The Canadian Urban Environmental Health Research Consortium - A protocol for building a national environmental exposure data platform for integrated analyses of urban form and health

Abstract

Background:
Multiple external environmental exposures related to residential location and urban form including, air pollutants, noise, greenness, and walkability have been linked to health impacts or benefits. The Canadian Urban Environmental Health Research Consortium (CANUE) was established to facilitate the linkage of extensive geospatial exposure data to existing Canadian cohorts and administrative health data holdings. We hypothesize that this linkage will enable investigators to test a variety of their own hypotheses related to the interdependent associations of built environment features with diverse health outcomes encompassed by the cohorts and administrative data.

Methods:
We developed a protocol for compiling measures of built environment features that quantify exposure; vary spatially on the urban and suburban scale; and can be modified through changes in policy or individual behaviour to benefit health. These measures fall into six domains: air quality, noise, greenness, weather/climate, and transportation and neighbourhood factors; and will be indexed to six-digit postal codes to facilitate merging with health databases. Initial efforts focus on existing data and include estimates of air pollutants, greenness, temperature extremes, and neighbourhood walkability and socioeconomic characteristics. Key gaps will be addressed for noise exposure, with a new national model being developed, and for transportation-related exposures, with detailed estimates of truck volumes and diesel emissions now underway in selected cities. Improvements to existing exposure estimates are planned, primarily by increasing temporal and/or spatial resolution given new satellite-based sensors and more detailed national air quality modelling. Novel metrics are also planned for walkability and food environments, green space access and function and life-long climate-related exposures based on local climate zones. Critical challenges exist, for example, the quantity and quality of input data to many of the models and metrics has changed over time, making it difficult to develop and validate historical exposures.

Discussion:
CANUE represents a unique effort to coordinate and leverage substantial research investments and will enable a more focused effort on filling gaps in exposure information, improving the range of exposures quantified, their precision and mechanistic relevance to health. Epidemiological studies may be better able to explore the common theme of urban form and health in an integrated manner, ultimately contributing new knowledge informing policies that enhance healthy urban living.

Full text

S T U D Y P R O T O C O L Open Access
The Canadian Urban Environmental Health
Research Consortium –a protocol for
building a national environmental exposure
data platform for integrated analyses of
urban form and health
Jeffrey R. Brook
1,2*
, Eleanor M. Setton
3
, Evan Seed
2
, Mahdi Shooshtari
3
, Dany Doiron
4
and CANUE –The Canadian
Urban Environmental Health Research Consortium
Abstract
Background: Multiple external environmental exposures related to residential location and urban form including,
air pollutants, noise, greenness, and walkability have been linked to health impacts or benefits. The Canadian Urban
Environmental Health Research Consortium (CANUE) was established to facilitate the linkage of extensive geospatial
exposure data to existing Canadian cohorts and administrative health data holdings. We hypothesize that this
linkage will enable investigators to test a variety of their own hypotheses related to the interdependent
associations of built environment features with diverse health outcomes encompassed by the cohorts and
administrative data.
Methods: We developed a protocol for compiling measures of built environment features that quantify exposure; vary
spatially on the urban and suburban scale; and can be modified through changes in policy or individual behaviour to
benefit health. These measures fall into six domains: air quality, noise, greenness, weather/climate, and transportation
and neighbourhood factors; and will be indexed to six-digit postal codes to facilitate merging with health databases.
Initial efforts focus on existing data and include estimates of air pollutants, greenness, temperature extremes, and
neighbourhood walkability and socioeconomic characteristics. Key gaps will be addressed for noise exposure, with a
new national model being developed, and for transportation-related exposures, with detailed estimates of truck
volumes and diesel emissions now underway in selected cities. Improvements to existing exposure estimates are
planned, primarily by increasing temporal and/or spatial resolution given new satellite-based sensors and more
detailed national air quality modelling. Novel metrics are also planned for walkability and food environments, green
space access and function and life-long climate-related exposures based on local climate zones. Critical challenges
exist, for example, the quantity and quality of input data to many of the models and metrics has changed over time,
making it difficult to develop and validate historical exposures.
(Continued on next page)
* Correspondence: Jeff.Brook@canue.ca
1
Processes Research Section, Air Quality Research Division, Environment and
Climate Change Canada, Toronto, ON, Canada
2
Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
Full list of author information is available at the end of the article
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Brook et al. BMC Public Health (2018) 18:114
DOI 10.1186/s12889-017-5001-5
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(Continued from previous page)
Discussion: CANUE represents a unique effort to coordinate and leverage substantial research investments and will
enable a more focused effort on filling gaps in exposure information, improving the range of exposures quantified,
their precision and mechanistic relevance to health. Epidemiological studies may be better able to explore the
common theme of urban form and health in an integrated manner, ultimately contributing new knowledge informing
policies that enhance healthy urban living.
Keywords: Urban form, Exposure, Air pollution, Noise pollution, Green space, Transportation, Climate, Public health
Background
Multiple external environmental exposures related to
residential location and urban form including, air pollut-
ants [1–3], noise [4–6], greenness [7], and walkability
[8–10] have been linked to health impacts or benefits. In
Canada, more than 80% of the population lives in urban
areas [11], and with clear evidence that health impacts
can occur even at exposure levels that are considered to
be low [12], there is an urgent need to learn how to
design and modify cities to improve, not degrade, popu-
lation health [13]. A concerted effort to address this
need could provide the informative science to support
urban planners and population health-related policy
makers who are faced with very real issues such as,
urban sprawl, traffic congestion, car-dependency, social
equity and sustainability.
We hypothesize that a coordinated program capitaliz-
ing on: 1)the opportunity of emerging big data relating
to our physical environment; 2) improvements in
methods for managing and analyzing large data streams;
3) learning from efforts to increase power for epidemio-
logical discovery by initiating large prospective cohorts
[14–17], combining existing cohorts [18] or building
large administrative cohorts [19–21]; can support the
production of substantial new knowledge about how the
environment contributes to chronic disease. Hu et al.
(2017) suggested that population health stands to benefit
from the big data and precision medicine agendas if a
parallel effort to introduce measures that capture poten-
tial health risks at multiple levels of influence can be re-
alized [22]. We view such an effort as bringing ‘big
environmental data’into the equation and the insights
gained could have applications from the individual to
the population level [23].
In 2015 the Canadian Institutes of Health Research
(CIHR) called for a new national consortium that would
bring together scientific and other expertise from a wide
variety of disciplines and fields from academia, government,
non-governmental organizations and industry to focus on
specific research priorities that can only be addressed
through interdisciplinary and intersectoral research. This
included developing a ‘data and methodological hub’where
environmental researchers could collaborate with cohorts
and health researchers on focused health projects using
innovative measurement models and ‘analysis-ready’data
[24]. Responding to this call, the Canadian Urban Envir-
onmental Health Research Consortium (CANUE) was
established and aims, through a coordinated program, to
capitalize on Canada’s growing big data capacity by facili-
tating the linkage of extensive geospatial exposure data to
the wealth of established cohorts and administrative
health data holdings (http://canue.ca). This linkage will
enable investigators to test a variety of hypotheses related
to the interdependent associations of built environment
features with diverse health outcomes encompassed by
the cohorts and administrative data.
The goal of this paper is to present CANUE’s protocol
for acquiring, developing and indexing exposure data for
integration with health databases, and to discuss some
of the challenges associated with developing accurate
exposure estimates related to urban form. In addition,
we provide examples of plans and opportunities to gen-
erate big environmental data to advance our understand-
ing of environmental health and help optimize urban
planning to benefit public health.
Methods
Data protocol
CANUE’s vision is to increase scientific understanding
of the interactions among the physical features of the
urban environment and health. This understanding will
lead to cost-effective actions that promote healthy child-
hood development and aging, reduce the burden of
chronic disease, and minimize the impact of changing
environments. To achieve this vision, CANUE is estab-
lishing and implementing a protocol for compiling envir-
onmental measures or metrics that: quantify exposure,
behaviour patterns or effect modifiers; vary spatially on
the urban and suburban scale; can be obtained for
multiple urbanized regions in Canada and; could be
modified through changes in policy or individual behav-
iour to benefit health. While urban areas are the focus,
exposures across rural Canada are also being compiled.
CANUE’s main data-related products are: (1) multiple
spatial maps or surfaces (Fig. 1), one for each environ-
mental exposure metric, that can linked to individuals
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included in health studies based upon the locations
where they spend time (e.g., home); (2) computational
tools to derive new exposure metrics; (3) documentation
of the methods and data, how they were derived, by
whom and key information relevant to data usage (e.g.,
potential limitations) and; (4) facilitation of exposure
data extraction for individual-level data merging with
major Canadian health databases (Table 1), including
consideration of residential mobility.
CANUE currently focuses on collating and generating
exposure metrics in six domains: Air Pollution, Noise,
Greenness, Weather and Climate, Transportation, and
Neighbourhood Factors, which include land-use, urban
design and social determinants. These factors are
grouped together, recognizing that much of our health
and well-being begins at the neighbourhood level and
there has been a great deal of theoretical guidance as to
which factors at this scale are paramount, influencing
key behaviours such physical activity and diet [25, 26].
Also, it is at this scale that patterns in socioeconomic
factors manifest, creating a backdrop of individual sus-
ceptibility that must be considered in the context of
Fig. 1 Schematic of the main data products and linkages being compiled through CANUE
Table 1 Major Canadian Health Databases
Type Name Participants Start Year
Cohort Canadian Partnership for Tomorrow Project 300,000 2000–2009
Cohort Canadian Longitudinal Study on Aging 50,000 2010
Cohort Canadian Healthy Infant Longitudinal Development Study 10,059 2008
Cohort All Our Babies and All Our Families 6774 2008
Cohort Alberta Pregnancy Outcomes and Nutrition 5841 2009
Cohort TARGet Kids! 5062 2008
Cohort Ontario Birth Study 2748 2013
Cohort 3D Study - Design, Develop, Discover 2456 2010
Cohort Maternal-Infant Research on Environmental Chemicals 2000 2008
Cohort Canadian Cohort of Obstructive Lung Disease 1400 2009
Cohort - administrative Canadian Census Health and Environment Cohort (1991) 2,500,000 1991
Cohort - administrative Canadian Census Health and Environment Cohort (1996) 3,500,000 1996
Cross-sectional survey Canadian Health Measures Survey 23,000 2007–2015
Linked Administrative Database Population Data BC > 5,000,000 1980s
Linked Administrative Database Manitoba Centre for Health Policy > 1,000,000 1970s
Linked Administrative Database Institute for Clinical Evaluative Science 13,000,000 1986
Network (26 pregnancy and birth cohorts) Research Advancement Through Cohort Cataloguing and Harmonization ~ 125,000 varies
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public health. Active within CANUE are domain-specific
working groups assessing the state of knowledge and re-
search nationally and internationally, identifying critical
gaps and conducting strategic research to improve the
available exposure metrics. Fig. 2 places the six domains
in the context of key external forces influencing urban
form: population growth, economic growth, and wea-
ther/climate which includes factors such as extreme heat
and cold events and longer-term climate change. In gen-
eral, the main public responses to these forces are land-
use planning and transportation infrastructure decisions;
in turn, this leads to individual options around housing,
employment and education locations. Choices made
based upon these options or constraints subsequently
impact an individual’s access to or interaction with
urban features of health relevance and dictates individual
behaviour such as time spent commuting and working
or time available for leisure and family. All ultimately
impact the magnitude of a range of harmful or beneficial
exposures and thus individual and public health.
The exposure data or metrics being compiled in
CANUE are georeferenced at the six-digit postal code
level (or other geographic level as appropriate) facilitat-
ing linkage with health research cohorts and administra-
tive health databases. Changes in the geographic
distribution of exposure over time are important to
consider given the potential time windows over which
environmental factors can contribute to adverse
health outcomes and chronic disease development.
The temporal resolution required and the number of
years back in time for which exposures can be
estimated varies across the domains based upon the
rate of change over time and available data sources.
Accurately accounting for short and long term expos-
ure time windows represents a considerable challenge
and will be discussed below.
Compilation of existing exposure information
Within each domain, existing data are being centralized
to improve accessibility for researchers and subsequent
integration with Canadian health data platforms. Work
with these initial datasets (Table 2) is also facilitating de-
velopment of CANUE’s infrastructure for data transfers,
storage, manipulation into analysis-ready formats and
documentation, including terms of use that assure
requirements of the data originators are respected. This
initial phase is helping to identify challenges related to
harmonization of environmental data within and be-
tween domains and with similar efforts internationally.
Several of these existing exposure datasets have been
previously used in health research, nationally or region-
ally within Canada and are thus in analysis-ready format;
while others require further processing and quality
assessment before linkage with health data.
Three air pollutants have been used most often in recent
epidemiological research in Canada; fine particulate matter
(PM
2.5
), nitrogen dioxide (NO
2
) and ozone (O
3
). National
coverage for PM
2.5
is derived from the recently developed
1 × 1 km satellite-derived PM
2.5
surface [27]. Exposures for
NO
2
are estimated empirically from a national Land-Use
Regression (LUR) model [28] and finer scale spatial patterns
in NO
2
are available from LUR models for 10 cities in
Canada [29]. Exposures to O
3
have been derived from a
combination of observations and output from the chemical
transport model developed by Environment and Climate
Change Canada for air quality prediction and used in recent
epidemiological studies [30, 31]. A national surface for
sulphur dioxide (SO
2
) is also available based upon recent
progress in satellite detection and extrapolation to surface
concentrations [32]. Temporal coverage of CANUE national
and urban-level air pollutant exposure surfaces will initially
extend from 2000 to the present.
The LUR method has also been used to model spatial
surfaces of urban environmental noise exposure in two
Canadian cities, Montreal [33] and Toronto [34], with
field monitoring also conducted in other cities (e.g., Van-
couver, Ottawa, and Halifax [35]). Vancouver noise maps
for 2003 have been generated using the deterministic
propagation model CadnaA [36]. A similar model is
currently being run for Montreal for 2008. CANUE is
documenting these noise exposure surfaces to make
them more-widely available for epidemiological research.
Fig. 2 Relationships among factors associated with urban form and
individual behaviours and environmental exposures. Land-use planning
controls the over-arching modifiable features of the urban environment
and, in addition to responding to external forces associated with population
and economic growth and local weather, including extreme events and
climate change, can potentially be optimized to have the greatest benefit
to public health
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Table 2 Existing metrics
Existing Metrics Geographic Extent Spatial Resolution Time Periods Description Ref.
Air Pollution
Surface concentrations of PM
2.5
National 1 km 1998 to present Satellite-derived annual mean
concentration
[27
Ambient concentrations of NO
2
National <100 m 1984–2012 National land use regression
model of annual mean
concentration, based on 2011
data and adjusted for historical
estimates
[28]
Ambient concentrations of NO
2
National <100 m 1984–2006 National model of annual mean
concentration based on 10 city
-specific land use regression
models (field monitoring between
2002 and 2010) and adjusted
using observational data for
each year
[29]
Ambient concentrations of O
3
National 10 km - 21 km 2003 - present Air quality forecast-based estimate,
adjusted with surface observation
data - annual and monthly
average concentration
[30]
Ambient concentrations of SO
2
National 30 km 2005–2015 Air quality model-based estimate
of 3-year running annual average
concentration
[32]
Noise Pollution
A-weighted sound pressure level
and related summary metrics
Regional
(Montreal)
10 m ~ 2014 Land use regression-based model
based on field monitoring
conducted 2010–2014
[33]
A-weighted sound pressure level
and related summary metrics
Regional
(Toronto)
10 m ~ 2013 Land use regression-based model
based on field monitoring
conducted 2012–2013
[34]
A-weighted sound pressure level
and related summary metrics
Regional
(Vancouver)
10 m 2003 Sound propagation model
(CadnaA)
[36]
Greenness
Normalized Difference Vegetation
Index
National + 30 m 1985 to present Satellite-derived (Landsat) [37,38]
Normalized Difference Vegetation
Index
National + 250 m 2000 to present Satellite-derived (MODIS) [39]
Normalized Difference Vegetation
Index
National + 1 km 1979 to present Satellite-derived (AVHRR) [40,41]
Green View Index National Postal code-specific 2017 only Google Street View-derived not published
(or use MIT)
Climate and Weather
Temperature metrics (daily, monthly,
annual averages, ranges, event
frequencies) and derived water
balance metrics (potential and actual
evapotranspiration)
National 10 km 1950–2010 Interpolated continuous
surfaces based on observation
station data
[42]
Temperature metrics (max, min,
mean, heat deg. days, cool deg. days),
total precipitation, snow on ground
National N/A 1950- to present National Climate Data and
Information Archive -
observation station data
[43]
Neighbourhood Factors
Walkability National Postal code-specific circa 2015 GIS-derived walkability (based
on land use mix, street
connectivity and population
density)
[47]
Walkability Regional Postal code-specific various specific
years
GIS-derived walkability
(land-use mix, residential
density, and street connectivity)
[48]
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The Normalized Difference Vegetation Index (NDVI),
which is derived from satellite measurements of near in-
frared and visible red radiation reflected by vegetation, is
readily available as an indicator of greenness and the ex-
posures this metric may represent. This includes already
developed annual and peak growing season NDVI prod-
ucts using Landsat 5 and Landsat 8 [37, 38], the Moder-
ate Resolution Imaging Spectroradiometer (MODIS)
[39] or the Advanced Very High Resolution Radiometer
(AVHRR) data which provides annual coverage and po-
tentially seasonal variations as far back as 1981 [40, 41].
To facilitate research on how extreme weather and cli-
mate relate to the incidence of chronic disease, CANUE
is including climate data. At present, the spatial reso-
lution available across Canada is limited and originates
from interpolation of the available, largely routine obser-
vations and/or from re-analysis products combining
models and observations. As such, an observation-based
dataset of daily maximum and minimum temperatures
and precipitation produced by the Canadian Forest Ser-
vice and Environment and Climate Change Canada is
available at 10 × 10 km [42] and raw data can also be
accessed by station [43] to derive proximity-based met-
rics of weather and climate (i.e., summary statistics
based on nearest stations). The Climate Forecast System
Reanalysis [44] or the Japanese 55-year Reanalysis [45]
are comparable, while higher resolution observed grid-
ded data, such as the ~800 m data covering British
Columbia through the Parameter-elevation Regressions
on Independent Slopes Model (PRISM) [46], are
expected to become available nationally in the future.
Geographic Information Systems (GIS) provide the
tools for calculation of a variety of exposure metrics at a
fine scale across urban areas and within neighbourhoods.
Walkability, for which multiple measures have been de-
veloped [47, 48], will be included early in the CANUE
data holdings. The Canadian Census data includes socio-
economic data for the country from which several indi-
ces can be computed and mapped. The Canadian
Marginalization Index (CanMarg) [49] and the Pampa-
lon Index [50] have been or are being determined for
multiple cycles of the Canadian census from the 1980s
to the present. Light at night, which is derived from
satellite observations with 1 km resolution, is also avail-
able and is listed as part of the neighbourhood factors
domain [51].
Building on the existing exposure information
Limitations associated with the exposure measures cur-
rently available for each domain are being addressed by
the CANUE Working Groups. This involves the initi-
ation of research projects and/or targeted workshops to
guide future projects. Priorities for this work were devel-
oped at a national workshop held in December 2016
(www.canue.ca/workshop). Clearly, CANUE will not be
able to address all limitations within five years. In
Table 3, selected key exposure metric advances planned
for this time period (i.e., through ~2021) are summa-
rized and through new partnerships CANUE will be able
to further expand the amount and type of new exposure
data available for health research.
New exposure metrics and spatial surfaces
Transportation infrastructure is a key element of urban
form (Fig. 2). There are multiple pathways through
which it can affect health, from the resulting air and
noise pollution to commute times and commuting mode
choice to changes in active transportation behaviour.
Therefore, improving Canadian urban scale data on
transportation has potential benefits across domains.
With this in mind, the Transportation Working Group
is focusing on developing nationally consistent traffic
volume and traffic emission maps. Initially this will in-
clude private vehicle travel behaviour for Canada’s three
largest cities; Vancouver, Montreal and Toronto, as well
as Halifax, Ottawa and Calgary. Maps have historically
been limited for trucks i.e., goods movement; however,
through CANUE, truck volumes and emissions will be
generated for Halifax (a single year) and the Greater To-
ronto and Hamilton areas (4 separate years), enabling
first ever maps for these cities of diesel emission pat-
terns and potential exposures, relative to gasoline engine
emissions, and applicable to urban populations. Depend-
ing on the level of success for this first set of cities and
on the availability of input information for modelling
private vehicle and truck flows, other cities will be
Table 2 Existing metrics (Continued)
Existing Metrics Geographic Extent Spatial Resolution Time Periods Description Ref.
Canadian Marginalization
Deprivation Index
National Census dissemination
area
Census years
1991–2011
Derived from census variables [49
Pampalon Deprivation Index National Census dissemination
area
Census years
1991–2011
Derived from census variables [50]
Nighttime light National + 1 km 1992 to present Satellite-derived from the
US Defense Meteorological
Satellite Program’s Operational
Linescan System (DMSP-OLS) -
annual average
[51]
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Table 3 Future Metrics
Future Metrics Geographic Extent Spatial Resolution Time Periods Description Ref.
Air Pollution
Surface concentrations
of NO2, SO2 and CO
National + 7 km 2016 onward New satellite and sensor, TropOMI,
upgrading the current OMI
measurements to provide increased
spatial resolution with daily global
coverage.
Surface concentrations
of PM2.5
2 km 2016 onward GOES-R geostationary satellite
observing aerosol optical depth
on an hourly or better time
resolution during daylight hours.
[72]
Surface concentrations
of PM2.5, NO2, SO2
National + 5+ km planned for 2019
onward
New geostationary satellite and
sensor, TEMPO, upgrading the
current OMI measurements and
enhancing TropOMI with hourly
or better time resolution during
daylight hours; similar satellites
planned for Europe (Sentinel-4)
and Asia (GEMS).
[69–71]
Ambient concentrations
of O3, PM2.5 and NO2
National (and city-
specific)
10 km Daily, monthly and
annual starting in 2000
Operational forecast chemical
transport model (GEM-MACH)
with objective analysis for NO2,
O3 and PM2.5 produced by
Environment and Climate Change
Canada; For NO2, additional
chemical transport model (CTM)
runs are being combined with
local LUR models (‘hybrid approach’)
by Health Canada. Where the LUR
and CTM are combined the spatial
resolution is ~50 m.
Ambient concentrations
of PM2.5 and NO2
National <100 m Annual National empirical models using
surface observations and multiple
predictors from diverse sources
such as satellites, CTMs, GIS,
transportation models.
Noise Pollution
A-weighted sound pressure
level and related summary
metrics
National <100 m 2017, with plans to adjust
for historical estimates
New LUR model(s) to be developed
based upon future noise measurements
in selected Canadian cities.
Greenness
Metrics reflecting greenness
accessibility and type (land
use and land cover)
National Neighbourhood-
level
To be determined Metrics to be identified by Greenness
Working Group, and may include
seasonal NDVI, measures of tree
cover/canopy, tree species inventories
at city scale, etc. and data from
Sentinel-2 or Planet satellites.
Climate and Weather
Local Climate Zones National + Varies depending
on landuse/cover
2017 with plans to adjust
for historical estimates
Method to be developed and
evaluated for using image classification
and deep machine learning to map
local climate zones based on
building type, height, and vegetation.
[62–64]
Long term climate metrics National + 32 km 1979 to present Derived from Climate Forecast
System Reananlysis data, metrics
to be identified by Weather and
Climate Working Group.
[44]
Long term climate metrics National + 60 km 1958 to 2012 Derived from Japanese 55-year
reanalysis, metrics to be identified
[45]
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added. Identification of areas of higher proportions of
truck traffic versus automobiles will enable new research
into the health effects of these major sources of near-
road exposure potentially leading to more-informed
transportation policies.
Another key function of CANUE is to facilitate inter-
action among Working Groups for consistency in devel-
opment of exposure data, sharing of measurements and
models, and to be better able to conduct integrated
studies of urban form and health. For example, the Air
and the Noise Pollution Working Groups are aligned
with the Transportation Working Group to enable each
to capitalize on the new traffic maps for development of
improved exposure surfaces. Due to the limited amount
of previous research, substantial gaps exist with regards
to noise exposure in Canada (i.e., spatially-resolved ex-
posure estimates are currently only available for dispar-
ate times for Montreal, Toronto and Vancouver).
However, building upon experience from these three
cities and improved traffic information from the
Transportation Working Group, a consistent method-
ology for estimating noise exposure will be developed
and applied for other major Canadian cities. Given
that the application of noise dispersion models such
as CadnaA to all of Canada or even all cities is not
feasible, a land-use regression-based approach will be
applied (Table 3). In parallel, a survey of existing field
data will be conducted and an approach will be devel-
oped for adjusting the new national LUR model to
represent historical noise levels.
National exposure surfaces and separate urban LUR
models are relatively well-developed for air pollution.
However, limitations remain and thus the Air Pollution
Working Group aims to update the national exposure
maps for PM
2.5
,NO
2
and O
3
. The currently available
maps were generated independently, with differences in
methodology and temporal coverage. For example, the
NO
2
surface includes the influence of near-road expo-
sures [31] while PM
2.5
and O
3
do not. To address incon-
sistencies and/or to improve the current exposure
estimates, two different approaches are being followed.
The first is based upon chemical transport models.
Hourly output from the current Environment and Cli-
mate Change Canada (ECCC) operational chemical
transport model - the Global Environmental Multi-scale
–Modelling Air Quality and Chemistry (GEM-MACH)
- which is combined with surface observations using an
objective analysis approach [52], is being provided to
CANUE for development of exposure metrics. This
approach is being further developed by Health Canada
to provide finer scale exposure estimates for NO
2
by
combining the chemical transport model with LUR
models in a ‘hybrid approach’. The second approach is
to update the national NO
2
and PM
2.5
surfaces, which
were empirically derived, through inclusion of larger
amounts of data, including near-road conditions, and
use of new methods (e.g., machine-learning) in the
model development.
Improvements in NDVI spatial resolution and devel-
opment of more health-relevant greenness exposure
Table 3 Future Metrics (Continued)
Future Metrics Geographic Extent Spatial Resolution Time Periods Description Ref.
by Weather and Climate
Working Group.
Long term climate
metrics
Regional
(British Columbia)
800 m Climate normal 30
year periods (1971–2000
and 1981–2010)
Derived from PRISM data,
metrics to be identified by
Weather and Climate Working
Group.
[46]
Neighbourhood Factors
Walkability National To be determined New metrics to be developed
reflecting age-specific and
season-specific patterns and
may consider landuse and
landcover data; Representativeness
of physical activity to be evaluated
with surveys, GPS and accelerometry.
Food environment National New metrics to be developed using
a variety of information sources
including GIS databases, ground-truth
observations and Google StreetView.
Transportation
Car and truck volumes
and traff emissions
(CO, PM2.5, NOx, BC,
selected VOCs)
Regional Road segment 2016 - Halifax; 2006,
2011 - Montreal,
Winnipeg; 1986, 2001,
2006 and 2011 -
Toronto, Hamilton
Method development to be extended
to other Canadian cities, and key
input data for noise and air quality
models.
Brook et al. BMC Public Health (2018) 18:114 Page 8 of 15
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metrics are being pursued through CANUE to advance
their utility. The integration of land use and land cover
data, biophysical measures of greenness such as tree
canopy cover, tree species data and NDVI seasonality is
being undertaken in order to explore how this approach
could lead to more accurate or representative greenness
metrics. Furthermore, increases in NDVI resolution to
better than 30 m may be feasible using a combination of
Planet images [53] and Landsat 8 data. The potential of
Sentinel-2 [54] multispectral imagery for providing fre-
quent (up to every 5 days) land use and land cover map-
ping, greenness and leaf area index at high spatial
resolution is also being explored.
NDVI by itself does not directly capture salient aspects
of the links between greenness and health outcomes.
Thus, other related metrics have employed additional
landcover information (i.e., percent canopy cover) and/
or land use information (park boundaries, accessibility
via transportation networks) [55, 56] in attempts to ad-
dress this limitation. The CANUE Greenness Working
Group is conducting a review to inform future develop-
ment of a larger suite of metrics that will reflect the
underlying features associated with greenness that could
impact health. For example, proximity to greenness
could influence physical activity levels within the popu-
lation if the areas observed to be ‘green’contain certain
infrastructure like walking paths.
Independent, but related to greenness is walkability.
Associations between walkability and health outcomes
such as obesity, cardiovascular health, and physical activ-
ity have been observed in many regions of the developed
world [57, 58]. Similar observations have been made re-
lating neighbourhood food environments, although not
consistently across regions and among countries [59].
Over the past several decades, many methods have been
used for quantitatively characterizing aspects of walk-
ability and food environments [60, 61]. The Neighbour-
hood Factors Working Group within CANUE is leading
a review of extant metrics with a focus on identifying
those that are applicable in Canada and can be imple-
mented nationally, given large geographic and seasonal
differences, and varying behaviours by age.
Urban morphology interacts with climate and extreme
weather creating local conditions that can potentially
impact population health. The sensitivity of the currently
available meteorological or climatological data to these
interactions is limited due to their complexity and the
spatial resolution of the data. The local climate zone
(LCZ) framework, which uses urban morphology charac-
teristics to estimate the magnitude of the urban heat is-
land and other hazards [62], will be assessed by the
Climate Working Group for its utility in health research.
LCZs were originally developed to characterize the en-
vironment surrounding meteorological field sites to
better account for urban influences on observed
temperature [63]. Factors evaluated include built types
(i.e., compact high-rise buildings, sparsely built, indus-
trial, etc.) and land cover types (i.e., dense trees, low
plant, water, etc.). Currently, the World Urban Database
and Access Portal Tools (WUDAPT) project is facilitat-
ing mapping LCZs using Google Earth and crowd-
sourcing techniques. City-specific volunteers around the
world [64] are providing valuable local-scale observa-
tions to reliably map LCZs. Through CANUE, LCZs will
be developed for all of Canada, and then linked to air
quality, vegetation, aeroallergen exposure, urban flood-
ing, and other hazard indicators as well as future climate
conditions, to assess how the LCZ framework can in-
form environmental health studies.
Increases in the volume, variety and velocity of big
environmental data
A range of new data sources have the potential to greatly
increase the quantity of the environmental exposure data
available for health research. Satellite-based measure-
ments of spatial patterns in a variety of physical and
chemical features at the Earth’s surface have been of tre-
mendous value to a wide range of disciplines. However,
the amount of data collected with each satellite overpass
or image is a challenging big data stream to manage. In
the study of atmospheric trace gases and aerosols, satel-
lite measurements, which have come of age in the last
two decades, have been highly beneficial. Estimates of
chronic exposure to air pollution are now possible for
much of the globe [65] leading to improved
characterization of exposure-response relationships [66,
67] and estimates of the role of particulate air pollution
in the global burden of disease [68] .
The volume and velocity and the potential variability
and value of satellite air pollution measurements are ex-
pected to increase substantially during the first five years
of CANUE’s program with the launch of new geostation-
ary satellites. The Tropospheric Emissions: Monitoring
of Pollution instrument (TEMPO) [69], Geostationary
Environment Monitoring Spectrometer (GEMS) [70]
and Sentinel-4 [71], for North America, Asia and Eur-
ope, respectively, will provide daytime hourly observa-
tions with increased spatial resolution compared to the
previous satellites providing information on trace gases
in the troposphere (Fig. 3). The full potential of this new
big data stream cannot be fully appreciated, but for
chronic and even sub-acute exposure estimation going
forward into the 2020’s these satellites and, the new
Geostationary Operational Environmental Satellite-R
series (GOES-R) [72] satellites enhancing information
on aerosol optical depth (PM
2.5
), will represent the state-
of-the-art. CANUE is developing the infrastructure and
algorithms to be able to capitalize on these data for
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environmental health research and monitoring. Given
the new data streams becoming available it may be feas-
ible in the future to link the temporal and spatial pat-
terns in urban NO
2
and PM
2.5
levels from the
geostationary satellites to traffic flow patterns derived
from tracking mobile phone locations (from global
positioning systems or tower signals) leading to new
understanding of the dynamic between urban form,
traffic, air pollution, and ultimately health. Mobile
phone data are already being used to refine air
pollution exposure estimates by tracking population
movements during the day [73, 74].
Climate, weather and air quality forecasting models
are another source of big data with potential value in en-
vironmental health research. The GEM-MACH model
and its objective analysis product, described above
(Table 3), has provided data for national ground-level O
3
exposure estimates (Table 2) [30]. CANUE is collaborat-
ing with ECCC to make data from 2013 to the present
available for a variety of exposure time windows. This
modelling system currently produces a large volume of
data year-round at 10 km resolution across North Amer-
ica. Methods are being developed through CANUE to
routinely capture data on hourly ozone, PM
2.5
and NO
2
concentrations in near real-time and preparing exposure-
relevant variables. Future versions of the model and
objective analysis product will likely increase spatial reso-
lution (e.g., 2.5 km) leading to larger volumes of data and
potentially better exposure precision. Ultimately, air qual-
ity researchers expect to integrate the hourly satellite data
with these modelling tools to further improve accuracy.
Such advances have the potential to benefit environmental
health research far into the future.
The meteorological models that support weather
forecasting and are essential to the air quality modelling
represent another big environmental data stream of po-
tential value to health research. In the near future these
models are expected to be capable of resolving urban scale
features leading to more-realistic characterization of cli-
mate phenomena such as heat islands. Such output, which
CANUE is aiming to utilize in partnership with OURA-
NOS [75], will support future research exploring how
current and future climate and extreme weather events
impacts public health. New knowledge in this area could
help Canada’s urban areas prepare for climate change (i.e.,
adaptation to build resiliency).
Google Earth Engine [76] was introduced in 2010 to en-
able the global-scale monitoring and measurement of
changes in the environment. The ‘Earth Engine’provides
two key functions: 1) the curating and management of his-
torical and ongoing satellite data; and 2) an easy to use ana-
lytical platform that allows researchers to create and
implement scripts and algorithms to process the data into
useful metrics of environmental characteristics and their
change over time. For example, with annual 30 m NDVI
data from Landsat in Google Earth Engine for 1984 onward
it will be possible to generate greenness exposure maps or
maps of areas of urban development (e.g., road coverage) at
a spatial resolution, temporal coverage and geographic ex-
tent not easily accomplished without the big data function-
ality of Google Earth Engine. This temporal information
has the potential to improve exposure estimates for cohorts
by integrating over a larger portion of each individual’slife-
time especially if residential history data can be obtained.
Useful metrics of green canopy coverage, which is
relevant to urban heat and likely a range of other is-
sues (e.g., aeroallergens), have recently been shown to
be computationally feasible from Google Street View
images [77], and are comparable to audits conducted
by direct observation [78]. While this virtual audit
saves time and money and it is repeatable among dif-
ferent observers, automation could lead to even
greater savings and consistency, also generating large
amounts of data from which to derive exposure metrics.
There is a rapidly growing literature illustrating the auto-
mation of index calculations using Google Street View, for
example, a Green Vegetation Index (GVI) [79]. CANUE
will explore a street level-based greenness indicator in the
near term and continue to refine and develop new
methods and indicators using available imagery.
Fig. 3 Relative differences in spatial resolution of trace gas
measurements (e.g., NO
2
) from satellite-based measurements over
Ottawa, Canada. Rectangles show the minimum sizes areas covered
(pixel size) with three generations of satellites. The blue square
corresponds to the less than daily observation frequency of GOME 2.
The green square, the daily frequency OMI measurements and, the
daylight, hourly frequency of TEMPO (yellow square). The new
TEMPO satellite will be capable of collecting data in the ultraviolet
and visible wavelengths at approximately 2 km × 5 km spatial
resolution. Once in operation TEMPO will produce data for
approximately 2.5 million grid cells every daylight hour, equivalent
to 1 terabyte of data daily
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Prospectively, new tools being developed for collecting
mobility data to inform transportation planners, including
smart-phone applications, which collect GPS coordinates
to infer locations, movement, mode of transportation and
activity can be used to determine individual activity-travel
diaries [80]. These ‘apps’could be adopted for use in large
cohorts (e.g., Canadian Partnership for Tomorrow Project
[15]) to obtain mobility data for tens of thousands of sub-
jects. They could also be enhanced to prompt, in a minim-
ally burdensome fashion, for longitudinal information
related to personal behaviours (e.g., recent meals and exer-
cise) and to process measurement data from sensors in
the phone (e.g., accelerometer, microphone) or from com-
panion sensors to improve exposure assessment. Consid-
erable effort is currently being focused in this area (e.g.,
The Pediatric Research using Integrated Sensor Monitor-
ing Systems (PRISMS) [81]; and, while not the primary
focus of CANUE, measurement sub-studies exploring
youth physical activity are being planned to support some
of Canada’s active birth cohorts (e.g., Canadian Healthy
Infant Longitudinal Development Study (CHILD) [82]).
Challenges
Key challenges for large environmental health studies,
particularly those aiming to implement an exposome-
based approach, continue to be enrichment of cohorts
with individual-level exposures, harmonization across
cohorts and, and ultimately identification of modifiable
risk factors leading to interventions that have benefits
on population health. To help meet these challenges
Stingone et al. [83] suggested that exposome studies
would be well-served by centralized support and coord-
ination to ensure that potential exposure assessment
strategies are rigorously evaluated. CANUE represents
an attempt to meet these challenges with respect to
exogenous factors and, while CANUE is the largest coor-
dinated effort in Canada around environmental exposure
data, many challenges remain.
There is a long-standing need to better understand
temporal change in spatial exposure patterns going back
decades and how this contributes to exposure misclassi-
fication and subsequent epidemiological results [84, 85].
Detailed characterization of spatial patterns with high
resolution that are indicative of chronic exposure is typ-
ically only accomplished for ‘snapshots’in time because
of the effort and expense required. It is therefore neces-
sary to estimate temporal changes in these spatial expos-
ure patterns by extrapolation of the spatial detail. This
could include estimates covering longer time periods
(i.e., decades) or particular months to years before or
after the time of the ‘snapshot’. For air pollution a variety
of extrapolation approaches have been used [84–87];
however, in order to have reasonable confidence in the
estimates it is necessary to have monitoring site data
with temporal coverage for the time periods and pollut-
ants of interest and ideally from multiple locations de-
pending upon the size of the spatial domain modelled.
This is problematic because long-term exposures over
relatively large geographic areas require estimates
much further back in time pre-dating the monitoring
of some pollutants (e.g., PM
2.5
). In these cases, there
is likely much greater uncertainty in the exposure
estimates [85], but they are difficult to quantify given
lack of evaluation data.
The need for temporal extrapolation and uncertainty
arising from lack of historical exposures are limitations
impacting most of the exposure domains of interest to
CANUE. The noise pollution maps are available for a
limited number of cities and specific snapshots in time.
New noise maps to be developed through CANUE will
also face this limitation and their applicability to other
time periods or longer time windows relies on the as-
sumption of temporal stability. Given that a key source
of noise is traffic and other transportation activities (e.g.,
airports) and the infrastructure for these is stable over
relatively long periods, extrapolation is reasonable.
However, road, air and train movements have changed
overtime as well as emissions; the locations of many
other noise sources can change more-rapidly; and even
changes such as construction of noise barriers will alter
exposure patterns. Further, fitting noise models to simi-
lar geospatial predictors as air pollution contributes to
collinearity hindering attempts to isolate effects due to
these two exposures [88].
Coordination through CANUE offers promise that
some progress on these and other challenges can be
achieved. Google’s Earth Engine, for example, is hypoth-
esized to facilitate the analysis of big geospatial data with
a temporal coverage that will be informative of changes
in urban environment exposure metrics going back into
the 1980s. CANUE provides the critical mass to explore
this idea. Given high resolution surfaces of noise and air
pollution, other health-relevant neighbourhood features
and maps of local climate zones that indicate poten-
tial for heat islands, it may be possible, using local
land use variables as model inputs, to develop algo-
rithms that can relate land use classifications derived
from the 30 m Landsat images. These algorithms, if
robust and mechanistically-based, could then enable
reliable estimation of a variety of urban form expos-
ure variables back to 1984.
Residential mobility is also an important cause of
misclassification when exposure assessment relies on
geographic location. Often exposure is based upon a sin-
gle home address, such as might be acquired at time of
study recruitment or baseline. The potential for differen-
tial exposure misclassification has been demonstrated in
birth cohorts [89], and can be expected to increase the
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longer the follow-up period or the longer the exposure
time window of interest. Crouse et al. [30] reported that
nearly 50% of the Canadian population moved at least
once in the 5 year period from 2001 to 2006. They
accounted for residential mobility during the 16 year of
follow-up of Canadian Census Health and Environment
Cohort and found that this led to larger hazard ratios
compared to those determined using exposures assigned
using a single baseline address. This attenuation in haz-
ard ratio was greatest for NO
2
, less for PM
2.5
and negli-
gible for O
3
.
Residential history on study individuals, if available,
can be used to determine time-weighted exposures,
assuming exposure data are available for the different
addresses reported. Ideally, such information is obtained
in prospective cohorts through questionnaires. In
practice this is not always the case and/or the data are
incomplete. Administrative data housed at the federal
and provincial level represents a different option, taking
the burden away from the subjects, while standardizing
the approach. CANUE is working with Statistics Canada
through the Social Data Linkage Environment [90] to
obtain annual residential history data for individuals in
some cohorts following the method used by Crouse
et al. [30]. Provincial health care records also retain
addresses and these data are being assessed for recon-
struction of residential history.
Daily mobility and time spent indoors poses another
challenge for exposure assessment. Accounting for time
at work or school and proximate exposures is feasible
given sufficient information and resources. While where
a person lives plays a major role in their relationship
with all of the urban form features related to CANUE’s
exposure domains, all locations where significant time is
spent, including in transit (i.e., commuting), are poten-
tially important (Fig. 2). CANUE is aiming to provide
exposure metrics for many potential locations allowing
for additional time-weighting of outdoor exposures.
However, reliable time activity behaviour at the individ-
ual level represents a key limitation.
Discussion
CANUE is compiling a wide range of geospatial datasets
of exposure metrics that are known to be or hypothe-
sized to be relevant to public health. However, these
postal code specific metrics are just that; metrics that act
as surrogates for more complex underlying processes
that manifest as a health effect, adverse or beneficial. It
is critical that we understand these processes as much as
possible and consider whether the metric or surrogate
being used is appropriate and ultimately informative of
the root causes. Consequently, one criterion for
CANUE’s efforts in refining exposure metrics is to im-
prove their ability to reflect the underlying processes or
mechanisms and to better understand these relation-
ships. Through this approach we aim to improve our un-
derstanding of the uncertainties in the exposure metrics,
which continue to be difficult to quantify. Furthermore,
future studies involving multiple, interacting exposures
can then be more informative.
The body of research is relatively large for impacts of
single air pollutants or urban form characteristics such
as greenness or walkability in isolation. There is less re-
search evaluating different features of the urban form or
exposures in combination [91–93]. Clearly, there is the
potential for joint as well as counter-acting effects. For
example, current understanding suggests that in coun-
tries with moderate to low air pollutant levels (e.g.,
Canada) the benefits of active transportation (i.e., phys-
ical activity) far outweigh the dis-benefits of enhanced
air pollution exposure from greater inhalation rates [94].
Furthermore, transportation polices that reduce air pol-
lution and increase active transportation are estimated
to have large economic benefits [95]. However, these ex-
amples are based upon risk analysis using current epi-
demiological data, while the original epidemiological
studies have tended to explore exposures separately.
With CANUE facilitating linkage of air pollutant expo-
sures and metrics related to physical activity, as well as
other exposures (e.g., noise, stress associated with neigh-
bourhood factors), to individuals cohorts, future epi-
demiological studies may be able to assess the effect of
interactions in different regions of Canada with different
socioeconomic and climatic conditions and for different
members of the population.
CANUE will also focus on data that are available inter-
nationally, such as those derived by satellite instruments or
global data collection initiatives such as those conducted by
Google. By building on existing methods for deriving useful
exposure metrics, implementing them nationally, and sharing
newly developed methods using widely available input data,
CANUE has the potential to contribute significantly to ad-
vancing environmental health studies globally. Making a wide
variety of standardized metrics available will increase the
comparability among studies, and potentially support the for-
mation of very large virtual cohorts by combining results of
studies from multiple countries. The statistical power these
meta-studies may be capable of achieving is likely key to un-
derstanding the subtle interactions among environmental ex-
posures related to urban form [87].
CANUE’s potential impact is based in large part on
the willingness of its members to share methods and in
some cases, proprietary input data or already-developed
exposure metrics suitable for a national platform.
CANUE is positioned as a neutral data broker, providing
standardized metadata for each shared dataset, as well as
a formal data sharing agreement with terms set by the
data developer. Exposure data will be provided to
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established cohorts and administrative data holders, who
then follow their own standard approval processes for
providing access to both the confidential health data and
related, and where possible, pre-linked exposure data.
The challenges of working with multiple data devel-
opers, data sharing requirements, and varying capacities
and procedures for data integration by health data
holders are complex, but not insurmountable.
CANUE’s protocol for establishing a centralized, coor-
dinated effort in deriving and linking urban-related en-
vironmental exposures to Canada’s wealth of cohorts
and administrative health data holdings will increase effi-
ciency by reducing duplication and insuring consistency
in the exposure measures used. As such, CANUE will
enable a more focused effort on filling gaps in exposure
information, improving the range of exposures quanti-
fied, their precision and mechanistic relevance to health.
Epidemiological studies will thus be better able to har-
ness big environmental data in order to explore the
common theme of urban form and health in an inte-
grated manner, ultimately contributing new knowledge
informing policies that enhance healthy urban living.
Abbreviations
AVHRR: Advanced very high resolution radiometer; BC: Black carbon;
CanMarg: Canadian marginalization index; CANUE : Canadian Urban
Environmental Health Research Consortium; CHILD: Canadian Healthy Infant
Longitudinal Development Study; CIHR: Canadian Institutes for Health
Research; CO: Carbon monoxide; ECCC: Environment and Climate Change
Canada; GEM-MACH: Global Environmental Multi-scale –Modelling Air
Quality and Chemistry; GEMS: Geostationary Environment Monitoring
Spectrometer; GIS: Geographic information systems; GOES-R: Geostationary
Operational Environmental Satellite-R series; GVI: Green vegetation index;
LCZ: Local climate zone; LUR: Land use regression; MODIS: Moderate
resolution imaging spectroradiometer; NDVI: Normalized Difference
Vegetation Index; NO2: Nitrogen dioxide; NOx: Nitrogen oxides; O3: Ozone;
PM2.5: Fine particulate matter; PRISM: Parameter-elevation regressions on
independent slopes model; PRISMS: Pediatric research using integrated
sensor monitoring systems; SO2: Sulphur dioxide; TEMPO: Tropospheric
Emissions: Monitoring of Pollution instrument; VOCs: Volatile organic
compounds; WUDAPT: World Urban Database and Access Portal
Acknowledgements
The Canadian Urban Environmental Health Research Consortium is funded
by the Canadian Institutes for Health Research. We would like to
acknowledge the contributions of our membership, now comprised of over
180 individuals, under the direction of CANUE’s leadership and the co-
authors of this paper.
The CANUE collaboration group (co-authors of this paper) includes:
Philip Awadalla, Michael Brauer, Howard Hu, Kim McGrail, Dave Stieb,
Padmaja Subarrao, Paul Demers, Doug Manuel, John McLaughlin, Chris
Carlsten, Meghan Azad, Stephanie Atkinson, Rick Burnett, Wendy Lou, Daniel
Rainham, Greg Evans, Ray Copes, Olimpia Pantelimon, Audrey Smargiassi,
Hugh Davies, Paul Villeneuve, Matilda van den Bosch, Diane Chaumont,
Johannes Feddema, Tim Takaro, Amir Hakami, Markey Johnson, Marianne
Hatzopoulou, Ahsan Habib, Daniel Fuller, Michael Widener.
Funding
The Canadian Urban Environmental Health Research Consortium is funded
by the Canadian Institutes for Health Research.
Availability of data and materials
Not applicable.
Authors’contributions
JRB was the major contributor to the manuscript. EMS and EDS also
contributed to the manuscript. DD and MS critically reviewed the
manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Publisher’sNote
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Author details
1
Processes Research Section, Air Quality Research Division, Environment and
Climate Change Canada, Toronto, ON, Canada.
2
Dalla Lana School of Public
Health, University of Toronto, Toronto, Canada.
3
Geography Department,
University of Victoria, Victoria, Canada.
4
Research Institute of McGill University
Health Centre, Montreal, Canada.
Received: 3 October 2017 Accepted: 19 December 2017
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