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Aviation-Related Impacts on Ultrafine Particle Number Concentrations Outside and Inside Residences near an Airport

Authors:
  • Volpe National Transportation Systems Center (US DOT)
  • Work within grass roots groups STEP and MVTF in Somerville MA

Abstract and Figures

Jet engine exhaust is a significant source of ultrafine particles and aviation-related emissions can adversely impact air quality over large areas surrounding airports. We investigated outdoor and indoor ultrafine particle number concentrations (PNC) from 16 residences located in two study areas in the greater Boston metropolitan area (MA, USA) for evidence of aviation-related impacts. During winds from the direction of Logan International Airport, that is, impact-sector winds, an increase in outdoor and indoor PNC was clearly evident at all seven residences in the Chelsea study area (∼4–5 km from the airport) and three out of nine residences in the Boston study area (∼5–6 km from the airport); the median increase during impact-sector winds compared to other winds was 1.7-fold for both outdoor and indoor PNC. Across all residences during impact-sector and other winds, median outdoor PNC were 19 000 and 10 000 particles/cm³, respectively, and median indoor PNC were 7000 and 4000 particles/cm³, respectively. Overall, our results indicate that aviation-related outdoor PNC infiltrate indoors and result in significantly higher indoor PNC. Our study provides compelling evidence for the impact of aviation-related emissions on residential exposures. Further investigation is warranted because these impacts are not expected to be unique to Logan airport.
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Aviation-Related Impacts on Ultrane Particle Number
Concentrations Outside and Inside Residences near an Airport
N. Hudda,*
,
M.C. Simon,
,
W. Zamore,
§
and J. L. Durant
Department of Civil and Environmental Engineering, Tufts University, 200 College Ave, 204 Anderson Hall, Medford,
Massachusetts 02155, United States
Department of Environmental Health, Boston University, 715 Albany Street, Boston, Massachusetts 02118, United States
§
Somerville Transportation Equity Partnership, 13 Highland Ave, #3, Somerville, Massachusetts 02143, United States
*
SSupporting Information
ABSTRACT: Jet engine exhaust is a signicant source of ultrane particles and aviation-related
emissions can adversely impact air quality over large areas surrounding airports. We investigated
outdoor and indoor ultrane particle number concentrations (PNC) from 16 residences located in
two study areas in the greater Boston metropolitan area (MA, USA) for evidence of aviation-related
impacts. During winds from the direction of Logan International Airport, that is, impact-sector winds,
an increase in outdoor and indoor PNC was clearly evident at all seven residences in the Chelsea
study area (45 km from the airport) and three out of nine residences in the Boston study area
(56 km from the airport); the median increase during impact-sector winds compared to other
winds was 1.7-fold for both outdoor and indoor PNC. Across all residences during impact-sector and
other winds, median outdoor PNC were 19 000 and 10 000 particles/cm3, respectively, and median
indoor PNC were 7000 and 4000 particles/cm3, respectively. Overall, our results indicate that
aviation-related outdoor PNC inltrate indoors and result in signicantly higher indoor PNC. Our
study provides compelling evidence for the impact of aviation-related emissions on residential
exposures. Further investigation is warranted because these impacts are not expected to be unique to
Logan airport.
INTRODUCTION
Aircraft engine exhaust emissions are a signicant source of
ultrane particles (UFP; aerodynamic diameter <100 nm) and
can cause several-fold increases in ground-level particle number
concentrations (PNC) over large areas downwind of air-
ports.
14
The spatial extent and magnitude of the impact varies
depending on factors including wind direction and speed,
runway use pattern, and ight activity but encompasses large
populations in cities where airports are located close to the
urban residential areas. For example, in Amsterdam, PNC (a
proxy for UFP) were found to be elevated 7 km downwind of
Schiphol Airport
2
while in Los Angeles, PNC were reported to
be elevated 18 km downwind of Los Angeles International
Airport.
1,3
Thus, it is important to characterize aviation-related
UFP.
Previous studies have shown that UFP can cross biological
boundaries (entering the circulatory system) due to their
extremely small size.
57
Exposure to UFP is of particular
concern because it is associated with inammation biomarkers,
oxidative stress and cardiovascular disease.
6
Recent exposure
assessment studies have started testing airport variables in UFP
predictive models,
812
but epidemiological studies that
incorporate airports in the exposure assessment are lacking;
currently, they primarily focus on trac-related UFP. To better
inform UFP exposure assessment eorts, it is also important to
distinguish aviation-related contributions from other urban
sources and to characterize them independently. This is
particularly challenging in urban areas with pervasive and
dense road networks. Furthermore, studies have shown that
residing in the vicinity of airports is signicantly associated with
hospitalization for cardiovascular disease;
13,14
however, there
the focus has been on association between cardiovascular health
eects and increased noise around airports, which can be
confounded by UFP. To date, no studies described in the
literature investigate the health eects of UFP, or of noise
controlling for UFP, around airports.
In a previous study, we found that during winds from the
direction of the Logan International Airport (Boston, MA)
PNC at two long-term, central monitoring stations located 4
km and 7.5 km downwind of the airport were 2-fold and 1.33-
fold higher, respectively, compared to average for all other
winds.
4
In the current study, we investigated residential data
sets from wider areas surrounding those two central sites. Our
primary objectives were (1) to investigate short-term residential
PNC monitoring data for evidence of aviation-related impacts
that could be identied despite the inuence of other urban
sources of UFP, and (2) to analyze the data for evidence of
indoor inltration of aviation-related PNC. To our knowledge,
Received: November 1, 2017
Revised: January 5, 2018
Accepted: January 9, 2018
Published: February 7, 2018
Article
pubs.acs.org/est
Cite This: Environ. Sci. Technol. 2018, 52, 17651772
© 2018 American Chemical Society 1765 DOI: 10.1021/acs.est.7b05593
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this is the rst study to report the impact of aviation-related
emissions inside residences.
MATERIALS AND METHODS
Logan International Airport and Central and Resi-
dential Monitoring Sites. The General Edward Lawrence
Logan International Airport is located 1.6 km east of downtown
Boston (Figure 1(a)). It has six runways and supports about
1000 ights per day. Flight statistics are shown in
the Supporting Information (SI) Figure S1. Prevailing winds
in the Boston region are westerly (northwest in winter and
southwest in summer, combined annual frequency 56%, see
Figure 1(b)). The downwind advection of airport-related
emissions occurs largely over urban areas located east and
northeast of the airport as well as over the ocean during
prevailing winds. During easterly winds, several other urban
areas are downwind of the airport. We studied two of these
areas: Chelsea and Boston.
In Chelsea, outdoor (i.e., ambient) and indoor monitoring
was conducted at seven residences that were located 3.74.9
km downwind from the airport along 133°165°azimuth
angles measured to the geographic center of the airport (Figure
1(a)). Each residence was monitored for six consecutive weeks
between February December 2014. Ambient monitoring
was also conducted continuously at a central site in Chelsea
(located on top of a three-story building) during the entire 11-
month period (Figure 1(a)). In Boston, monitoring was
conducted at nine residences between May 2012 and October
2013. The residences were located 5.010.0 km downwind
from the airport along 43°74°azimuth angles measured to
the geographic center of the airport. Monitoring was also
conducted continuously during this 18-month period at a
central site in Bostonthe U.S. Environmental Protection
Agency Speciation Trends Network site (ID: 250250042).
Central sites were selected based on their proximity to the
geographic center and representativeness for the study area.
Residential sites were selected based on their proximity to
highways and major roads (the latter dened as annual average
daily trac >20 000): four sites were <100 m, seven between
100 and 200 m, and ve >200 m from highways or major roads.
Monitoring schedule, meteorological parameter summary,
residence characteristics, and distance to major roadways are
shown in SI Tables S1S6.
During the six-weeks of monitoring at each residence, a
HEPA lter (HEPAirX, Air Innovations, Inc., North Syracuse,
NY) was operated in the room where the condensation particle
counter (CPC) was located for three consecutive weeks
followed by three consecutive weeks of sham ltration or vice
versa. Only nonsmoking residences were recruited and we
found no evidence of smoking in residences. Residences were
monitored one or two at a time with limited overlap between
monitoring periods. For further details of residential monitor-
ing and ltration, see Simon et al.
15
and Brugge et al.,
16
respectively.
Instruments and Data Acquisition. PNC were moni-
tored using four identical water-based CPCs (model 3783, TSI
Inc., Shoreview MN), which recorded 30 s or 1 min average
concentrations. The CPCs were annually calibrated at TSI and
measured to within ±10% of one another, consistent with
manufacturer-stated error. Ambient PNC were monitored
continuously at the central-sites. At residences, a solenoid
valve connected to the inlet switched the air ow between
outdoor and indoor air every 15 min. Thus, residential outdoor
and indoor PNC were monitored for 30 min per hour. To
ensure that the sampling lines (1-m-long conductive silicon
tubing for both indoor and outdoor carrying transport ow of 3
L per minute) were fully ushed, the rst and last data points
per switch were discarded (713% of the total). Any data that
were agged by the instruments (<1% of the total) and hours
with <50% data recovery were not included in the analysis.
Flight records for individual aircraft were obtained from the
Massachusetts Port Authority (East Boston, MA) and counted
to obtain hourly totals for landings, takeos and the sum of the
two (LTO). Meteorological data (a 2 min running average at 1
min resolution for wind direction and speed) were obtained
from the National Weather Service station at the airport and
processed through AERMINUTE
17
(a meteorological process-
or developed by EPA for use in AERMET and AERMOD) to
obtain hourly values.
Data and Statistical Analysis. Each PNC data set
(residential indoor, residential outdoor, and central-site) was
Figure 1. (a) Map of the runways at Logan International Airport and
the locations of the central and residential monitoring sites in Chelsea
and Boston. Base layers were obtained from mass.gov. (b) Windrose is
based on 1 min data for 2014 reported by National Weather Service
Automated Surface Station located at the airport.
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aggregated separately to calculate hourly medians. Hourly
medians were further aggregated by 10°-wide wind-direction
sectors, and medians were calculated for each sector. Wind-
direction sectors were centered on even 10°and spanned ±5°.
Data were also classied as impact-sector versus other based on
the wind direction. Winds that positioned monitoring sites
downwind of the airport were called impact-sector winds.
Impact-sector boundaries (Table 1) correspond to the azimuth
angles measured from a monitoring site to the widest distance
across the airport complex (SI Figure S2).
For indoor data we also calculated the hourly minimum in
addition to hourly medians. Indoor data were also classied by
ltration scenario (HEPA or sham). Indoor measurements
reect contributions from both particles generated indoors and
particles of outdoor origin that inltrate indoors. We did not
quantify fraction of indoor- versus outdoor-origin particles.
Instead, we compared hourly indoor minimums (less likely to
be inuenced by indoor-generated PNC spikes) with outdoor
PNC to determine if higher indoor PNC occurred during
impact-sector winds. During periods of elevated outdoor
concentrations, indoor concentrations are also expected to be
elevated due to air exchange between residences and their
surroundings.
Spearmans rank correlation (coecients reported as rS) was
calculated between PNC and wind speed and PNC and LTO.
Inferences based on Spearmans rank correlation were limited
to ordinal associations. Correlations were considered signicant
if p-values were <0.05. Bootstrapped 95% condence intervals
for the correlation coecients were also calculated. Further,
impact-sector wind data sets at residences were relatively small;
they ranged from 30 to 119 h or 3.011.8% of the total data.
To take the resulting uncertainty into account, we compared
distributions of correlation coecient estimates generated
using bootstrap resampling methods (1 ×104random samples
with replacement) for impact-sector winds to other winds.
Subsamples (1 ×104random samples without replacement)
from other-wind data sets but of size comparable to impact-
sector-winds were also compared where appropriate.
RESULTS AND DISCUSSION
We found strong evidence of aviation-related particle
inltration. Outdoor and indoor PNC were statistically
signicantly higher during impact-sector winds compared to
other winds. Wilcoxon rank sum tests indicated that the median
of 10°-wide-sector medians from all residences for impact
sector winds was higher than other winds for outdoor
concentrations (p-value <0.0001, z-value = 8.1) as well as
for indoor concentrations during both sham ltration (p-value
<0.0001, z-value = 5.1) and HEPA ltration (p-value =
0.0037, z-value = 2.7). Table 1 summarizes indoor and
outdoor concentrations.
We present detailed results in the following sections where
we have organized our lines of reasoning as follows: rst, we
demonstrate elevated outdoor PNC during dierent impact-
sector winds in the two study areas (each showing an impact
when it was oriented downwind of the airport) including sites
upwind and downwind of a highway; second, we discuss
correlation of outdoor PNC with wind speed and ight activity,
which indicated the aviation-related origin of elevated PNC
during impact-sector winds; and third, we report indoor trends
at all residences and discuss indoor inltration of aviation-
related, elevated, outdoor PNC for two residences in detail.
Wind Direction and Ambient PNC Patterns at
Residences. Higher ambient PNC were observed during
winds that positioned the sites downwind of the airport (i.e.,
impact-sector winds). Impact sector diered by study area and
from residence to residence within the study areas. In Chelsea
(located NW of the airport) PNC were elevated during SE
winds and in Boston (located SW of the airport) PNC were
elevated during NE winds (Figure 1). This impact is thus
spatially widely distributed in the Boston area.
Chelsea. During impact-sector winds in the Chelsea study
area (ESE-S, 111°182°), PNC were elevated at the central site
and all seven residences. Residences that were upwind of the
highway during impact-sector winds are denoted with a U,
residences that were downwind of the highway during impact-
sector winds are denoted as D, and community sites that are
Table 1. Impact Sector Denitions and Summary of Particle Number Concentration Statistics for Residential Sites
impact-sector winds hourly PNC
statistics other winds hourly PNC statistics
ID distance to
airport (km) impact sector
denition (WD°)impact sector winds
frequency, hours outdoor
median indoor
median indoor
minimum outdoor
median indoor
median indoor
minimum
Chelsea Residences
D1 4.3 111155 4.7%, 47 36 000 11 100 7600 13 200 4400 3700
D2 4.4 111154 5%, 50 37 100 14 600 7500 16 200 5100 3500
U1 4.9 142176 5.3%, 53 14 900 2300 1400 7800 1900 1600
U2 4.0 117164 11.8%, 119 18 600 2500 1800 10 700 2400 1800
C1 4.2 145182 5.2%, 50 12 800 3500 2800 8100 2500 1900
C2 4.4 130171 5.4%, 54 19 700 1900 1300 9700 2200 1700
C3 3.7 124173 10.8%, 111 26 600 6400 4700 8900 2800 2200
Boston Residences
D1 6.1 3159 6.9%, 63 27 800 8400 4300 10 700 5300 4000
U1 5.0 2861 8.4%, 79 25 100 22 700 17 500 14 700 7400 6100
U2 5.6 3059 8.2%, 70 19 700 10 900 6900 9700 6100 3700
C1 6.8 5379 9.6%, 97 9400 3700 2600 8000 2300 1800
C2 7.1 5378 3%, 30 11 900 7900 6400 10 000 4100 2800
C3 7.8 6286 9.6%, 94 21 000 7700 5800 14 300 3900 3300
B1 10.0 3353 3.4%, 34 13 500 4900 4200 10 100 4500 3400
B2 8.8 4867 6%, 65 8200 4900 3200 7200 4500 3000
B3 9.2 6078 4%, 39 12 900 15 400 11 600 8100 6300 5100
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not in proximity of a highway are denoted as C (Figure 2).
Median PNC during impact-sector winds were 1.6- to 3.0-fold
higher than the medians for all other winds (Table 1). Highest
and lowest residential impact-sector medians were 37 000 and
13 000 particles/cm3, respectively, as compared to 16 000 and
8000 particles/cm3during all other winds.
Impact-sector winds occurred for 4.711.8% of the time
(annually, 7% in 2014) during the residential monitoring, but
their weighted contributions to the monitoring averages were
826%. It should be noted that these contributions likely
include some input from other sources in impact sectors, such
as, trac. Heatmaps of PNC by wind direction and hour of the
day for the central site and all seven residences studied in
Chelsea (SI Figure S3 (a) and (c)) indicate PNC peaks
coincided with morning and evening vehicular and aviation
trac rush-hours. However, these peaks were highly elevated
during impact-sector winds even though trac impacts are not
particularly concentrated in the impact sector; only two of the
seven residences (D1 and D2) were downwind of major
roadways and highways during impact-sector winds.
Boston. In the Boston study area, a pronounced increase in
PNC during impact-sector winds was evident at three sites 5.0
6.1 km downwind of the airport (Figure 3). At residences U1
and U2 (NNE-ENE, 28°61°), which were both also upwind
of Interstate 93 (I-93) (Figure 3(b)), median PNC during
impact-sector winds were 25 000 and 20 000 particles/cm3,
respectively, as compared to 15 000 and 10 000 particles/cm3
during all other winds. At site D1, which was 6.1 km downwind
of the airport and 200 m downwind of I-93 during impact-
sector (NE) winds, but impacted by the highway during both
NE (31°59°) and SE (115°145°) winds, median PNC were
greater during NE winds than during SE winds (29 000 vs
19 000 particles/cm3, respectively; means were 29 000 ±46%
vs 21 000 ±70% particles/cm3, respectively) for similar I-93
trac volume (hourly tracow was 7000 ±47% during times
of NE vs 8000 ±39% during SE winds).
At the other six sites in Boston, which were 6.810.0 km
from the airport, increases in PNC during impact-sector winds
were not as distinct (Figure 3(c)). Ambient median PNC
during impact-sector winds, which likely included considerable
contributions from upwind sources including busy roadways
and highways in Boston, were 1.1- to 1.6-fold higher at these six
residences than the medians for all other winds (Table 1).
Heatmaps for PNC by wind direction and time of day for the
central site and all residences (SI Figure S3 (b) and (d))
indicate PNC peaks coincided with morning and evening
vehicular and aviation trac rush-hours. The impact-sector
PNC were lower in Boston compared to Chelsea.
15
Correlations between PNC and Wind Speed. Because
higher wind speeds generally promote greater dispersion and
mixing, PNC and wind speed are typically negatively correlated.
However, for buoyant aviation emissions plumes, higher wind
speeds promote faster ground arrival counterbalancing the
increased dilution.
18
Thus, a distinct feature of aviation
emissions impacts (unlike road trac emissions impacts) is a
lack of negative correlation between PNC and wind
speed.
4,19,20
We too observed this phenomenon. During
impact-sector winds at Chelsea and Boston central-sites, the
negative correlation between PNC and wind speed was lacking;
correlation coecients were rS= 0.17 and 0.19, n= 435 and
408 h, respectively, and p-value < 0.001. In contrast, during
other winds, the expected negative correlation between PNC
and wind speed was observed (rS=0.24 and 0.05, n= 7552
and 10 537 h, respectively, and p-value < 0.001). Similar trends
Figure 2. (a) Locations of the central site (C0, black) and seven residences monitored in Chelsea. Residences were classied as upwind (U, dark
blue) of the highway during impact-sector winds, downwind of the highway (D, orange ) during impact-sector winds and community sites that were
not in proximity of the highway (C, light blue). (b)(e) Normalized (by the maximum) PNC roses are based on hourly medians; concentric circles
are increments of 0.2 on a 01 scale.
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were found at the residences in both study areas: correlation
between PNC and wind speed was either lacking or even
positive during impact-sector winds but it was negative during
other winds. Correlation coecients for residences are shown
in Figure 4 where points have been jittered along the
categorical x-axis to reduce overlap.
Because impact-sector winds were a small fraction of all
winds (312% of the total data set) we conducted bootstrap
resampling of correlation estimates (rS) and bootstrap
subsampling of a similarly small data set from other wind
conditions to ensure that the lack of negative correlation was
not by chance. The correlation estimates during impact-sector
winds were dierent from the negative estimates obtained for
other winds; results are shown in SI Figure S4S19. The
contrast in correlation was most evident in Chelsea and sites
upwind of I-93 in Boston. Notable exceptions were sites
downwind of both a highway and the airport during impact-
sector winds likely because they were dominantly impacted by
highway emissions given their proximity to the highways. For
example, at site D1 in Boston, we observed no dierence in
correlation estimates between impact-sector and other winds
(SI Figure S11). In comparison, at sites U1 and U2 in Boston,
which were upwind of the highway during impact-sector winds
but still downwind of the airport, correlation estimates were
positive during impact-sector winds and negative during other
winds (SI Figure S12S13).
Correlations between PNC and Flight Activity. PNC at
both central sites were previously reported to be positively
correlated with aviation activity (measured as LTO, the hourly
total landings and takeos) after controlling for trac volume,
time of day and week, and meteorological factors (wind speed,
temperature, and solar radiation).
4
Because the central sites
both had relatively large data sets (several years of monitoring),
we were able to control for these factors; however, the relatively
small PNC data sets for residences and the lack of local trac
volume information limited meaningful controls in the current
analysis. Also, because the temporal patterns of ight activity
and vehicle trac are similar, some confounding was observed
between PNC and LTO irrespective of the wind direction. For
example, Pearsons correlation coecient for hourly LTO and
tracvolumeonI-93in2012was0.85.Nonetheless,
Spearmans correlations and the bootstrap analysis (SI Figure
S20S35) indicate that PNC versus LTO correlation estimates
during impact-sector winds were generally higher than during
other winds; that is, rsranged from 0.29 to 0.67 during impact-
sector winds compared to 0.100.54 during other winds, but
there were exceptions (see discussion in SI).
Indoor Inltration of PNC during Impact-Sector
Winds. Overall Trend at Residences. Inltration of aviation-
related outdoor PNC was evident in the data as higher indoor
concentrations during impact-sector winds compared to other
winds. The median increase in indoor concentrations during
impact-sector winds compared to other winds was 1.7-fold
(range: 0.93.1-fold). PNC measurements (median and
minimums) are summarized in Table 1 for all residences. For
trends with respect to wind direction for individual residences
see SI Figures S36S51, which show an increase in indoor
medians coincident with impact-sector winds is more apparent
for residences in Chelsea and Boston closer to the airport, while
Figure 3. (a) Locations of the central site (C0, black) and nine
residences monitored in Boston. Residences were classied as upwind
(U, dark blue) of the highway during impact-sector winds, downwind
of the highway (D, orange) during impact-sector winds, community
sites (C, light blue) and background sites (B, green). (b)(c)
Normalized (by the maximum) PNC roses are based on hourly
medians; concentric circles are increments of 0.2 on a 01 scale.
Figure 4. Correlation coecients between outdoor PNC and wind
speed (a, b) and LTO (c, d) for seven Chelsea and nine Boston
residences during impact-sector and other winds. Filled squares
represent signicant correlation (p-value <0.05) and unlled squares
represent insignicant correlations. X-axis is categorical but points
have been jittered to enhance visual clarity by reducing overlap. For
description of colors, see captions for Figures 2 and 3.
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some residences located farthest away (like B1 and B2) showed
no trend with respect to wind direction for either outdoor or
indoor PNC.
HEPA ltration lowered the indoor concentrations; indoor-
to-outdoor PNC ratios were 0.33 ±0.17 lower during HEPA
ltration as compared to sham ltration (see Brugge et al.
16
).
Figure 5 compares 10°-wide-sector PNC medians for impact-
sector and other winds separately for sham and HEPA ltration
scenarios in all 16 homes. Because ltration eciency is not
preferential to ambient wind direction, higher concentrations
(despite lower indoor-to-outdoor ratios) were still observed
during impact-sector winds. Further, this trend was apparent in
both the hourly medians and hourly minimums (range: 0.8
2.9-fold) of indoor PNC even though hourly medians are more
likely to be skewed by contributions from indoor sources than
the hourly minimums (SI Figure S52).
Previous studies have shown that ambient PNC inltrate
indoors via multiple pathways such as forced air ventilation
systems, open windows, or cracks in the building envelope.
21
Inltration factors vary from 0.03 to 1.0
21,22
in the ultrane
range, the size range for the majority of the aviation-related
particulate emissions.
3
Inltration of aviation-related PNC and,
resultantly, an increase in indoor PNC and residential
exposures can thus be expected in near-airport residences.
Our results clearly indicate that to be the case; particles of
aviation-related origin inltrate residences. Two cases are
illustrated in detail in the following section.
Illustration of Inltration at Select Residences. Inltration
of PNC is illustrated for residence C3 in Chelsea in Figure 6
(a). Time series of indoor PNC closely followed the same
pattern as outdoor PNC during an 18-h period of consistent
impact-sector winds (from 1900 h on Oct 6 to 1200 h on Oct
7, 2014). During hours of minimal ight activity (01000500
h; LTO = 1.5 h1), PNC indoors and outdoors at C3 and the
central site were all low but increased as ight activity resumed
after 0500 h. Residential outdoor PNC was also remarkably
highly correlated (Pearsonsr= 0.96) with the central site
located 1 km away indicating the spatial homogeneity of the
aviation-related impact over a large area. Further, even though
it was past the evening trac rush-hour period (and thus trac
would have contributed minimally to the observations or for
that matter particle formation) when the winds shifted (at
1900 h) to the impact sector, outdoor and central-site
concentrations increased to high levels (1 min averages were
between 50 000 and 100 000 particles/cm3), which underscores
the magnitude of this impact. In comparison, Simon et al.
15
reported mean 1 min on-road PNC from 180 h of mobile
monitoring across Chelsea including trac rush-hours was
32 000 particles/cm3which was about one third to one half of
the observed PNC at C3 during impact-sector winds. Overall,
at C3, the median indoor PNC was nearly 3-fold higher for
impact-sector winds compared to other winds (8900 versus
2800 particle/cm3)(Figure 6(c), SI Figure S42).
Figure 5. (a) Tukeys boxplots of indoor and outdoor PNC data
during sham and HEPA ltration from all 16 homes. The horizontal
line inside each box is the median; the boxes extend from the 25th to
the 75th percentile and the whiskers extend to 1.5*interquartile range.
In (b) and (c) each point in the scatterplots represents the median of
hourly medians classied into 10-degree-wide wind sectors.
Figure 6. PNC time series for October 67, 2014 for site C3 in
Chelsea is shown in (a). Impact-sector winds are highlighted in gray.
Tukeys boxplots in (b) and (c) show outdoor and indoor PNC. The
horizontal line inside each box is the median, the boxes extend from
the 25th to the 75th percentile and the whiskers extend to
1.5*interquartile range.
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Another example of inltration is shown in Figure S53(a)
where a 22-h period of generally consistent impact-sector winds
is highlighted (from 1900 h on Nov 6 to 1700 h on Nov 7,
2012) for residence U1 from the Boston study area. U1 is
relatively close to I-93 but it is upwind of the highway during
impact-sector winds. Outdoor concentrations during impact-
sector winds from 1900 h to as late as midnight on Nov 67,
2012 were 40 000 particles/cm3but then decreased to as low
as 2000 particles/cm3during the hours of low ight activity at
the airport (LTO decreased from 32 h1to 2.8 h1during
19000000 h to 00000500 h). The indoor PNC time series
was consistent with the outdoor concentration during these
hours. Both outdoor and indoor concentration started
increasing again around 0500 h when ight activity resumed
at the airport; however, around 0800 h indoor PNC spiked,
likely from an indoor particle-generation event that dominated
indoor PNC during the following hours despite impact-sector
winds. Overall, the median indoor PNC was 2-fold higher for
impact-sector winds compared to other winds (15 000 versus
7400 particles/cm3)(Figure S53(c) and Figure S44).
Strength and Limitations. To our knowledge this is the
rst investigation of the impacts of aviation-related emissions at
residences around airports. Our results show an increase in
outdoor as well as indoor PNC. These ndings point to the
need for studies to provide further characterization of these
impacts (e.g., measure additional pollutants in a greater number
and variety of residences both near and far from airports and
under a greater diversity of meteorological conditions and
indoor activities).
Our study also had limitations. The foremost is that
monitoring was not specically designed for quantifying the
impacts of aviation-related emissions on indoor and outdoor
PNC. Data were collected as part of the Boston Puerto Rican
Health Study (a study of exposure to urban air pollution and
cardiovascular health eects in a Puerto Rican cohort
23
), but it
allowed for the reported analysis because of the residences
proximity to and distribution around the airport. Ideally, for
quantifying the aviation-related impacts and distinguishing
them from other outdoor sources (such as trac) and indoor
sources (such as cooking), continuous indoor and outdoor
monitoring at several locations in carefully characterized
residences with indoor time-activity records would be
necessary. In addition, the study was not designed to
characterize the air exchange rates or inltration factors for
ambient particles. As a result, we could not quantify the
contribution of indoor- versus outdoor-origin PNC to total
indoor observations, or more pertinently the contribution from
aviation-related outdoor PNC to indoor observations. Further,
the lack of concurrent data from all or even multiple residences
precluded spatial analysis. Residence-to-residence dierences in
outdoor and indoor PNC (Figure 7 and Table 1) were
observed. For example, at sites closer to the airport PNC were
generally higher than farther away, but at sites immediately
downwind of highways, even though they were farther
downwind of the airport, PNC were even higher, likely due
to impacts from both aviation-related and trac emissions.
Such spatial dierences were not investigated. Observed
outdoor concentration dierences were likely not solely due
to the dierences in spatial location with respect to the airport
or other sources; temporal dierences (e.g., meteorological and
seasonal factors) likely also contributed signicantly, but they
could not be controlled for due to lack of concurrent data.
Signicance of the Results. Altogether, our results make a
compelling case for further investigation of aviation-related air
pollution impacts and resulting exposures because these
impacts are not expected to be unique to Logan airport.
Extrapolating from Correia et al.
13
, we estimate that in the
United States 40 million people live near 89 major airports
(i.e., within areas with 45 dB noise levels near airports).
Inclusion of aviation-related impacts may also improve
predictive models for exposure assessments. Future studies of
this impact with concurrently located sites that allow analysis of
the spatial gradient and comparison with trac impacts could
be very informative for ultrane particle epidemiology.
ASSOCIATED CONTENT
*
SSupporting Information
The Supporting Information is available free of charge on the
ACS Publications website at DOI: 10.1021/acs.est.7b05593.
Information related to ight activity at Logan Interna-
tional Airport (Figure S1), details of monitoring schedule
residence characteristics, and summary statistics of the
Figure 7. Outdoor PNC at residences during six-week monitoring periods in Chelsea (a) and Boston (b). Median of hourly medians classied as
impact-sector and other winds are shown.
Environmental Science & Technology Article
DOI: 10.1021/acs.est.7b05593
Environ. Sci. Technol. 2018, 52, 17651772
1771
data (Table S1S6, Figure S2), heatmaps of PNC by
wind direction and time of the day (Figure S3),
correlation coecient estimates from bootstrap subsam-
pling and resampling (Figure S4S35), additional
graphics related to particle number concentration trends
with respect to wind direction at monitoring sites (Figure
S36S52) and an example of inltration (Figure S53)
(PDF)
AUTHOR INFORMATION
Corresponding Author
*Phone: 617.627.5489; fax: 617.627.3994; e-mail: neelakshi.
hudda@tufts.edu.
ORCID
N. Hudda: 0000-0002-2886-5458
Notes
The authors declare no competing nancial interest.
ACKNOWLEDGMENTS
We are grateful to Alex Bob, Dana Harada, Joanna Stowell,
Hanaa Rohman, Ruhui Zhao, and Andrew Shapero for
eldwork assistance. Alexis Soto and Nancy Figueroa recruited
participants for residential monitoring. Marianne Ray helped
with data analysis. We would like to thank The Neighborhood
Developers (Chelsea, MA) and Massachusetts Department of
Environmental Protection (Roxbury, MA) for providing space
and electricity for our monitoring equipment. This work was
funded by NIH grants P01 AG023394 and P50 HL105185 to
the University of Massachusetts Lowell, NIH-NIEHS grant
ES015462 to Tufts University, and by the Somerville
Transportation Equity Partnership (STEP).
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Supplementary resource (1)

... The recently completed MOV-UP study in King County, Washington, identified a clear, aircraft-associated footprint of ultrafine particles under flight paths. Monitoring campaigns conducted in communities near airports in Seattle, [2][3][4] Los Angeles, [5][6][7][8] , Atlanta, 9 Boston, 10 New York 11 and Amsterdam 12 have all identified elevated levels of total UFP in proximity to international airports. This work has also highlighted differences in the pollutant mixtures between aircraft and roadway traffic sources, 6,11,13,14 as well as differences in fuel-based emissions of UFP from aircraft and roadway traffic sources. ...
... Monitoring campaigns conducted in communities near airports in Seattle, [2][3][4] Los Angeles, [5][6][7][8] , Atlanta, 9 Boston, 10 New York 11 and Amsterdam 12 have all identified elevated levels of total UFP in proximity to international airports. This work has also highlighted differences in the pollutant mixtures between aircraft and roadway traffic sources, 6,11,13,14 as well as differences in fuel-based emissions of UFP from aircraft and roadway traffic sources. 2,7 Evidence is emerging that exposure to aircraft emissions is associated with negative health impacts. ...
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Background Exposure to airborne ultrafine particle (UFP; <100 nm in aerodynamic diameter) is an emerging public health problem. Nevertheless, the benefit of using high efficiency particulate arrestance (HEPA) filtration to reduce UFP concentrations in homes is not yet clear. Methods We conducted a randomized crossover study of HEPA filtration without a washout period in 23 homes of low-income Puerto Ricans in Boston and Chelsea, MA (USA). Most participants were female, older adults who were overweight or obese. Particle number concentrations (PNC, a proxy for UFP) were measured indoors and outdoors at each home continuously for six weeks. Homes received both HEPA filtration and sham filtration for three weeks each in random order. Results Median PNC under HEPA filtration was 50–85% lower compared to sham filtration in most homes, but we found no benefit in terms of reduced inflammation; associations between hsCRP, IL-6, or TNFRII in blood samples and indoor PNC were inverse and not statistically significant. Conclusions Limitations to our study design likely contributed to our findings. Limitations included carry-over effects, a population that may have been relatively unresponsive to UFP, reduction in PNC even during sham filtration that limited differences between HEPA and sham filtration, window opening by participants, and lack of fine-grained (room-specific) participant time-activity information. Our approach was similar to other recent HEPA intervention studies of particulate matter exposure and cardiovascular risk, suggesting that there is a need for better study designs.
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Traffic-related ultrafine particles (UFP; <100 nm diameter) are ubiquitous in urban air. While studies have shown that UFP are toxic, epidemiological evidence of health effects, which is needed to inform risk assessment at the population scale, is limited due to challenges of accurately estimating UFP exposures. Epidemiologic studies often use empirical models to estimate UFP exposures; however, the monitoring strategies upon which the models are based have varied between studies. Our study compares particle number concentrations (PNC; a proxy for UFP) measured by three different monitoring approaches (central-site, short-term residential-site, and mobile on-road monitoring) in two study areas in metropolitan Boston (MA, USA). Our objectives were to quantify ambient PNC differences between the three monitoring platforms, compare the temporal patterns and the spatial heterogeneity of PNC between the monitoring platforms, and identify factors that affect correlations across the platforms. We collected >12,000 h of measurements at the central sites, 1000 h of measurements at each of 20 residential sites in the two study areas, and >120 h of mobile measurements over the course of ∼1 year in each study area. Our results show differences between the monitoring strategies: mean 1 min PNC on-roads were higher (64,000 and 32,000 particles/cm³ in Boston and Chelsea, respectively) compared to central-site measurements (23,000 and 19,000 particles/cm³) and both were higher than at residences (14,000 and 15,000 particles/cm³). Temporal correlations and spatial heterogeneity also differed between the platforms. Temporal correlations were generally highest between central and residential sites, and lowest between central-site and on-road measurements. We observed the greatest spatial heterogeneity across monitoring platforms during the morning rush hours (06:00-09:00) and the lowest during the overnight hours (18:00-06:00). Longer averaging times (days and hours vs. minutes) increased temporal correlations (Pearson correlations were 0.69 and 0.60 vs. 0.39 in Boston; 0.71 and 0.61 vs. 0.45 in Chelsea) and reduced spatial heterogeneity (coefficients of divergence were 0.24 and 0.29 vs. 0.33 in Boston; 0.20 and 0.27 vs. 0.31 in Chelsea). Our results suggest that combining stationary and mobile monitoring may lead to improved characterization of UFP in urban areas.
Article
Long-term Ultrafine Particle (UFP) exposure estimates at a fine spatial scale are needed for epidemiological studies. Land Use Regression (LUR) models were developed and evaluated for six European areas based on repeated 30-minute monitoring following standardized protocols. In each area; Basel (Switzerland), Heraklion (Greece), Amsterdam, Maastricht and Utrecht (‘the Netherlands’), Norwich (United Kingdom), Sabadell (Spain), and Turin (Italy), 160-240 sites were monitored to develop LUR models by supervised stepwise selection of GIS predictors. For each area and all areas combined, ten models were developed in stratified random selections of 90% of sites. UFP prediction robustness was evaluated with the Intraclass Correlation Coefficient (ICC) at 31-50 external sites per area. Models from Basel and the Netherlands were validated against repeated 24-hour outdoor measurements. Structure and Model R2 of local models were similar within, but varied between areas (e.g. 38-43% Turin; 25-31% Sabadell). Robustness of predictions within areas was high (ICC 0.73-0.98). External validation R2 was 53% in Basel and 50% in the Netherlands. Combined area models were robust (ICC 0.93-1.00) and explained UFP variation almost equally well as local models. In conclusion, robust UFP LUR models could be developed on short-term monitoring, explaining around 50% of spatial variance in longer-term measurements.
Article
Mobile and short-term monitoring campaigns are increasingly used to develop land use regression (LUR) models for ultrafine particles (UFP) and black carbon (BC). It is not yet established whether LUR models based on mobile or short-term stationary measurements result in comparable models and concentration predictions. The goal of this paper is to compare LUR models based on stationary (30 minutes) and mobile UFP and BC measurements from a single campaign. An electric car collected both repeated stationary and mobile measurements in Amsterdam and Rotterdam, The Netherlands. A total of 2,964 road segments and 161 stationary sites were sampled over two seasons. Our main comparison was based on predicted concentrations of the mobile and stationary monitoring LUR models at 12,682 residential addresses in Amsterdam. Predictor variables in the mobile and stationary LUR model were comparable resulting in highly correlated predictions at external residential addresses (R2 of 0.89 for UFP and 0.88 for BC). Mobile model predictions were on average 1.41 and 1.91 times higher than stationary model predictions for UFP and BC respectively. LUR models based upon mobile and stationary monitoring predicted highly correlated UFP and BC concentration surfaces, but predicted concentrations based on mobile measurements were systematically higher.
Article
We measured particle size distributions and spatial patterns of particle number (PN) and particle surface area concentrations downwind from the Los Angeles International Airport (LAX) where large increases (over local background) in PN concentrations routinely extended 18 km downwind. These elevations were mostly comprised of ultrafine particles smaller than 40 nm. For a given downwind distance, the greatest increases in PN concentrations, along with the smallest mean sizes, were detected at locations under the landing jet trajectories. The smaller size of particles in the impacted area, as compared to the ambient urban aerosol, increased calculated lung deposition fractions to 0.7-0.8 from 0.5-0.7. A diffusion charging instrument (DiSCMini), that simulates alveolar lung deposition, measured a fivefold increase in alveolar-lung deposited surface area concentrations 2-3 km downwind from the airport (over local background), decreasing steadily to a twofold increase 18 km downwind. These ratios (elevated lung-deposited surface area over background) were lower than the corresponding ratios for elevated PN concentrations, which decreased from tenfold to twofold over the same distance, but the spatial patterns of elevated concentrations were similar. It appears that PN concentration can serve as a nonlinear proxy for lung deposited surface area downwind of major airports.