Air Quality in New York City: A GIS
Analysis of Vehicular Traffic
Michael Horowitz
CRP 408
Final Project
May 5, 2008
Table of Contents
Summary………………………………….. 1
Introduction………………………………. 2
Data and Methodology………………........ 3
Analysis……………………………………. 6
Conclusion………………………………… 8
Appendix A................................................... 11
Appendix B………………………………... 15
References and Data Sources……………. 23
Summary
Modern urban environments have been attempting to manage the environmental
problems created by decades of anthropogenic activities. Today, air quality in New York
City is so poor that it is a direct threat to human and ecosystem health. This study utilizes
GIS to examine correlations between vehicular traffic and concentrations of harmful
gases and also provides mass estimates for nitrogen emitted due to vehicle exhaust.
1
Introduction
Air pollution is one of the major causes of poor health in urban areas today.
Anthropogenic activity since the industrial revolution has been degrading air quality in all
populated places, but most notably in heavily developed metropolitan areas. Fossil fuel
burning in vehicles, power plants, and other industrial processes generate significant
amounts of air pollution. The four major boroughs of New York City have the highest
vehicular traffic densities out of all counties in the United States (Lipfert et al., 2006).
New York City also ranks among the lowest for annual air quality out of all the major
metropolitan cities in the United States (American Lung Association, 2004).
Furthermore, automobile traffic throughout the United States increased by nearly 10%
between 1995 and 2001 (US DOT, 2006). If these trends persist, air quality in New York
will continue to decline.
Significant amounts of aerosols and particulate matter are produced by fossil fuel
combustion. Increased levels of aerosols in the atmosphere have been linked to heart
disease, lung cancer, and other lung conditions (Ross et al., 2007). Particulate matter
smaller than 2.5 microns in size (PM-2.5) pass into the human respiratory system much
more easily and are, thus, particularly harmful. Children and the elderly are especially
sensitive to poor air quality, developing a myriad of conditions as a result. In turn, this
creates an unnecessary burden on previously-strained healthcare systems. In an era of
growth, the issue of air pollution must be tackled immediately before it becomes any
worse.
Nitrogen gases are also released into the atmosphere by the burning of fossil fuels.
Large amounts of oxidized forms of nitrogen, such as NO2, are emitted in vehicle exhaust
2
and are eventually deposited on the landscape. Smaller amounts of reduced nitrogen,
such as NH3, are also emitted by vehicle exhaust and deposited, but this has been poorly
studied (Cape et al., 2004). These forms of nitrogen are readily absorbed by primary
producers and can have adverse ecological effects in large amounts, such as the
eutrophication of estuaries and the creation of hypoxic “dead zones” (Schlesinger, 1997).
Furthermore, the spatial variability is too uncertain in current models that are used to
predict atmospheric nitrogen deposition in smaller-scale urban areas. In order to better
understand their interactions with vulnerable ecosystems, we must determine
anthropogenic contributions of these important gases.
It is the intent of this study to use GIS to examine traffic densities and road
networks in New York City in relation to vehicular emissions and air quality. It will
examine the spatial relationship, if any, between traffic volume and the mean annual
atmospheric concentration of fine particulate matter (PM-2.5) and nitrogen dioxide
(NO2). This study will also utilize GIS to estimate average daily emissions of ammonia
(NH3) and NO2 within New York City due to vehicle exhaust. The findings will be
valuable in determining the best management practices for planning the development of
urban environments.
Data and methods
Data
The following data was acquired for this project:

New York State Zip Code Shapefiles
3
Source: New York State Office of Cyber Security and Critical Infrastructure
Coordination

NBPM Highway Network
Description: New York Metropolitan road network shapefile with measured and
modeled data
Source: New York Metropolitan Transportation Council (NYMTC)

2002 Atmospheric Pollutant Monitor Data – New York State
Source: Environmental Protection Agency AirData
Data Preparation
New York State Zip Codes were used to designate the boundaries of each New
York City borough. Rather than using New York County boundaries derived from
census data, which shows the boundaries of these counties extending into the water, Zip
Code boundaries were used to more accurately reflect the physical boundaries of New
York City. Postal code boundaries were dissolved so that only the county boundaries
remained and the five counties of New York City (Bronx, New York, Kings, Queens, and
Richmond) were exported into a final shapefile using the NAD 1983 UTM Zone 18N
projection (NYC_Zips_Dissolve.shp).
The NYMTC highway data was initially composed of many different shapefiles
based on measured and modeled data. The measured data was created from traffic counts
performed in 2002. This data was split into shapefiles for morning, mid-day (peak),
evening, and night time periods. Additional highway data from NYMTC was available
based on modeled projections of traffic for 2008 and 2030. Because projected air quality
values are unavailable for these years, this traffic data was not used. In order to isolate
4
roadway data for New York City, the 2002 mid-day NYMTC highway data was clipped
to the boundaries of New York City represented by the Zip Code shapefile described
above. The method by which NYMTC conducts its traffic study dictates that not all
roadways can feasibly be counted. Thus, “connectors” are created to link counted roads
so that no traffic is lost between analysis zones. These connectors were, essentially, fake,
and were deleted from the data. Deleted segments include those named “Centroid
Connector” and “PTZ Connector.” The final shapefile (2002B_md_NYC_UTM.shp)
was projected to NAD 1983 UTM Zone 18N (Appendix A, Map 1).
Using the EPA’s AirData website, PM-2.5 air quality data for New York State in
2002 was downloaded in the form of a comma-delineated text file - an example of which
can be found in Appendix A, Table 1. The most useful elements of this data are the site
ID, latitude and longitude coordinates, and mean annual PM-2.5 concentration, though
there are many more attributes included. Conserving all attributes, the monitoring sites in
New York City were saved as a dBase IV file. These sites were then imported as XY
data into ArcMap by means of their respective latitudes and longitudes (Appendix A,
Map 2) and saved as a shapefile (PM25_NYC.shp). The same method was used to obtain
NO2 data and prepare it for use in ArcMap (Appendix A, Map 3).
Assumptions
In order to simplify this analysis, it was assumed that daily peak flow occurring
mid-day would accurately represent the daily traffic flow as a whole. The data that were
received via personal correspondence from NYMTC were partially corrupted – the
2002B_am_nwk+assn.shp file would neither open nor allow access to its attribute data.
As a result, this study interprets mid-day traffic flow as representative of total daily flow.
5
This study also assumed that vehicle exhaust deposits within 300m of the location
from which it is emitted. This was crucial in making the spatial correlation between a
site’s annual mean concentration of PM-2.5 and NO2 and the traffic flow within a certain
distance. Previous studies have made similar assumptions (Ross et al., 2007).
In calculating the amount of NO2 and NH3 emitted daily due to vehicle exhaust,
this study made assumptions regarding the composition of traffic. Heavy trucks that run
on diesel emit different proportions of these gases than do gasoline-fueled light vehicles.
It was assumed that heavy trucks make up 10% of the traffic, while light vehicles make
up the remaining 90%.
Analysis
In order to visualize the mean annual concentrations of PM-2.5 throughout New
York City, a continuous dataset was created based on the mean annual PM-2.5
concentrations at each discrete monitoring station. This data was interpolated to a raster
using the Inverse Distance Weighted method (IDW) with the Spatial Analyst toolkit. The
concentrations were interpolated to the rectangular extent of the New York City
boundaries with the units of µg / m3 (Appendix B, Map 1). According to the map, the
highest annual concentrations are within New York and Bronx counties. The same
method was applied to the data from NO2 monitoring stations; however, the NO2 dataset
is much smaller and only allowed for a partial coverage of continuous interpolation
(Appendix B, Map 2).
The second step of this analysis was to create a 300 meter buffer around each
monitoring station (Appendix B, Map 3) and clip all NYMTC roadways to this buffer and
6
create a new shapefile (NYC_roads_300_clip.shp). As part of the original NYMTC data,
the field “TOT_FLOW” indicates the daily vehicular traffic flow based on peak mid-day
traffic. Two new fields were added to this shapefile: “calc_lengt” and “flow_x_len”.
Using the field calculator, the length of each clipped road segment was calculated in
meters. Next, the total vehicle kilometers per segment was calculated by the following
equation: [flow_x_len] = [TOT_FLOW] x [calc_lengt] / 1000. A summary report was
created and its first page is included in Appendix B (Table 1). The above procedure was
repeated for the 2002 NO2 data (Appendix B, Map 4; Table omitted).
The clipped road segments (NYC_roads_300_clip.shp) were spatially joined to
the buffer polygons within which each was contained – a process that added the essential
information of the Site ID to each road segment. Site ID’s and vehicle kilometers
traveled were culled from the NYC_roads_300_clip.dbf file and placed into a new Excel
spreadsheet. Using a pivot table in Excel, the sum of vehicle kilometers for each Site ID
was calculated (Appendix B, Table 2), which gives the mean daily peak traffic flow
within 300 meters of each monitoring station.
The mean annual concentrations of PM-2.5 (µg/m3) for each site were culled from
the PM25_NYC.dbf table associated with the sites’ shapefile. Sites with more than one
value for mean annual PM-2.5 were averaged (Appendix B, Table 3). A regression
analysis was performed that correlated the mean annual concentration of PM-2.5 with
daily peak vehicular traffic within 300m of each monitoring station and the results plotted
(Appendix B, Figure 5). The vehicle kilometers within 300m of each NO2 monitoring
station were also calculated (Appendix B, Table 4), as well as the annual mean NO2
concentration in parts per million (ppm) (Appendix B, Table 5). A regression between
7
the two variable for each site was plotted (R2 = 0.868) (Appendix B, Figure 6) using the
same method as used for PM-2.5.
In order to estimate the total daily vehicle emissions from New York City, the
method to obtain “calc_lengt” and “flow_x_len” as described above was utilized for the
entire 2002B_md_NYC_UTM.shp shapefile. After extracting the calculated values from
the associated dBase file, a pivot table was used to calculate the total sum of vehiclekilometers for New York City – the subsequent calculations require that vehicle distance
be in meters, so the conversion was duly made. Using the traffic composition
assumptions made previously and emissions estimates determined by the US DOT and
Emmenegger et al. (2004), the mass of nitrogen as NO2 and NH3 was calculated with
Microsoft Excel (Appendix B, Table 6).
Conclusions and Discussion
According to the regression analysis performed, 34% of the variation in mean
annual concentrations of PM-2.5 is explained by the daily peak vehicular traffic passing
within 300m of each station. Although this may initially seem like a weak correlation, it
is a significant finding which can be extrapolated beyond monitoring stations to roadways
themselves. Based on this study, we can conclude that over one-third of the variation in
PM-2.5 concentrations within 300m of roadways can be explained by the simple volume
of daily traffic flow.
According to the regression performed for NO2, 87% of the variation in NO2
concentrations can be explained by the variation in traffic volume within 300m of each
station. Unfortunately, this cannot be concluded with confidence, as there are only five
8
data points (i.e., stations monitoring NO2). As a result, more monitoring of NO2 is
needed to make this correlation.
Using GIS to calculate road length and traffic volume in vehicle-kilometers has
allowed for the estimation of total nitrogen emissions from vehicle exhaust in New York
City. Nearly 17,000 metric tons of nitrogen are released daily, and over 6 million tons
annually, in vehicle exhaust. These figures imply strong ecological implications and
further study must be done to quantify and correlate their effects. Policy-makers should
take note of these results in order to tighten emissions standards and mitigate
environmental effects.
Due the initial assumptions made, this study can be improved upon several ways.
If daily total traffic flow is used to calculate vehicular traffic, a stronger correlation
between traffic and average annual PM-2.5 and NO2 concentrations may present itself.
Furthermore, vehicular traffic fluctuates throughout the day, depending on the time of
day. A strong correlation might be made between vehicle exhaust and the two emission
factors by comparing traffic and concentration data collected on an hourly timescale.
The implications of this result are significant and can be easily enacted. By using
traffic volume as an predictor of air quality, more traffic counts can be conducted to study
air quality. Rather than use costly and technically intricate atmospheric sampling
equipment, simple traffic studies can be conducted to help predict a region’s air quality
trends. Furthermore, urban municipalities should focus on reducing traffic flow in order
to mitigate air quality. By reducing peak daily congestion, cities have the potential to
drastically improve their air quality by reducing the amount of pollutants emitted.
9
Appendix A: Base Maps
Figure 1: Base map of New York City roadways included in NYMTC data.
10
Figure 2: US EPA PM-2.5 monitoring stations across New York City.
11
Figure 3: US EPA NO2 monitoring stations across New York City
Table 1: Example AirData acquired from US EPA
* US EPA - AirData Monitor Values Report - Criteria Air Pollutants
*
6-May-2008 at 12:55:25 AM (USA Eastern time
Tuesday zone)
12
* Geographic Area: New York
* Pollutant: Particles < 2.5 micrometers diameter
* Year: 2002
*
* File Size : 46 Rows
* File Format: CSV - Comma Separated Values
* Field 1: # Obs (24-Hour PM2.5)
* Field 2: 1st Max (24-Hour PM2.5)
* Field 3: 2nd Max (24-Hour PM2.5)
* Field 4: 3rd Max (24-Hour PM2.5)
* Field 5: 4th Max (24-Hour PM2.5)
* Field 6: 98th Pct (24-Hour PM2.5)
* Field 7: # Exceed (24-Hour PM2.5)
* Field 8: Annual Mean (PM2.5)
* Field 9: Annual # Exceed (PM2.5)
* Field 10: Site ID
* Field 11: County
* Field 12: Latitude (Degrees)
* Field 13: Longitude (Degrees)
13
14
9
9
8
14
0
7.1
0
360010005
Albany Co
42.6424
-73.75453
29
42
34
27
26
42
0
12.6
0
360010005
Albany Co
42.6424
-73.75453
115
77
36
34
33
34
0
11.4
0
360010012
Albany Co
42.68069
-73.75689
120
78
38
37
35
37
0
15.9
1
360050080
Bronx Co
40.83608
-73.92021
122
80
35
34
33
34
0
14
0
360050083
Bronx Co
40.86586
-73.88075
118
78
40
37
37
37
0
15
0
360050110
Bronx Co
40.81616
-73.90207
350
80
53
48
45
43
0
14.5
0
360050110
Bronx Co
40.81616
-73.90207
110
47
41
39
39
39
0
11.8
0
360070009
42.10873
-75.88027
114
43
42
38
33
38
0
11.3
0
360130011
Broome Co
Chautauqua
Co
42.29073
-79.58658
120
62
32
31
31
31
0
11.2
0
360271004
Dutchess Co
41.69486
-73.91441
114
46
40
38
38
38
0
12.1
0
360290002
Erie Co
42.99292
-78.77142
117
50
47
43
40
43
0
13.7
0
360290005
Erie Co
42.87684
-78.80988
116
50
45
38
37
38
0
13.4
0
360291007
Erie Co
42.82728
-78.84989
305
41
40
35
35
31
0
6.9
0
360310003
Essex Co
44.39309
-73.85892
113
86
35
32
32
32
0
14.7
0
360470052
Kings Co
40.64154
-74.01835
119
81
40
32
31
32
0
13.8
0
360470076
Kings Co
40.67185
-73.97824
121
81
38
36
34
36
0
14.6
0
360470122
Kings Co
40.7198
-73.94788
102
32
32
26
23
26
0
10.3
0
360552002
Monroe Co
43.15838
-77.56458
115
42
36
31
31
36
0
11.1
0
360556001
Monroe Co
43.161
-77.60357
113
42
32
32
30
32
0
11.2
0
360556001
Monroe Co
43.161
-77.60357
120
81
39
32
32
32
0
11.9
0
360590008
Nassau Co
40.63102
-73.73388
119
79
38
32
30
32
0
11.8
0
360590012
Nassau Co
40.78909
-73.63648
120
77
39
34
31
34
0
11.8
0
360590013
Nassau Co
40.76078
-73.4906
122
80
41
38
36
38
0
16.7
1
360610056
New York Co
40.75917
-73.96651
122
79
40
39
36
39
0
16.4
1
360610056
New York Co
40.75917
-73.96651
113
83
43
39
34
39
0
16
1
360610062
New York Co
40.72052
-74.00409
120
78
37
36
36
36
0
14.7
0
360610079
New York Co
40.79937
-73.93334
113
82
41
38
36
38
0
16.2
1
360610128
New York Co
40.73003
-73.98446
107
45
38
34
28
34
0
11.8
0
360632008
Niagara Co
43.08216
-79.00099
116
59
44
40
38
40
0
12.5
0
360652001
Oneida Co
43.0994
-75.22519
13
110
44
39
39
35
39
0
11.6
0
360670019
Onondaga Co
43.04823
-76.16479
102
44
40
38
35
38
0
11
0
360670020
Onondaga Co
43.02014
-76.16075
106
52
45
40
39
45
0
11.5
0
360671015
Onondaga Co
43.05238
-76.0592
114
52
45
39
39
39
0
11.1
0
360671015
Onondaga Co
43.05238
-76.0592
115
71
32
32
31
32
0
11.5
0
360710002
Orange Co
41.49947
-74.00973
23
34
24
21
19
34
0
13.3
0
360810094
Queens Co
40.77798
-73.84318
23
34
24
20
19
34
0
13.3
0
360810094
Queens Co
40.77798
-73.84318
119
79
36
35
33
35
0
13.7
0
360810096
Queens Co
40.77039
-73.82841
329
76
51
47
44
39
0
13
0
360810124
Queens Co
40.7362
-73.82317
119
85
45
40
39
40
0
14.4
0
360850055
Richmond Co
40.63302
-74.13713
118
83
39
28
27
28
0
12.1
0
360850067
40.59733
-74.12619
88
59
43
42
31
43
0
9.9
0
360893001
44.67778
-74.94999
117
75
42
36
35
36
0
11.5
0
360930003
Richmond Co
St. Lawrence
Co
Schenectady
Co
42.79963
-73.94019
332
52
46
45
42
37
0
10.4
0
361010003
Steuben Co
42.09071
121
80
40
36
31
36
0
12
0
361030001
40.745833
121
77
33
33
33
33
0
12.3
0
361191002
Suffolk Co
Westchester
Co
-77.21025
73.420278
40.93006
-73.76924
14
Appendix B: Analysis
Figure 1: Spatial Analysis of annual mean PM-2.5 concentrations throughout New York
City based on US EPA data, Inverse-Distance Weighted Interpolation.
15
Figure 2: Spatial Analysis of annual mean NO2 concentrations throughout New York
City based on US EPA data, Inverse-Distance Weighted Interpolation.
16
Figure 3: A map designating different traffic flows throughout lower New York, western
Queens, and northern Kings counties. Also shown are 300 meter buffers around PM-2.5
monitoring stations used to isolate nearby roadways.
17
Table 1: PM-2.5 300m buffer Vehicle-km summary report for each road segment,
displaying calculated lengths in meters and vehicle meters per segment.
18
Figure 4: A map designating different traffic flows throughout eastern New York,
western Queens, and northern Kings counties. Also shown are 300 meter buffers around
NO2 monitoring stations used to isolate nearby roadways.
19
Table 2: Microsoft Excel pivot table used to calculate total vehicle-kilometers within
300m of each PM-2.5 site.
Sum of
flow_x_len
SITE_ID
360050080
360050083
360050110
360470052
360470076
360470122
360610056
360610062
360610079
360610128
360810094
360810096
360810124
360850055
360850067
Grand Total
Total
4780.443317
5505.21
1418.8
26553.04
3124.9
28469.28223
41717.66
20174.74
13135.85
11942.49
4569.82
727.3
2431
1568.67
3307.1
169426.3055
Table 3: Excel pivot table used to average annual mean PM-2.5 concentrations (µg/m3)
for stations with more than one value.
Average of
YRLY_MEAN
SITE_ID
360050080
360050083
360050110
360470052
360470076
360470122
360610056
360610062
360610079
360610128
360810094
360810096
360810124
360850055
360850067
Grand Total
Total
15.9
14
14.75
14.7
13.8
14.6
16.55
16
14.7
16.2
13.3
13.7
13
14.4
12.1
14.57222222
20
Annual Mean PM-2.5 (ug/m3)
Vehicular Traffic within 300m of PM-2.5 Stations
17
y = 6E-05x + 13.842
R2 = 0.3435
16
15
14
PM-2.5
13
Linear (PM-2.5)
12
11
10
0
10000
20000
30000
40000
50000
Daily Peak Vehicle-km
Figure 5: Regression analysis of vehicle-kilometers within 300m of each site and 2002
annual mean PM-2.5 concentration (R2 = 0.344).
Sum of
len_x_flw
Site_ID
360050083
360050110
360610056
360810098
360810124
(blank)
Grand Total
Total
5505.21
709.4
20858.83
930.16
2431
30434.6
Table 4: Excel pivot table used to calculate total vehicle-kilometers within 300m of each
NO2 monitoring station.
Site_ID
360050083
360050110
360610056
360810098
360810124
Yrly_Mean
0.028
0.03
0.038
0.028
0.028
Table 5: Annual mean NO2 concentrations at each station in parts-per-million (ppm).
21
Annual Mean NO2 (ppm)
Vehicular Traffic within 300m of NO2 Stations
0.04
0.035
y = 5E-07x + 0.0275
R2 = 0.8679
0.03
0.025
NO2
0.02
Linear (NO2)
0.015
0.01
0.005
0
0
5000
10000
15000
20000
25000
Peak Daily Vehicle-km
Figure 6: Regression analysis of vehicle-kilometers within 300m of NO2 stations and
their 2002 annual mean NO2 concentrations.
Daily Vehicle-meters
g N from NOx per
meter
Tons N from NOx
daily
g N from NH3 per
meter
Tons N from NH3
daily
Total Tons N daily
Total Tons N
Annually
Heavy Trucks
2675660238
Light Vehicles
24080942138
Total
26756602376
3.953306131
0.226984084
Assume NOx = NO2
10577.70402
5465.990595
16043.69462
0.011529412
0.025529412
30.84878861
10608.55281
614.7722874
6080.762883
645.621076
16689.31569
3872121.776
2219478.452
6091600.229
Table 5: Calculations for total N by gaseous species emitted based on GIS calculated
distances.
22
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