Embed Size (px)
Citation preview
Atmospheric Environment 38 (2004) 1547–1558
ARTICLE IN PRESS
AE International – North America
*Correspond
E-mail addr
1352-2310/$ - se
doi:10.1016/j.at
Temporal, spatial and meteorological variations in hourlyPM2.5 concentration extremes in New York City
Arthur T. DeGaetano*, Owen M. Doherty
Northeast Regional Climate Center, Department of Earth and Atmospheric Science, Cornell University, 1119 Bradfield Hall,
Ithaca, NY 14853, USA
Received 10 August 2003; accepted 24 December 2003
Abstract
Variations in the extreme percentiles of empirical hourly PM2.5 concentration distributions from a unique high-
density network of 20 stations within New York City are statistically analyzed. Significant diurnal, seasonal and day-of-
week variations are noted, with the highest concentrations typically found between 7:00 and 9:00 a.m., during summer,
and on weekdays. The lowest concentrations are generally found during early morning hours (4:00–6:00 a.m.), in winter
and on weekends. The amplitudes of these seasonal and diurnal cycles vary with percentile, with less pronounced cycles
for the lowest and in some cases highest percentiles. The diurnal and day-of-week patterns suggest that although
anthropogenic factors may be primarily responsible for the observed diurnal cycle, meteorological conditions also have
some influence.
There is little spatial variation in concentration across the city. Highly significant between-station correlations are
obtained for all seasons. However, lower correlation is found in winter. Meteorologically, the highest PM2.5
concentrations occur with moderate southwesterly winds and high temperatures and humidity during summer. These
conditions are related to the westward expansion of the Bermuda high-pressure system. Calm winds are conducive to
the highest winter particulate concentrations. Relatively strong northerly winds are typically associated with the lowest
PM2.5 concentrations. It appears that regional-scale processes dominate day-to-day changes in particulate
concentrations across the city.
r 2004 Elsevier Ltd. All rights reserved.
Keywords: PM2.5 particulate matter; Diurnal variations; Meteorology; New York City; Spatial correlation
1. Introduction
Recent studies have shown that high levels of atmo-
spheric aerosol particles, particularly those with dia-
meters of less than 2.5mm (PM2.5), have significant
health effects (e.g. Pope et al., 2002; Samet et al., 2000;
Schwartz et al., 1996). In addition to these human health
impacts, trace elements associated with PM2.5 can be
deposited on soils and in coastal waters, with potentially
adverse consequences for ecosystem health (Gao et al.,
2002). Kim et al. (1999) show that these trace elements
may be transported substantial distances over the open
ing author. Fax: +1-607-255-2106.
ess: [email protected] (A.T. DeGaetano).
e front matter r 2004 Elsevier Ltd. All rights reserve
mosenv.2003.12.020
ocean. High PM2.5 levels have been shown to reduce
visibility (Shendriker and Steinmetz, 2003) which may
affect transportation safety and aesthetics.
Elevated atmospheric concentrations of fine particu-
lates can be associated with both local sources of
emission and regional transport. Diesel engine combus-
tion accounts for a significant portion of urban
PM2.5 loads (Fraser et al., 2003) . Other anthropogenic
sources, like smelting, as well as natural phenomena
such as wildfires also emit particulate matter in this
size range. Atmospheric chemical reactions can also
contribute to total PM2.5 mass. Parkhurst et al.
(1999) show correlations as high as 0.74 between daily
average PM2.5 concentrations and 1-h maximum ozone
levels.
d.
ARTICLE IN PRESSA.T. DeGaetano, O.M. Doherty / Atmospheric Environment 38 (2004) 1547–15581548
Spatial and temporal variations in PM2.5 concentra-
tion can be influenced by a variety of anthropogenic and
meteorological factors. Laakso et al. (2003), found that
particle mass had distinct seasonal and diurnal cycles.
Hien et al. (2002) found 24-h concentration minima
during the June–August monsoon season in Vietnam,
and a maximum of >300mgm�3 during winter. The
highest concentration events were observed in conjunc-
tion with atmospheric temperature inversions. They also
found a distinct diurnal concentration cycle during
radiational inversions, with the highest PM2.5 levels at
night. Bogo et al. (2003) illustrated the influence of both
traffic volume and wind speed on the PM2.5 climatology
of Buenos Aires.
Since meteorological conditions can influence the
formation and transport of PM2.5, the efficacy of
regulatory actions taken to improve air quality cannot
be adequately established without reference to the state
of the atmosphere. For instance, changes in air quality
may be an artifact of more beneficial or unfavorable
meteorological conditions, rather than changes in air
quality regulations or human activity. Likewise, meteor-
ological conditions can confound analyses examining
the impacts of high particulate concentrations. Samet
et al. (2000) introduced a smoothing function to account
for the effects of temperature and dew point on
mortality. However, apparently they did not specifically
consider the relationship between weather conditions
and particulate concentrations. Jung et al. (2002) found
that high PM2.5 concentrations were often associated
with high daily temperatures. These meteorological
conditions, themselves, may be related to increased
mortality (Kalkstein and Greene, 1997). Vukovich and
Sherwell (2002) found dew point to be a driver of 7-year
PM2.5 trends in Washington, DC.
Jung et al. (2002) argue that atmospheric transport is
a component of high PM2.5 observations in Ohio.
Pathak et al. (2003) demonstrated the role of long-range
atmospheric transport on ionic PM2.5 species in Hong
Kong. Bari et al. (2003) examined the behavior of PM2.5
observations at two sites in New York City and found
that PM2.5 mass has a significant regional component,
with little influence from very local sources.
In this study, a dense network of over 20 hourly PM2.5
sampling sites within New York City is evaluated.
Temporal, spatial and meteorological variations in the
data are assessed. Likewise, the behavior of the city data
is compared with two sites in highly suburbanized areas
adjacent to the city and a third remote non-urban
location. Unlike previous studies that are restricted to
mean concentrations, variations in extreme concentra-
tion percentiles are analyzed here as well. Documenta-
tion of these variations is a necessary prerequisite for
quantifying the contribution of atmospheric transport to
ambient concentrations in this highly urban environ-
ment and also establishing causes for the spatial
variability of PM2.5 concentration and temporal varia-
tions on different time scales.
2. Data
Hourly PM2.5 observations were obtained from the
New York Department of Environmental Conservation.
This state agency operates a network of 20 real-time
tapered element oscillating microbalance (TEOM)
monitors within New York City and will provide these
data to researchers. Bari et al. (2003) discuss the
characteristics of these sampling devices. Unfortunately,
a detailed description of the environment around these
sensors is not readily available. Nonetheless, Table 1
summarizes those site characteristics that were described
in US EPA AirData {http://www.epa.gov/air/data/
index.htmlc. Similar monitors are also operated in nine
other, primarily urban, locations throughout the state.
Data from two of these sites in adjacent suburban
locations and a third site located at the base of
Whiteface Mountain, approximately 550 km north of
New York City, were also analyzed. Of the nine sites
outside of New York City, Whiteface best exemplified a
non-urban environment. To our knowledge, such a
spatially dense PM2.5 observation network is unique.
Other urban networks documented on the AirData
website contain, at best, only about half the number of
sites (per area) as are available in New York.
Since the network was deployed incrementally over
the last three years, the period of record varies from
monitor to monitor (Table 1). Several new stations were
added in 2002. However, their deployment was not
uniform, with the majority of new stations being located
in lower (southern) Manhattan. In subsequent analyses,
between-station comparisons were limited to common
hours, while analyses on individual stations utilized all
available data. In general, the monitoring sites are
rooftop locations and hence observations reflect differ-
ent elevations above street level and a mix of residential
and commercial neighborhoods.
Hourly meteorological data were obtained for La-
Guardia Airport (LGA in Fig. 3). Although weather
data are available from other stations near the city, the
LaGuardia observations were assumed to represent the
broad meteorological conditions across the metropoli-
tan area. No significant differences were noted in results
based on data from Newark, New Jersey or JFK
Airport.
3. Methodology
Monte-Carlo resampling analyses were the primary
technique used to assess the statistical significance of the
temporal and meteorological concentration variations.
ARTICLE IN PRESS
Table 1
Site characteristics and periods of record for analyzed stations
Site Borougha Probe elev. (m) Site character Traffic
volumebStart date End date Fig. 3a
concentration
Comment
P.S. 199 Queens 10 Residential 10 4/00 12/02 52
P.S. 219 Queens 9 Residential 10 3/01 12/02 51
Maspeth Queens 6 Commercial 10 5/01 12/02 49
I.S. 293 Brooklyn 13 Commercial 10 10/01 12/02 52
P.S. 274 Brooklyn Unknown Residential 5 2/00 12/02 46
Fresh Kills Staten Island 10 Industrial 5 1/00 12/02 47 Tower
P.S. 44 Staten Island 14 Commercial 10 2/01 12/02 54
P.S. 154 Bronx 15 Commercial 10 3/00 12/02 49
P.S. 52 Bronx 11 Commercial 5 1/00 12/02 47 Roof
P.S. 74 Bronx 15 Commercial 5 3/00 12/02 51
I.S. 143 Manhattan U 15 Residential 10 4/00 12/02 51
Manhattanville Manhattan U 13 Commercial 10 11/00 12/02 50 Roof
P.S. 64 Manhattan M 13 Residential 10 2/00 12/02 48
Mabel Dean Manhattan L 38 Residential n/a 1/00 6/01 50 Roof
Albany St Manhattan L 8 Residential 10 11/01 12/02 58
Park Row Manhattan L n/a Residential 10 10/01 12/02 n/a
34th Street Manhattan L 3 Commercial n/a 4/02 12/02 54
MBCC Manhattan L 3 Commercial 20 9/01 12/02 45
Battery Park Manhattan L 3 Commercial 1 10/01 12/01 52
Pace Univ. Manhattan L n/a Commercial 20 9/01 12/02 78
White Plains Westchester 2 Residential 50 4/01 12/02 47
Eisenhower Nassau 6 Commercial n/a 1/02 12/02 49 Roof
White Face Essex 5 Forest n/a 1/00 12/02 n/a Roof
aCounties are given for stations outside New York. U, M. and L signify Upper (northern), Middle, and Lower Manhattan.bTraffic volume has units of vehicles per day.
A.T. DeGaetano, O.M. Doherty / Atmospheric Environment 38 (2004) 1547–1558 1549
The observed diurnal, seasonal, and day-of-the-week
variations in concentration were compared to those
obtained from randomized PM2.5 concentration series.
Random series were constructed by arbitrary assigning
data values to a different hour, month or weekday
depending on the analysis. This allowed 1000 resampled
concentration time series to be constructed. Given the
resampling for the seasonal analysis, the 8:00 a.m. data
value on a ‘‘June’’ day may actually correspond to an
8:00 a.m. December observation. Percentiles of the
actual (non-randomized) PM2.5 time series were then
compared to those based on the resampled distributions.
Observed values that either exceeded the 97.5th percen-
tile of the random distribution or failed to exceed the
2.5th percentile were considered statistically significant
variations.
The significance of the meteorological variations was
assessed in a similar fashion. Randomized hourly
meteorological time series were constructed for 2000–
2002 by arbitrary selecting data, in monthly blocks,
from the period 1976–2002. Thus in the randomized
series, meteorological data for July 1978 may have been
paired with PM2.5 concentration data from July 2002.
Resampling in monthly blocks preserved the hourly
and daily persistence that is typical of meteorological
data.
Resampling approaches, such as this, appear to be
novel in the air quality literature. However, their use in
climatology is common (e.g. Wilks, 1993; Janis et al.,
2004). Resampling tests are appealing, as they require no
assumptions regarding the underlying theoretical dis-
tribution of the data. Furthermore, any statistic that is
suggested as important by the physical nature of the
application can form the basis for the test, as long as it
can be computed from the available data.
Pearson correlations were used to assess between-
station correlations and within- station autocorrelations.
The day-to-day lag-1 autocorrelation for particular
hours (i.e. the correlation between yesterday’s 7:00 a.m.
observation and today’s 7:00 a.m. concentration) was
generally less than 0.05 at all stations and thus there was
no need to account for this autocorrelation in assessing
the significance of the between-station correlation. On
an hour-to-hour basis, however, the lag-1 autocorrela-
tion of the concentration anomalies was high, typically
exceeding 0.88 at all sites.
Principal components analysis was also used to assess
the pattern of spatial variability exhibited by the
concentration data. These analyses were limited to data
from 2002 to assure that an ample number of coincident
observations were available from as dense a station
network as possible. Sufficient data were available from
ARTICLE IN PRESSA.T. DeGaetano, O.M. Doherty / Atmospheric Environment 38 (2004) 1547–15581550
16 of the 20 New York City stations in 2002, with most
combinations of seasons and hours (e.g. morning rush
hour in summer) having at least 100 individual
observations. Although the spatial concentration pat-
terns were interpreted based on rotated components, the
results of the principal component analyses were
resilient to the use of rotation and the use of variance–
covariance or correlation matrices.
4. Results
4.1. Temporal PM2.5 concentration patterns
Hourly PM2.5 observations across New York City
exhibit distinct seasonal, diurnal and day-of-week
variations. For individual months, the 95th percentile
hourly concentrations show a marked summer (June–
August) peak , with minimum PM2.5 concentrations
occurring during the winter (December–March) months
(Fig. 1a). On average, the peak 95th percentile values are
near the 40 mgm�3 threshold the EPA considers
unhealthy for sensitive individuals. The 95th percentile
at some individual stations exceeds the 65.4mgm�3
unhealthy (to all) criterion. This pattern is evident at
each sampling site, with the exception of those with a
limited data record. Based on Monte-Carlo resampling,
the enhanced summer and diminished winter PM2.5
concentrations are significant at the a ¼ 0:05 level. The
99th and median concentrations display a similar season
pattern. However, a seasonal pattern is absent when the
lowest hourly concentration is considered during each
month. There is considerable station-to-station varia-
bility in the 5th percentile hourly PM2.5 concentrations
for each month and only sporadic excursions of the city-
Jan MarFeb Apr May Jul Sep NovJun Aug DecOct
18
28
38
48
58
68
78
88
PM
2.5
Con
cent
ratio
n (u
g/m
3 )
Month
(a)
Fig. 1. Seasonal (a) and diurnal (b) patterns of PM2.5 concentration
stations with dashed lines those stations having less than 50% of th
pooled (all station) percentile. The resampled 95% confidence interval
(a) and the 5th percentile is shown in (b).
average value outside the resampled 95% confidence
interval. These seasonal relationships are also reflected
in data from the three sites outside of New York City.
Likewise, the seasonal cycles for individual hours and
days of the week are analogous to that shown
for concentrations based on all hours and days (e.g.
Fig. 1a).
A pronounced diurnal cycle in PM2.5 concentrations
is also evident (Fig. 1b). The 1st and 5th percentile
concentrations for individual hours display a bi-modal
pattern with significant peaks between 7:00 and
9:00 a.m. (morning rush hours) and 5:00 and 11:00 pm.
Lower than expected PM2.5 concentrations are also
evident overnight (1:00–5:00 a.m.) and around noon
(12:00–1:00 p.m.). The secondary afternoon maximum is
not apparent when the highest hourly concentrations are
considered. Only a pronounced 7:00–10:00 a.m. peak
exists for the hourly 95th percentile concentrations.
Likewise, the noontime minimum is absent, while the
overnight minimum is retained. When the 99th percen-
tile concentration is considered, the amplitude of the
diurnal cycle decreases considerably. However, a sig-
nificant minimum still exists overnight and is followed
by significantly higher concentrations from 9:00–
10:00 a.m.
Away from the city, these diurnal PM2.5 patterns are
also evident (not shown). Even at Whiteface Mountain,
the 95th percentile PM2.5 concentration is 10–20%
higher at 8:00 a.m. compared to overnight or midday
hours. Given the remote location of White Face, this
could suggest that enhanced anthropogenic activity is
not solely responsible for the morning rush hour PM2.5
peak. It could be argued that the rush hour peak occurs
near the time that atmospheric stability is normally at a
maximum. As the diurnal pattern of atmospheric
2
3
4
5
6
7
8
Hour (local time)
1 3 5 7 9 11 13 17 21 231915
PM
2.5
Con
cent
ratio
n (u
g/m
3 )
(b)
s at New York City stations. Gray lines represent individual
e possible observations available. The heavy black line is the
is shown by the thin black lines. The 95th percentile is shown in
ARTICLE IN PRESSA.T. DeGaetano, O.M. Doherty / Atmospheric Environment 38 (2004) 1547–1558 1551
stability is influenced by insolation, the time of
occurrence of the morning peak should vary through
the year with a later maximum in winter and an earlier
summer peak. This is not the case as there is no
systematic variation in the timing of the morning
concentration maximum through the year. Likewise,
one would expect stability to increase through the
nighttime hours producing a gradual increase in
particulate concentration, rather than the abrupt morn-
ing increase that is observed.
Alternatively, an anthropogenic morning peak should
be less pronounced during the weekend. There is some
indication of this in Fig. 2a, where the increase in PM2.5
concentration between 5:00 a.m. and 9:00 a.m. is 30% on
Wednesday compared to only 10% on Sunday. Collec-
tively, these results suggest that human activity is
primarily responsible for the morning particulate peak.
However, the influence of meteorological conditions,
appear to exaggerate the morning peak, either through
enhanced morning stability or conversely increased
mixing (decreased stability) during the late morning and
afternoon hours. This assertion is supported by a
noticeable change in the diurnal concentration cycles
during July and to some extent June. During these
months the morning rush hour peak is either absent (July)
or of considerably less amplitude. In both cases a broad
late afternoon concentration maximum is also apparent.
Regardless of hour or season, PM2.5 concentrations
across the city are significantly lower on Saturdays and
Sundays and uniformly high on the other days of the week.
The pattern of median concentrations for specific days
of the week is shown as a representative case in Fig. 2b.
Here the median concentrations rise to a significant peak
on Thursday and Friday before falling to a Sunday
minimum. At stations outside of New York City, PM2.5
concentrations also tend to be the lowest during the
weekends. The strength of this pattern is similar to that
observed at the city stations in neighboring counties, but
1 3 5 7 9 11 13 17 21 231915Hour (local time)
50
45
40
35
30
25
PM
2.5
Con
cent
ratio
n (u
g/m
)
(a) (
Fig. 2. Diurnal pattern of PM2.5 concentration over all stations for (a)
the week pattern of median PM2.5 concentration at New York City s
barely discernable at the remote White Face Mountain
site.
Given these temporal concentration patterns, subse-
quent analyses were limited to weekdays. The data were
further segregated into summer (June–August) and
winter (January–March) seasons. Within each season,
concentrations were further divided into overnight
(1:00–5:00 a.m.), morning rush hour (7:00–9:00 a.m.)
and afternoon–evening (2:00–11:00 p.m.) time cate-
gories. Grouping the data into these six categories, was
intended to filter the strong seasonal, diurnal and, to a
lesser degree, day-of-the-week effects from the subse-
quent spatial and meteorological analyses.
4.2. Spatial PM2.5 patterns
Fig. 3 shows the spatial patterns of PM2.5 concentra-
tions across the city. Regardless of season or time of
day, both the 95th and 5th percentile concentrations
tend to be highest in lower Manhattan. A distinct
secondary maximum over Upper Manhattan, extending
into the Bronx, is evident in the majority of cases
(Fig. 3a). The lone exception to this general pattern
occurs for the 5th percentile concentrations during
summer (Fig. 3b). Here, the maximum shifts to the east
with the highest values found over Queens and a secondary
maximum over Staten Island. A local concentration
minimum replaces the maximum over lower Manhattan.
The patterns of afternoon PM2.5 concentrations (not
shown) typically follow those based on the morning data.
Collectively there does not appear to be a preferred
location for minimum particulate concentrations.
Table 2 compares the concentrations measured at
stations within New York City to those from adjacent
counties and the remote White Face Mountain monitor-
ing site. Concentrations at White Plains (White Face)
are generally 75% (50%) of those observed at the city
sites, except for the 95th percentile values during
Mon Tues Wed Thur Fri Sat Sun8
9
10
11
12
13
14
15
16
17
18
Day of week
PM
2.5
Con
cent
ratio
n (u
g/m
)
b)
weekdays (thin black) and weekends (thick gray), and (b) day of
tations with lines as in Fig. 1.
ARTICLE IN PRESS
(a) (b)
Staten Island
Manhattan
Bronx
LGA
Queens
Brooklyn
White
EisenhowerPark
Plains
Fig. 3. Spatial pattern of 7:00 a.m. PM2.5 concentrations mgm�3 representing the (a) 95th percentile and (b) 5th percentile during
summer. Areas of relative maximum concentrations are highlighted by a contour.
Table 2
PM2.5 concentrations for stations outside of New York Citya expressed as a percentage of the average concentration at the 20 city
monitoring sites
95th percentile 5th percentile
Season Hours E. Park W. Plains W. Face E. Park W. Plains W. Face
Winter 7–9 a.m. 105 72 39 84 81 45
Winter 1–5 a.m. 114 83 54 68 89 52
Winter 2–11 p.m. 86 70 44 83 75 48
Summer 7–9 a.m. 100 95 66 64 67 49
Summer 1–5 a.m. 121 95 75 62 86 59
Summer 2–11 p.m. 112 93 58 63 73 53
aE. Park: Eisenhower Park, W. Plains: White Plains; W.Face: White Face Mountain.
A.T. DeGaetano, O.M. Doherty / Atmospheric Environment 38 (2004) 1547–15581552
summer, which average about 95% (66%) of the city
values. At Eisenhower Park, 95th percentile concentra-
tions typically exceed the average city value, with values
similar to those found at the individual city sites with the
highest concentrations. The 5th percentile Eisenhower
Park values are similar to those found at White Plains.
Overall the between-station correlation of PM2.5
observations is quite high. Fig. 4 shows the correlation
between 7:00 a.m PM2.5 concentration anomalies at P.S.
199 in Queens and those at the other New York City
area stations, based on 2002 data. The Queens site was
chosen as a base for its proximity to the geographic
center of the city. With the exception of Manhattan
Borough Community College, in Lower Manhattan, the
between-station correlations are at least 0.85. None-
theless, the correlations at all stations are significant at
the a ¼ 0:01 level. Although the correlation between the
Queens site and White Face Mountain is considerably
lower ðr ¼ 0:16Þ; this value is still higher than can be
expected by chance at the a = 0.01 level, given a sample
size of 305.
During the overnight concentration minimum (5 a.m.
observations), the correlation between P.S. 199 and the
other stations is similar to that found during the
morning peak, with the exception of White Plains (not
shown). Here the correlation is considerably lower at
5:00 a.m. reaching only 0.28. Overall, these analyses
suggest that variations in particulate concentrations
across the city result from primarily city-wide anthro-
pogenic and meteorological features. However, local
scale effects appear to play some role in the southern
part of Manhattan. A high density of tall buildings
dominates the landscape and presumably influence the
accumulation and dispersion of particulates. The avail-
ARTICLE IN PRESS
P.S. 199
Fig. 4. Correlation, expressed as percentages, between the
7:00 a.m. PM2.5 concentration anomalies at PS. 199 and those
at the other New York area stations. Correlations greater than
95% are highlighted by the contour.
A.T. DeGaetano, O.M. Doherty / Atmospheric Environment 38 (2004) 1547–1558 1553
ability of hourly PM2.5 observations at stations in
southern Manhattan was limited prior to 2002, therefore
it was not possible to assess whether the lower
correlations in the area may be a consequence of local
sources following the collapse of the World Trade
Center. However, some of the highest hourly PM2.5
values are noted at lower Manhattan stations in the
period from October–December 2001.
The magnitude of the correlation between PM2.5
concentrations at different sites across the city is
influenced strongly by season, but only slightly by wind
direction (Fig. 5). Between-station correlation is con-
siderably higher during the summer months than in
winter. However, with the exception of White Plains,
even the winter correlations are higher than would be
expected by chance ða ¼ 0:01Þ: Although the top and
bottom panels in Fig. 5 reflect both different seasons and
times of day, time of day has little effect on the pattern
and magnitude of between-station correlation. The
influence of wind direction is relatively minor. During
summer there is a tendency for lower correlations under
northeasterly winds, whereas in winter, the lowest
correlations are found when hours with northwesterly
winds are considered. It is hard to argue that the spatial
pattern of correlation is influenced by wind direction, as
in each case the area of highest correlation is typically
centered on P.S. 199.
These correlation results are further corroborated by
a principal component analysis. Standard component
truncation rules indicated the retention of a single
component characterized by uniform loading on each of
the New York City stations. Typically, the first principal
component explained over 85% of the variation in
PM2.5 across (and at stations adjacent to) the city. This
explained variance tended to be higher in summer (89–
95%) than in winter (85–93%), with the range repre-
senting different time periods during the day. The
second component explained an additional 2–6% of
the spatial variability in concentration. In general, this
component represented a contrast in concentration
between stations in southern parts of the city (Lower
Manhattan, Brooklyn, Queens, and P.S. 44 in Staten
Island) and those in Upper Manhattan and the Bronx.
4.3. Meteorological influences
During the morning rush hour period (7:00–9:00 a.m.)
the highest summer PM 2.5 concentrations are char-
acterized by either calm or southwesterly winds. Over
40% of the >95 percentile PM2.5 concentrations occur
with wind directions between 210� and 260� (Fig. 6a).
An additional 14.5% of these events occur in conjunc-
tion with calm or variable wind directions. Other than
for calm winds, there is not a statistically significant
tendency for high PM2.5 concentrations to be associated
with particular wind speeds. However, the majority of
high PM2.5 events occur with fairly high, 5.4ms�1
(12miles h�1) wind speeds (Fig. 6b). On the other hand,
high PM2.5 concentrations are most pronounced when
early morning summer temperatures are high (Fig. 6c).
All recorded hourly exceedences of the 95th percentile
occurred at a temperature >21�C (70�F). Despite less
than 20% of all morning hours (regardless of PM2.5
concentration) having temperaturesX29�C (85�F), over
half of the 95th percentile concentration exceedences
were associated with such temperatures (Fig. 6c).
Similarly, the majority of hours with high particulate
concentrations tend to occur in association with high
humidity levels (not shown).
Low summer PM2.5 concentrations are also associated
with a relatively narrow set of meteorological condi-
tions. These typically include winds from a northeasterly
or northerly direction (Fig. 6a), cool (o16�C) tempera-
tures (Fig. 6c), and low humidity (dew points o13�C).
In addition, the percentage of low concentration hours
occurring in conjunction with wind speeds greater than
9ms�1 (20miles h�1) was more than would be expected
by chance (Fig. 6b).
During winter, southwesterly winds were responsible
for only a modest number of high PM2.5 events (Fig. 7a).
In this season, calm and variable winds occurred with
the highest PM2.5 events more frequently than would be
expected by chance. This is reflected in the comparison
of high PM2.5 occurrence and wind speed. Almost 80%
of the high winter concentrations occur with wind
speeds less than 2.7ms�1 (Fig. 7b). Despite the
ARTICLE IN PRESS
(a) (b)
(c) (d)
Fig. 5. As in Fig. 4 but for summer morning rush hours conditional on the occurrence of (a) northeast, and (b) northwest wind
direction and for winter nighttime hours conditional on the occurrence of (c) northeast, and (d) northwest wind direction.
A.T. DeGaetano, O.M. Doherty / Atmospheric Environment 38 (2004) 1547–15581554
difference in wind direction behavior between winter
and summer, the warmest (>13�C) morning tempera-
tures (and to a lesser degree the most humid hours) still
tend to be associated with the highest concentration
events during winter (not shown).
Approximately, 70% of the low winter morning PM2.5
concentrations occurred when the wind was between
west and north (270–360�). The frequency of 300� and
320�, winds, conditional on low PM2.5 concentrations,
was higher than would be expected by chance (Fig. 7a).
Wind speed is also a factor, as a higher than expected
number of the lowest particulate concentration events is
associated with wind speeds in excess of 9ms�1 (Fig. 7b).
There is not a clear indication of an influence from
temperature or humidity in these low winter events.
Although these meteorological relations are based on
hours during the morning particulate peak, similar
meteorological effects are observed during other por-
tions of the diurnal cycle. One notable exception is the
lack of a significant PM2.5 concentration maximum
under calm winds when the overnight hours are
evaluated. During the intermediate seasons, the meteor-
ological influences represent a transition between the
conditions that characterize the winter and summer
seasons.
Meteorologically, the wind, temperature and humid-
ity values that favor high (and low) PM2.5 concentra-
tions represent a distinct weather pattern. Fig. 8 shows
composite weather maps of standardized surface pres-
sure anomalies for days on which more than one New
York City station recorded at least one hourly PM2.5
concentration that exceeded (fell below) the 95th (5th)
percentile value. During summer, high PM2.5 concentra-
tions are characterized by higher-than-normal pressure
off the southeast US coast and lower-than-expected
pressures over northeastern Canada (Fig. 8a). This is
ARTICLE IN PRESS
Var 0 20 40 60 80 100
120
140
160
180
200
220
240
260
280
300
320
340
360
0
2
4
6
8
10
12
14
16
18
20F
requ
ency
(%
)
Wind Direction (degrees)
0
20
30
40
Fre
quen
cy (
%)
45
35
25
15
10
5
4.4 10.0 15.6 21.1 26.7 32.2 37.8
Temperature (°C)
0 1.8 3.6 5.4 7.2 9.0 10.8 12.6
Wind Speed (ms-1)
5
10
15
25
35
Fre
quen
cy (
%)
20
30
40
0
(b)
(c)
(a)
Fig. 6. Frequency of summer morning rush hour PM2.5
concentrations above the 95th percentile (thick black) and
below the 5th percentile (thick gray) conditional upon the
occurrence of (a) wind direction, (b) wind speed and (c)
temperature. The frequency of each meteorological variable,
without regard for PM2.5 concentration is given by the dotted
line. The 95% resampled confidence interval is shown by the
thin solid lines. For wind direction Var=variable and 0=calm.
A.T. DeGaetano, O.M. Doherty / Atmospheric Environment 38 (2004) 1547–1558 1555
indicative of a westward displacement of the Bermuda
high. Conversely, the lowest summer PM2.5 values
typically occur with lower-than-normal pressure over
the southwestern Atlantic and a center of stronger-than-
normal pressure north of the Great Lakes (Fig. 8b). This
pressure pattern, in concert with a strong temperature
anomaly gradient across the Northeast, suggests the
presence of a frontal boundary in the proximity of New
York on these days.
During winter the highest PM2.5 concentrations tend
to occur in association with a relatively strong positive
surface pressure anomaly over the Canadian Maritime
Provinces and a negative pressure anomaly to the south
of Lake Michigan (Fig. 8c) Such a pattern produces a
broad area of southerly winds over the New York
Metropolitan Area which supports the relatively high
frequency of winds observed from the south to south-
west in Fig. 7a. In contrast, the pressure pattern
associated with the lowest winter concentrations is
reversed, showing a large positive anomaly over the
central US and a fairly lax pressure gradient over New
York (Fig. 8d).
5. Conclusions
PM2.5 concentration variations over a relatively dense
network of monitoring sites within and adjacent to New
York City are empirically related to a number of
temporal and meteorological factors. Primarily, the data
show marked diurnal and seasonal cycles with maximum
PM2.5 concentrations during the summer and morning
(7:00–9:00 a.m.) hours. Minimum PM2.5 concentrations
are noted during the early morning hours (4:00–
6:00 a.m.) and in winter. Concentration is also a
function of the day of the week, with significantly lower
concentrations observed on Saturday and Sunday. The
magnitude of the diurnal cycle varies, with a less
amplified cycle during summer months and the weekend.
This implies that the concentration peaks are likely
related to a blend of anthropogenic and meteorological
influences. Clearly, more rigorous chemical analyses are
required to ascribe sources to these concentration peaks.
The diurnal and seasonal cycles do not represent a
simple translation of the hourly PM2.5 distribution.
Rather, the diurnal cycle becomes bi-modal when the
lowest concentrations (e.g. values p the 5th percentile)
are considered, with a sharp peak during the morning
rush hour and a broad evening maximum. When
concentrations above the 99th percentile are considered,
the diurnal cycle becomes less amplified. Similarly the
seasonal cycle is less pronounced for concentrations
below the 5th and 1st percentiles.
The spatial variation of extreme PM2.5 concentrations
is not strong. In most cases, over 90% of the observed
spatial variation in PM2.5 concentration can be ex-
plained by a single principal component with approxi-
mately equal weight ascribed to each monitoring site.
Empirically, this argues that variations in PM2.5
concentration across New York City and its surround-
ing counties are primarily driven by regional processes,
rather than local sources. This is also supported by an
examination of between-station correlations conditional
upon wind direction. These show very little change in
the correlation pattern for different wind directions. A
similar conclusion was reached by Bari et al. (2003)
based on two New York sites.
ARTICLE IN PRESS
Var 0 20 40 60 80 100
120
140
160
180
200
220
240
260
280
300
320
340
360
0
2
4
6
8
10
12
14
16
18
20
Fre
quen
cy (
%)
Wind Direction (degrees)
0
5
10
15
20
25
30
35
Fre
quen
cy (
%)
0 1.8 3.6 5.4 7.2 9.0 10.8 12.6 14.4 16.2 18.0
Wind Speed (ms-1)(a) (b)
Fig. 7. As in Fig. 6, but for winter (a) wind direction and (b) wind speed.
(a) (b)
(d)(c)
Fig. 8. Composite 1200 Universal Coordinate Time surface weather maps showing standardized sea level pressure anomalies for days
on which at least one hourly PM2.5 concentration reading was (a) above the 95th percentile in summer (b) below the 5th percentile in
summer (c) above the 95th percentile in winter or (d) below the 5th percentile in winter.
A.T. DeGaetano, O.M. Doherty / Atmospheric Environment 38 (2004) 1547–15581556
ARTICLE IN PRESSA.T. DeGaetano, O.M. Doherty / Atmospheric Environment 38 (2004) 1547–1558 1557
It is also noteworthy that there are relatively few
hours in which PM2.5 concentrations fall in upper
quartile at some stations and lower quartile at more
than one other observation site. Only 10% of the
summer days had at least 1 h in which such a disparity
was observed. This occurrence was more common in
winter, in agreement with lower between-station correla-
tion values during this season. On about 40% of the
winter days two or more stations have at least one
coincident hourly observation that falls in opposite
quartiles of the empirical concentration distribution.
However, the probability of this occurrence drops to
13% when the percentile extremes are increased to the
85th and 15th. This suggests that local-scale sources of
PM2.5 may play a more dominant role in winter.
Meteorologically, high summer PM2.5 concentrations
are observed in association with the westward displace-
ment of the Bermuda high-pressure system. Thus
particulate concentrations are highest under moderately
strong southwesterly winds. Under these conditions
temperatures typically exceed 29�C and humidity levels
are high. Such conditions are also typical of high ozone
events (e.g. Cox and Chu, 1996) suggesting that atmo-
spheric chemistry, rather than transport, may be
responsible for these high summer concentrations.
High winter PM2.5 concentrations are associated with
similar conditions (a southerly wind component and
relatively warm temperatures), however, during this
season, a significant portion of the highest PM2.5
concentrations occur in conjunction with calm or light,
variable winds. Conversely, the lowest particulate
concentrations tend to occur with relatively strong
northerly component winds, and in summer, relatively
cool temperatures.
Acknowledgements
This material is based upon work supported by
the Cooperative State Research, Education, and Exten-
sion Service, US Department of Agriculture, under
Agreement No. 2003-06232. Partial support was also
provided through NOAA Cooperative Agreements
NA17RJ1222. We are grateful to Russ Twaddell of the
New York State Department of Environmental Con-
servation for providing us with the PM2.5 concentration
data and providing insight into the data quality and
monitoring techniques.
References
Bari, A., Ferraro, V., Wilson, L.R., Luttinger, D., Husain, L.,
2003. Measurements of gaseous HONO, HNO3, SO2, HCL,
NH3, particulate sulfate and PM2.5 in New York, NY.
Atmospheric Environment 37, 2825–2835.
Bogo, H., Otero, M., Castro, P., Ozafran, M.J., Kreiner, A.,
Calvo, E.J., Negri, R.M., 2003. Study of atmospheric
particulate matter in Buenos Ares city. Atmospheric
Environment 37, 1135–1147.
Cox, W.M., Chu, S-H., 1996. Assessment of interannual ozone
variation in urban areas from a climatological perspective.
Atmospheric Environment 30, 2615–2625.
Fraser, M.P., Yue, Z.W., Buzcu, B., 2003. Source apportion-
ment of fine particulate matter in Houston, TX, using
organic molecular markers. Atmospheric Environment 37,
2117–2123.
Gao, Y., Nelson, E.D., Field, M.P., Ding, Q., Li, H., Sherrell,
R.M., Gigliotti, C.L., Van Ry, D.A., Glenn, T.R.,
Eisenreich, S.J., 2002. Characterization of atmospheric
trace elements on PM2.5 particulate matter over the New
York–New Jersey harbor estuary. Atmospheric Environ-
ment 36, 1077–1086.
Hien, P.D., Bac, V.T., Tham, H.C., Nhan, D.D., Vinh, L.D.,
2002. Influence of meteorological conditions on PM2.5 and
PM2.5–10 concentrations during the monsoon season in
Hanoi, Vietnam. Atmospheric Environment 36, 3473–3484.
Janis, M.J., Hubbard, K.G., Redmond, K.T., 2004. Station
density strategy for monitoring long-term climatic change
in the contiguous United States. Journal of Climate 17,
151–162.
Jung, I., Kumar, S., Kuruvilla, J., Crist, K., 2002. Impact of
meteorology on the fine particulate matter distribution in
central and southeastern Ohio. Preprints American Meteor-
ological Society 12th Joint Conference on Applications of
Air Pollution Meteorology with the Air and Waste
Management Association Norfolk, VA. American Meteor-
ological Society, Boston, MA.
Kalkstein, L.S., Greene, J.S., 1997. An evaluation of climate/
mortality relationships in large US cities and the possible
impacts of climate change. Environmental Health Perspec-
tives 105, 84–93.
Kim, G., Alleman, L., Church, T., 1999. Atmospheric
depositional fluxes of trace elements 210Pb and 7Be to the
Sargasso Sea. Global Biogeochemical Cycles 13, 1183–1192.
Laakso, L., Hussein, T., Aarnio, P., Komppula, M., Hiltunen,
V., Viisanen, Y., Kulmala, M., 2003. Diurnal and annual
characteristics of particle mass and number concentrations
in urban, rural and Arctic environments in Finland.
Atmospheric Environment 37, 2629–2641.
Parkhurst, W.J., Tanner, R.L., Weatherford, F.P., Valente,
R.J., Meagher, J.F., 1999. Historic PM2.5/PM10 concentra-
tions in the southeastern United States—Potential implica-
tions of the revised particulate matter standard. Journal
of the Air and Waste Management Association 49,
1060–1067.
Pathak, R.K., Yao, X., Lau, A.K.H., Chan, C.K., 2003. Acidity
and concentrations of ionic species of PM2.5 in Hong Kong.
Atmospheric Environment 37, 1113–1124.
Pope, C.A., Burnett, R., Thun, M.J., Calle, E.E., Krewskik, D.,
Ito, K., Thurston, G.D., 2002. Lung cancer, cardiopulmon-
ary mortality, and long term exposure to fine particulate air
pollution. Journal of the American Medical Association
287, 1132–1141.
Samet, J.M., Dominici, F., Curriero, F.C., Coursac, I., Zeger,
S.L., 2000. Fine particulate air pollution and mortality in 20
ARTICLE IN PRESSA.T. DeGaetano, O.M. Doherty / Atmospheric Environment 38 (2004) 1547–15581558
US cities, 1987–1994. The New England Journal of
Medicine 343, 1742–1749.
Schwartz, J., Dockery, D.W., Neas, L.M., 1996. Is daily
mortality associated specifically with fine particles? Journal
of the Air and Waste Management Association 46, 927–939.
Shendriker, A.D., Steinmetz, W.K., 2003. Integrating nephol-
ometer measurements for air-borne fine particulate matter
(PM2.5) mass concentration. Atmospheric Environment 37,
1383–1392.
Vukovich, F.M., Sherwell, J., 2002. Comparison of fine
particles and the relationship between particle variations
and meteorology at an urban site and a remote site in the
eastern United States. Journal of the Air and Waste
Management Association 51, 573–854.
Wilks, D.S., 1993. Comparison of three-parameter probability
distributions for representing annual extreme and partial
duration precipitation series. Water Resources Research 29,
3543–3549.
