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Lovasi et al. ) document the relationship between the density of
street trees and the prevalence of childhood asthma in New York City.
Their findings suggest street trees are associated with a lower
prevalence, although no causality was inferred. I would like to point out
a number of methodological issues which should benefit future studies on
Prevalence of asthma was determined for 4-year-old a...
Prevalence of asthma was determined for 4-year-old and 5-year-old
children using data from school screenings in 1999. Street tree density
was derived from the 1995 street tree census completed by the Parks and
Recreation Department of the City of New York and expressed as the total
number of trees on streets segments divided by the land area. Data was
aggregated at the level of United Hospital Fund (UHF) areas. Additional
variables used in the analysis were population density, racial/ethnic
composition and a measure of proximity to pollution sources. The initial
correlation analysis suggested a negative association between street tree
density and prevalence of asthma. However, one of the strongest positive
associations was between street tree density and population density. This
initially appears somewhat counterintuitive, until it is recognized
exactly which types of trees are included in the analysis. The street tree
census conducted by the Parks and Recreation Department of the City of New
York only considered trees along city streets, and trees in parks and open
space are not included. As a result, the street tree density derived by
Lovasi et al.  is a substantial underestimate of the actual number
of trees within a UHF area, in particular in areas with large areas of
parks and open space. In fact, Nowak et al.  estimate that New York
City has about 5.2 million trees, while the latest street tree census in
2005-2006 counted 592,130 trees . Street trees,
therefore, account only for around 11% of trees within the study area. The
strong correlation between street tree density and population density is
strongly driven by the fact that the total length of streets segments per
unit area increases with population density. Logic suggests that overall
tree density and population density are likely negatively associated since
many parks and open space occur in areas with lower (average) population
density. The pattern in street tree density by UHF areas is therefore a
poor representation of the overall tree density.
Figure 1 below provides an example of a park in Brooklyn adjacent to
a residential area. While numerous trees are visible on the residential
streets, the number of trees in the park far exceeds the number of street
trees. While Figure 1 is not representative for the entire study area, it
illustrates how not including trees in parks and open space presents a
misleading picture of the potential effects of trees on local air quality
in urban areas
Figure 1 Digital orthophoto of a portion of Prospect Park in
Booklyn, New York City adjacent to a residential area.
Source: United States Geological Survey 2006.
The argument could be made that street trees are more relevant than
those in parks and open space since street trees are much closer to the
residential homes. However, Lovasi et al  do not make this argument
and instead aggregate all variables at the level of UHFs. This aggregation
does not allow for a determination of street tree density in close
proximity to the residential addresses of asthma cases. If street trees in
close proximity are deemed of greater relevance than trees in parks and
open spaces at greater distances, an individual or street segment level
analysis is required.
A second methodological issue relates to the determination of the
measure of proximity to pollution sources. Relying on the methodology
presented by Maantay , Lovasi et al.  create uniform distance
buffers around toxic release inventory sites, stationary point sources and
major truck routes and then determine the percentage of each UHF falling
within one or more of these buffers. While the specific distances and
types of sources were derived from the Maantay  study, the authors
fail to highlight the many limitations of this approach as detailed at
length in the original study, including the use of single buffer
distances, treating all pollution sources as being similar, and ignoring
cumulative effects from multiple sources. Perhaps more importantly,
Maantay  used the individual geocoded residential locations of
asthma hospitalization cases and determined if they fell within a
particular buffer or not. Lovasi et al.  instead determine the
prevalence of asthma as a rate based on the number of children within each
UHF and compared this to the percentage of the area of the UHF falling
within one of more of the buffers around the pollution sources, without
considering the proximity of individual cases to pollution sources. The
data aggregation to the level of UHFs represents a very substantial loss
of information. No evidence is presented that aggregation at the level of
UHF areas is justified given the nature of the research question since it
remains unclear at what (spatial) scale the potential effects of street
trees on air quality are expected to occur.
Future research efforts in this area should consider the following
1) Developing a more robust measure of tree density
which includes trees in parks and open spaces. This could be addressed by
using land use or land cover maps supplemented with field sampling as
employed by Nowak et al. .
2) Using individual level analysis
instead of aggregation to coarse units. This would involve geocoding
individual address locations of asthma cases and creating individual-level
measures of tree density and proximity to pollution sources, as well as
creating a meaningful sample of non-asthma cases for comparison.
Employing more robust measures of proximity to pollution sources. One
approach to accomplishing this is to use cumulative distribution functions
as employed by Waller et al.  and Zandbergen and Chakraborty .
While each of these three elements requires considerable effort, they
should contribute to a much improved understanding of the complex
relationships between tree density and asthma prevalence.
University of New Mexico
Department of Geography
1. Lovasi GS, Quin JW, Neckerman KM, Perzanowksi MS Rundle A. 2008.
Children living in areas with more street trees have lower prevalence of
asthma. J Epidemiol Community Health 2008;62:647-649.
2. Nowak DJ, Hoehn III RE, Crane D E, Stevens JC, Walton JT. Assessing
urban forest effects and values, New York City's urban forest. Resource
Bulletin NRS-9. United States Department of Agriculture, Forest Service,
Northern Research Station, 2007.
3. Peper PJ, Mcpherson EG, Simpson JR, Gardner SL, Vargas KE, Xiao Q.
New York City, New York municipal forest resource analysis. Center for
Urban Forest Research, United States Department of Agriculture, Forest
Service, Pacific Southwest Research Station, 2007.
4. Maantay J. Asthma and air pollution in the Bronx: methodological and
data considerations in using GIS for environmental justice and health
research. Health Place 2007;13:32-56
5. Waller LA, Louis TA, Carlin BP. 2007. Environmental justice and
statistical summaries of differences in exposure distributions. J Exposure
Analysis and Env Epi 1999;9:56–65.
6. Zandbergen PA, Chakraborty J. Improving environmental exposure
analysis using cumulative distribution functions and individual geocoding.
Int J Health Geographics 2006;5:23.
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Lovasi et al. ) document the relationship between the density of street trees and the prevalence of childhood asthma in New York City. Their findings suggest street trees are associated with a lower prevalence, although no causality was inferred. I would like to point out a number of methodological issues which should benefit future studies on this subject.
Prevalence of asthma was determined for 4-year-old a...