [i.e., normalized mean bias: -43%/-46% and root mean square error: 1.12/1.21 (1015 molecules/cm2)]. Close agreement of TROPOMI-derived observations to modeled NO2 VCDs from two power plants with continuous emissions monitors was found. The net effect of aviation-related emissions was an increase in UFP (j mode in CMAQ), PM2.5 (i + j mode), and O3 concentrations by up to 6.5 × 102 particles/cm3 (∼38%), 0.7 μg/m3 (∼8%), and 2.7 ppb (∼4%), respectively. Overall, the results show (1) that the spatial allocation of airport emissions has notable effects on air quality modeling results and will be of further importance as airports become a larger part of the total urban emissions and (2) the applicability of high-resolution satellite retrievals to better understand emissions from facilities such as airports.
... A study of residential populations within 10 km of California's 12 largest airports found a significant contemporaneous increase in respiratory and heart-related hospital admissions among those age 65 and older from aviation-related carbon monoxide exposure [28]. Elevated concentrations of fine and ultrafine particulates and other criteria pollutants have been found in residential areas and downwind areas up to several kilometers from major airports [29][30][31]. ...
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Leaded fuel used by piston-engine aircraft is the largest source of airborne lead emissions in the United States. Previous studies have found higher blood lead levels in children living near airports where leaded aviation fuel is used. However, little is known about the health effects on adults. This study is the first to examine the association between exposure to aircraft operations that use leaded aviation fuel and adult cardiovascular mortality. We estimated the association between annual piston-engine air traffic and cardiovascular mortality among adults age 65 and older near 40 North Carolina airports during 2000 to 2017. We used several strategies to minimize the potential for bias due to omitted variables and confounding from other health hazards at airports, including coarsened exact matching, location-specific intercepts, and adjustment for jet-engine and other air traffic that does not use leaded fuel. Our findings are mixed but suggestive of adverse effects. We found higher rates of cardiovascular mortality within a few kilometers downwind of single- and multi-runway airports, though these results are not always statistically significant. We also found significantly higher cardiovascular mortality rates within a few kilometers and downwind of single-runway airports in years with more piston-engine air traffic. We did not consistently find a statistically significant association between cardiovascular mortality rates and piston-engine air traffic near multi-runway airports, where there was greater uncertainty in our measure of the distance between populations and aviation exposures. These results suggest that (i) reducing lead emissions from aviation could yield health benefits for adults, and (ii) more refined data are needed to obtain more precise estimates of these benefits. Subject Areas: Toxic Substances, Health, Epidemiology, Air Pollution, Ambient Air Quality. JEL codes: Q53, I18
... Mobile monitoring platforms, such as those based on cars and bicycles, can further improve the spatial and temporal resolution of PM 2.5 monitoring by providing real-time snapshots of PM 2.5 concentration profiles along chosen paths. Some mobile systems consist of research-grade monitors mounted in special vehicle-based laboratories [7,[20][21][22][23]. Others are based on commercial, portable monitors mounted on bicycles [24][25][26][27][28][29]. ...
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The concentration of fine particulate matter (PM2.5) is known to vary spatially across a city landscape. Current networks of regulatory air quality monitoring are too sparse to capture these intra-city variations. In this study, we developed a low-cost (60 USD) portable PM2.5 monitor called Smart-P, for use on bicycles, with the goal of mapping street-level variations in PM2.5 concentration. The Smart-P is compact in size (85 × 85 × 42 mm) and light in weight (147 g). Data communication and geolocation are achieved with the cyclist’s smartphone with the help of a user-friendly app. Good agreement was observed between the Smart-P monitors and a regulatory-grade monitor (mean bias error: −3.0 to 1.5 μg m−3 for the four monitors tested) in ambient conditions with relative humidity ranging from 38 to 100%. Monitor performance decreased in humidity > 70% condition. The measurement precision, represented as coefficient of variation, was 6 to 9% in stationary mode and 6% in biking mode across the four tested monitors. Street tests in a city with low background PM2.5 concentrations (8 to 9 μg m−3) and in two cities with high background concentrations (41 to 74 μg m−3) showed that the Smart-P was capable of observing local emission hotspots and that its measurement was not sensitive to bicycle speed. The low-cost and user-friendly nature are two features that make the Smart-P a good choice for empowering citizen scientists to participate in local air quality monitoring.
Ultrafine particles (UFP) contribute to adverse health outcomes such as asthma, obstructive pulmonary disease, cardiovascular disease, and lung cancer. Recent research draws attention to elevated ambient UFP number concentrations near airports. In this study, high time-resolution UFP measurements were conducted along public roads near Mohammad Ali International Airport (SDF; Louisville, KY) which is a commercial passenger airport and a major air cargo hub. Short-duration (∼3 h) measurements with two instrumented vehicles were designed and executed to capitalize on the distinct features of the air cargo hub including periods of high flight activity (and either all landings or all take-offs) at night and early morning when the atmospheric mixing layer depth is shallow. We present preliminary measurements for quantifying individual aircraft contributions and showcase the complexities involved in interpreting these data. For example, during periods with high arrivals frequency, UFP plumes from multiple aircraft on approach are superposed and it is challenging to apportion impacts to individual aircraft. Ground-level impacts for individual aircraft on climb-out are difficult to discern because the planes rapidly ascend above the atmospheric mixed layer height and take different flight paths soon after take-off. Elevated UFP concentrations are observed downwind of the airport, in some cases admixed with approach/climb-out emissions. Although from these data UFP concentrations are difficult to associate with specific aircraft characteristics, UFP concentrations are elevated downwind of the airport. These impacts decrease with increasing distance from the airport yet are clearly discernible at least 3 km downwind.
Sustainable aviation fuels (SAFs) must demonstrate specific physical and chemical properties as well as material compatibility (i.e., seal swell) to be used as aviation turbine fuels. Several alternative jet fuels incorporated in ASTM D7566 are comprised mainly of n/iso-alkanes and can only be blended up to 50 vol% due to material compatibility and density issues. Prior work illustrated the ability of cycloalkanes to replace the swelling potential of aromatics required for material compatibility. Here, we report the first archival documentation of a feedstock and chemical process to yield a product composition that could complement 5 existing SAF ASTM D7566 annexes. A lignin-based jet fuel (LJF) blend component is generated and composed of mostly C6–C18 mono, di, and tri-cycloalkanes. The neat LJF was blended with conventional jet fuel at 10 vol% (LJF blend) to simulate a potential qualification goal. Fuel properties critical to engine operability (ATSM D4054 Tier 3 & 4) were either predicted or experimentally tested based on the volume availability. All LJF-blended operability properties fall within the experience range of conventional jet fuel, with neat o-ring swelling exceeding the typical range of conventional fuels. These results support the potential use of this LJF pathway to complement other SAF pathways and achieve 100% drop-in SAF.
Introduction Air pollution has been linked to preterm birth (PTB) while findings for noise exposure have been mixed. Few studies – none considering airports – have investigated combined exposures. We explore the relationship between joint exposure to airport-related noise, airport ultrafine particles (UFP), and vehicle traffic-related air pollution (TRAP) on risk of PTB near Los Angeles International Airport (LAX). Methods We used comprehensive birth data for mothers living ≤15 km from LAX from 2008 to 2016 (n = 174,186) Noise data were generated by monitor-validated models. NO2 was used as a TRAP proxy, estimated with a seasonally-adjusted, validated land-use regression model. We estimated the effects of exposure to airport-related noise and TRAP on PTB employing logistic regression models that adjusted for known maternal risk factors for PTB as well as aircraft-origin UFP and neighborhood characteristics. Results The adjusted odds ratio (aOR) for PTB from high noise exposure (i.e. > 65 dB) was 1.10 (95% CI: 1.01–1.19). Relative to the first quartile, the aORs for PTB in the second, third, and fourth TRAP quartiles were 1.10 (95% CI: 1.05–1.16), 1.11 (95% CI: 1.05–1.16), and 1.15 (95% CI: 1.10–1.22), respectively. When stratifying by increasing TRAP quartiles, the aORs for PTB with high airport-related noise were 1.04 (95% CI: 0.91–1.18), 1.02 (95% CI: 0.88–1.19), 1.24 (95% CI: 1.03–1.48), and 1.44 (95% CI: 1.08–1.91) (p-interaction = 0.06). Conclusion Our results suggest a potential synergism between airport-related noise and TRAP exposures on increasing the risk of PTB in this metropolitan area.
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. NOx, NO2 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 NO2, SO2 and PM2.5 decreased by 77.3%, 8.2%, 29.5%, right after the closure of airport WUH on 23 January 2020. The average concentrations of NO, NO2 and NOx 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 O3 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 O3 pollution due to a lower titration by NO under strong reduction in NOx emissions.
Urban air pollution is quite complex and exhibits significant spatial variability within communities. Traditional centralized monitoring captures temporal variability as well as the long-term trends of air pollution very well, but mapping spatial variability of air pollution in communities at high resolution would require large number of air quality monitors distributed across the community. Mobile monitoring complements stationary monitoring approaches by measuring pollution levels on accessible road networks in urban communities. This paper presents the application of an integrated mobile measurement and data analysis approach to study community-level air pollution patterns, separate the regional background and local contributions, and identify high pollution zones for black carbon (BC), ultrafine particles (UFP), and fine particulate matter (PM2.5). This study was conducted during different periods from 2017 to 2019 in three California cities (Richmond, Stockton, and Commerce) with diverse community and source characteristics. The study found that traffic was the dominant primary source of air pollution in both urban and suburban settings. Urban areas adjacent to large area sources, such as ports and railyards had increased pollutant enhancements due to either direct or indirect emissions, while suburban areas containing unpaved roads were observed to have large PM2.5 enhancement due to the resuspended dust. The analysis disaggregated the contribution of regional and local sources to local air pollution and found that regional background sources contributed up to 75% of the PM2.5 concentrations, while local sources contributed more than half of BC and UFP. The study suggests that 15–30 repeated measurements may be sufficient to map the general air pollution patterns within the community, while some extreme high pollution zones can be identified with fewer repeats (5–10). These techniques can be used for initial screening of air pollution variability within the community, and help identify priority areas for conducting follow-up long-term air quality measurements. The techniques also informs the relative importance of regional and local actions to reduce community pollutant levels.
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La pollution atmosphérique par les particules en suspension « PM » dans l’air constitue une préoccupation actuelle majeure en raison de ses effets délétères sur la santé humaine. Aujourd’hui, un manque de connaissances demeure vis-à-vis du rôle spécifique de certains constituants des particules et des différentes fractions des PM sur l’apparition du stress oxydant. La capacité des PM à déclencher la production d’espèces réactives de l’oxygène (ERO) en cause dans le stress oxydant peut également être renseignée par des méthodes chimiques de potentiel oxydant. Cette thèse vise à étudier le potentiel oxydant et le stress oxydant induit par les différentes fractions d’échantillons de particules PM₂,₅ collectées sur des sites de typologies urbaine, trafic routier et industrielle. Le potentiel oxydant (OP) a été évalué par la mesure de la vitesse d'oxydation de l’acide ascorbique (AA) et du dithiothréitol (DTT). Il a été montré que les deux tests de potentiel apportaient des informations complémentaires, OP-AA normalisé au volume étant relié aux concentrations de PM₂,₅ et aux sources de combustion, tandis que OP-DTT s’avère être sensible aux sources de particules émettant des métaux. Le stress oxydant a été étudié en quantifiant les ERO, les atteintes oxydatives aux protéines (protéines carbonylées), à la membrane plasmique (8-isoprostane) et à l’ADN (8-OHdG). Les particules totales sont capables d'induire la surproduction des ERO et de causer des dommages aux protéines, à des niveaux plus élevés que les autres fractions. Des altérations plus grandes de la membrane plasmique et de l’ADN ont été constatées avec la fraction particulaire et les extraits organiques des échantillons de PM₂,₅-₀,₃ provenant du site urbain. Il n’a pas été trouvé de corrélation entre le potentiel oxydant et les dommages oxydatifs aux macromolécules biologiques, ce qui signifie que ces mesures de OP ne permettraient pas de prédire les dommages cellulaires in vitro que nous avons suivis dans cette étude. Une approche plus globale, considérant notamment la mobilisation du système anti-oxydant dans la réponse cellulaire, pourrait permettre de mieux appréhender l’apport de la mesure du potentiel oxydant dans la compréhension des effets des PM en lien avec le stress oxydant cellulaire.
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Using a subset of stations that are included in the Western Regional Climate Center's Southern California climate regions, the purpose of this investigation is to survey and characterize hourly/monthly wind climatologies for the given stations, and in the process demonstrate a graphical methodology which comprehensively depicts seasonal and diurnal wind variation on single page layouts. The regions include the Sonoran, South Interior, Mohave, South Coast, Central Coast, and San Joaquin Valley, The graphical methodology is the Two-Way Time-Section climogram, an analog to the topographical map - calendar month replacing North/South or the y-axis, and hour of the day replacing East-West or the the X-axis. The various points on the graph represent the wind climatological parameter statistics for a given month and hour of the day. Among other features, the points can be contoured and areas of the graph with similar properties colored to lend additional insights. Three types of graphs are employed: Mean Vector Wind/Constancy, Prevailing Winds (Direction, Pct Occurrence Frequencies, and Mean Speeds from that direction), and Mean overall scalar speeds/Pct of Time with Calms. Such charts may be useful as quick study decision-aid and planning tools for a number of applications
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This guidance provides a method and rationale for designing monitoring networks to determine compliance with newly enacted PM 2.5 and PM 10 national ambient air quality standards. It defines concepts and terms of network design, presents a methodology for defining planning areas and community monitoring zones, identifies data resources and the uses of those resources for network design, and provides some practical examples of applying the guidance. Existing metropolitan statistical areas are first examined to determine where the majority of the people live in each state. These are then broken down into smaller populated entities which may include country, zip code, census tract, or census block boundaries. Combinations of these population entities are combined to define metropolitan planning areas. These may be further sub-divided into community monitoring zones, based on examination of existing PM measurements, source locations, terrain, and meteorology. Finally, PM 2.5 monitors are located at specific sites that represent neighborhood or urban scales to determine compliance with the annual standard and at maximum, population oriented locations for comparison with the 24-hour standard.
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Particulate emission indices (per kg fuel) have been determined by sampling the advected plumes of in-use commercial aircraft at two different airports using a novel approach. Differences are observed in the number, magnitude, and composition of the particle emissions between idle and take-off. At the first airport, Electrical Low Pressure Impactor (ELPI) data indicate that number based emission indices (EIn) vary by an order of magnitude for take-off plumes from different aircraft. Additionally, EIn values for idle plumes are greater than take-off. At the second airport, EIn values derived from condensation particle counter (CPC) measurements span ∼ an order of magnitude (3–50 × 10 particles per kg fuel). The median values of the idle and take-off plumes were 1.8 × 10 and 7.6 × 10 particles per kg fuel, respectively. For take-off plumes, the magnitude of the particulate emission index is not correlated with NOx at either airport. The surface properties of the particulate emissions in take-off and idle plumes differ significantly as measured by diffusion charging (DC) and photoelectric aerosol sensor (PAS) instruments. Results indicate that take-off plumes are characterized by particles with photoelectric-active surfaces, presumably elemental carbon, whereas idle plumes are composed of non-photoelectric-active constituents and coated soot particles. Measurements of the particulate size distribution (ELPI) show evidence for two modes, one at ∼ 90 nm aerodynamic diameter and a second mode at or below the instrument cutoff (
Real time number concentrations and size distributions of ultrafine particles (UFPs, diameter <100 nm) and time integrated black carbon, PM2.5 mass, and chemical species were studied at the Los Angeles International Airport (LAX) and a background reference site. At LAX, data were collected at the blast fence (similar to 140 m from the takeoff position) and five downwind sites up to 600 m from the takeoff runway and upwind of the 405 freeway. Size distributions of UFPs collected at the blast fence site showed very high number concentrations, with the highest numbers found at a particle size of approximately 14 nm. The highest spikes in the time series profile of UFP number concentrations were correlated with individual aircraft takeoff. Measurements indicate a more than 100-fold difference in particle number concentrations between the highest spikes during takeoffs and the lowest concentrations when no takeoff is occurring. Total UFP counts exceeded 10(7) particles cm(-3) during some monitored takeoffs. Time averaged concentrations of PM2.5 mass and two carbonyl compounds, formaldehyde and acrolein, were statistically elevated at the airport site relative to a background reference site. Peaks of 15 nm particles, associated with aircraft takeoffs, that occurred at the blast fence were matched with peaks observed 600 m downwind, with time lags of less than 1 min. The results of this study demonstrate that commercial aircraft at LAX emit large quantities of UFP at the lower end of currently measurable particle size ranges. The observed highly elevated UFP concentrations downwind of LAX associated with aircraft takeoff activities have significant exposure and possible health implications.
High concentrations of air pollutants on roadways, relative to ambient concentrations, contribute significantly to total personal exposure. Estimation of these exposures requires measurements or prediction of roadway concentrations. Our study develops, compares and evaluates linear regression and non-linear generalized additive models (GAMs) to estimate on-road concentrations of four key air pollutants, particle-bound polycyclic aromatic hydrocarbons (PB-PAH), particle number count (PNC), nitrogen oxides (NOx), and particulate matter with diameter <2.5 µm (PM2.5) using traffic, meteorology, and elevation variables. Critical predictors included wind speed and direction for all the pollutants, traffic-related variables for PB-PAH, PNC, and NOx, and air temperatures and relative humidity for PM2.5. GAMs explained 50%, 55%, 46%, and 71% of the variance for log or square-root transformed concentrations of PB-PAH, PNC, NOx, and PM2.5 respectively, an improvement of 5 to over 15% over the linear models. Accounting for temporal autocorrelation in the GAMs further improved the prediction, explaining 57-89% of the variance. We concluded that traffic and meteorological data are good predictors in estimating on-road traffic-related air pollutant concentrations and GAMs perform better for non-linear variables, such as meteorological parameters.
Predictions as to the transport of aircraft NOx emissions to ground level in the vicinity of an airport are made, taking account of the vortical dynamics of the aircraft wake. A model is used to calculate mean ground-level concentrations. It employs a kinematic approach, harnessing results from dynamical models in the literature. Two aircraft types are considered, a B737-300 (twin turbofan), taken as representative of short-range aircraft, and a B747-400, taken as representative of long-range aircraft. Airport and meteorological parameters are assigned values as holding in the case of Manchester Airport, UK, during a fortnight in May 1999 (prior to the opening of a second runway there). The airport operated at a capacity of about 45 aircraft movements per hour in the peak morning period, with frequencies in the middle of the day being about half this. Aircraft of maximum takeoff weight in excess of 120tonnes accounted for some 6% of all movements. The simulations predict mean NOx concentrations of maximum 3μgm−3 at the centre of the runway arising as a result of the vortex-mediated transport (as expressed on conversion of all NO to NO2), with concentrations on the order of 1μgm−3 arising 0.5km laterally from the runway.
An aircraft exerts a downward force on the air, so aircraft exhausts tend to descend toward the ground within the aircraft's wake. The wake of an aeroplane descends in an essentially inviscid and thus long-lived manner, through the action of a pair of trailing counterrotating vortices on one another. The vortices derive from the circulation about the wings yielding the lift. Exhaust pollutants may thus be conveyed to the ground close to airports, far more effectively than through ambient atmospheric dispersion alone, as has hitherto been assumed occurs within air-quality models. The presence of the vortices in the vicinity of airports is well-established, as it is the need to wait for their decay or movement out of the flight corridor—for the safety of the following aircraft—that caps departure and arrival rates at busy periods. The dynamics of the transport are elucidated, so that the impact of the transport on pollutant concentrations at ground level in an airport environs may be established.
Background: While commercial aircraft are known sources of ultrafine particulate matter (UFP), the relationship between airport activity and local real-time UFP concentrations has not been quantified. Understanding these associations will facilitate interpretation of the exposure and health risk implications of UFP related to aviation emissions. Objectives: We used time-resolved UFP data along with flight activity and meteorological information to determine the contributions of aircraft departures and arrivals to UFP concentrations. Methods: Aircraft flight activity and near-field continuous UFP concentrations (≧ 6 nm) were measured at five monitoring sites over a 42-day field campaign at Los Angeles International Airport (LAX). We developed regression models of UFP concentrations as a function of time-lagged landing and take-off operations (LTO) activity, in the form of arrivals or departures weighted by engine-specific estimates of fuel consumption. Results: Our regression models demonstrate a strong association between departures and elevated total UFP concentrations at the end of the departure runway, with diminishing magnitude and time-lagged impacts with distance from the source. LTO activity contributed a median (95th, 99th percentile) UFP concentration of approximately 150,000 particles/cm(3) (2,000,000, 7,100,000) at a monitor at the end of the departure runway, versus 19,000 particles/cm(3) (80,000, 140,000), and 17,000 particles/cm(3) (50,000, 72,000) for monitors 250 m and 500 m further downwind, respectively. Conclusions: We demonstrated significant contributions from aircraft departure activities to UFP concentrations in close proximity to departure runways, with evidence of rapid plume evolution in the near field. Our methods can inform source attribution and interpretation of dispersion modeling outputs.